diff --git a/.gitattributes b/.gitattributes index 3ec79ccb76896d566234ea8d4e02e46c2229f1bb..d6cde4f8df428a37bfe7ee1cb1180529f777c92e 100644 --- a/.gitattributes +++ b/.gitattributes @@ -3862,3 +3862,63 @@ n9AyT4oBgHgl3EQflfie/content/2301.00454v1.pdf filter=lfs diff=lfs merge=lfs -tex jNE2T4oBgHgl3EQfdAe8/content/2301.03902v1.pdf filter=lfs diff=lfs merge=lfs -text NNFQT4oBgHgl3EQfWDbE/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text SdE2T4oBgHgl3EQfCAaW/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +AtE1T4oBgHgl3EQfpAWX/content/2301.03327v1.pdf filter=lfs diff=lfs merge=lfs -text +WNAyT4oBgHgl3EQf8_py/content/2301.00866v1.pdf filter=lfs diff=lfs merge=lfs -text +wdAzT4oBgHgl3EQf7f5k/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +ZtAyT4oBgHgl3EQf9vp1/content/2301.00880v1.pdf filter=lfs diff=lfs merge=lfs -text +PtE1T4oBgHgl3EQfagQG/content/2301.03161v1.pdf filter=lfs diff=lfs merge=lfs -text +KdE3T4oBgHgl3EQfvQvs/content/2301.04693v1.pdf filter=lfs diff=lfs merge=lfs -text +QdE3T4oBgHgl3EQfyAvv/content/2301.04717v1.pdf filter=lfs diff=lfs merge=lfs -text +ndAyT4oBgHgl3EQflfhA/content/2301.00452v1.pdf filter=lfs diff=lfs merge=lfs -text +AtE1T4oBgHgl3EQfpAWX/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +B9AzT4oBgHgl3EQfTfz1/content/2301.01252v1.pdf filter=lfs diff=lfs merge=lfs -text +KdE3T4oBgHgl3EQfvQvs/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +ftAzT4oBgHgl3EQf3_6L/content/2301.01838v1.pdf filter=lfs diff=lfs merge=lfs -text +k9FST4oBgHgl3EQfIzgr/content/2301.13730v1.pdf filter=lfs diff=lfs merge=lfs -text +O9AyT4oBgHgl3EQfUffK/content/2301.00128v1.pdf filter=lfs diff=lfs merge=lfs -text +iNFJT4oBgHgl3EQfWSzz/content/2301.11517v1.pdf filter=lfs diff=lfs merge=lfs -text +ZtAyT4oBgHgl3EQf9vp1/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +ydE2T4oBgHgl3EQfMQZF/content/2301.03722v1.pdf filter=lfs diff=lfs merge=lfs -text +xtE5T4oBgHgl3EQfNA53/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +k9FST4oBgHgl3EQfIzgr/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +w9E_T4oBgHgl3EQf_xxJ/content/2301.08394v1.pdf filter=lfs diff=lfs merge=lfs -text +SdE2T4oBgHgl3EQfWgfj/content/2301.03835v1.pdf filter=lfs diff=lfs merge=lfs -text +gtE0T4oBgHgl3EQfpAHr/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +ctE0T4oBgHgl3EQfoAE4/content/2301.02519v1.pdf filter=lfs diff=lfs merge=lfs -text +ftAzT4oBgHgl3EQf3_6L/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +O9AyT4oBgHgl3EQfUffK/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +ctE0T4oBgHgl3EQfoAE4/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +UdE3T4oBgHgl3EQf0Atf/content/2301.04733v1.pdf filter=lfs diff=lfs merge=lfs -text +VdAyT4oBgHgl3EQfuvn4/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +cNE1T4oBgHgl3EQfdgR_/content/2301.03196v1.pdf filter=lfs diff=lfs merge=lfs -text +ldE3T4oBgHgl3EQfiAoP/content/2301.04575v1.pdf filter=lfs diff=lfs merge=lfs -text +Y9FOT4oBgHgl3EQf-TSC/content/2301.12973v1.pdf filter=lfs diff=lfs merge=lfs -text +p9AyT4oBgHgl3EQfZPfT/content/2301.00221v1.pdf filter=lfs diff=lfs merge=lfs -text +ntE1T4oBgHgl3EQfhgSw/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +7tAyT4oBgHgl3EQfpvj9/content/2301.00533v1.pdf filter=lfs diff=lfs merge=lfs -text +UdE3T4oBgHgl3EQf0Atf/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +p9AyT4oBgHgl3EQfZPfT/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +_tAyT4oBgHgl3EQf3vm5/content/2301.00775v1.pdf filter=lfs diff=lfs merge=lfs -text +StE0T4oBgHgl3EQfUwAJ/content/2301.02253v1.pdf filter=lfs diff=lfs merge=lfs -text +m9AyT4oBgHgl3EQflPjP/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +OdE1T4oBgHgl3EQfaAQ0/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +ntE1T4oBgHgl3EQfhgSw/content/2301.03242v1.pdf filter=lfs diff=lfs merge=lfs -text +CNE1T4oBgHgl3EQfVwTa/content/2301.03107v1.pdf filter=lfs diff=lfs merge=lfs -text +idAyT4oBgHgl3EQf-_pB/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +jNE2T4oBgHgl3EQfdAe8/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +Y9FOT4oBgHgl3EQf-TSC/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +mNE_T4oBgHgl3EQf6xyS/content/2301.08366v1.pdf filter=lfs diff=lfs merge=lfs -text +mNFIT4oBgHgl3EQfsyss/content/2301.11337v1.pdf filter=lfs diff=lfs merge=lfs -text +79E1T4oBgHgl3EQfnQTu/content/2301.03308v1.pdf filter=lfs diff=lfs merge=lfs -text +HNE4T4oBgHgl3EQfgg1T/content/2301.05117v1.pdf filter=lfs diff=lfs merge=lfs -text +K9E4T4oBgHgl3EQf8A5c/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +QdE3T4oBgHgl3EQfyAvv/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +l9FLT4oBgHgl3EQfeS-E/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +cNE1T4oBgHgl3EQfdgR_/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +K9E4T4oBgHgl3EQf8A5c/content/2301.05344v1.pdf filter=lfs diff=lfs merge=lfs -text +3NFQT4oBgHgl3EQf3DZF/content/2301.13426v1.pdf filter=lfs diff=lfs merge=lfs -text +8NE2T4oBgHgl3EQflQeg/content/2301.03987v1.pdf filter=lfs diff=lfs merge=lfs -text +SdE2T4oBgHgl3EQfWgfj/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +iNFJT4oBgHgl3EQfWSzz/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +ydE2T4oBgHgl3EQfMQZF/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text +79E1T4oBgHgl3EQfnQTu/vector_store/index.faiss filter=lfs diff=lfs merge=lfs -text diff --git a/0tAyT4oBgHgl3EQfbfeR/content/tmp_files/2301.00264v1.pdf.txt b/0tAyT4oBgHgl3EQfbfeR/content/tmp_files/2301.00264v1.pdf.txt new file mode 100644 index 0000000000000000000000000000000000000000..0822d1040628585d61d96a07977b4eba728961a7 --- /dev/null +++ b/0tAyT4oBgHgl3EQfbfeR/content/tmp_files/2301.00264v1.pdf.txt @@ -0,0 +1,605 @@ +Application Of ADNN For Background Subtraction In +Smart Surveillance System +University Of Alberta +Department of Computing Science +Piyush Batra, Gagan Raj Singh, Neeraj Goyal +December 7, 2022 +Brief Abstract +Object movement identification is one of the most researched problems in the +field of computer vision. In this task, we try to classify a pixel as foreground or +background. Even though numerous traditional machine learning and deep learning +methods already exist for this problem, the two major issues with most of them +are the need for large amounts of ground truth data and their inferior performance +on unseen videos. Since every pixel of every frame has to be labeled, acquiring +large amounts of data for these techniques gets rather expensive. Recently, Zhao +et al. [1] proposed one of a kind Arithmetic Distribution Neural Network (ADNN) +for universal background subtraction which utilizes probability information from +the histogram of temporal pixels and achieves promising results. Building onto +this work, we developed an intelligent video surveillance system that uses ADNN +architecture for motion detection, trims the video with parts only containing motion, +and performs anomaly detection on the trimmed video. +1 +Literature review +Motion detection aims to find regions related to moving objects, and background subtraction is a +widely used technique for this task. Herein, every pixel of each video frame is compared against +their historical counterparts or a background model, depending on the technique, and then +classified into foreground or background. Pixels that differ significantly from the reference are +classified as moving objects or foreground, and static pixels are referred to as background. This +section will discuss some of the previously proposed methods related to background subtraction, +video surveillance, and anomaly detection. +Many background modeling techniques based on mathematical theories, like the temporal +average [2], temporal median [3], or the histogram over time [4], have been proposed for motion +detection by a stationary camera. But these are not robust to challenges in surveillance videos +such as dynamic background, object shadow, camera jitter, weather conditions (either snow or +rain), and variations in illumination. To overcome these problems, several motion-detection +methods, like Temporal Differencing [5], Three-frame Difference [6], Gaussian mixture model [7], +1 + +DSTEI [9], etc, have been presented over the past years. +Temporal Differencing, proposed by Cheung et al. [5], is used for detecting temporal changes +in intensity in video frames. However, its main drawback is that the detected objects are +incomplete and poorly presented. +In the Gaussian mixture model proposed by Stauffer and Grimson [7], the temporal histogram +of each pixel is modeled using a mixture of K Gaussian distributions to precisely model a dynamic +background. This method produced a real-time tracker which can deal with lighting changes, +repetitive motions from clutter, and long-term scene changes. Later, Chan et al. [8] proposed a +Generalized Stauffer–Grimson (GSG) algorithm for background subtraction in dynamic scenes. +In this method, the statistics required for online learning of dynamic texture are derived from +generalizing the GMM proposed by Stauffer and Grimson [7]. +The Difference-based Spatio-Temporal Entropy Image (DSTEI) by Jing et al. [9] is an entropy- +based method for human motion detection. A Spatio-temporal histogram is generated by +accumulated pixels obtained by the difference between consecutive images. This histogram is +then normalized to calculate the degree of randomness and magnitude of entropy to denote +the significance of motion. In this method, noises are assumed to follow Gaussian distribution. +However, these assumptions, such as heavy shadows or sudden illumination changes, will be +violated in some cases. +Soumyadip Sengupta et al. [10] proposed a background matting technique that generated +high-quality foreground and alpha mattes in natural settings. In this method, a deep learning +framework is developed and trained on synthetic-composite data and then adapted to actual data +using an adversarial network. Even though providing an additional photo of the background +requires a small amount of foresight, it is far less tedious than creating a trimap for traditional +matting methods. +In 2017, Dan Yang et al. [11] proposed a multi-feature background approach for complex +video scenes that measures the stability of features and then selects different dominant features +to model the background from the pixel and time-sequence domains. This Stability of Adaptive +Features approach showed promising results on both complex and baseline scenes. +For applications of background subtraction in real time, Z. Kuang et al. [12] proposed a +combination of the Horn-Schunck optical-flow estimation technique [13] and autoencoder neural +networks that solve the problem of motion blur in real-time background subtraction during +video conferencing. This method uses an optical-flow-based model to extract motion features +between every two frames and then combine these features with the appearance feature from +the original frame. An encoder-decoder network in combination with CNN is then used to learn +and predict a mask output for the human head and shoulders for background subtraction. +Similarly, for real-time background subtraction, DK Yadav et al. [14] proposed a Pixel +Intensity Based (PIBBS) system that first models the background, then extracts moving objects +with a threshold and updates the background using a feedback-based background updation +scheme. To improve the detection quality, this system also uses morphological operators as the +last step. +Bruno Sauvalle et al. [15] proposed using an autoencoder to model the background of a +video as a low-dimensional manifold. The output of this autoencoder is then compared with +the original image to compute the segmentation masks. In this method, the autoencoder is also +trained to predict the background noise, which allows it to compute a pixel-dependent threshold +for each frame to perform the foreground segmentation. Without using temporal or motion +information, this method could perform at par with state-of-the-art solutions on CDnet 2014 [16] +2 + +and LASIESTA [17] datasets. +To overcome the problem of camera jitter and sudden changes in illumination, Ye Tao et al. +[18] proposed a generative architecture for unsupervised deep background modeling, which +learns the parameters automatically and uses intensity and optical flow features between a +reference and a target frame. This system generates a background with a probabilistic heat map +of the color values for a given input frame. This method could also be applied to unseen videos +without re-training. When tested, this method shows promising results over state-of-the-art +[19][20][21] methods on the SBMnet dataset[22]. +Guanfang Dong et al. [23] proposed a novel denoising neural network model called Feature- +guided Denoising Convolutional Neural Network (FDCNN) to denoise the images produced +by portable devices. This technique employed a hierarchical denoising framework driven +by a feature masking layer. The feature extraction algorithm used in this method is based +on Explainable Artificial Intelligence (XAI) for medical images. Similarly, Yingnan Ma et al. +[24] proposed an Edge-guided Denoising Convolutional Neural Network which can preserve +important edge information in ultrasound images when removing noise. This method increases +the recognition of various organs in ultrasound images. +Jhony H. Giraldo et al. [25] proposed a new algorithm called Graph Background Subtraction +(GraphBGS). It is composed of instance segmentation, background initialization, graph construc- +tion, and graph sampling. Unlike Deep Learning methods for background subtraction which +require vast amounts of data, this method is a semi-supervised algorithm inspired by the theory +of recovery of graph signals. +To generate descriptions of human actions and their interactions, Zijian Kuang et al. [26] +proposed a technique that utilizes an Actor Relation Graph (ARG) based model with novel +improvements for group activity recognition. This method also used MobileNet as the backbone +to extract features from each video frame. +To accurately perform background subtraction in a freely moving camera, Zhao et al. [27] +developed a novel method called “the integration of foreground and background cues.” The +underlying motivation in this technique is to utilize the exclusiveness between these cues to +compensate for their corresponding defects. The foreground is segmented by combining super- +pixels with proximity under multiple levels. +As video resolution and, subsequently, the video size is increasing daily, Ruixing et al. [28] +proposed a method to compute the optimal image resolution adaptively. This is achieved by +exploiting the correlation between an image’s gray-value distribution and resolution. This +approach was proposed to increase the performance of multi-object online tracking and learning. +A novel tracklet reliability assessment metric was also introduced in this paper to eliminate the +incorrect samples and can recover occluded targets. +As a unique application of neural networks in multimedia, C. Sun et al. [29] proposed a 2-step +product re-identification (Re-ID) method which involves image feature extraction and a feature +search and retrieval engine. To extract the features of the input image, a novel AlphaAlexNet, an +extended version of the AlexNet, is being used. Vearch, a visual search system, is used as the +image search similarity engine. The new model - AlphaAlexNet, demonstrated improved object +detection accuracy of Vearch. +To classify two distributions without using just histograms and incorporating a deep learning +network to learn and classify distributions automatically, Chunqiu Zhao and Anup Basu [30] +proposed a novel vessel segmentation method based on distribution learning using a spatial +distribution descriptor (RPoSP) under multiple scales. Here, statistical distributions are indirectly +forced as an input to a CNN for distribution learning. The proposed approach showed promising +3 + +results when compared to existing state-of-the-art methods[31][32] on the DRIVE[33] dataset. +Yongxin Ge et al. [34] proposed the Deep Variation Transformation Network (DVTN) model, +which uses pixel variations to detect the background. This model assigns the probability to +each pixel, and then by using thresholding, it computes whether it’s background or foreground. +This model compares the pixel variation instead of distributions. Previously used models +in background detection usually fail when they encounter similar observations, causing false +detections. The DVTN analyzes the pixel variations in a new space, where the above observations +are classified easily. This model outperforms the traditional background detection models by +showing astonishing results on the CDnet2014 dataset. +However, all of the methods mentioned above require either a large amount of ground truth +data or result in inadequate performance on unseen videos. Zhao and Basu [35] proposed a +Deep Pixel Distribution Learning (DPDL) technique to overcome these issues. Unlike typical +approaches, which compare new frames to a formulated background model, this technique +focuses on comparing pixels’ current and historical frames. This method uses a novel pixel- +based feature called the Random Permutation of Temporal Pixels (RPoTP) to represent the +distribution of past observations for a particular pixel. Subsequently, a CNN is used to learn +whether the current pixel is foreground or background. Adding on to this method, Zhao et al. +[36] later proposed a new Dynamic Deep Pixel Distribution Learning (D-DPDL) technique. In +this method, the RPoTP feature is dynamically permuted in this method for every training epoch. +To compensate for the random noise generated in this process, a Bayesian Refinement model is +used and improve the accuracy. +Zhao et al. [1] also proposed an Arithmetic Distribution Neural Network architecture demon- +strating even better performance than the D-DPDL method. The input in the ADNN method is +histograms of subtractions between current pixels and their historical counterparts. The sum +and product arithmetic distribution layers proposed here demonstrate a better ability to clas- +sify distributions than the convolutional layers in D-DPDL. Moreover, the number of learning +parameters used in ADNN architecture (0.1 Million) is significantly less than that used in the +D-DPDL method (7 Million). +Coming onto detecting anomalies in videos, Virender Singh et al. [37] proposed an approach +to detect variation from the norm in real-world CCTV recordings. This method uses two deep +learning models (CNN and RNN) to learn a general anomaly detection model with a poorly +labeled dataset. The training dataset has been doubled by flipping the videos horizontally, thus +increasing the testing accuracy. The overall accuracy of the model is 97.23 +Y Fan et al. [38] proposed a technique that first converts the video clips of an ongoing event +into Dynamic Images, which can simultaneously capture the appearance and temporal evolution +of the occurrence. The approach uses dynamic images of two categories of video clips and +involves training a detector based on deep-learning techniques. +Yu Tian et al. [39] proposed a weakly-supervised anomaly detection algorithm, Robust +Temporal Feature Magnitude learning (RTFM), aiming to identify snippets containing abnormal +events. This method trains a feature magnitude learning function to effectively recognize the +positive instances, substantially enhancing the robustness of this method to the negative instances +from abnormal videos. RTFM achieves significantly improved subtle anomaly discriminability +and sample efficiency. +The Weakly Supervised Video Anomaly Detection(WSVAD) [40]-[42] method for anomaly +detection suffers from the wrong identification of normal and abnormal instances during the +training process. Kapil Deshpande et al. [43] proposed better-quality transformer-based features +named Videoswin Features, followed by an attention layer to capture long and short-range +4 + +dependencies in the temporal domain. This method extracts better-quality features from available +videos resulting in better performance. +2 +Method +In this work, we implemented an Arithmetic Distribution Neural Network [1] to develop a video +surveillance system for identifying object movement in a static video. In this ADNN model, +the arithmetic operations are utilized to introduce the arithmetic distribution layers, including +the product and sum distribution layers. Outputs from these layers are combined and passed +through a classifier for accurate classification. We chose this architecture because it requires +training only one network, with limited training data, and it works well with unseen test videos. +Figure 1: The flow diagram of our proposed approach +Upon successful object movement detection using background subtraction, we further ana- +lyzed the results obtained from ADNN to filter out their anomalous activities. +2.1 +Motion detection - Arithmetic Distribution Neural Network +In this work, we used ADNN proposed by Zhao et al. [1] to detect motion in the input surveil- +lance video. This paper proposed arithmetic distribution layers, which are a new type of network +layer that is designed to improve distribution analysis in classification tasks. These layers, which +include product and sum distribution layers, are an alternative to convolution layers. During +the forward pass of the proposed arithmetic distribution layers, the input distributions are +processed using the distributions in the learning kernels to generate the output distributions. +In the backpropagation process, the gradient of the distributions in the learning kernels with +respect to the network output is calculated to update the learning kernels. These operations are +based on histograms and arithmetic distribution operations rather than the matrix arithmetic +operations used in traditional convolution layers. +To improve the accuracy of the foreground mask generated, an improved Bayesian refinement +model is used. This model takes into account the correlations between pixels by using a mixture +of Gaussian approximation functions rather than just Euclidean distance, as in the original +Bayesian refinement model. The Bayesian refinement model is used to iteratively refine the +foreground mask, with the output of the arithmetic distribution neural network serving as the +initial binary mask for the iteration process. +5 + +Sum Distribution +Layer +Convolution +Classifier +Anomalydetection +ramework +Basedonpixe +classification +A Set of trimmed videos +Input Video +with activity +A Set of anomaly +detected videos +Product +Distribution LayerFigure 2: Arithmetic distribution neural network for background subtraction +After obtaining the refined foreground masks from the ADNN architecture, we utilize a +python script to generate a trimmed video from a set of input frames by using a threshold value +on the frames generated by the Bayesian refinement model. The threshold value determines the +minimum number of white pixels (foreground pixels) that must be present in a frame in order +for it to be included in the trimmed video. For this work, we are using a threshold value of 5% to +generate the trimmed videos. +Figure 3: Generation of trimmed video from input frames passed through the ADNN (arithmetic +distribution neural network) +2.2 +Anomaly Detection +Following the works of Waqas Sultani et al. [44], we have put into use their novel Multiple +Instance Learning framework for the second part of our system. Once we obtain the trimmed +video from the previous step, we use that as the input in this step. In this, a training set of +positive (containing an abnormality someplace) and negative (having no anomaly) videos are +used to train the anomaly detection model. Then each video is divided into a sequence of +non-overlapping temporal segments. +6 + +Convolution, +Full Connection +Relu +Convolution +Product +Distribution Layer +Foreground +Size:B × 3 × 201 × 2 +Background +Size:B × 3 × 201 × 1 +Size:B × 2×1 × 1 +Size:B × 10 × 201 × 1 +Sum Distribution +Layer +Size:B × 512 × 1 × 1 +Datadimensions:BatchSize×Channels×Width×Height +Size:B × 3 × 201 × 2Frames from ADNN Output +CombinedTrimmed Videc +Motion Video1 +Motion Video 2 +Framesfrom inputvideo +Input VideoFigure 4: Anomaly detection flow diagram +Each video in the training set can be represented as a bag, and each video segment represents +an instance in the bag. After extracting C3D features from video segments using a pre-trained +3D convNet, a fully connected neural network is trained using the novel ranking loss function; it +computes the ranking loss between the top-rated occurrences in the positive bag and the negative +bag. +In conclusion, the proposed method for detecting anomalies in surveillance videos consists +of two main steps. First, the ADNN architecture is used to detect motion in the input video +and generate a refined foreground mask. This mask is then used to create a trimmed video, +which is used as input for the second step of the system. In this step, we used a pre-trained +multiple instance learning model trained on a set of positive and negative videos and used to +classify each temporal segment in the test video as normal or anomalous. The predicted scores +for each segment are then combined to generate a prediction (anomaly graph) for the entire +video. By combining these two approaches, the system is able to effectively detect abnormalities +in surveillance videos, even when they only occur for a short period of time or are only present +in a small number of segments. +3 +Results +In this section, we will discuss our experimental results for two different videos. Table 1 compares +the full video and trimmed video for two different videos, labeled Video 1 and Video 2. For +Video 1, the full video had a duration of 06:37 minutes, a size of 90.5 MB, and contained 11937 +frames. The anomaly detection process for this video took 789 seconds. The trimmed video +for Video 2 had a duration of 04:09 minutes, a size of 68.5 MB, and contained 7470 frames. The +anomaly detection process for this video took 540 seconds, which is lower than the time taken +for the full video. +For Video 2, the full video had a duration of 04:59 minutes, a size of 40.6 MB, and contained +8990 frames. The anomaly detection process for this video took 610 seconds. On the other hand, +the trimmed video for Video 2 had a duration of 1:04 minutes, a size of 10.2 MB, and contained +1950 frames. The anomaly detection process for this video took 137 seconds, which is also lower +than the time taken for the full video. +7 + +Positive bag +Instance scores in positive bag +Anomaly video +Bag instance (video segment) +4096 +MIL Ranking Loss with sparsity + and smoothness constraints +609 +512 +N +C3D feature extraction +32 +for each video segment +0.1 +... +32 temporal segments +32 temporal segments +(anomaly score) +... +pre-trained 3D ConvNet +Normal video +Negative bag +Instance scores in negative bagThe graphs obtained after anomaly detection are shown below. These are the relative anomaly +scores of each video segment (32 in this case). We can see that the anomalous regions in the +trimmed video are more focused, and there are comparatively fewer inactive regions. Moreover, +the overall structure of the graphs is similar for both the original and trimmed videos, indicating +that trimming down the video does not affect the anomaly identification and the relative scores +of different segments. +Figure 5: The graphs indicate anomaly scores of the video2 (left) and its trimmed version (right) +Overall, the results in Table 1 show that the trimmed videos had shorter durations and +smaller sizes compared to the full videos. Additionally, the anomaly detection process for the +trimmed videos took much less time than the full videos in both examples. This suggests that +using trimmed videos leads to a more efficient anomaly detection process. +4 +Discussion +The results presented above demonstrate the effectiveness of our ADNN-based video surveillance +system in identifying object movement and filtering out anomalous activities. As shown, the +trimmed videos had shorter durations, smaller sizes and required less time for anomaly detection +compared to the full videos in both examples. This suggests that the ADNN model and the use +of trimmed videos lead to a more efficient and effective video surveillance system. Additionally, +the ADNN model we employed has the advantage of requiring only limited training data and +8 + +Duration +Size +Frames +Anomaly +(mm: ss) +(MB) +Detection +(cpu - sec) +Full Video +06:37 +90.5 +11937 +789 +Video 1 +Trimmed Video (Combined) +04:09 +68.5 +7470 +540 +Full Video +04:59 +40.6 +8990 +610 +Vide02 +Trimmed Video (Combined) +1:04 +10.2 +1950 +137 +Table I: Comparison of results for trimmed and full-length videos0.40 +0.35 +0.8 +0.30 +0.25 +0.6 +0.20 +0.4 +0.15 +0.10 +0.2 +0.05 +0.00 +0.0 +5 +10 +15 +20 +25 +30 +n +10 +15 +20 +25 +30being able to perform well with unseen test videos. This makes it a suitable choice for practical +implementation in real-world scenarios. +In conclusion, our ADNN-based video surveillance system has demonstrated its ability to +accurately detect object movement and filter out anomalous activities, making it a promising +solution for video surveillance applications. +5 +Future Work +In the future, we plan to work on making the ADNN model more efficient at inferring foreground +masks, as it currently takes a significant amount of time to process videos. This will be a major +challenge, but we believe it is necessary in order to make the system more practical and useful in +real-world scenarios. +Additionally, we will work on generating a better test dataset to further evaluate the adapt- +ability of this system. This will help us to better understand the limitations and potential +improvements of the system. Overall, the goal would be to improve the efficiency of the ADNN +model in order to make it a useful tool for video surveillance and anomaly detection applications. +References +[1] Zhao, C., Hu, K., Basu, A. (2022). Universal Background Subtraction Based on Arith- +metic Distribution Neural Network. IEEE Transactions on Image Processing, 31, 2934–2949. +https://doi.org/10.1109/tip.2022.3162961 +[2] Lee, B.; Hedley, M. Background estimation for video surveillance. In: +Image +Vi- +sion Computing New Zealand (IVCNZ ’02); 2002; Auckland, NZ. 2002. 315-320. +http://hdl.handle.net/102.100.100/199802 +[3] Graszka, P. (2014). Median mixture model for background–foreground segmentation in +video sequences. +[4] Roy, S. M., Ghosh, A. (2018). Real-Time Adaptive Histogram Min-Max Bucket (HMMB) +Model for Background Subtraction. IEEE Transactions on Circuits and Systems for Video +Technology, 28(7), 1513–1525. https://doi.org/10.1109/tcsvt.2017.2669362 +[5] Cheung, S.-C. S., Kamath, C. (2005). Robust Background Subtraction with Foreground +Validation for Urban Traffic Video. EURASIP Journal on Advances in Signal Processing, +2005(14). https://doi.org/10.1155/asp.2005.2330 +[6] Chen, C., Zhang, X. (2012). Moving Vehicle Detection Based on Union of Three-Frame +Difference. Lecture Notes in Electrical Engineering, 459–464. https://doi.org/10.1007/978- +3-642-27296-7 71 +[7] Stauffer, C., Grimson, W. E. L. (2022). Adaptive background mixture models for real-time +tracking. Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and +Pattern Recognition (Cat. No PR00149). https://doi.org/10.1109/cvpr.1999.784637 +[8] Chan, A. B., Mahadevan, V., Vasconcelos, N. (2010). Generalized Stauffer–Grimson back- +ground subtraction for dynamic scenes. Machine Vision and Applications, 22(5), 751–766. +https://doi.org/10.1007/s00138-010-0262-3 +9 + +[9] Guo-Jing, Chng Eng Siong, Rajan, D. (2022). Foregroung motion detection by difference- +based spatial temporal entropy image. 2004 IEEE Region 10 Conference TENCON 2004. +https://doi.org/10.1109/tencon.2004.1414436 +[10] Sengupta, S., Jayaram, V., Curless, B., Seitz, S. M., Kemelmacher-Shlizerman, I. (2020). Back- +ground Matting: The World Is Your Green Screen. 2020 IEEE/CVF Conference on Computer +Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr42600.2020.00236 +[11] Yang, D., Zhao, C., Zhang, X., Huang, S. (2018). Background Modeling by Stability of Adap- +tive Features in Complex Scenes. IEEE Transactions on Image Processing, 27(3), 1112–1125. +https://doi.org/10.1109/tip.2017.2768828 +[12] Kuang, Z., Tie, X. (2021). Flow-based Video Segmentation for Human Head and Shoulders. +ArXiv.org. https://doi.org/10.48550/arXiv.2104.09752 +[13] sniklaus. (2022, March 16). sniklaus/pytorch-pwc: a reimplementation of PWC-Net in Py- +Torch that matches the official Caffe version. GitHub. https://github.com/sniklaus/pytorch- +pwc +[14] Yadav, D. K., Sharma, L., Bharti, S. K. (2014). Moving object detection in real-time visual +surveillance using background subtraction technique. 2014 14th International Conference +on Hybrid Intelligent Systems. https://doi.org/10.1109/his.2014.7086176 +[15] Sauvalle, +B., +de. +(2021). +Autoencoder-based +background +reconstruction +and +foreground +segmentation +with +background +noise +estimation. +ArXiv.org. +https://doi.org/10.48550/arXiv.2112.08001 +[16] Wang, Y., Jodoin, P.-M., Porikli, F., Konrad, J., Benezeth, Y., Ishwar, P. (2014). CDnet 2014: +An Expanded Change Detection Benchmark Dataset. 2014 IEEE Conference on Computer +Vision and Pattern Recognition Workshops. https://doi.org/10.1109/cvprw.2014.126 +[17] Cuevas, C., Y´a˜nez, E. M., Garc´ıa, N. (2016). Labeled dataset for integral evaluation of mov- +ing object detection algorithms: LASIESTA. Computer Vision and Image Understanding, +152, 103–117. https://doi.org/10.1016/j.cviu.2016.08.005 +[18] Tao, Y., Palasek, P., Ling, Z., Patras, I. (2017). Background modelling based on genera- +tive unet. 2017 14th IEEE International Conference on Advanced Video and Signal Based +Surveillance (AVSS). https://doi.org/10.1109/avss.2017.8078483 +[19] De Gregorio, M., Giordano, M. (2017). Background estimation by weightless neural net- +works. Pattern Recognition Letters, 96, 55–65. https://doi.org/10.1016/j.patrec.2017.05.029 +[20] Ramirez-Alonso, G., Ramirez-Quintana, J. A., +Chacon-Murguia, M. I. (2017). Tem- +poral weighted learning model for background estimation with an automatic re- +initialization stage and adaptive parameters update. Pattern Recognition Letters, 96, 34–44. +https://doi.org/10.1016/j.patrec.2017.01.011 +[21] Laugraud, B., Pierard, S., Van Droogenbroeck, M. (2016). LaBGen-P: A pixel-level stationary +background generation method based on LaBGen. 2016 23rd International Conference on +Pattern Recognition (ICPR). https://doi.org/10.1109/icpr.2016.7899617 +[22] Jodoin, P.-M., Maddalena, L., Petrosino, A., Wang, Y. (2017). Extensive Benchmark and +Survey of Modeling Methods for Scene Background Initialization. IEEE Transactions on +Image Processing, 26(11), 5244–5256. https://doi.org/10.1109/tip.2017.2728181 +10 + +[23] Dong, +G., +Ma, +Y., +Basu, +A. +(2021). +Feature-Guided +CNN +for +Denoising +Images +From +Portable +Ultrasound +Devices. +IEEE +Access, +9, +28272–28281. +https://doi.org/10.1109/access.2021.3059003 +[24] Ma, Y., Yang, F., Basu, A. (2021). Edge-guided CNN for Denoising Images from Portable +Ultrasound Devices. 2020 25th International Conference on Pattern Recognition (ICPR). +https://doi.org/10.1109/icpr48806.2021.9412758 +[25] Giraldo, J. H., Bouwmans, T. (2020). GraphBGS: Background Subtraction via Recovery of +Graph Signals. ArXiv.org. https://doi.org/10.48550/arXiv.2001.06404 +[26] Kuang, Z., Tie, X. (2020). Improved Actor Relation Graph based Group Activity Recognition. +ArXiv.org. https://doi.org/10.48550/arXiv.2010.12968 +[27] Zhao, C., Sain, A., Qu, Y., Ge, Y., Hu, H. (2019). Background Subtraction Based on Integra- +tion of Alternative Cues in Freely Moving Camera. IEEE Transactions on Circuits and Sys- +tems for Video Technology, 29(7), 1933–1945. https://doi.org/10.1109/tcsvt.2018.2854273 +[28] Yu, R., Cheng, I., Zhu, B., Bedmutha, S., +Basu, A. (2018). Adaptive Resolution +Optimization and Tracklet Reliability Assessment for Efficient Multi-Object Tracking. +IEEE Transactions on Circuits and Systems for Video Technology, 28(7), 1623–1633. +https://doi.org/10.1109/tcsvt.2017.2668278 +[29] Sun, C., Song, L. B. (2021). Product Re-identification System in Fully Automated Defect +Detection. ArXiv.org. https://doi.org/10.48550/arXiv.2112.10324 +[30] Zhao, C., Basu, A. (2021). Pixel Distribution Learning for Vessel Segmentation under +Multiple Scales. 2021 43rd Annual International Conference of the IEEE Engineering in +Medicine Biology Society (EMBC). https://doi.org/10.1109/embc46164.2021.9629614 +[31] Fu, H., Xu, Y., Lin, S., Kee Wong, D. W., Liu, J. (2016). DeepVessel: Retinal Vessel Segmen- +tation via Deep Learning and Conditional Random Field. Medical Image Computing and +Computer-Assisted Intervention – MICCAI 2016, 132–139. https://doi.org/10.1007/978-3- +319-46723-8 16 +[32] Dasgupta, A., Singh, S. (2017). A fully convolutional neural network based structured +prediction approach towards the retinal vessel segmentation. 2017 IEEE 14th International +Symposium on Biomedical Imaging (ISBI 2017). https://doi.org/10.1109/isbi.2017.7950512 +[33] Staal, J., Abramoff, M. D., Niemeijer, M., Viergever, M. A., van Ginneken, B. (2004). Ridge- +Based Vessel Segmentation in Color Images of the Retina. IEEE Transactions on Medical +Imaging, 23(4), 501–509. https://doi.org/10.1109/tmi.2004.825627 +[34] Ge, Y., Zhang, J., Ren, X., Zhao, C., Yang, J., Basu, A. (2021). Deep Variation Transformation +Network for Foreground Detection. IEEE Transactions on Circuits and Systems for Video +Technology, 31(9), 3544–3558. https://doi.org/10.1109/tcsvt.2020.3042559 +[35] Zhao, C., Cham, T.-L., Ren, X., Cai, J., Zhu, H. (2018). Background Subtraction Based on +Deep Pixel Distribution Learning. 2018 IEEE International Conference on Multimedia and +Expo (ICME). https://doi.org/10.1109/icme.2018.8486510 +[36] Zhao, C., Basu, A. (2020). Dynamic Deep Pixel Distribution Learning for Background Sub- +traction. IEEE Transactions on Circuits and Systems for Video Technology, 30(11), 4192–4206. +https://doi.org/10.1109/tcsvt.2019.2951778 +11 + +[37] Singh, V., Singh, S., +Gupta, P. (2020). Real-Time Anomaly Recognition Through +CCTV +Using +Neural +Networks. +Procedia +Computer +Science, +173, +254–263. +https://doi.org/10.1016/j.procs.2020.06.030 +[38] Fan, Y., Wen, G., Li, D., Qiu, S., Levine, M. D. (2018). Early event detection based on dynamic +images of surveillance videos. Journal of Visual Communication and Image Representation, +51, 70–75. https://doi.org/10.1016/j.jvcir.2018.01.002 +[39] Tian, Y., Pang, G., Chen, Y., Singh, R., Verjans, Johan W, Carneiro, G. (2021). Weakly- +supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning. +ArXiv.org. https://doi.org/10.48550/arXiv.2101.10030 +[40] Liu, W., Luo, W., Li, Z., Zhao, P., +Gao, S. (2019). Margin Learning Embedded Pre- +diction for Video Anomaly Detection with A Few Anomalies. Ijcai.org, 3023–3030. +https://www.ijcai.org/proceedings/2019/419 +[41] Pang, G., Cao, L., Chen, L., +Liu, H. (2018). Learning Representations of Ultrahigh- +dimensional Data for Random Distance-based Outlier Detection. Proceedings of the +24th ACM SIGKDD International Conference on Knowledge Discovery Data Mining. +https://doi.org/10.1145/3219819.3220042 +[42] Pang, G., Shen, C., Anton. (2019). Deep Anomaly Detection with Deviation Networks. +ArXiv.org. https://doi.org/10.48550/arXiv.1911.08623 +[43] Deshpande, K., Punn, N. S., Sonbhadra, Sanjay Kumar, Agarwal, S. (2022). Anomaly +detection in surveillance videos using transformer-based attention model. ArXiv.org. +https://doi.org/10.48550/arXiv.2206.01524 +[44] Sultani, W., Chen, C., Shah, M. (2018). Real-world Anomaly Detection in Surveillance +Videos. ArXiv.org. https://doi.org/10.48550/arXiv.1801.04264 +[45] zhaochenqiu. +(2022, +June +6). +zhaochenqiu/UBgS ADNNet. +GitHub. +https://github.com/zhaochenqiu/UBgS ADNNet +12 + diff --git a/0tAyT4oBgHgl3EQfbfeR/content/tmp_files/load_file.txt b/0tAyT4oBgHgl3EQfbfeR/content/tmp_files/load_file.txt new file mode 100644 index 0000000000000000000000000000000000000000..3655711f1e0a440f7ed099457c3455d432549017 --- /dev/null +++ b/0tAyT4oBgHgl3EQfbfeR/content/tmp_files/load_file.txt @@ -0,0 +1,723 @@ +filepath=/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf,len=722 +page_content='Application Of ADNN For Background Subtraction In Smart Surveillance System University Of Alberta Department of Computing Science Piyush Batra, Gagan Raj Singh, Neeraj Goyal December 7, 2022 Brief Abstract Object movement identification is one of the most researched problems in the field of computer vision.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' In this task, we try to classify a pixel as foreground or background.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Even though numerous traditional machine learning and deep learning methods already exist for this problem, the two major issues with most of them are the need for large amounts of ground truth data and their inferior performance on unseen videos.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Since every pixel of every frame has to be labeled, acquiring large amounts of data for these techniques gets rather expensive.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Recently, Zhao et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [1] proposed one of a kind Arithmetic Distribution Neural Network (ADNN) for universal background subtraction which utilizes probability information from the histogram of temporal pixels and achieves promising results.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Building onto this work, we developed an intelligent video surveillance system that uses ADNN architecture for motion detection, trims the video with parts only containing motion, and performs anomaly detection on the trimmed video.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' 1 Literature review Motion detection aims to find regions related to moving objects, and background subtraction is a widely used technique for this task.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Herein, every pixel of each video frame is compared against their historical counterparts or a background model, depending on the technique, and then classified into foreground or background.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Pixels that differ significantly from the reference are classified as moving objects or foreground, and static pixels are referred to as background.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This section will discuss some of the previously proposed methods related to background subtraction, video surveillance, and anomaly detection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Many background modeling techniques based on mathematical theories, like the temporal average [2], temporal median [3], or the histogram over time [4], have been proposed for motion detection by a stationary camera.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' But these are not robust to challenges in surveillance videos such as dynamic background, object shadow, camera jitter, weather conditions (either snow or rain), and variations in illumination.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' To overcome these problems, several motion-detection methods, like Temporal Differencing [5], Three-frame Difference [6], Gaussian mixture model [7], 1 DSTEI [9], etc, have been presented over the past years.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Temporal Differencing, proposed by Cheung et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [5], is used for detecting temporal changes in intensity in video frames.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' However, its main drawback is that the detected objects are incomplete and poorly presented.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' In the Gaussian mixture model proposed by Stauffer and Grimson [7], the temporal histogram of each pixel is modeled using a mixture of K Gaussian distributions to precisely model a dynamic background.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This method produced a real-time tracker which can deal with lighting changes, repetitive motions from clutter, and long-term scene changes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Later, Chan et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [8] proposed a Generalized Stauffer–Grimson (GSG) algorithm for background subtraction in dynamic scenes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' In this method, the statistics required for online learning of dynamic texture are derived from generalizing the GMM proposed by Stauffer and Grimson [7].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' The Difference-based Spatio-Temporal Entropy Image (DSTEI) by Jing et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [9] is an entropy- based method for human motion detection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' A Spatio-temporal histogram is generated by accumulated pixels obtained by the difference between consecutive images.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This histogram is then normalized to calculate the degree of randomness and magnitude of entropy to denote the significance of motion.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' In this method, noises are assumed to follow Gaussian distribution.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' However, these assumptions, such as heavy shadows or sudden illumination changes, will be violated in some cases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Soumyadip Sengupta et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [10] proposed a background matting technique that generated high-quality foreground and alpha mattes in natural settings.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' In this method, a deep learning framework is developed and trained on synthetic-composite data and then adapted to actual data using an adversarial network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Even though providing an additional photo of the background requires a small amount of foresight, it is far less tedious than creating a trimap for traditional matting methods.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' In 2017, Dan Yang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [11] proposed a multi-feature background approach for complex video scenes that measures the stability of features and then selects different dominant features to model the background from the pixel and time-sequence domains.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This Stability of Adaptive Features approach showed promising results on both complex and baseline scenes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' For applications of background subtraction in real time, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Kuang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [12] proposed a combination of the Horn-Schunck optical-flow estimation technique [13] and autoencoder neural networks that solve the problem of motion blur in real-time background subtraction during video conferencing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This method uses an optical-flow-based model to extract motion features between every two frames and then combine these features with the appearance feature from the original frame.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' An encoder-decoder network in combination with CNN is then used to learn and predict a mask output for the human head and shoulders for background subtraction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Similarly, for real-time background subtraction, DK Yadav et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [14] proposed a Pixel Intensity Based (PIBBS) system that first models the background, then extracts moving objects with a threshold and updates the background using a feedback-based background updation scheme.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' To improve the detection quality, this system also uses morphological operators as the last step.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Bruno Sauvalle et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [15] proposed using an autoencoder to model the background of a video as a low-dimensional manifold.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' The output of this autoencoder is then compared with the original image to compute the segmentation masks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' In this method, the autoencoder is also trained to predict the background noise, which allows it to compute a pixel-dependent threshold for each frame to perform the foreground segmentation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Without using temporal or motion information, this method could perform at par with state-of-the-art solutions on CDnet 2014 [16] 2 and LASIESTA [17] datasets.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' To overcome the problem of camera jitter and sudden changes in illumination, Ye Tao et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [18] proposed a generative architecture for unsupervised deep background modeling, which learns the parameters automatically and uses intensity and optical flow features between a reference and a target frame.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This system generates a background with a probabilistic heat map of the color values for a given input frame.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This method could also be applied to unseen videos without re-training.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' When tested, this method shows promising results over state-of-the-art [19][20][21] methods on the SBMnet dataset[22].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Guanfang Dong et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [23] proposed a novel denoising neural network model called Feature- guided Denoising Convolutional Neural Network (FDCNN) to denoise the images produced by portable devices.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This technique employed a hierarchical denoising framework driven by a feature masking layer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' The feature extraction algorithm used in this method is based on Explainable Artificial Intelligence (XAI) for medical images.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Similarly, Yingnan Ma et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [24] proposed an Edge-guided Denoising Convolutional Neural Network which can preserve important edge information in ultrasound images when removing noise.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This method increases the recognition of various organs in ultrasound images.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Jhony H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Giraldo et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [25] proposed a new algorithm called Graph Background Subtraction (GraphBGS).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' It is composed of instance segmentation, background initialization, graph construc- tion, and graph sampling.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Unlike Deep Learning methods for background subtraction which require vast amounts of data, this method is a semi-supervised algorithm inspired by the theory of recovery of graph signals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' To generate descriptions of human actions and their interactions, Zijian Kuang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [26] proposed a technique that utilizes an Actor Relation Graph (ARG) based model with novel improvements for group activity recognition.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This method also used MobileNet as the backbone to extract features from each video frame.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' To accurately perform background subtraction in a freely moving camera, Zhao et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [27] developed a novel method called “the integration of foreground and background cues.” The underlying motivation in this technique is to utilize the exclusiveness between these cues to compensate for their corresponding defects.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' The foreground is segmented by combining super- pixels with proximity under multiple levels.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' As video resolution and, subsequently, the video size is increasing daily, Ruixing et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [28] proposed a method to compute the optimal image resolution adaptively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This is achieved by exploiting the correlation between an image’s gray-value distribution and resolution.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This approach was proposed to increase the performance of multi-object online tracking and learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' A novel tracklet reliability assessment metric was also introduced in this paper to eliminate the incorrect samples and can recover occluded targets.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' As a unique application of neural networks in multimedia, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Sun et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [29] proposed a 2-step product re-identification (Re-ID) method which involves image feature extraction and a feature search and retrieval engine.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' To extract the features of the input image, a novel AlphaAlexNet, an extended version of the AlexNet, is being used.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Vearch, a visual search system, is used as the image search similarity engine.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' The new model - AlphaAlexNet, demonstrated improved object detection accuracy of Vearch.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' To classify two distributions without using just histograms and incorporating a deep learning network to learn and classify distributions automatically, Chunqiu Zhao and Anup Basu [30] proposed a novel vessel segmentation method based on distribution learning using a spatial distribution descriptor (RPoSP) under multiple scales.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Here, statistical distributions are indirectly forced as an input to a CNN for distribution learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' The proposed approach showed promising 3 results when compared to existing state-of-the-art methods[31][32] on the DRIVE[33] dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Yongxin Ge et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [34] proposed the Deep Variation Transformation Network (DVTN) model, which uses pixel variations to detect the background.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This model assigns the probability to each pixel, and then by using thresholding, it computes whether it’s background or foreground.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This model compares the pixel variation instead of distributions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Previously used models in background detection usually fail when they encounter similar observations, causing false detections.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' The DVTN analyzes the pixel variations in a new space, where the above observations are classified easily.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This model outperforms the traditional background detection models by showing astonishing results on the CDnet2014 dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' However, all of the methods mentioned above require either a large amount of ground truth data or result in inadequate performance on unseen videos.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Zhao and Basu [35] proposed a Deep Pixel Distribution Learning (DPDL) technique to overcome these issues.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Unlike typical approaches, which compare new frames to a formulated background model, this technique focuses on comparing pixels’ current and historical frames.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This method uses a novel pixel- based feature called the Random Permutation of Temporal Pixels (RPoTP) to represent the distribution of past observations for a particular pixel.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Subsequently, a CNN is used to learn whether the current pixel is foreground or background.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Adding on to this method, Zhao et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [36] later proposed a new Dynamic Deep Pixel Distribution Learning (D-DPDL) technique.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' In this method, the RPoTP feature is dynamically permuted in this method for every training epoch.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' To compensate for the random noise generated in this process, a Bayesian Refinement model is used and improve the accuracy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Zhao et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [1] also proposed an Arithmetic Distribution Neural Network architecture demon- strating even better performance than the D-DPDL method.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' The input in the ADNN method is histograms of subtractions between current pixels and their historical counterparts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' The sum and product arithmetic distribution layers proposed here demonstrate a better ability to clas- sify distributions than the convolutional layers in D-DPDL.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Moreover, the number of learning parameters used in ADNN architecture (0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1 Million) is significantly less than that used in the D-DPDL method (7 Million).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Coming onto detecting anomalies in videos, Virender Singh et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [37] proposed an approach to detect variation from the norm in real-world CCTV recordings.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This method uses two deep learning models (CNN and RNN) to learn a general anomaly detection model with a poorly labeled dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' The training dataset has been doubled by flipping the videos horizontally, thus increasing the testing accuracy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' The overall accuracy of the model is 97.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='23 Y Fan et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [38] proposed a technique that first converts the video clips of an ongoing event into Dynamic Images, which can simultaneously capture the appearance and temporal evolution of the occurrence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' The approach uses dynamic images of two categories of video clips and involves training a detector based on deep-learning techniques.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Yu Tian et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [39] proposed a weakly-supervised anomaly detection algorithm, Robust Temporal Feature Magnitude learning (RTFM), aiming to identify snippets containing abnormal events.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This method trains a feature magnitude learning function to effectively recognize the positive instances, substantially enhancing the robustness of this method to the negative instances from abnormal videos.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' RTFM achieves significantly improved subtle anomaly discriminability and sample efficiency.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' The Weakly Supervised Video Anomaly Detection(WSVAD) [40]-[42] method for anomaly detection suffers from the wrong identification of normal and abnormal instances during the training process.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Kapil Deshpande et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [43] proposed better-quality transformer-based features named Videoswin Features, followed by an attention layer to capture long and short-range 4 dependencies in the temporal domain.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This method extracts better-quality features from available videos resulting in better performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' 2 Method In this work, we implemented an Arithmetic Distribution Neural Network [1] to develop a video surveillance system for identifying object movement in a static video.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' In this ADNN model, the arithmetic operations are utilized to introduce the arithmetic distribution layers, including the product and sum distribution layers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Outputs from these layers are combined and passed through a classifier for accurate classification.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' We chose this architecture because it requires training only one network, with limited training data, and it works well with unseen test videos.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Figure 1: The flow diagram of our proposed approach Upon successful object movement detection using background subtraction, we further ana- lyzed the results obtained from ADNN to filter out their anomalous activities.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1 Motion detection - Arithmetic Distribution Neural Network In this work, we used ADNN proposed by Zhao et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [1] to detect motion in the input surveil- lance video.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This paper proposed arithmetic distribution layers, which are a new type of network layer that is designed to improve distribution analysis in classification tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' These layers, which include product and sum distribution layers, are an alternative to convolution layers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' During the forward pass of the proposed arithmetic distribution layers, the input distributions are processed using the distributions in the learning kernels to generate the output distributions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' In the backpropagation process, the gradient of the distributions in the learning kernels with respect to the network output is calculated to update the learning kernels.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' These operations are based on histograms and arithmetic distribution operations rather than the matrix arithmetic operations used in traditional convolution layers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' To improve the accuracy of the foreground mask generated, an improved Bayesian refinement model is used.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This model takes into account the correlations between pixels by using a mixture of Gaussian approximation functions rather than just Euclidean distance, as in the original Bayesian refinement model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' The Bayesian refinement model is used to iteratively refine the foreground mask, with the output of the arithmetic distribution neural network serving as the initial binary mask for the iteration process.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' 5 Sum Distribution Layer Convolution Classifier Anomalydetection ramework Basedonpixe classification A Set of trimmed videos Input Video with activity A Set of anomaly detected videos Product Distribution LayerFigure 2: Arithmetic distribution neural network for background subtraction After obtaining the refined foreground masks from the ADNN architecture,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' we utilize a python script to generate a trimmed video from a set of input frames by using a threshold value on the frames generated by the Bayesian refinement model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' The threshold value determines the minimum number of white pixels (foreground pixels) that must be present in a frame in order for it to be included in the trimmed video.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' For this work, we are using a threshold value of 5% to generate the trimmed videos.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Figure 3: Generation of trimmed video from input frames passed through the ADNN (arithmetic distribution neural network) 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2 Anomaly Detection Following the works of Waqas Sultani et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [44], we have put into use their novel Multiple Instance Learning framework for the second part of our system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Once we obtain the trimmed video from the previous step, we use that as the input in this step.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' In this, a training set of positive (containing an abnormality someplace) and negative (having no anomaly) videos are used to train the anomaly detection model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Then each video is divided into a sequence of non-overlapping temporal segments.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' 6 Convolution,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='Full Connection ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='Relu ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='Convolution ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='Product ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='Distribution Layer ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='Foreground ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='Size:B × 3 × 201 × 2 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='Background ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='Size:B × 3 × 201 × 1 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='Size:B × 2×1 × 1 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='Size:B × 10 × 201 × 1 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='Sum Distribution ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='Layer ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='Size:B × 512 × 1 × 1 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='Datadimensions:BatchSize×Channels×Width×Height ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='Size:B × 3 × 201 × 2Frames from ADNN Output ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='CombinedTrimmed Videc ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='Motion Video1 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='Motion Video 2 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='Framesfrom inputvideo ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='Input VideoFigure 4: Anomaly detection flow diagram ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='Each video in the training set can be represented as a bag,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' and each video segment represents an instance in the bag.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' After extracting C3D features from video segments using a pre-trained 3D convNet, a fully connected neural network is trained using the novel ranking loss function;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' it computes the ranking loss between the top-rated occurrences in the positive bag and the negative bag.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' In conclusion, the proposed method for detecting anomalies in surveillance videos consists of two main steps.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' First, the ADNN architecture is used to detect motion in the input video and generate a refined foreground mask.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This mask is then used to create a trimmed video, which is used as input for the second step of the system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' In this step, we used a pre-trained multiple instance learning model trained on a set of positive and negative videos and used to classify each temporal segment in the test video as normal or anomalous.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' The predicted scores for each segment are then combined to generate a prediction (anomaly graph) for the entire video.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' By combining these two approaches, the system is able to effectively detect abnormalities in surveillance videos, even when they only occur for a short period of time or are only present in a small number of segments.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' 3 Results In this section, we will discuss our experimental results for two different videos.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Table 1 compares the full video and trimmed video for two different videos, labeled Video 1 and Video 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' For Video 1, the full video had a duration of 06:37 minutes, a size of 90.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='5 MB, and contained 11937 frames.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' The anomaly detection process for this video took 789 seconds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' The trimmed video for Video 2 had a duration of 04:09 minutes, a size of 68.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='5 MB, and contained 7470 frames.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' The anomaly detection process for this video took 540 seconds, which is lower than the time taken for the full video.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' For Video 2, the full video had a duration of 04:59 minutes, a size of 40.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='6 MB, and contained 8990 frames.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' The anomaly detection process for this video took 610 seconds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' On the other hand, the trimmed video for Video 2 had a duration of 1:04 minutes, a size of 10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2 MB, and contained 1950 frames.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' The anomaly detection process for this video took 137 seconds, which is also lower than the time taken for the full video.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' 7 Positive bag Instance scores in positive bag Anomaly video Bag instance (video segment) 4096 MIL Ranking Loss with sparsity and smoothness constraints 609 512 N C3D feature extraction 32 for each video segment 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1 .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='..' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' 32 temporal segments 32 temporal segments (anomaly score) .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='..' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' pre-trained 3D ConvNet Normal video Negative bag Instance scores in negative bagThe graphs obtained after anomaly detection are shown below.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' These are the relative anomaly scores of each video segment (32 in this case).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' We can see that the anomalous regions in the trimmed video are more focused, and there are comparatively fewer inactive regions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Moreover, the overall structure of the graphs is similar for both the original and trimmed videos, indicating that trimming down the video does not affect the anomaly identification and the relative scores of different segments.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Figure 5: The graphs indicate anomaly scores of the video2 (left) and its trimmed version (right) Overall, the results in Table 1 show that the trimmed videos had shorter durations and smaller sizes compared to the full videos.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Additionally, the anomaly detection process for the trimmed videos took much less time than the full videos in both examples.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This suggests that using trimmed videos leads to a more efficient anomaly detection process.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' 4 Discussion The results presented above demonstrate the effectiveness of our ADNN-based video surveillance system in identifying object movement and filtering out anomalous activities.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' As shown, the trimmed videos had shorter durations, smaller sizes and required less time for anomaly detection compared to the full videos in both examples.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This suggests that the ADNN model and the use of trimmed videos lead to a more efficient and effective video surveillance system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Additionally, the ADNN model we employed has the advantage of requiring only limited training data and 8 Duration Size Frames Anomaly (mm: ss) (MB) Detection (cpu - sec) Full Video 06:37 90.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='5 11937 789 Video 1 Trimmed Video (Combined) 04:09 68.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='5 7470 540 Full Video 04:59 40.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='6 8990 610 Vide02 Trimmed Video (Combined) 1:04 10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2 1950 137 Table I: Comparison of results for trimmed and full-length videos0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='40 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='35 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='8 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='30 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='25 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='20 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='15 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='10 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='05 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='0 5 10 15 20 25 30 n 10 15 20 25 30being able to perform well with unseen test videos.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This makes it a suitable choice for practical implementation in real-world scenarios.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' In conclusion, our ADNN-based video surveillance system has demonstrated its ability to accurately detect object movement and filter out anomalous activities, making it a promising solution for video surveillance applications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' 5 Future Work In the future, we plan to work on making the ADNN model more efficient at inferring foreground masks, as it currently takes a significant amount of time to process videos.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This will be a major challenge, but we believe it is necessary in order to make the system more practical and useful in real-world scenarios.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Additionally, we will work on generating a better test dataset to further evaluate the adapt- ability of this system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' This will help us to better understand the limitations and potential improvements of the system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Overall, the goal would be to improve the efficiency of the ADNN model in order to make it a useful tool for video surveillance and anomaly detection applications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' References [1] Zhao, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Hu, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Basu, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Universal Background Subtraction Based on Arith- metic Distribution Neural Network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' IEEE Transactions on Image Processing, 31, 2934–2949.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1109/tip.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2022.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='3162961 [2] Lee, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Hedley, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Background estimation for video surveillance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' In: Image Vi- sion Computing New Zealand (IVCNZ ’02);' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' 2002;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Auckland, NZ.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' 2002.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' 315-320.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' http://hdl.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='handle.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='net/102.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='100.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='100/199802 [3] Graszka, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2014).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Median mixture model for background–foreground segmentation in video sequences.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' [4] Roy, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Ghosh, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Real-Time Adaptive Histogram Min-Max Bucket (HMMB) Model for Background Subtraction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' IEEE Transactions on Circuits and Systems for Video Technology, 28(7), 1513–1525.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1109/tcsvt.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2017.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2669362 [5] Cheung, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='-C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Kamath, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2005).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Robust Background Subtraction with Foreground Validation for Urban Traffic Video.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' EURASIP Journal on Advances in Signal Processing, 2005(14).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1155/asp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2005.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2330 [6] Chen, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Zhang, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2012).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Moving Vehicle Detection Based on Union of Three-Frame Difference.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Lecture Notes in Electrical Engineering, 459–464.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1007/978- 3-642-27296-7 71 [7] Stauffer, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Grimson, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Adaptive background mixture models for real-time tracking.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Proceedings.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' No PR00149).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1109/cvpr.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1999.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='784637 [8] Chan, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Mahadevan, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Vasconcelos, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2010).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Generalized Stauffer–Grimson back- ground subtraction for dynamic scenes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Machine Vision and Applications, 22(5), 751–766.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1007/s00138-010-0262-3 9 [9] Guo-Jing, Chng Eng Siong, Rajan, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Foregroung motion detection by difference- based spatial temporal entropy image.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' 2004 IEEE Region 10 Conference TENCON 2004.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1109/tencon.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2004.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1414436 [10] Sengupta, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Jayaram, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Curless, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Seitz, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Kemelmacher-Shlizerman, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Back- ground Matting: The World Is Your Green Screen.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1109/cvpr42600.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='00236 [11] Yang, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Zhao, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Zhang, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Huang, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Background Modeling by Stability of Adap- tive Features in Complex Scenes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' IEEE Transactions on Image Processing, 27(3), 1112–1125.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1109/tip.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2017.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2768828 [12] Kuang, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Tie, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Flow-based Video Segmentation for Human Head and Shoulders.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' ArXiv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='48550/arXiv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2104.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='09752 [13] sniklaus.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2022, March 16).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' sniklaus/pytorch-pwc: a reimplementation of PWC-Net in Py- Torch that matches the official Caffe version.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' GitHub.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://github.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='com/sniklaus/pytorch- pwc [14] Yadav, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Sharma, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Bharti, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2014).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Moving object detection in real-time visual surveillance using background subtraction technique.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' 2014 14th International Conference on Hybrid Intelligent Systems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1109/his.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2014.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='7086176 [15] Sauvalle, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', de.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Autoencoder-based background reconstruction and foreground segmentation with background noise estimation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' ArXiv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='48550/arXiv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2112.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='08001 [16] Wang, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Jodoin, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='-M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Porikli, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Konrad, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Benezeth, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Ishwar, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2014).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' CDnet 2014: An Expanded Change Detection Benchmark Dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1109/cvprw.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2014.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='126 [17] Cuevas, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Y´a˜nez, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Garc´ıa, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2016).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Labeled dataset for integral evaluation of mov- ing object detection algorithms: LASIESTA.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Computer Vision and Image Understanding, 152, 103–117.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1016/j.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='cviu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2016.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='08.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='005 [18] Tao, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Palasek, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Ling, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Patras, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Background modelling based on genera- tive unet.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1109/avss.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2017.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='8078483 [19] De Gregorio, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Giordano, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Background estimation by weightless neural net- works.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Pattern Recognition Letters, 96, 55–65.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1016/j.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='patrec.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2017.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='05.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='029 [20] Ramirez-Alonso, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Ramirez-Quintana, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Chacon-Murguia, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Tem- poral weighted learning model for background estimation with an automatic re- initialization stage and adaptive parameters update.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Pattern Recognition Letters, 96, 34–44.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1016/j.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='patrec.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2017.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='01.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='011 [21] Laugraud, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Pierard, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Van Droogenbroeck, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2016).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' LaBGen-P: A pixel-level stationary background generation method based on LaBGen.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' 2016 23rd International Conference on Pattern Recognition (ICPR).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1109/icpr.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2016.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='7899617 [22] Jodoin, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='-M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Maddalena, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Petrosino, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Wang, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Extensive Benchmark and Survey of Modeling Methods for Scene Background Initialization.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' IEEE Transactions on Image Processing, 26(11), 5244–5256.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1109/tip.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2017.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2728181 10 [23] Dong, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Ma, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Basu, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Feature-Guided CNN for Denoising Images From Portable Ultrasound Devices.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' IEEE Access, 9, 28272–28281.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1109/access.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2021.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='3059003 [24] Ma, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Yang, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Basu, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Edge-guided CNN for Denoising Images from Portable Ultrasound Devices.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' 2020 25th International Conference on Pattern Recognition (ICPR).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1109/icpr48806.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2021.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='9412758 [25] Giraldo, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Bouwmans, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' GraphBGS: Background Subtraction via Recovery of Graph Signals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' ArXiv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='48550/arXiv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2001.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='06404 [26] Kuang, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Tie, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Improved Actor Relation Graph based Group Activity Recognition.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' ArXiv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='48550/arXiv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2010.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='12968 [27] Zhao, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Sain, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Qu, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Ge, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Hu, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2019).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Background Subtraction Based on Integra- tion of Alternative Cues in Freely Moving Camera.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' IEEE Transactions on Circuits and Sys- tems for Video Technology, 29(7), 1933–1945.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1109/tcsvt.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2018.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2854273 [28] Yu, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Cheng, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Zhu, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Bedmutha, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Basu, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Adaptive Resolution Optimization and Tracklet Reliability Assessment for Efficient Multi-Object Tracking.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' IEEE Transactions on Circuits and Systems for Video Technology, 28(7), 1623–1633.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1109/tcsvt.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2017.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2668278 [29] Sun, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Song, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Product Re-identification System in Fully Automated Defect Detection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' ArXiv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='48550/arXiv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2112.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='10324 [30] Zhao, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Basu, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Pixel Distribution Learning for Vessel Segmentation under Multiple Scales.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' 2021 43rd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1109/embc46164.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2021.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='9629614 [31] Fu, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Xu, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Lin, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Kee Wong, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Liu, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2016).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' DeepVessel: Retinal Vessel Segmen- tation via Deep Learning and Conditional Random Field.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, 132–139.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1007/978-3- 319-46723-8 16 [32] Dasgupta, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Singh, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1109/isbi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2017.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='7950512 [33] Staal, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Abramoff, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Niemeijer, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Viergever, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', van Ginneken, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2004).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Ridge- Based Vessel Segmentation in Color Images of the Retina.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' IEEE Transactions on Medical Imaging, 23(4), 501–509.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1109/tmi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2004.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='825627 [34] Ge, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Zhang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Ren, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Zhao, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Yang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Basu, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Deep Variation Transformation Network for Foreground Detection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' IEEE Transactions on Circuits and Systems for Video Technology, 31(9), 3544–3558.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1109/tcsvt.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='3042559 [35] Zhao, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Cham, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='-L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Ren, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Cai, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Zhu, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Background Subtraction Based on Deep Pixel Distribution Learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' 2018 IEEE International Conference on Multimedia and Expo (ICME).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1109/icme.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2018.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='8486510 [36] Zhao, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Basu, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Dynamic Deep Pixel Distribution Learning for Background Sub- traction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' IEEE Transactions on Circuits and Systems for Video Technology, 30(11), 4192–4206.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1109/tcsvt.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2019.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2951778 11 [37] Singh, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Singh, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Gupta, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Real-Time Anomaly Recognition Through CCTV Using Neural Networks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Procedia Computer Science, 173, 254–263.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1016/j.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='procs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='06.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='030 [38] Fan, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Wen, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Li, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Qiu, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Levine, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Early event detection based on dynamic images of surveillance videos.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Journal of Visual Communication and Image Representation, 51, 70–75.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1016/j.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='jvcir.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2018.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='01.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='002 [39] Tian, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Pang, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Chen, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Singh, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Verjans, Johan W, Carneiro, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Weakly- supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' ArXiv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='48550/arXiv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2101.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='10030 [40] Liu, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Luo, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Li, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Zhao, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Gao, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2019).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Margin Learning Embedded Pre- diction for Video Anomaly Detection with A Few Anomalies.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Ijcai.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org, 3023–3030.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://www.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='ijcai.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/proceedings/2019/419 [41] Pang, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Cao, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Chen, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Liu, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Learning Representations of Ultrahigh- dimensional Data for Random Distance-based Outlier Detection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery Data Mining.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1145/3219819.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='3220042 [42] Pang, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Shen, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Anton.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2019).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Deep Anomaly Detection with Deviation Networks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' ArXiv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='48550/arXiv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1911.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='08623 [43] Deshpande, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Punn, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Sonbhadra, Sanjay Kumar, Agarwal, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Anomaly detection in surveillance videos using transformer-based attention model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' ArXiv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='48550/arXiv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='2206.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='01524 [44] Sultani, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Chen, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=', Shah, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' Real-world Anomaly Detection in Surveillance Videos.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' ArXiv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='48550/arXiv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='1801.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='04264 [45] zhaochenqiu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' (2022, June 6).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' zhaochenqiu/UBgS ADNNet.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' GitHub.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content=' https://github.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} +page_content='com/zhaochenqiu/UBgS ADNNet 12' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAyT4oBgHgl3EQfbfeR/content/2301.00264v1.pdf'} diff --git a/2NAzT4oBgHgl3EQf8_6P/content/tmp_files/2301.01913v1.pdf.txt b/2NAzT4oBgHgl3EQf8_6P/content/tmp_files/2301.01913v1.pdf.txt new file mode 100644 index 0000000000000000000000000000000000000000..57b8382323d40b2540b72b1acffa784b08574d33 --- /dev/null +++ b/2NAzT4oBgHgl3EQf8_6P/content/tmp_files/2301.01913v1.pdf.txt @@ -0,0 +1,1130 @@ +Training a Deep Q-Learning Agent Inside a +Generic Constraint Programming Solver +Tom Marty1,2, Tristan François2, Pierre Tessier2, Louis Gautier2, +Louis-Martin Rousseau1, and Quentin Cappart1 +1 Polytechnique Montréal, Montreal, Canada +{tom.marty,louis-martin.rousseau,quentin.cappart}@polymtl.ca +2 Ecole Polytechnique, Palaiseau, France +{tristan.francois,pierre.tessier,louis.gautier}@polytechnique.edu +Abstract. Constraint programming is known for being an efficient ap- +proach to solving combinatorial problems. Important design choices in +a solver are the branching heuristics, designed to lead the search to the +best solutions in a minimum amount of time. However, developing these +heuristics is a time-consuming process that requires problem-specific ex- +pertise. This observation has motivated many efforts to use machine +learning to learn efficient heuristics without expert intervention automat- +ically. To our knowledge, it is still an open research question. Although +several generic variable-selection heuristics are available in the literature, +the options for a generic value-selection heuristic are more scarce. In this +paper, we propose to tackle this issue by introducing a generic learning +procedure that can be used to obtain a value-selection heuristic inside +a constraint programming solver. This has been achieved thanks to the +combination of a deep Q-learning algorithm, a tailored reward signal, and +a heterogeneous graph neural network architecture. Experiments on graph +coloring, maximum independent set, and maximum cut problems show +that our framework is able to find better solutions close to optimality +without requiring a large number of backtracks while being generic. +Keywords: Constraint Programming · Reinforcement Learning · Graph +Representation Learning +1 +Introduction +Combinatorial optimization has countless industrial applications, such as schedul- +ing, routing, or finance. Unfortunately, most of these problems are NP-hard and, +thereby, challenging to solve efficiently in practice. It is why finding good solu- +tions have motivated intense research efforts for many years. Traditional methods +for tackling them are somehow based on a search procedure: A clever enumer- +ation of the solution space is performed to find a feasible and possibly optimal +solution. Among these methods, constraint programming (CP) is an exact algo- +rithm. It constitutes a popular approach as they offer the possibility to find the +optimal solution or good feasible approximations by stopping the search early. +arXiv:2301.01913v1 [cs.AI] 5 Jan 2023 + +2 +T. Marty et al. +An additional asset is its declarative paradigm in modeling, which makes the +technology easier for the end user to grasp. This aspect has been greatly facili- +tated by introducing solver-agnostic modeling languages, such as MiniZinc [30]. +Aligned with this goal, the propagation engine inside a CP solver is mostly hid- +den from the end user. However, ensuring a generic search procedure is trickier +as non-trivial heuristics must be designed to make the solving process efficient +for an arbitrary problem. That being said, generic variable-selection heuristics +(i.e., selecting the next variable to branch on) have been successfully designed. +Notable examples are impact-based search [32] or activity-based search [26]. On +the other hand, there is no similar generic heuristic for the value-selection (i.e., +selecting the next value to branch one). As a concrete example, the current +version of MiniZinc3 does not propose generic value-selection heuristics, except +in(out)domain. In practice, this heuristic is often designed thanks to problem- +specific expert knowledge, which is often out of reach for end-users that do not +have a solid background in artificial intelligence. +In another context, machine learning (ML) has been recently considered +for automating the design of branching heuristics, both in constraint program- +ming [11], mixed-integer programming [14,20], or SAT solving [35]. Specifically, +reinforcement learning (RL) [38] or imitation learning [19] approaches, often +combined with deep learning [23], have gained special attention. Although this +idea seems appealing, this is not an easy task to achieve in practice as several +technical considerations must be taken into account in order to ensure both the +efficiency and the genericity of the approach. In constraint programming, we +identified three questions to resolve when learning a generic branching heuristic +inside a solver. They are as follows: +1. How to train the machine learning model? An intuitive way is to leverage an +RL agent that would explore the tree search by making branching decisions +and rewarding it based on the quality of the solution found on a terminal +node. For getting a certificate of optimality, this would typically be done with +a depth-first search traversal of the tree. However, as pointed out by several +authors [33,36], the backtracking operations inside a solver raise difficulties +when formalizing the task as a Markov decision process and may require +redefining it. Besides, this training scheme intensifies the credit assignment +problem [27], which is ubiquitous in reinforcement learning. +2. How to evaluate the quality of a value selection? A core component of an +RL environment is the reward function, which gives a score to each decision +performed. The end goal for the agent is to perform a sequence of decisions +leading to the best-accumulated sum of rewards. In our case, an intuitive +solution would be to reward the agent according to the quality of the solution +found. However, this information is only available at terminal nodes, and +only a zero reward is provided in branching nodes. This is related to sparse +reward problematic, which is known to complicate the training process. +3. How to learn from a CP model? This question relates to the type of archi- +tecture that can be used to obtain a value-selection heuristic from a search +3 https://www.minizinc.org/doc-2.5.5/en/lib-stdlib.html + +Training a DQN Agent Inside a Generic CP Solver +3 +node (i.e., a partially solved CP model). A promising direction has been +proposed by Gasse et al. [14] for binary mixed-integer programs. They intro- +duced a bipartite graph linking variables and constraints (i.e., the two types +of nodes) when a variable is involved in a given constraint. The subsequent +architecture is a heterogeneous graph neural network. However, this encod- +ing is not directly applicable in constraint programming, as a CP model +generally involves non-binary variables and combinatorial constraints. This +has been partially addressed by Chalumeau et al. [12], who introduced a +tripartite graph where variables, values, and constraints are specific types +of nodes. Their approach, however, suffers from a lack of genericity as their +method requires retraining when the number of variables changes. +To our knowledge, answering such questions is still an open challenge in the +research community. This paper proposes to progress in this direction. It intro- +duces a generic learning procedure that can be used to obtain a value-selection +heuristic from a constraint programming model given as input. The approach +has been designed to be generic in that it can be used in whatever the CP model +is. In practice, a specific way to extract features from a constraint should be +designed for any available constraint, but this has to be done only once per +constraint type. In this proof of concept, we limit our experiments to three com- +binatorial optimization problems, namely graph coloring, maximum independent +set, and maximum cut. Specifically, we propose three main contributions, each +dedicated to addressing one of the aforementioned difficulties. They are as fol- +lows: (1) a learning procedure, based on restarts, for training a reinforcement +learning agent directly inside a CP solver, (2) a reward function able to assign +non-zero intermediate rewards based on the propagation that has been carried +out on the node, and (3) a neural architecture based on a tripartite graph and a +heterogeneous graph neural network. Experimental results show that combining +these three ideas enables the search to find good solutions without requiring +many backtracks. As shown by limiting the search tree’s size with a budget. +The paper is structured as follows. The next section presents other ap- +proaches related to our contribution. Then, Section 3 introduces succinctly tech- +nical background on reinforcement learning and graph neural networks. The core +contribution is then presented in Section 4. Finally, Section 5 provides experi- +mental results and closes with a discussion of the results and of some limitations. +2 +Related Work +Bengio et al. [5] identified three ways to leverage machine learning for com- +binatorial optimization. First, end-to-end learning aims to solve the problem +only with a trained ML model. This has been, for instance, considered for the +traveling salesman problem [4,21]. However, such an approach does not guar- +antee the validity nor the optimality of the solution returned. Second, learning +to configure is dedicated to providing insights to a solver before its execution. +This can be, for instance, the decision to linearize the problem in the context +of quadratic programs [7] or to learn when a decomposition is appropriate [22]. + +4 +T. Marty et al. +This approach is also referred to as parameter tuning [18]. We refer to the initial +survey for extended information about these two families of approaches. Third, +learning within a search procedure uses machine learning within the solver. Our +contribution belongs to this last category of methods. Although the idea of com- +bining learning and searching for solving combinatorial optimization problems +was already discussed in the nineties [31], it has re-emerged only recently with +the rise of deep learning. Most combinatorial optimization solvers are based +on branch-and-bound and backtracking. In this context, ML is often used with +branching rules to follow. Imitation learning [19] has been for instance used to +replicate the expensive strong branching strategy for mixed-integer programming +solvers [14,20]. One limitation of imitation learning is that the performances are +bounded by the relevance of the imitated strategy, which remains heuristic and +perfectible [37]. This opens the door for RL approaches that have the guarantee +to find the best branching strategy [25] eventually. A branching strategy can be +split into two challenging decisions, variable selection, and value selection. Both +of them have been addressed by reinforcement learning approaches. +Concerning the learning for selecting the next variable to branch on, Song +et al. [36] propose to combine a double deep Q-network algorithm [40] with a +graph neural network for carrying out this task. The approach is trained to +minimize the expected number of nodes to reach a leaf node using the first-fail +principle. Although this is a good proxy for pruning a maximum of infeasible +solutions for a constraint satisfaction problem, it does not extend naturally to +optimization variants, for which one should consider a trade-off between the +quality of the solution found and the number of nodes required to reach that +solution. For the value-selection heuristic, Cappart et al. [11] proposed to train +a model with reinforcement learning outside the CP solver and to integrate +the agent, once trained, subsequently in the solver. This has been achieved by +reaping the benefits of a dynamic programming formulation of a combinatorial +problem. An important limitation of this work is that no information related to +the CP solver, such as the propagation achieved on a node, can be used to drive +the decision. Chalumeau et al. [12] proposed to carry out the learning inside the +solver. The model is trained to find the optimal solution and to prove it with +the least number of search nodes possible. However, this goal is disconnected +from finding the best solution as quickly as possible and is practically hard +to achieve, even with a good heuristic. A more realistic goal is to find a good +solution quickly without closing the search. This is how the contribution of this +paper is positioned. +We would like to point out that learning how to branch is not the only way to +leverage ML inside a combinatorial optimization solver. Related works have also +been proposed on learning tight optimization bounds [8,10] or for accelerating +column generation approaches [29]. A recurrent design choice is an architecture +based on graph neural networks. We refer to the following survey for more in- +formation about combinatorial optimization with graph neural networks [9]. + +Training a DQN Agent Inside a Generic CP Solver +5 +3 +Technical Background +3.1 +Reinforcement Learning +Let ⟨S, A, T, R⟩ be a 4-tuple representing a Markov decision process where S is +the set of states in the environment, A is the set of actions that the agent can +do, T : S × A → S is a transition function leading the agent from one state +to another, given the action taken, and R : S × A → R is a reward function +of taking action from a specific state. The sequence [s1, . . . , sT ] from the initial +state (s1) of an agent towards a terminal state (sT ) is referred to as an episode. +The returned reward within a partial episode [st, . . . , sT ] can be formalized as +follows: Gt = �T +i=t R(si, ai). We intentionally omitted the discounting factor +as we do not want to discount the late rewards in our application. The agent +is governed by a policy π : S → A, which indicates the action that must be +taken on a given state. The agent’s goal is to find the policy that will lead +it to maximize the accumulated reward until a terminal state is reached. The +core idea of reinforcement learning is to determine this policy by letting the +agent interact with the environment and by increasing the probability of taking +action if it leads to high amounts of subsequent rewards. There are a plethora +of reinforcement learning algorithms dedicated to this task, such as trust region +policy optimization [34] or soft actor-critic [15]. We refer to SpinningUp website +for explanations of the main algorithms [1]. +This section presents the core principles of deep Q-learning [28], which is +the algorithm used in this paper. The idea of this algorithm is to compute +an action-value function Qπ(st, at) = Gt. Intuitively, this function gives the +accumulated reward that the agent will obtain when performing the action a +at state s while subsequently following a policy π. The output of this func- +tion for a specific action is referred to as a Q-value. Provided that the action- +value function can be computed exactly, the optimal policy π⋆ turns to be sim- +ply the selection of the action having the highest Q-value on a specific state: +π∗ = argmaxπQπ(s, a), ∀(s, a) ∈ (S, A). Although the exact computation of Q- +values can theoretically be performed, a specific value must be computed for each +pair of states and actions, which is not tractable for realistic situations. It is why +a tremendous amount of work has been carried out to approximate accurately +and efficiently Q-values. Among them, deep Q-learning aims to provide a neural +estimator ˆQ(s, a, θ) ≈ Q(s, a), where θ is a tensor of parameters that must be +learned during a training phase. This algorithm is commonly enriched with other +mechanisms dedicated to speed-up or stabilizing the training process, such as +the double deep Q-network variant [40]. Concerning the neural architecture, we +opted for a graph neural network, which is explained in the next section. +3.2 +Graph Neural Network +Intuitively, the goal of a graph neural network (GNN) is to embed information +contained in a graph (e.g., the structure of the graph, spatial properties, features +of the nodes, etc.) into a d-dimensional tensor for each node u ∈ V of the graph. + +6 +T. Marty et al. +To do so, information on a node is iteratively refined by aggregating information +from neighboring nodes. Each iteration of aggregation is referred to as a layer +of the GNN and involves parameters that must be learned. Let hk +u ∈ Rd×1 be +the tensor representation of node a u at layer k of the GNN, hk+1 +u +∈ Rl×1 be +the tensor representation of this node at the next layer (l being the dimension +of a node at the layer k + 1), and θ1 ∈ Rl×d and θ2 ∈ Rl×d be two matrices of +parameters, respectively. Each GNN layer carries out the following update: +hk+1 +u += g +� +θ1hk +u ⋆ ( +� +v∈N(u) +θ2hk +v) +� +∀u ∈ V +(1) +Three operations are involved in this update: (1) � is an aggregation operator +that is dedicated to aggregating information of neighbors (e.g., mean-pooling or +sum-pooling), (2) ⋆ is a merging which enables to combine of the information +of a node with the ones from the neighbors (e.g., a concatenation), and (3) g is +an element-wise non-linear activation function, such as the ones commonly used +in fully connected neural networks (e.g., ReLU). Without loss of generality, the +bias term is not included in the equation. A concrete implementation of a GNN +turns out to define these three functions adequately. The training is carried out +in a fully connected neural network through back-propagation and an optimizer +based on gradient descent. +4 +Learning a Value-Selection Heuristic Inside a Solver +This section presents how a value-selection heuristic can be learned with re- +inforcement learning in a CP solver from a model given as input. This is the +core contribution of this paper. Three mechanisms are introduced: (1) a train- +ing procedure based on restarts, (2) a reward function leveraging propagation of +domains, and (3) a heterogeneous graph neural network architecture. They are +described individually in the next subsections. They have been implemented in +recently introduced SeaPearl.jl solver [12]. The main specificity of this solver +is to natively integrate support for learning inside the search procedure. This +greatly facilitates the prototyping of new search algorithms based on learning. +4.1 +Restart-Based Training +Generally speaking, the performance of a reinforcement learning agent is tightly +correlated with the definition on an episode. This corresponds to the agent’s +interactions with the CP solver’s search procedure and is related to the goal +desired for the agent. Two options are discussed in this section, (1) an episode +based on depth-first search, which has been introduced by Chalumeau et al. [12], +and (2) an episode based on restarts is one contribution of the paper. +Building branching heuristics for solving exact combinatorial optimization +problems often concurrently targets two objectives: finding quickly good solu- +tions and proving the optimality of a solution. The approach of Chalumeau et + +Training a DQN Agent Inside a Generic CP Solver +7 +al. [12] relies heavily on the second objective and aims to minimize the number +of visited search nodes before proving optimality (e.g. closing the search). To do +so, they defined a training episode as a complete solving process carried out by +the depth-first search of a solver and penalized through the reward function the +generation of each node. This is illustrated in the left picture of Fig. 1. However, +this approach suffers from an important difficulty. An episode only terminates +when the search is completed, which is often intractable for realistic problems as +it requires exploring an exponentially large search tree. This is especially prob- +lematic during the training phase, where the heuristic is still mediocre. This has +also been pointed out by Scavuzzo et al. [33] for mixed-integer programming. +Fig. 1: The two training procedures (left: depth-first search, right: restart-based) +Unlike this approach, we propose to train the model to find good solutions +quickly. To do so, we followed the approach proposed by Cappart et al. [11]: +an episode is defined as a diving heuristic. No backtrack is allowed; the episode +stops when a complete solution is found or when a failure is generated. Once +the episode is terminated, a restart from the root node is performed, and a +new episode is generated, whereas the name of restart-based episode. This is +illustrated in the right picture of Fig. 1. One limitation of Cappart et al. [11] is +that episodes are executed outside the CP solver during the training and are then +unable to use information based on the propagation for the branching. Inspired +by Song et al. [36] for variable-selection heuristics, we addressed this limitation +by executing each episode inside the solver during the training. Formally, this +requires defining the dynamics of the environment as a Markov Decision Process +(i.e., a tuple ⟨S, A, T, R⟩, see Section 3.1). +Set of states Let P = ⟨X, D(X), C, O⟩ be the expression of a combinatorial +optimization problem (COP), defined by its variables (X), the domains (D), +its constraints (C), and an objective function (O). Each state st ∈ S is +defined as the pair st = (Pt, xt), where Pt is a partially solved COP (i.e., +some variables may have been assigned), and xt ∈ X is a variable selected for +branching, at step t of the episode. The initial state s1 ∈ S corresponds to +the situation after the execution of the fix-point. A terminal node is reached +either if all the variables are assigned (∀x ∈ X : |Dt(x)| = 1), or if a failure + +Decision branching +Back-tracking +Reward Signal +End of the episode8 +T. Marty et al. +is detected (∃x ∈ X : |Dt(x)| = 0). The variable selected for branching is +obtained through a standard heuristic such as first-fail. +Set of actions Given a state st = (Pt, xt), an action at corresponds to the +selection of a value v ∈ D(xt) for branching at step t. Finding the most +promising value to branch on is the problem addressed in this paper. +Transition function Given a state st = (Pt, xt) and an action at = v, the +transition function executes three successive operations. First, it assigns the +value v to the variable x (i.e., D(xt+1) = v). Second, it executes the fix- +point on Pt in order to prune the domains (i.e., Pt+1 = fixPoint(Pt)). Third, +it selects the next variable to branch on (i.e., xt+1 = nextVariable(Pt)). This +results in a new state st+1 = (Pt+1, xt+1). Integrating the propagation inside +the transition is the main difference from the work of Cappart et al. [11]. +Reward function The function is defined separately in Section 4.2. +Concerning the training, we opted for a double deep Q-learning algorithm, +known to perform well for discrete action space, but other RL algorithms could +also be used. Finally, we compared both training procedures for the maximum +independent set problem with instances with 50 nodes using performance pro- +files [13]. The ratio is computed using the optimal solution as a reference. As a +non-learned baseline, we added the performances of an agent performing only +random decisions. Training is carried out on randomly generated Barabási-Albert +graphs [2]. Evaluation is performed on 20 other graphs following the same dis- +tribution. A detailed explanation of the experimental protocol is proposed in +Section 5. Figure 2 shows performance profiles. As expected, we observe that +our new agent (single dive learning) can obtain better solutions quickly, with a +comparable ability to prove optimality compared to Chalumeau et al. [12]. +(a) Value of the solution obtained. +(b) Node visited until optimality. +Fig. 2: Comparison of both training methods on max. independent set (50 nodes). +4.2 +Propagation-Based Reward +The goal of the reward is to lead the agent to good solutions to the combinato- +rial problem. Based on our training procedure, an intuitive function is to reward + +1.0 +20 instances +0.8 +0.6 +0.4 +Single Dive Learning +0.2 +DFS-based Learning +Random +0.0 +1.00 +1.25 +1.50 +1.75 +2.00 +2.25 +2.50 +2.75 +3.00 +Within this factor of the best score1.0 +20 instances +0.8 +0.6 +Proportion of the +0.4 +Single Dive Learning +0.2 +DFS-based Learning +Random +0.0 +1.0 +1.2 +1.4 +1.6 +1.8 +2.0 +2.2 +Within this factor of the smallest number of node visitedTraining a DQN Agent Inside a Generic CP Solver +9 +the agent proportionally to the quality of the solution found at the end of an +episode. In case of an infeasible solution is found, a penalty can be given. The +main drawback of this rewarding scheme is that this information is only available +at terminal nodes, and only a zero reward is provided in branching nodes. This is +related to the sparse reward problem, which is known to complicate the training +process [39]. To address this difficulty, we propose a new rewarding scheme based +on the domain reduction of the objective variable (i.e., the variable that must be +minimized or maximized). This happens either thanks to the branching assign- +ment or the application of the fix-point. There are two main components: (1) +an intermediate reward (rmid) collected at branching nodes, and (2) a terminal +reward (rend) collected only at the end of an episode. +Fig. 3: Intermediate reward when four values are pruned from the domain. +Assuming a minimization problem, the intermediate reward follows two prin- +ciples: each domain reduction of the largest values of the domain is rewarded, +and each domain reduction of the lowest values of the domain is penalized. The +rationale is to lead the agent to a situation where the minimum cost can be even- +tually obtained while removing costly solutions. It is formalized in Equations (2) +to (4), where rmid +t +is the reward obtained at step t, and is illustrated in Fig 3. As +shown in Equation 5, the terminal reward is set to -1 if the leaf node corresponds +to an infeasible solution and 0 if it is feasible. Finally, the total reward (racc) +accumulated during an episode of T steps is the sum of all intermediate rewards +with the final term, as proposed in Equation (6). +rub +t += # +� +v ∈ Dt(xobj) +��� v /∈ Dt+1(xobj) ∧ v > max +� +Dt(xobj) +�� +(2) +rlb +t = # +� +v ∈ Dt(xobj) +��� v /∈ Dt+1(xobj) ∧ v < min +� +Dt(xobj) +�� +(3) +rmid +t += rub +t − rlb +t +��D1(xobj) +�� +(4) +rend +t += −1 if unfeasible solution found (0 otherwise) +(5) +racc = +T −1 +� +t=1 +rmid +t ++ rend +T +(6) +An experimental analysis of this new reward scheme (propagation-based re- +ward) is carried out for the maximum cut, graph coloring, and maximum inde- +pendent set problems. As a baseline, we consider a reward (score reward) that + +1 +2 +3 +4 +5 +6 +7 +8 +9 +10 +D+ +1 +2 +3 +4 +5 +6 +7 +↓ +3-1 +2 +D++1 +2 +3 +4 +10 +1010 +T. Marty et al. +only gives a value at terminal nodes (rend +T ) without an intermediate reward. Be- +sides, we present the values of the optimal solution and the solutions obtained +by a random value-selection heuristic. Fig. 4 shows the evolution of the objective +value (y-axis, averaged on 20 instances of the validation step) with the training +time (number of episodes in the x-axis). Instances are Barabási-Albert randomly +generated graphs with 50 nodes. Except for the reward scheme, the other parts +of the architecture are unchanged. We observe that the propagation-based reward +provides a more stable training (Fig. 4a) and can converge to a better model or, +at least, to an equally good model as the sparse reward (Figs. 4b and 4c). +(a) Graph coloring. +(b) Maximum cut. +(c) Max. independent set. +Fig. 4: Training curve for the two rewarding schemes. +4.3 +Heterogeneous Graph Neural Network Architecture +An important part of our framework is the neural network architecture that we +designed to perform a prediction of the next value to branch on. A high-level +representation is proposed in Fig. 5. Four steps are carried out: (1) a CP model +encoder, (2) a graph neural network encoder, (3) a neural network decoder, and +(4) an action-selection policy. They are detailed in the next subsections. +Fig. 5: High-level overview of the neural architecture designed. + +50 +Propagation-based reward +Score reward +40 +Optimal Score +Random +30 +20 +10 +0 +2 +4 +6 +8 +10 +Training episode +(x1000)0 +Propagation-based reward +-20 +Score reward +Optimal Score +-40 +Random +-60 +-80 +-100 +-120 +0 +2 +4 +6 +8 +10 +Training episode (x1000)-2 +-3 +Propagation-based reward +Score reward +-4 +Optimal Score +-5 +Random +-6 +-7 +-8 +-9 +-10 +0 +2 +4 +6 +10 +Training episode (x1000)GNN Encoder (2) +NN Decoder (3) +Solver state and +Predicted +selected variable +Q-Table +St = (Pt,&t) +*=3 +Extract +*=2 +value +GNN layers +CP Encoder +features +*=1 +Q(St, X1 = 3) +X1 +*=3 +Q(St, Xi = 2) +X1 +X1 +*=1 +Q(St, Xi = 1) +Extract +variable +X1 +C1 +C2 +Action-Selection +features +4' +PolicyTraining a DQN Agent Inside a Generic CP Solver +11 +Step 1: CP Model Encoder This module’s core idea is to learn for any CP +model given as input, unlike Cappart et al. [11], who require a specific encod- +ing for each combinatorial problem. This has been achieved for mixed-integer +programs thanks to a bipartite graph representation [14] and by Chalumeau et +al. [12] for CP models thanks to a tripartite graph. This last work does not lever- +age any feature related to the variables, values, or constraints. We built upon +this last approach by adding such features. Specifically, let P = ⟨X, D(X), C, O⟩ +be the combinatorial problem we want to encode. The idea consists in building +a simple undirected graph G(V1, V2, V3, f1, f2, f3, E1, E2) encoding all the infor- +mation of Pt from a state st = (Pt, xt). In this representation, V1, V2, and V3 are +three types of vertices, f1, f2, and f3 are three vectors of features, and E1 with +E2 are two distinct sets of edges. This yields a graph with three types of nodes +decorated with features. The first part of the encoding we propose is as follows: +(1) each variable, constraint, and value corresponds to a specific type of node +(V1 = X, V2 = C, and V3 = D), (2) each time a variable x ∈ V1 is involved in +a constraint c ∈ V2, an edge (x, c) ∈ E1 is added between both nodes, (3) each +time a value v ∈ V3 is in the domain of a variable x ∈ V1, an edge (v, x) ∈ E2 is +added between both nodes. This gives a tripartite graph representation of a CP +model generically. This is illustrated in Fig. 6. The second part of the encoding +is to add features to each node. Intuitively, the features will provide meaningful +information and thus improve the quality of the model. The features we consid- +ered are proposed below. We note that we can easily extend this encoding by +integrating new features. +1. Features attached to variables (f1): the current domain size, the initial do- +main size, a binary indication if the variable is already assigned, and a binary +indication if the variable corresponds to the objective. +2. Features attached to constraints (f2): the constraint type (one-hot encoding), +and a binary indication if the constraint propagation has reduced domains. +3. Features attached to values (f3): its numerical value. +Fig. 6: Representation computed by the CP encoder on a simple example. +Step 2: Graph Neural Network Encoder Once the CP model has been +encoded as a graph, the next step is to embed this representation as a latent + +Solver State +Tripartite Heterogeneous Graph +Vi +V2 +XiE1,2.X2E1,21,X3E[1,2,3] +CP Encoder +fi(X1) +f2(C)) +.C1 = X1 ≤ X2 +fi(X2) +.C2 = X2 ≤X3 +f2(Ca) +.C3 = AllDifferent(Xi, X2, X3) +fi(Xs) +f2(C2) +E +Ei12 +T. Marty et al. +vector of features for each node of the graph (see Section 3.2). We propose +to carry out this operation with a graph neural network. Unlike the standard +prediction scheme presented in Equation (1), our graph has three types of nodes. +For this reason, we opted for a heterogeneous architecture. Concretely, a specific +convolution is carried out for each node type. The architecture is detailed in +Equations (7) to (9), where � is the sum-pooling or mean-pooling aggregation, +operator (.∥.) is a concatenation of vectors, Nx(n) is the set of neighbouring +nodes of n from V1 (variable), Nc(n) is the set of neighbouring nodes of n from V2 +(constraint), Nv(n) is the set of neighbouring nodes of n from V3 (value), θk +1,...,10 +are weight matrices at layer k, and g is the leakyReLU activation function [24]. +Another difference with the canonical GNN equation is the integration of skip +connections (h0 +x, h0 +c, and h0 +c) allowing to keep at each layer information from +the input features. This technique is ubiquitous in deep convolutional networks +such as in ResNet [17]. Finally, the initial embedding are initialized as follows: +h0 +x = θ11f1, h0 +c = θ12f2, and h0 +v = θ13f3, where θ11,...,13 are new weight matrices. +hk+1 +x += g +� +θk +1h0 +x +�� θk +2hk +x +�� ( +� +c∈Nc(x) +θk +3hk +c) +�� ( +� +v∈Nv(x) +θk +4hk +v) +� +∀x ∈ V1 +(7) +hk+1 +c += g +� +θk +5h0 +c +�� θk +6hk +c +�� ( +� +x∈Nx(c) +θk +7hk +x) +� +∀c ∈ V2 +(8) +hk+1 +v += g +� +θk +8h0 +v +�� θk +9hk +v +�� ( +� +x∈Nx(v) +θk +10hk +x) +� +∀v ∈ V3 +(9) +Step 3: Neural Network Decoder At this step, a d-dimensional tensor is +obtained for each node of the graph. Let x ∈ V1 be the node representing the +current variable selected for branching, and Vx ⊆ V3 the subset of nodes repre- +senting the values available for x (i.e., the values that are in the domain of the +variable). The goal of the decoder is to predict a Q-value (see Section 3.1) for each +v ∈ Vx. The computation is formalized in Equation (10), where hK +x and hK +v are +the node embedding of variable x and value v, respectively, after K iterations of +the GNN architecture. The functions ϕx : Rd → Rl, ϕv : Rd → Rl, ϕq : R2l → R +are fully-connected neural networks. Such a Q-value must computed for each +value v ∈ Vx. It is internally done thanks to matrix operations, allowing a more +efficient computation. +ˆQ(hK +x , hK +v ) = ϕq +� +ϕx(hK +x ) +�� ϕv(hK +v ) +� +∀v ∈ Vx +(10) +Step 4: Action-Selection Policy Once all the Q-values have been computed +for the current variable, the branching policy π on variable x consists simply +by taking the highest Q-value, according to the standard Q-learning algorithm +shown in Equation (11). Once trained, this value should represent the branching +choice leading to the best decision according to the reward of Equation (6). +π(v|x) = argmaxv∈Vx ˆQ(hK +x , hK +v ) +(11) + +Training a DQN Agent Inside a Generic CP Solver +13 +Assembling all the pieces together, this architecture gives a generic approach to +obtain a data-driven value-selection heuristic inside a CP solver. Concerning the +search strategy, we propose to embed our predictions inside an iterative limited +discrepancy search (ILDS) [16]. This strategy is commonly used when we are +confident on the quality of the heuristic. The core idea is to restrict the number +of branching choices deviating from the heuristic (i.e., a discrepancy). By doing +so, the search will explore a subset of solutions expected to be good while giving +a chance to reconsider the value-heuristic selection which is nevertheless prone +to errors. This mechanism is enriched with a procedure that iteratively increases +the number of discrepancies allowed once a level has been explored. +5 +Experiments +The goal of the experiments is to evaluate the quality of the learned value- +selection heuristic and the efficiency of the approach when solving combinatorial +optimization problems. Three problems are considered: graph coloring (COL), +maximum independent set (MIS), and maximum cut (MAXCUT). +5.1 +Experimental Protocol +Three configurations are proposed for each problem: small (20 to 30 nodes), +medium (40 to 50 nodes) and large (80 to 100 nodes) instances, except for +MAXCUT which was already challenging for the medium size. Training is carried +out on randomly generated Barabási-Albert graph [2] with a density factor vary- +ing between 4 and 15 according to the size of the instances. A specific model is +trained for each configuration using randomly generated instances. Evaluation is +then performed on 20 other graphs following the same distributions. The models +are trained on an Nvidia Tesla V100 32Go GPU until convergences. It took up +to 72 hours of training time for the most difficult cases (graph coloring with +80 nodes) and less than 1 hour for the simplest cases (graph coloring with 20 +nodes). Each operation of the CP solver during training and evaluation is carried +out on a CPU Intel Xeon Silver 4116 at 2.10GHz. The approach has been imple- +mented in Julia and is integrated into the solver Seapearl. The implementation +is available on GitHub with MIT open-source licence4. +Our approach (ILDS-Learned) is compared with the optimal solution (OPT) +which is obtained by an exact solver, with a standard depth-first search strategy +based on a random value selection (DFS-Random), and with the application of +the learned heuristic without any backtrack (Dive-Learned). A standard first- +fail variable-selection heuristic is used for all the methods. Finally, a maximum +budget in terms of the number of nodes visited is enforced. The idea is to show +that we can obtain solutions close to optimality with few backtracks. +4 https://github.com/corail-research/SeaPearl.jl + +14 +T. Marty et al. +5.2 +Quantitative Results +Table 1 summarizes the main results of our approach. A first observation is that +the learned value-selection, even without backtrack (Dive-Learned) can find solu- +tions close to optimality. For instance, a single dive for MAXCUT with 50 nodes +yields a solution with an optimality gap of 17% in less than 1 second, whereas +DFS-Random required 22 seconds and roughly 51,000 nodes explored to find a +solution with the same gap. Within the same budget, ILDS-Learned improves +the solution but with a longer execution time. This increased computation time +is mainly due to the fact that calling the graph neural network architecture +(Section 4.3) at each tree search node is more expensive than calling a random +heuristic. Experimental results are also proposed in Fig. 7 using performance +profiles [13] for the hardest instances of each problem (80 for graph coloring, 100 +for max independent set, and 50 for maximum cut. The ratio is computed using +the optimal solution as a reference. Within the same maximal number of nodes +visited (1000), we observe that ILDS-Learned dominate DFS-Random. Besides, +when restricting ten times the budget for ILDS-Learned, we still perform better +than the competitor. +Table 1: Results for the three problems. For each configuration, the average +result (rounded) on the 20 test instances is reported, and a specific node budget +is enforced for DFS-Random and ILDS-Learned. Gap indicates the optimality gap, +Node gives the number of nodes explored before finding the best solution within +the budget, and Time gives the time, in seconds, before finding this solution. +DFS-Random +Dive-Learned +ILDS-Learned +(with budget) +(no backtrack) +(with budget) +Size +OPT Gap +Node Time Gap +Time Gap +Node Time Budget +COL +20 +4.95 2,33 +85 +< 1 0.00 +< 1 0.00 +21 +< 1 +102 +40 +7.90 0.00 +1,559 +< 1 0.00 +< 1 0.00 +41 +< 1 +104 +80 +12.00 0.00 +6,698 +11 0.02 +2 0.00 +85 +2 +104 +MIS +30 +10.20 0.00 +291 +< 1 0.05 +< 1 0.00 +41 +< 1 +104 +50 +14.90 0.00 +8,011 +< 1 0.19 +< 1 0.00 +2,749 +2 +105 +100 +21.75 0.09 51,174 +7 0.19 +< 1 0.01 28,483 +170 +105 +MAXCUT +20 +46.45 0.03 +5,071 +< 1 0.19 +< 1 0.03 +3,059 +2 +104 +50 135.19 0.17 51,222 +22 0.17 +< 1 0.10 35,977 +172 +105 +5.3 +Discussions +Previous experiments showed the capacity of our approach to obtain a value- +selection heuristic in a CP solver, thanks to historical instances of the same +distribution. Unlike many related works based on imitation learning [14,20], the +training is not supervised and thus does not require labels from an expert (e.g., + +Training a DQN Agent Inside a Generic CP Solver +15 +(a) Graph coloring. +(b) Maximum cut. +(c) Max. independent set. +Fig. 7: Best solutions found on largest instances for the three problems. +an expensive heuristic or an exact solving). One major difficulty encountered +by our approach is the increased computation time due to the inference of the +graph neural network at each node of the tree search. A first solution would +be to reduce the complexity of the model by compressing its knowledge, e.g., +using network pruning tools [41]. Another idea is to call the model only in a +few nodes, in a similar fashion as Cappart et al. [8] did for decision-diagram- +based branch-and-bound [6]. The last idea to tackle this scaling issue would be +to restrict the learning only to small instances and transfer the model to solve +larger instances. The architecture has been designed to do so, and experiments +on this aspect are part of future work. A second difficulty is the size of the CP +encoding as a tripartite graph, which involves a specific node for each variable, +value, and constraint. This grows proportionally with the problem size and slows +down the training phase. As a concrete example, graph coloring instances with +80 nodes require 72 hours of training time. An interesting research question is +how to build such a generic encoding more compactly. +6 +Conclusion +The efficiency of constraint programming solvers is partially due to the branch- +ing heuristics used to guide the search. Unlike the variable selection, there is +no available generic and efficient heuristic for the value selection. In practice, +value-selection heuristics are often designed thanks to problem-specific expert +knowledge, often out of reach for non-practitioners. In this paper, we proposed +a learning-based approach for obtaining such a heuristic thanks to historical +data, characterized by problem instances following the same distribution of the +one that must be solved. This has been achieved thanks to the combination of +a restart-based training procedure, a non-sparse reward signal, and a hetero- +geneous graph neural network architecture. Experiments on three combinato- +rial optimization problems show that the framework can find better solutions +close to optimality without requiring many backtracks. Several limitations (e.g., +tractability for larger instances) have been identified, and addressing them is +part of future work. We also plan to consider other combinatorial problems, +such as the ones proposed in XCSP3 competitions [3]. + +20 instances +1.0 +0.8 +0.6 +Proportion of the 2 +0.4 +RL Agent - ILDS - 100 +0.2 +RL Agent - ILDS - 1000 +Random - DFS - 1oo0 +0.0 +1 +2 +3 +4 +5 +6 +Within this factor of the best score1.0 +0.8 +0.6 +0.4 +RL Agent - ILDS - 1o0 +0.2 +RL Agent - ILDS - 1000 +Random - DFS - 1oo0 +0.0 +1.0 +1.1 +1.2 +1.3 +1.4 +Within this factor of the best score1.0 +0.8 +0.6 +0.4 +RL Agent - ILDS - 1o0 +0.2 +RL Agent - ILDS - 1000 +Random - DFS - 1oo0 +0.0 +1.0 +1.1 +1.2 +1.3 +1.4 +1.5 +1.6 +Within this factor of the best score16 +T. Marty et al. +References +1. Achiam, J.: Spinning up as a deep rl researcher (Oct 2018), spinningup.openai. +com/en/latest/spinningup/spinningup.html +2. Albert, R., Barabási, A.L.: Statistical mechanics of complex networks. Reviews of +modern physics 74(1), 47 (2002) +3. Audemard, G., Lecoutre, C., Lonca, E.: Proceedings of the 2022 xcsp3 competition. +arXiv preprint arXiv:2209.00917 (2022) +4. Bello, I., Pham, H., Le, Q.V., Norouzi, M., Bengio, S.: Neural combinatorial opti- +mization with reinforcement learning. arXiv preprint arXiv:1611.09940 (2016) +5. Bengio, Y., Lodi, A., Prouvost, A.: Machine learning for combinatorial optimiza- +tion: a methodological tour d’horizon. European Journal of Operational Research +290(2), 405–421 (2021) +6. Bergman, D., Cire, A.A., Van Hoeve, W.J., Hooker, J.N.: Discrete optimization +with decision diagrams. INFORMS Journal on Computing 28(1), 47–66 (2016) +7. Bonami, P., Lodi, A., Zarpellon, G.: Learning a classification of mixed-integer +quadratic programming problems. In: International Conference on the Integration +of Constraint Programming, Artificial Intelligence, and Operations Research. pp. +595–604. Springer (2018) +8. Cappart, Q., Bergman, D., Rousseau, L.M., Prémont-Schwarz, I., Parjadis, A.: +Improving variable orderings of approximate decision diagrams using reinforcement +learning. INFORMS Journal on Computing (2022) +9. Cappart, Q., Chételat, D., Khalil, E., Lodi, A., Morris, C., Veličković, P.: Com- +binatorial optimization and reasoning with graph neural networks. arXiv preprint +arXiv:2102.09544 (2021) +10. Cappart, Q., Goutierre, E., Bergman, D., Rousseau, L.M.: Improving optimization +bounds using machine learning: Decision diagrams meet deep reinforcement learn- +ing. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 33, pp. +1443–1451 (2019) +11. Cappart, Q., Moisan, T., Rousseau, L.M., Prémont-Schwarz, I., Cire, A.A.: Com- +bining reinforcement learning and constraint programming for combinatorial opti- +mization. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 35, +pp. 3677–3687 (2021) +12. Chalumeau, F., Coulon, I., Cappart, Q., Rousseau, L.M.: Seapearl: A constraint +programming solver guided by reinforcement learning. In: International Conference +on Integration of Constraint Programming, Artificial Intelligence, and Operations +Research. pp. 392–409. Springer (2021) +13. Dolan, E.D., Moré, J.J.: Benchmarking optimization software with performance +profiles. Mathematical programming 91(2), 201–213 (2002) +14. Gasse, M., Chételat, D., Ferroni, N., Charlin, L., Lodi, A.: Exact combinatorial +optimization with graph convolutional neural networks. Advances in Neural Infor- +mation Processing Systems 32 (2019) +15. Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft actor-critic: Off-policy maxi- +mum entropy deep reinforcement learning with a stochastic actor. In: International +conference on machine learning. pp. 1861–1870. PMLR (2018) +16. Harvey, W.D., Ginsberg, M.L.: Limited discrepancy search. In: IJCAI (1). pp. +607–615 (1995) +17. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: +Proceedings of the IEEE conference on computer vision and pattern recognition. +pp. 770–778 (2016) + +Training a DQN Agent Inside a Generic CP Solver +17 +18. Hoos, H.H.: Automated algorithm configuration and parameter tuning. In: Au- +tonomous search, pp. 37–71. Springer (2011) +19. Hussein, A., Gaber, M.M., Elyan, E., Jayne, C.: Imitation learning: A survey of +learning methods. ACM Computing Surveys (CSUR) 50(2), 1–35 (2017) +20. Khalil, E., Le Bodic, P., Song, L., Nemhauser, G., Dilkina, B.: Learning to branch in +mixed integer programming. In: Proceedings of the AAAI Conference on Artificial +Intelligence. vol. 30 (2016) +21. Kool, W., van Hoof, H., Welling, M.: Attention, learn to solve routing prob- +lems! In: International Conference on Learning Representations (2019), https: +//openreview.net/forum?id=ByxBFsRqYm +22. Kruber, M., Lübbecke, M.E., Parmentier, A.: Learning when to use a decomposi- +tion. In: International conference on AI and OR techniques in constraint program- +ming for combinatorial optimization problems. pp. 202–210. Springer (2017) +23. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. nature 521(7553), 436–444 +(2015) +24. Maas, A.L., Hannun, A.Y., Ng, A.Y., et al.: Rectifier nonlinearities improve neural +network acoustic models. In: Proc. icml. vol. 30, p. 3. Atlanta, Georgia, USA (2013) +25. Mazyavkina, N., Sviridov, S., Ivanov, S., Burnaev, E.: Reinforcement learning for +combinatorial optimization: A survey. Computers & Operations Research 134, +105400 (2021) +26. Michel, L., Hentenryck, P.V.: Activity-based search for black-box constraint pro- +gramming solvers. In: International Conference on Integration of Artificial Intelli- +gence (AI) and Operations Research (OR) Techniques in Constraint Programming. +pp. 228–243. Springer (2012) +27. Minsky, M.: Steps toward artificial intelligence. Proceedings of the IRE 49(1), 8–30 +(1961). https://doi.org/10.1109/JRPROC.1961.287775 +28. Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., +Riedmiller, M.: Playing atari with deep reinforcement learning. arXiv preprint +arXiv:1312.5602 (2013) +29. Morabit, M., Desaulniers, G., Lodi, A.: Machine-learning–based column selection +for column generation. Transportation Science 55(4), 815–831 (2021) +30. Nethercote, N., Stuckey, P.J., Becket, R., Brand, S., Duck, G.J., Tack, G.: Miniz- +inc: Towards a standard cp modelling language. In: International Conference on +Principles and Practice of Constraint Programming. pp. 529–543. Springer (2007) +31. Potvin, J.Y., Dubé, D., Robillard, C.: A hybrid approach to vehicle routing using +neural networks and genetic algorithms. Applied Intelligence 6(3), 241–252 (1996) +32. Refalo, P.: Impact-based search strategies for constraint programming. In: Inter- +national Conference on Principles and Practice of Constraint Programming. pp. +557–571. Springer (2004) +33. Scavuzzo, L., Chen, F.Y., Chételat, D., Gasse, M., Lodi, A., Yorke-Smith, N., +Aardal, K.: Learning to branch with tree mdps. arXiv preprint arXiv:2205.11107 +(2022) +34. Schulman, J., Levine, S., Abbeel, P., Jordan, M., Moritz, P.: Trust region policy +optimization. In: International conference on machine learning. pp. 1889–1897. +PMLR (2015) +35. Selsam, D., Bjørner, N.: Guiding high-performance SAT solvers with unsat-core +predictions. In: International Conference on Theory and Applications of Satisfia- +bility Testing. pp. 336–353. Springer (2019) +36. Song, W., Cao, Z., Zhang, J., Xu, C., Lim, A.: Learning variable ordering heuristics +for solving constraint satisfaction problems. Engineering Applications of Artificial +Intelligence 109, 104603 (2022) + +18 +T. Marty et al. +37. Sun, H., Chen, W., Li, H., Song, L.: Improving learning to branch via reinforcement +learning. In: Learning Meets Combinatorial Algorithms at NeurIPS2020 (2020) +38. Sutton, R.S., Barto, A.G.: Reinforcement learning: An introduction. MIT press +(2018) +39. Trott, A., Zheng, S., Xiong, C., Socher, R.: Keeping your distance: Solving sparse +reward tasks using self-balancing shaped rewards. Advances in Neural Information +Processing Systems 32 (2019) +40. Van Hasselt, H., Guez, A., Silver, D.: Deep reinforcement learning with double q- +learning. In: Proceedings of the AAAI conference on artificial intelligence. vol. 30 +(2016) +41. Yu, X., Serra, T., Ramalingam, S., Zhe, S.: The combinatorial brain surgeon: Prun- +ing weights that cancel one another in neural networks. In: International Confer- +ence on Machine Learning. pp. 25668–25683. PMLR (2022) + diff --git a/2NAzT4oBgHgl3EQf8_6P/content/tmp_files/load_file.txt b/2NAzT4oBgHgl3EQf8_6P/content/tmp_files/load_file.txt new file mode 100644 index 0000000000000000000000000000000000000000..41dd80533c2347978e3fa4e3b991813388ddaacc --- /dev/null +++ b/2NAzT4oBgHgl3EQf8_6P/content/tmp_files/load_file.txt @@ -0,0 +1,865 @@ +filepath=/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf,len=864 +page_content='Training a Deep Q-Learning Agent Inside a Generic Constraint Programming Solver Tom Marty1,2, Tristan François2, Pierre Tessier2, Louis Gautier2, Louis-Martin Rousseau1, and Quentin Cappart1 1 Polytechnique Montréal, Montreal, Canada {tom.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='marty,louis-martin.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='rousseau,quentin.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='cappart}@polymtl.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='ca 2 Ecole Polytechnique, Palaiseau, France {tristan.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='francois,pierre.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='tessier,louis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='gautier}@polytechnique.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='edu Abstract.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Constraint programming is known for being an efficient ap- proach to solving combinatorial problems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Important design choices in a solver are the branching heuristics, designed to lead the search to the best solutions in a minimum amount of time.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' However, developing these heuristics is a time-consuming process that requires problem-specific ex- pertise.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This observation has motivated many efforts to use machine learning to learn efficient heuristics without expert intervention automat- ically.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' To our knowledge, it is still an open research question.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Although several generic variable-selection heuristics are available in the literature, the options for a generic value-selection heuristic are more scarce.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In this paper, we propose to tackle this issue by introducing a generic learning procedure that can be used to obtain a value-selection heuristic inside a constraint programming solver.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This has been achieved thanks to the combination of a deep Q-learning algorithm, a tailored reward signal, and a heterogeneous graph neural network architecture.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Experiments on graph coloring, maximum independent set, and maximum cut problems show that our framework is able to find better solutions close to optimality without requiring a large number of backtracks while being generic.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Keywords: Constraint Programming · Reinforcement Learning · Graph Representation Learning 1 Introduction Combinatorial optimization has countless industrial applications, such as schedul- ing, routing, or finance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Unfortunately, most of these problems are NP-hard and, thereby, challenging to solve efficiently in practice.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' It is why finding good solu- tions have motivated intense research efforts for many years.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Traditional methods for tackling them are somehow based on a search procedure: A clever enumer- ation of the solution space is performed to find a feasible and possibly optimal solution.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Among these methods, constraint programming (CP) is an exact algo- rithm.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' It constitutes a popular approach as they offer the possibility to find the optimal solution or good feasible approximations by stopping the search early.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' arXiv:2301.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='01913v1 [cs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='AI] 5 Jan 2023 2 T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Marty et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' An additional asset is its declarative paradigm in modeling, which makes the technology easier for the end user to grasp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This aspect has been greatly facili- tated by introducing solver-agnostic modeling languages, such as MiniZinc [30].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Aligned with this goal, the propagation engine inside a CP solver is mostly hid- den from the end user.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' However, ensuring a generic search procedure is trickier as non-trivial heuristics must be designed to make the solving process efficient for an arbitrary problem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' That being said, generic variable-selection heuristics (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', selecting the next variable to branch on) have been successfully designed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Notable examples are impact-based search [32] or activity-based search [26].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' On the other hand, there is no similar generic heuristic for the value-selection (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', selecting the next value to branch one).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' As a concrete example, the current version of MiniZinc3 does not propose generic value-selection heuristics, except in(out)domain.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In practice, this heuristic is often designed thanks to problem- specific expert knowledge, which is often out of reach for end-users that do not have a solid background in artificial intelligence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In another context, machine learning (ML) has been recently considered for automating the design of branching heuristics, both in constraint program- ming [11], mixed-integer programming [14,20], or SAT solving [35].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Specifically, reinforcement learning (RL) [38] or imitation learning [19] approaches, often combined with deep learning [23], have gained special attention.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Although this idea seems appealing, this is not an easy task to achieve in practice as several technical considerations must be taken into account in order to ensure both the efficiency and the genericity of the approach.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In constraint programming, we identified three questions to resolve when learning a generic branching heuristic inside a solver.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' They are as follows: 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' How to train the machine learning model?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' An intuitive way is to leverage an RL agent that would explore the tree search by making branching decisions and rewarding it based on the quality of the solution found on a terminal node.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' For getting a certificate of optimality, this would typically be done with a depth-first search traversal of the tree.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' However, as pointed out by several authors [33,36], the backtracking operations inside a solver raise difficulties when formalizing the task as a Markov decision process and may require redefining it.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Besides, this training scheme intensifies the credit assignment problem [27], which is ubiquitous in reinforcement learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' How to evaluate the quality of a value selection?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' A core component of an RL environment is the reward function, which gives a score to each decision performed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The end goal for the agent is to perform a sequence of decisions leading to the best-accumulated sum of rewards.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In our case, an intuitive solution would be to reward the agent according to the quality of the solution found.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' However, this information is only available at terminal nodes, and only a zero reward is provided in branching nodes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This is related to sparse reward problematic, which is known to complicate the training process.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' How to learn from a CP model?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This question relates to the type of archi- tecture that can be used to obtain a value-selection heuristic from a search 3 https://www.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='minizinc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='org/doc-2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='5/en/lib-stdlib.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='html Training a DQN Agent Inside a Generic CP Solver 3 node (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', a partially solved CP model).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' A promising direction has been proposed by Gasse et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' [14] for binary mixed-integer programs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' They intro- duced a bipartite graph linking variables and constraints (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', the two types of nodes) when a variable is involved in a given constraint.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The subsequent architecture is a heterogeneous graph neural network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' However, this encod- ing is not directly applicable in constraint programming, as a CP model generally involves non-binary variables and combinatorial constraints.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This has been partially addressed by Chalumeau et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' [12], who introduced a tripartite graph where variables, values, and constraints are specific types of nodes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Their approach, however, suffers from a lack of genericity as their method requires retraining when the number of variables changes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' To our knowledge, answering such questions is still an open challenge in the research community.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This paper proposes to progress in this direction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' It intro- duces a generic learning procedure that can be used to obtain a value-selection heuristic from a constraint programming model given as input.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The approach has been designed to be generic in that it can be used in whatever the CP model is.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In practice, a specific way to extract features from a constraint should be designed for any available constraint, but this has to be done only once per constraint type.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In this proof of concept, we limit our experiments to three com- binatorial optimization problems, namely graph coloring, maximum independent set, and maximum cut.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Specifically, we propose three main contributions, each dedicated to addressing one of the aforementioned difficulties.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' They are as fol- lows: (1) a learning procedure, based on restarts, for training a reinforcement learning agent directly inside a CP solver, (2) a reward function able to assign non-zero intermediate rewards based on the propagation that has been carried out on the node, and (3) a neural architecture based on a tripartite graph and a heterogeneous graph neural network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Experimental results show that combining these three ideas enables the search to find good solutions without requiring many backtracks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' As shown by limiting the search tree’s size with a budget.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The paper is structured as follows.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The next section presents other ap- proaches related to our contribution.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Then, Section 3 introduces succinctly tech- nical background on reinforcement learning and graph neural networks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The core contribution is then presented in Section 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Finally, Section 5 provides experi- mental results and closes with a discussion of the results and of some limitations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 2 Related Work Bengio et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' [5] identified three ways to leverage machine learning for com- binatorial optimization.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' First, end-to-end learning aims to solve the problem only with a trained ML model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This has been, for instance, considered for the traveling salesman problem [4,21].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' However, such an approach does not guar- antee the validity nor the optimality of the solution returned.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Second, learning to configure is dedicated to providing insights to a solver before its execution.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This can be, for instance, the decision to linearize the problem in the context of quadratic programs [7] or to learn when a decomposition is appropriate [22].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 4 T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Marty et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This approach is also referred to as parameter tuning [18].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' We refer to the initial survey for extended information about these two families of approaches.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Third, learning within a search procedure uses machine learning within the solver.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Our contribution belongs to this last category of methods.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Although the idea of com- bining learning and searching for solving combinatorial optimization problems was already discussed in the nineties [31], it has re-emerged only recently with the rise of deep learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Most combinatorial optimization solvers are based on branch-and-bound and backtracking.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In this context, ML is often used with branching rules to follow.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Imitation learning [19] has been for instance used to replicate the expensive strong branching strategy for mixed-integer programming solvers [14,20].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' One limitation of imitation learning is that the performances are bounded by the relevance of the imitated strategy, which remains heuristic and perfectible [37].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This opens the door for RL approaches that have the guarantee to find the best branching strategy [25] eventually.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' A branching strategy can be split into two challenging decisions, variable selection, and value selection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Both of them have been addressed by reinforcement learning approaches.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Concerning the learning for selecting the next variable to branch on, Song et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' [36] propose to combine a double deep Q-network algorithm [40] with a graph neural network for carrying out this task.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The approach is trained to minimize the expected number of nodes to reach a leaf node using the first-fail principle.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Although this is a good proxy for pruning a maximum of infeasible solutions for a constraint satisfaction problem, it does not extend naturally to optimization variants, for which one should consider a trade-off between the quality of the solution found and the number of nodes required to reach that solution.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' For the value-selection heuristic, Cappart et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' [11] proposed to train a model with reinforcement learning outside the CP solver and to integrate the agent, once trained, subsequently in the solver.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This has been achieved by reaping the benefits of a dynamic programming formulation of a combinatorial problem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' An important limitation of this work is that no information related to the CP solver, such as the propagation achieved on a node, can be used to drive the decision.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Chalumeau et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' [12] proposed to carry out the learning inside the solver.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The model is trained to find the optimal solution and to prove it with the least number of search nodes possible.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' However, this goal is disconnected from finding the best solution as quickly as possible and is practically hard to achieve, even with a good heuristic.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' A more realistic goal is to find a good solution quickly without closing the search.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This is how the contribution of this paper is positioned.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' We would like to point out that learning how to branch is not the only way to leverage ML inside a combinatorial optimization solver.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Related works have also been proposed on learning tight optimization bounds [8,10] or for accelerating column generation approaches [29].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' A recurrent design choice is an architecture based on graph neural networks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' We refer to the following survey for more in- formation about combinatorial optimization with graph neural networks [9].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Training a DQN Agent Inside a Generic CP Solver 5 3 Technical Background 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='1 Reinforcement Learning Let ⟨S, A, T, R⟩ be a 4-tuple representing a Markov decision process where S is the set of states in the environment, A is the set of actions that the agent can do, T : S × A → S is a transition function leading the agent from one state to another, given the action taken, and R : S × A → R is a reward function of taking action from a specific state.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The sequence [s1, .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' , sT ] from the initial state (s1) of an agent towards a terminal state (sT ) is referred to as an episode.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The returned reward within a partial episode [st, .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' , sT ] can be formalized as follows: Gt = �T i=t R(si, ai).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' We intentionally omitted the discounting factor as we do not want to discount the late rewards in our application.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The agent is governed by a policy π : S → A, which indicates the action that must be taken on a given state.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The agent’s goal is to find the policy that will lead it to maximize the accumulated reward until a terminal state is reached.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The core idea of reinforcement learning is to determine this policy by letting the agent interact with the environment and by increasing the probability of taking action if it leads to high amounts of subsequent rewards.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' There are a plethora of reinforcement learning algorithms dedicated to this task, such as trust region policy optimization [34] or soft actor-critic [15].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' We refer to SpinningUp website for explanations of the main algorithms [1].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This section presents the core principles of deep Q-learning [28], which is the algorithm used in this paper.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The idea of this algorithm is to compute an action-value function Qπ(st, at) = Gt.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Intuitively, this function gives the accumulated reward that the agent will obtain when performing the action a at state s while subsequently following a policy π.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The output of this func- tion for a specific action is referred to as a Q-value.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Provided that the action- value function can be computed exactly, the optimal policy π⋆ turns to be sim- ply the selection of the action having the highest Q-value on a specific state: π∗ = argmaxπQπ(s, a), ∀(s, a) ∈ (S, A).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Although the exact computation of Q- values can theoretically be performed, a specific value must be computed for each pair of states and actions, which is not tractable for realistic situations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' It is why a tremendous amount of work has been carried out to approximate accurately and efficiently Q-values.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Among them, deep Q-learning aims to provide a neural estimator ˆQ(s, a, θ) ≈ Q(s, a), where θ is a tensor of parameters that must be learned during a training phase.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This algorithm is commonly enriched with other mechanisms dedicated to speed-up or stabilizing the training process, such as the double deep Q-network variant [40].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Concerning the neural architecture, we opted for a graph neural network, which is explained in the next section.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='2 Graph Neural Network Intuitively, the goal of a graph neural network (GNN) is to embed information contained in a graph (e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', the structure of the graph, spatial properties, features of the nodes, etc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=') into a d-dimensional tensor for each node u ∈ V of the graph.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 6 T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Marty et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' To do so, information on a node is iteratively refined by aggregating information from neighboring nodes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Each iteration of aggregation is referred to as a layer of the GNN and involves parameters that must be learned.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Let hk u ∈ Rd×1 be the tensor representation of node a u at layer k of the GNN, hk+1 u ∈ Rl×1 be the tensor representation of this node at the next layer (l being the dimension of a node at the layer k + 1), and θ1 ∈ Rl×d and θ2 ∈ Rl×d be two matrices of parameters, respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Each GNN layer carries out the following update: hk+1 u = g � θ1hk u ⋆ ( � v∈N(u) θ2hk v) � ∀u ∈ V (1) Three operations are involved in this update: (1) � is an aggregation operator that is dedicated to aggregating information of neighbors (e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', mean-pooling or sum-pooling), (2) ⋆ is a merging which enables to combine of the information of a node with the ones from the neighbors (e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', a concatenation), and (3) g is an element-wise non-linear activation function, such as the ones commonly used in fully connected neural networks (e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', ReLU).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Without loss of generality, the bias term is not included in the equation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' A concrete implementation of a GNN turns out to define these three functions adequately.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The training is carried out in a fully connected neural network through back-propagation and an optimizer based on gradient descent.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 4 Learning a Value-Selection Heuristic Inside a Solver This section presents how a value-selection heuristic can be learned with re- inforcement learning in a CP solver from a model given as input.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This is the core contribution of this paper.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Three mechanisms are introduced: (1) a train- ing procedure based on restarts, (2) a reward function leveraging propagation of domains, and (3) a heterogeneous graph neural network architecture.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' They are described individually in the next subsections.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' They have been implemented in recently introduced SeaPearl.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='jl solver [12].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The main specificity of this solver is to natively integrate support for learning inside the search procedure.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This greatly facilitates the prototyping of new search algorithms based on learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='1 Restart-Based Training Generally speaking, the performance of a reinforcement learning agent is tightly correlated with the definition on an episode.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This corresponds to the agent’s interactions with the CP solver’s search procedure and is related to the goal desired for the agent.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Two options are discussed in this section, (1) an episode based on depth-first search, which has been introduced by Chalumeau et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' [12], and (2) an episode based on restarts is one contribution of the paper.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Building branching heuristics for solving exact combinatorial optimization problems often concurrently targets two objectives: finding quickly good solu- tions and proving the optimality of a solution.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The approach of Chalumeau et Training a DQN Agent Inside a Generic CP Solver 7 al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' [12] relies heavily on the second objective and aims to minimize the number of visited search nodes before proving optimality (e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' closing the search).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' To do so, they defined a training episode as a complete solving process carried out by the depth-first search of a solver and penalized through the reward function the generation of each node.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This is illustrated in the left picture of Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' However, this approach suffers from an important difficulty.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' An episode only terminates when the search is completed, which is often intractable for realistic problems as it requires exploring an exponentially large search tree.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This is especially prob- lematic during the training phase, where the heuristic is still mediocre.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This has also been pointed out by Scavuzzo et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' [33] for mixed-integer programming.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 1: The two training procedures (left: depth-first search, right: restart-based) Unlike this approach, we propose to train the model to find good solutions quickly.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' To do so, we followed the approach proposed by Cappart et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' [11]: an episode is defined as a diving heuristic.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' No backtrack is allowed;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' the episode stops when a complete solution is found or when a failure is generated.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Once the episode is terminated, a restart from the root node is performed, and a new episode is generated, whereas the name of restart-based episode.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This is illustrated in the right picture of Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' One limitation of Cappart et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' [11] is that episodes are executed outside the CP solver during the training and are then unable to use information based on the propagation for the branching.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Inspired by Song et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' [36] for variable-selection heuristics, we addressed this limitation by executing each episode inside the solver during the training.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Formally, this requires defining the dynamics of the environment as a Markov Decision Process (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', a tuple ⟨S, A, T, R⟩, see Section 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='1).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Set of states Let P = ⟨X, D(X), C, O⟩ be the expression of a combinatorial optimization problem (COP), defined by its variables (X), the domains (D), its constraints (C), and an objective function (O).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Each state st ∈ S is defined as the pair st = (Pt, xt), where Pt is a partially solved COP (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', some variables may have been assigned), and xt ∈ X is a variable selected for branching, at step t of the episode.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The initial state s1 ∈ S corresponds to the situation after the execution of the fix-point.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' A terminal node is reached either if all the variables are assigned (∀x ∈ X : |Dt(x)| = 1), or if a failure Decision branching Back-tracking Reward Signal End of the episode8 T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Marty et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' is detected (∃x ∈ X : |Dt(x)| = 0).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The variable selected for branching is obtained through a standard heuristic such as first-fail.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Set of actions Given a state st = (Pt, xt), an action at corresponds to the selection of a value v ∈ D(xt) for branching at step t.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Finding the most promising value to branch on is the problem addressed in this paper.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Transition function Given a state st = (Pt, xt) and an action at = v, the transition function executes three successive operations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' First, it assigns the value v to the variable x (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', D(xt+1) = v).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Second, it executes the fix- point on Pt in order to prune the domains (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Pt+1 = fixPoint(Pt)).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Third, it selects the next variable to branch on (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', xt+1 = nextVariable(Pt)).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This results in a new state st+1 = (Pt+1, xt+1).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Integrating the propagation inside the transition is the main difference from the work of Cappart et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' [11].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Reward function The function is defined separately in Section 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Concerning the training, we opted for a double deep Q-learning algorithm, known to perform well for discrete action space, but other RL algorithms could also be used.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Finally, we compared both training procedures for the maximum independent set problem with instances with 50 nodes using performance pro- files [13].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The ratio is computed using the optimal solution as a reference.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' As a non-learned baseline, we added the performances of an agent performing only random decisions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Training is carried out on randomly generated Barabási-Albert graphs [2].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Evaluation is performed on 20 other graphs following the same dis- tribution.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' A detailed explanation of the experimental protocol is proposed in Section 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Figure 2 shows performance profiles.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' As expected, we observe that our new agent (single dive learning) can obtain better solutions quickly, with a comparable ability to prove optimality compared to Chalumeau et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' [12].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' (a) Value of the solution obtained.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' (b) Node visited until optimality.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 2: Comparison of both training methods on max.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' independent set (50 nodes).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='2 Propagation-Based Reward The goal of the reward is to lead the agent to good solutions to the combinato- rial problem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Based on our training procedure, an intuitive function is to reward 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='0 20 instances 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='8 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='4 Single Dive Learning 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='2 DFS-based Learning Random 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='0 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='00 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='25 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='50 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='75 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='00 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='25 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='50 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='75 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='00 Within this factor of the best score1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='0 20 instances 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='8 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='6 Proportion of the 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='4 Single Dive Learning 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='2 DFS-based Learning Random 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='0 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='0 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='2 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='4 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='6 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='8 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='0 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='2 Within this factor of the smallest number of node visitedTraining a DQN Agent Inside a Generic CP Solver 9 the agent proportionally to the quality of the solution found at the end of an episode.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In case of an infeasible solution is found, a penalty can be given.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The main drawback of this rewarding scheme is that this information is only available at terminal nodes, and only a zero reward is provided in branching nodes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This is related to the sparse reward problem, which is known to complicate the training process [39].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' To address this difficulty, we propose a new rewarding scheme based on the domain reduction of the objective variable (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', the variable that must be minimized or maximized).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This happens either thanks to the branching assign- ment or the application of the fix-point.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' There are two main components: (1) an intermediate reward (rmid) collected at branching nodes, and (2) a terminal reward (rend) collected only at the end of an episode.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 3: Intermediate reward when four values are pruned from the domain.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Assuming a minimization problem, the intermediate reward follows two prin- ciples: each domain reduction of the largest values of the domain is rewarded, and each domain reduction of the lowest values of the domain is penalized.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The rationale is to lead the agent to a situation where the minimum cost can be even- tually obtained while removing costly solutions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' It is formalized in Equations (2) to (4), where rmid t is the reward obtained at step t, and is illustrated in Fig 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' As shown in Equation 5, the terminal reward is set to -1 if the leaf node corresponds to an infeasible solution and 0 if it is feasible.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Finally, the total reward (racc) accumulated during an episode of T steps is the sum of all intermediate rewards with the final term, as proposed in Equation (6).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' rub t = # � v ∈ Dt(xobj) ��� v /∈ Dt+1(xobj) ∧ v > max � Dt(xobj) �� (2) rlb t = # � v ∈ Dt(xobj) ��� v /∈ Dt+1(xobj) ∧ v < min � Dt(xobj) �� (3) rmid t = rub t − rlb t ��D1(xobj) �� (4) rend t = −1 if unfeasible solution found (0 otherwise) (5) racc = T −1 � t=1 rmid t + rend T (6) An experimental analysis of this new reward scheme (propagation-based re- ward) is carried out for the maximum cut, graph coloring, and maximum inde- pendent set problems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' As a baseline, we consider a reward (score reward) that 1 2 3 4 5 6 7 8 9 10 D+ 1 2 3 4 5 6 7 ↓ 3-1 2 D++1 2 3 4 10 1010 T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Marty et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' only gives a value at terminal nodes (rend T ) without an intermediate reward.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Be- sides, we present the values of the optimal solution and the solutions obtained by a random value-selection heuristic.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 4 shows the evolution of the objective value (y-axis, averaged on 20 instances of the validation step) with the training time (number of episodes in the x-axis).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Instances are Barabási-Albert randomly generated graphs with 50 nodes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Except for the reward scheme, the other parts of the architecture are unchanged.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' We observe that the propagation-based reward provides a more stable training (Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 4a) and can converge to a better model or, at least, to an equally good model as the sparse reward (Figs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 4b and 4c).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' (a) Graph coloring.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' (b) Maximum cut.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' (c) Max.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' independent set.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 4: Training curve for the two rewarding schemes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='3 Heterogeneous Graph Neural Network Architecture An important part of our framework is the neural network architecture that we designed to perform a prediction of the next value to branch on.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' A high-level representation is proposed in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Four steps are carried out: (1) a CP model encoder, (2) a graph neural network encoder, (3) a neural network decoder, and (4) an action-selection policy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' They are detailed in the next subsections.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 5: High-level overview of the neural architecture designed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 50 Propagation-based reward Score reward 40 Optimal Score Random 30 20 10 0 2 4 6 8 10 Training episode (x1000)0 Propagation-based reward 20 Score reward Optimal Score 40 Random 60 80 100 120 0 2 4 6 8 10 Training episode (x1000)-2 3 Propagation-based reward Score reward 4 Optimal Score 5 Random 6 7 8 9 10 0 2 4 6 10 Training episode (x1000)GNN Encoder (2) NN Decoder (3) Solver state and Predicted selected variable Q-Table St = (Pt,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='&t) =3 Extract =2 value GNN layers CP Encoder features =1 Q(St,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' X1 = 3) X1 =3 Q(St,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Xi = 2) X1 X1 =1 Q(St,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=" Xi = 1) Extract variable X1 C1 C2 Action-Selection features 4' PolicyTraining a DQN Agent Inside a Generic CP Solver 11 Step 1: CP Model Encoder This module’s core idea is to learn for any CP model given as input," metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' unlike Cappart et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' [11], who require a specific encod- ing for each combinatorial problem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This has been achieved for mixed-integer programs thanks to a bipartite graph representation [14] and by Chalumeau et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' [12] for CP models thanks to a tripartite graph.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This last work does not lever- age any feature related to the variables, values, or constraints.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' We built upon this last approach by adding such features.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Specifically, let P = ⟨X, D(X), C, O⟩ be the combinatorial problem we want to encode.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The idea consists in building a simple undirected graph G(V1, V2, V3, f1, f2, f3, E1, E2) encoding all the infor- mation of Pt from a state st = (Pt, xt).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In this representation, V1, V2, and V3 are three types of vertices, f1, f2, and f3 are three vectors of features, and E1 with E2 are two distinct sets of edges.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This yields a graph with three types of nodes decorated with features.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The first part of the encoding we propose is as follows: (1) each variable, constraint, and value corresponds to a specific type of node (V1 = X, V2 = C, and V3 = D), (2) each time a variable x ∈ V1 is involved in a constraint c ∈ V2, an edge (x, c) ∈ E1 is added between both nodes, (3) each time a value v ∈ V3 is in the domain of a variable x ∈ V1, an edge (v, x) ∈ E2 is added between both nodes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This gives a tripartite graph representation of a CP model generically.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This is illustrated in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The second part of the encoding is to add features to each node.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Intuitively, the features will provide meaningful information and thus improve the quality of the model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The features we consid- ered are proposed below.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' We note that we can easily extend this encoding by integrating new features.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Features attached to variables (f1): the current domain size, the initial do- main size, a binary indication if the variable is already assigned, and a binary indication if the variable corresponds to the objective.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Features attached to constraints (f2): the constraint type (one-hot encoding), and a binary indication if the constraint propagation has reduced domains.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Features attached to values (f3): its numerical value.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 6: Representation computed by the CP encoder on a simple example.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Step 2: Graph Neural Network Encoder Once the CP model has been encoded as a graph, the next step is to embed this representation as a latent Solver State Tripartite Heterogeneous Graph Vi V2 XiE1,2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='X2E1,21,X3E[1,2,3] CP Encoder fi(X1) f2(C)) .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='C1 = X1 ≤ X2 fi(X2) .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='C2 = X2 ≤X3 f2(Ca) .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='C3 = AllDifferent(Xi, X2, X3) fi(Xs) f2(C2) E Ei12 T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Marty et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' vector of features for each node of the graph (see Section 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='2).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' We propose to carry out this operation with a graph neural network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Unlike the standard prediction scheme presented in Equation (1), our graph has three types of nodes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' For this reason, we opted for a heterogeneous architecture.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Concretely, a specific convolution is carried out for each node type.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The architecture is detailed in Equations (7) to (9), where � is the sum-pooling or mean-pooling aggregation, operator (.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='∥.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=') is a concatenation of vectors, Nx(n) is the set of neighbouring nodes of n from V1 (variable), Nc(n) is the set of neighbouring nodes of n from V2 (constraint), Nv(n) is the set of neighbouring nodes of n from V3 (value), θk 1,.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='..' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=',10 are weight matrices at layer k, and g is the leakyReLU activation function [24].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Another difference with the canonical GNN equation is the integration of skip connections (h0 x, h0 c, and h0 c) allowing to keep at each layer information from the input features.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This technique is ubiquitous in deep convolutional networks such as in ResNet [17].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Finally, the initial embedding are initialized as follows: h0 x = θ11f1, h0 c = θ12f2, and h0 v = θ13f3, where θ11,.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='..' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=',13 are new weight matrices.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' hk+1 x = g � θk 1h0 x �� θk 2hk x �� ( � c∈Nc(x) θk 3hk c) �� ( � v∈Nv(x) θk 4hk v) � ∀x ∈ V1 (7) hk+1 c = g � θk 5h0 c �� θk 6hk c �� ( � x∈Nx(c) θk 7hk x) � ∀c ∈ V2 (8) hk+1 v = g � θk 8h0 v �� θk 9hk v �� ( � x∈Nx(v) θk 10hk x) � ∀v ∈ V3 (9) Step 3: Neural Network Decoder At this step, a d-dimensional tensor is obtained for each node of the graph.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Let x ∈ V1 be the node representing the current variable selected for branching, and Vx ⊆ V3 the subset of nodes repre- senting the values available for x (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', the values that are in the domain of the variable).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The goal of the decoder is to predict a Q-value (see Section 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='1) for each v ∈ Vx.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The computation is formalized in Equation (10), where hK x and hK v are the node embedding of variable x and value v, respectively, after K iterations of the GNN architecture.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The functions ϕx : Rd → Rl, ϕv : Rd → Rl, ϕq : R2l → R are fully-connected neural networks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Such a Q-value must computed for each value v ∈ Vx.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' It is internally done thanks to matrix operations, allowing a more efficient computation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' ˆQ(hK x , hK v ) = ϕq � ϕx(hK x ) �� ϕv(hK v ) � ∀v ∈ Vx (10) Step 4: Action-Selection Policy Once all the Q-values have been computed for the current variable, the branching policy π on variable x consists simply by taking the highest Q-value, according to the standard Q-learning algorithm shown in Equation (11).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Once trained, this value should represent the branching choice leading to the best decision according to the reward of Equation (6).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' π(v|x) = argmaxv∈Vx ˆQ(hK x , hK v ) (11) Training a DQN Agent Inside a Generic CP Solver 13 Assembling all the pieces together, this architecture gives a generic approach to obtain a data-driven value-selection heuristic inside a CP solver.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Concerning the search strategy, we propose to embed our predictions inside an iterative limited discrepancy search (ILDS) [16].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This strategy is commonly used when we are confident on the quality of the heuristic.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The core idea is to restrict the number of branching choices deviating from the heuristic (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', a discrepancy).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' By doing so, the search will explore a subset of solutions expected to be good while giving a chance to reconsider the value-heuristic selection which is nevertheless prone to errors.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This mechanism is enriched with a procedure that iteratively increases the number of discrepancies allowed once a level has been explored.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 5 Experiments The goal of the experiments is to evaluate the quality of the learned value- selection heuristic and the efficiency of the approach when solving combinatorial optimization problems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Three problems are considered: graph coloring (COL), maximum independent set (MIS), and maximum cut (MAXCUT).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='1 Experimental Protocol Three configurations are proposed for each problem: small (20 to 30 nodes), medium (40 to 50 nodes) and large (80 to 100 nodes) instances, except for MAXCUT which was already challenging for the medium size.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Training is carried out on randomly generated Barabási-Albert graph [2] with a density factor vary- ing between 4 and 15 according to the size of the instances.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' A specific model is trained for each configuration using randomly generated instances.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Evaluation is then performed on 20 other graphs following the same distributions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The models are trained on an Nvidia Tesla V100 32Go GPU until convergences.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' It took up to 72 hours of training time for the most difficult cases (graph coloring with 80 nodes) and less than 1 hour for the simplest cases (graph coloring with 20 nodes).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Each operation of the CP solver during training and evaluation is carried out on a CPU Intel Xeon Silver 4116 at 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='10GHz.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The approach has been imple- mented in Julia and is integrated into the solver Seapearl.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The implementation is available on GitHub with MIT open-source licence4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Our approach (ILDS-Learned) is compared with the optimal solution (OPT) which is obtained by an exact solver, with a standard depth-first search strategy based on a random value selection (DFS-Random), and with the application of the learned heuristic without any backtrack (Dive-Learned).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' A standard first- fail variable-selection heuristic is used for all the methods.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Finally, a maximum budget in terms of the number of nodes visited is enforced.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The idea is to show that we can obtain solutions close to optimality with few backtracks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 4 https://github.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='com/corail-research/SeaPearl.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='jl 14 T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Marty et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='2 Quantitative Results Table 1 summarizes the main results of our approach.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' A first observation is that the learned value-selection, even without backtrack (Dive-Learned) can find solu- tions close to optimality.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' For instance, a single dive for MAXCUT with 50 nodes yields a solution with an optimality gap of 17% in less than 1 second, whereas DFS-Random required 22 seconds and roughly 51,000 nodes explored to find a solution with the same gap.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Within the same budget, ILDS-Learned improves the solution but with a longer execution time.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This increased computation time is mainly due to the fact that calling the graph neural network architecture (Section 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='3) at each tree search node is more expensive than calling a random heuristic.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Experimental results are also proposed in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 7 using performance profiles [13] for the hardest instances of each problem (80 for graph coloring, 100 for max independent set, and 50 for maximum cut.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The ratio is computed using the optimal solution as a reference.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Within the same maximal number of nodes visited (1000), we observe that ILDS-Learned dominate DFS-Random.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Besides, when restricting ten times the budget for ILDS-Learned, we still perform better than the competitor.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Table 1: Results for the three problems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' For each configuration, the average result (rounded) on the 20 test instances is reported, and a specific node budget is enforced for DFS-Random and ILDS-Learned.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Gap indicates the optimality gap, Node gives the number of nodes explored before finding the best solution within the budget, and Time gives the time, in seconds, before finding this solution.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' DFS-Random Dive-Learned ILDS-Learned (with budget) (no backtrack) (with budget) Size OPT Gap Node Time Gap Time Gap Node Time Budget COL 20 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='95 2,33 85 < 1 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='00 < 1 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='00 21 < 1 102 40 7.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='90 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='00 1,559 < 1 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='00 < 1 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='00 41 < 1 104 80 12.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='00 6,698 11 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='02 2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='00 85 2 104 MIS 30 10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='20 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='00 291 < 1 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='05 < 1 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='00 41 < 1 104 50 14.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='90 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='00 8,011 < 1 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='19 < 1 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='00 2,749 2 105 100 21.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='75 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='09 51,174 7 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='19 < 1 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='01 28,483 170 105 MAXCUT 20 46.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='45 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='03 5,071 < 1 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='19 < 1 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='03 3,059 2 104 50 135.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='19 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='17 51,222 22 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='17 < 1 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='10 35,977 172 105 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='3 Discussions Previous experiments showed the capacity of our approach to obtain a value- selection heuristic in a CP solver, thanks to historical instances of the same distribution.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Unlike many related works based on imitation learning [14,20], the training is not supervised and thus does not require labels from an expert (e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Training a DQN Agent Inside a Generic CP Solver 15 (a) Graph coloring.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' (b) Maximum cut.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' (c) Max.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' independent set.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 7: Best solutions found on largest instances for the three problems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' an expensive heuristic or an exact solving).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' One major difficulty encountered by our approach is the increased computation time due to the inference of the graph neural network at each node of the tree search.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' A first solution would be to reduce the complexity of the model by compressing its knowledge, e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', using network pruning tools [41].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Another idea is to call the model only in a few nodes, in a similar fashion as Cappart et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' [8] did for decision-diagram- based branch-and-bound [6].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The last idea to tackle this scaling issue would be to restrict the learning only to small instances and transfer the model to solve larger instances.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' The architecture has been designed to do so, and experiments on this aspect are part of future work.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' A second difficulty is the size of the CP encoding as a tripartite graph, which involves a specific node for each variable, value, and constraint.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This grows proportionally with the problem size and slows down the training phase.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' As a concrete example, graph coloring instances with 80 nodes require 72 hours of training time.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' An interesting research question is how to build such a generic encoding more compactly.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 6 Conclusion The efficiency of constraint programming solvers is partially due to the branch- ing heuristics used to guide the search.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Unlike the variable selection, there is no available generic and efficient heuristic for the value selection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In practice, value-selection heuristics are often designed thanks to problem-specific expert knowledge, often out of reach for non-practitioners.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In this paper, we proposed a learning-based approach for obtaining such a heuristic thanks to historical data, characterized by problem instances following the same distribution of the one that must be solved.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' This has been achieved thanks to the combination of a restart-based training procedure, a non-sparse reward signal, and a hetero- geneous graph neural network architecture.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Experiments on three combinato- rial optimization problems show that the framework can find better solutions close to optimality without requiring many backtracks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Several limitations (e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', tractability for larger instances) have been identified, and addressing them is part of future work.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' We also plan to consider other combinatorial problems, such as the ones proposed in XCSP3 competitions [3].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 20 instances 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='8 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='6 Proportion of the 2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='4 RL Agent - ILDS - 100 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='2 RL Agent - ILDS - 1000 Random - DFS - 1oo0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='0 1 2 3 4 5 6 Within this factor of the best score1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='8 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='4 RL Agent - ILDS - 1o0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='2 RL Agent - ILDS - 1000 Random - DFS - 1oo0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='0 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='0 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='1 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='2 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='3 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='4 Within this factor of the best score1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='8 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='4 RL Agent - ILDS - 1o0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='2 RL Agent - ILDS - 1000 Random - DFS - 1oo0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='0 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='0 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='1 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='2 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='3 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='4 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='5 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='6 Within this factor of the best score16 T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Marty et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' References 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Achiam, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Spinning up as a deep rl researcher (Oct 2018), spinningup.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='openai.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' com/en/latest/spinningup/spinningup.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='html 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Albert, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Barabási, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' : Statistical mechanics of complex networks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Reviews of modern physics 74(1), 47 (2002) 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Audemard, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Lecoutre, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Lonca, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Proceedings of the 2022 xcsp3 competition.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' arXiv preprint arXiv:2209.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='00917 (2022) 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Bello, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Pham, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Le, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Norouzi, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Bengio, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Neural combinatorial opti- mization with reinforcement learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' arXiv preprint arXiv:1611.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='09940 (2016) 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Bengio, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Lodi, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Prouvost, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Machine learning for combinatorial optimiza- tion: a methodological tour d’horizon.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' European Journal of Operational Research 290(2), 405–421 (2021) 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Bergman, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Cire, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Van Hoeve, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Hooker, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' : Discrete optimization with decision diagrams.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' INFORMS Journal on Computing 28(1), 47–66 (2016) 7.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Bonami, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Lodi, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Zarpellon, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Learning a classification of mixed-integer quadratic programming problems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In: International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 595–604.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Springer (2018) 8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Cappart, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Bergman, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Rousseau, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Prémont-Schwarz, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Parjadis, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Improving variable orderings of approximate decision diagrams using reinforcement learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' INFORMS Journal on Computing (2022) 9.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Cappart, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Chételat, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Khalil, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Lodi, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Morris, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Veličković, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Com- binatorial optimization and reasoning with graph neural networks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' arXiv preprint arXiv:2102.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='09544 (2021) 10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Cappart, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Goutierre, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Bergman, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Rousseau, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' : Improving optimization bounds using machine learning: Decision diagrams meet deep reinforcement learn- ing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In: Proceedings of the AAAI Conference on Artificial Intelligence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 33, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 1443–1451 (2019) 11.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Cappart, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Moisan, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Rousseau, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Prémont-Schwarz, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Cire, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Com- bining reinforcement learning and constraint programming for combinatorial opti- mization.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In: Proceedings of the AAAI Conference on Artificial Intelligence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 35, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 3677–3687 (2021) 12.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Chalumeau, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Coulon, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Cappart, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Rousseau, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' : Seapearl: A constraint programming solver guided by reinforcement learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In: International Conference on Integration of Constraint Programming, Artificial Intelligence, and Operations Research.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 392–409.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Springer (2021) 13.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Dolan, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Moré, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Benchmarking optimization software with performance profiles.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Mathematical programming 91(2), 201–213 (2002) 14.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Gasse, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Chételat, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Ferroni, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Charlin, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Lodi, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Exact combinatorial optimization with graph convolutional neural networks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Advances in Neural Infor- mation Processing Systems 32 (2019) 15.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Haarnoja, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Zhou, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Abbeel, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Levine, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Soft actor-critic: Off-policy maxi- mum entropy deep reinforcement learning with a stochastic actor.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In: International conference on machine learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 1861–1870.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' PMLR (2018) 16.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Harvey, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Ginsberg, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' : Limited discrepancy search.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In: IJCAI (1).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 607–615 (1995) 17.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' He, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Zhang, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Ren, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Sun, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Deep residual learning for image recognition.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In: Proceedings of the IEEE conference on computer vision and pattern recognition.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 770–778 (2016) Training a DQN Agent Inside a Generic CP Solver 17 18.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Hoos, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' : Automated algorithm configuration and parameter tuning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In: Au- tonomous search, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 37–71.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Springer (2011) 19.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Hussein, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Gaber, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Elyan, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Jayne, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Imitation learning: A survey of learning methods.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' ACM Computing Surveys (CSUR) 50(2), 1–35 (2017) 20.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Khalil, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Le Bodic, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Song, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Nemhauser, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Dilkina, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Learning to branch in mixed integer programming.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In: Proceedings of the AAAI Conference on Artificial Intelligence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 30 (2016) 21.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Kool, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', van Hoof, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Welling, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Attention, learn to solve routing prob- lems!' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In: International Conference on Learning Representations (2019), https: //openreview.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='net/forum?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='id=ByxBFsRqYm 22.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Kruber, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Lübbecke, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Parmentier, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Learning when to use a decomposi- tion.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In: International conference on AI and OR techniques in constraint program- ming for combinatorial optimization problems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 202–210.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Springer (2017) 23.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' LeCun, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Bengio, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Hinton, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Deep learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' nature 521(7553), 436–444 (2015) 24.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Maas, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Hannun, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Ng, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' : Rectifier nonlinearities improve neural network acoustic models.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In: Proc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' icml.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 30, p.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Atlanta, Georgia, USA (2013) 25.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Mazyavkina, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Sviridov, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Ivanov, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Burnaev, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Reinforcement learning for combinatorial optimization: A survey.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Computers & Operations Research 134, 105400 (2021) 26.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Michel, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Hentenryck, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' : Activity-based search for black-box constraint pro- gramming solvers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In: International Conference on Integration of Artificial Intelli- gence (AI) and Operations Research (OR) Techniques in Constraint Programming.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 228–243.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Springer (2012) 27.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Minsky, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Steps toward artificial intelligence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Proceedings of the IRE 49(1), 8–30 (1961).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='1109/JRPROC.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='1961.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='287775 28.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Mnih, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Kavukcuoglu, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Silver, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Graves, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Antonoglou, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Wierstra, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Riedmiller, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Playing atari with deep reinforcement learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' arXiv preprint arXiv:1312.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='5602 (2013) 29.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Morabit, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Desaulniers, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Lodi, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Machine-learning–based column selection for column generation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Transportation Science 55(4), 815–831 (2021) 30.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Nethercote, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Stuckey, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Becket, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Brand, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Duck, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Tack, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Miniz- inc: Towards a standard cp modelling language.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In: International Conference on Principles and Practice of Constraint Programming.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 529–543.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Springer (2007) 31.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Potvin, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Dubé, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Robillard, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': A hybrid approach to vehicle routing using neural networks and genetic algorithms.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Applied Intelligence 6(3), 241–252 (1996) 32.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Refalo, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Impact-based search strategies for constraint programming.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In: Inter- national Conference on Principles and Practice of Constraint Programming.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 557–571.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Springer (2004) 33.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Scavuzzo, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Chen, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Chételat, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Gasse, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Lodi, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Yorke-Smith, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Aardal, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Learning to branch with tree mdps.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' arXiv preprint arXiv:2205.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='11107 (2022) 34.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Schulman, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Levine, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Abbeel, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Jordan, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Moritz, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Trust region policy optimization.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In: International conference on machine learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 1889–1897.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' PMLR (2015) 35.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Selsam, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Bjørner, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Guiding high-performance SAT solvers with unsat-core predictions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In: International Conference on Theory and Applications of Satisfia- bility Testing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 336–353.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Springer (2019) 36.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Song, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Cao, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Zhang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Xu, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Lim, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Learning variable ordering heuristics for solving constraint satisfaction problems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Engineering Applications of Artificial Intelligence 109, 104603 (2022) 18 T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Marty et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 37.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Sun, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Chen, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Li, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Song, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Improving learning to branch via reinforcement learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In: Learning Meets Combinatorial Algorithms at NeurIPS2020 (2020) 38.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Sutton, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Barto, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content='G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Reinforcement learning: An introduction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' MIT press (2018) 39.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Trott, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Zheng, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Xiong, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Socher, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Keeping your distance: Solving sparse reward tasks using self-balancing shaped rewards.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Advances in Neural Information Processing Systems 32 (2019) 40.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Van Hasselt, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Guez, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Silver, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': Deep reinforcement learning with double q- learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In: Proceedings of the AAAI conference on artificial intelligence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 30 (2016) 41.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' Yu, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Serra, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Ramalingam, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=', Zhe, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=': The combinatorial brain surgeon: Prun- ing weights that cancel one another in neural networks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' In: International Confer- ence on Machine Learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' 25668–25683.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} +page_content=' PMLR (2022)' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/2NAzT4oBgHgl3EQf8_6P/content/2301.01913v1.pdf'} diff --git a/3NFQT4oBgHgl3EQf3DZF/content/2301.13426v1.pdf b/3NFQT4oBgHgl3EQf3DZF/content/2301.13426v1.pdf new file mode 100644 index 0000000000000000000000000000000000000000..1398e4bf0ee59bac7e420a97c40ee43ae467b9f6 --- /dev/null +++ b/3NFQT4oBgHgl3EQf3DZF/content/2301.13426v1.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c5da70dc47636aff10e8c7423489f0e910afa33545fabff25a80a859b002dc58 +size 536836 diff --git a/3NFQT4oBgHgl3EQf3DZF/vector_store/index.pkl b/3NFQT4oBgHgl3EQf3DZF/vector_store/index.pkl new file mode 100644 index 0000000000000000000000000000000000000000..e573a63a5513ffe0203a53044f33ac8af307096c --- /dev/null +++ b/3NFQT4oBgHgl3EQf3DZF/vector_store/index.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:390a71ed47d155ab47685f0f4875522d08ccf7cc33d6d86da9292e3cc9a483e1 +size 48167 diff --git a/4dA0T4oBgHgl3EQfNf-w/content/tmp_files/2301.02148v1.pdf.txt b/4dA0T4oBgHgl3EQfNf-w/content/tmp_files/2301.02148v1.pdf.txt new file mode 100644 index 0000000000000000000000000000000000000000..48c4401cb70abf42994b3a18672f38b658586b51 --- /dev/null +++ b/4dA0T4oBgHgl3EQfNf-w/content/tmp_files/2301.02148v1.pdf.txt @@ -0,0 +1,2458 @@ +An electromechanics-driven fluid dynamics model for the +simulation of the whole human heart +Alberto Zingaro1, Michele Bucelli1, Roberto Piersanti1, Francesco Regazzoni1, +Luca Dede’1, and Alfio Quarteroni1, 2 +1MOX, Laboratory of Modeling and Scientific Computing, Dipartimento di Matematica, Politecnico di +Milano, Piazza Leonardo da Vinci 32, 20133, Milano, Italy +2Institute of Mathematics, ´Ecole Polytechnique F´ed´erale de Lausanne, Station 8, Av. Piccard, CH-1015 +Lausanne, Switzerland (Professor Emeritus). +Abstract +We introduce a multiphysics and geometric multiscale computational model, suitable to +describe the hemodynamics of the whole human heart, driven by a four-chamber electrome- +chanical model. +We first present a study on the calibration of the biophysically detailed +RDQ20 activation model (Regazzoni et al., 2020) that is able to reproduce the physiological +range of hemodynamic biomarkers. Then, we demonstrate that the ability of the force gener- +ation model to reproduce certain microscale mechanisms, such as the dependence of force on +fiber shortening velocity, is crucial to capture the overall physiological mechanical and fluid +dynamics macroscale behavior. This motivates the need for using multiscale models with high +biophysical fidelity, even when the outputs of interest are relative to the macroscale. We show +that the use of a high-fidelity electromechanical model, combined with a detailed calibration +process, allows us to achieve a remarkable biophysical fidelity in terms of both mechani- +cal and hemodynamic quantities. Indeed, our electromechanical-driven CFD simulations – +carried out on an anatomically accurate geometry of the whole heart – provide results that +match the cardiac physiology both qualitatively (in terms of flow patterns) and quantitatively +(when comparing in silico results with biomarkers acquired in vivo). Moreover, we consider +the pathological case of left bundle branch block, and we investigate the consequences that +an electrical abnormality has on cardiac hemodynamics thanks to our multiphysics integrated +model. The computational model that we propose can faithfully predict a delay and an in- +creasing wall shear stress in the left ventricle in the pathological condition. The interaction +of different physical processes in an integrated framework allows us to faithfully describe and +model this pathology, by capturing and reproducing the intrinsic multiphysics nature of the +human heart. +1 +Introduction +The study of cardiac blood flows aims at enhancing the knowledge of heart physiology, assessing +pathological conditions, and possibly improving clinical treatments and therapeutics. In the last +decades, the role of mathematical models in the study of cardiac hemodynamics has increasingly +gained relevance, for their non-invasiveness and flexibility with respect to geometries and flow +1 +arXiv:2301.02148v1 [math.NA] 5 Jan 2023 + +conditions [1–9]. Computational Fluid Dynamics (CFD) is largely employed to provide a detailed +description of cardiac flows and to estimate hemodynamic indicators, like e.g., the wall shear stress, +that standard image-based techniques might not capture [1]. A biophysically detailed mathematical +model of cardiac hemodynamics entails the complex interplay among different processes such as the +interaction with cardiac electromechanics (EM), valve dynamics, transition-to-turbulence effects, +and coupling with the surrounding circulation. +Furthermore, while the literature about CFD +models of the left ventricle [3, 4, 10–13], left atrium [8, 14–22] and left heart [1, 9, 23–26] is relevant, +the fluid dynamics simulation of the right heart is the subject of few works, and they focus on the +sole right ventricle [27–29] , neglecting the right atrium description. Conversely, whole-heart fluid +dynamics modeling is a much more challenging topic and has been addressed only recently in few +works [30–35]. As a matter of fact, to the best of our knowledge, hemodynamics simulations of the +whole heart are presented only in the following papers. A four-chamber CFD model is proposed +by the Siemens group in [30]: the displacement of cardiac walls is obtained from patient-specific +images and, since the left and right sides are not connected to the surrounding circulation, they +performed simulations separately on the two parts. In [31], the authors introduce a Fluid Structure +Interaction (FSI) simulation of the whole heart based on the UT-Heart simulator developed at the +University of Tokyo. Moreover, FSI simulations of the whole heart have been performed in the +context of the Living Heart Project [32]. A sequentially-coupled FSI model of the whole heart +is devised in [33], showing that few iterations of the solver are needed to reach convergence in +terms of mechanical indicators. Recently, a patient-specific whole-heart CFD model is introduced +in [34], consisting of the Navier-Stokes-Brinkman equations [36] with prescribed boundary motion +and, similarly to [30], simulations of the left and right parts are carried out separately. Finally, +a GPU-accelerated fully-coupled electro-mechano-fluid computational model of the whole heart, +based on the immersed boundary method, is presented in [35]. +In this paper, we propose a four-chamber electromechanical-driven fluid dynamics model, which +is characterized by a remarkable biophysical fidelity and able to faithfully describe potential +pathologies. Moreover, to +improve the virtual representation of the heart physiology enabled +by our model, we present a meticulous calibration of the activation model (at the cellular level) to +reproduce mechanical and hemodynamic biomarkers in the physiological range. +The blood flow in heart chambers is commonly modeled by Navier-Stokes equations for Newto- +nian fluids [37]. A crucial aspect in heart flows modeling is the treatment of boundary displacement, +i.e. the way the deformation of cardiac walls is accounted for into the model. The boundary dis- +placement can be the solution of a suitable mathematical model for the dynamics of the walls, +fully coupled to the fluid dynamics model in an FSI framework, by imposing geometric, kinematic, +and dynamic coupling conditions at the fluid-solid interface. The motion of the myocardium is in +turn driven by muscular EM, resulting in a coupled electro-mechano-fluid problem (see e.g. [9, 23, +24, 38–42]). This approach, while being very comprehensive and physically motivated, entails a +significant computational effort, due to the number of subsystems involved and to the non-linearity +induced by the coupling. To mitigate this large computational cost, the boundary displacement +can be prescribed as a datum, without any feedback from the fluid flow, in a CFD modeling +framework. The displacement may be prescribed by suitable analytical laws [3, 4, 11, 13, 16, 17, +25, 43–45], from patient-specific image-based reconstructions [1, 5, 7, 8, 14, 15], or else obtained +from a previously performed EM simulation [2, 6, 26, 46–48]. The latter corresponds to a one-way +coupled approach between EM and CFD, since only the kinematic coupling is enforced, without +foreseeing any dynamic feedback from the fluid to the structure problem. This approach is also +referred to as “kinematic uncoupling” [2, 49]. +A critical issue in 3D cardiovascular hemodynamics modeling is the prescription of boundary +2 + +conditions at inlet and outlet sections, since boundary data are generally unavailable, but also +because the circulatory system is a closed-loop network and the mathematical model should account +for it. A possible approach is the so called geometric multiscale modeling [50]: the region of interest +(in our case, the whole-heart) is described by a 3D model, while the remaining part of the circulation +is addressed by means of lumped-parameter models, as 0D [50–54], or 1D [40, 50, 55–57]. The +geometric multiscale modeling allows to account for the mutual interaction between the heart and +the circulatory system, especially if the lumped parameter model provides a closed-loop description +of the vascular network, as done in [26, 51, 58–61]. +An additional key aspect in cardiac CFD simulations is the modeling of the cardiac valves. In +principle, these can be treated by considering a structural model for the solid (leaflets of the valve +and possibly its chordae tendinae) and a fluid dynamics model for the surrounding blood flow. +This approach yields a coupled FSI model of the blood-valve system [24, 62–70], characterized by +contact phenomena and fast dynamics. Thus, FSI valve models are commonly associated to a huge +computational burden, to be added to the overall cost of the heart CFD simulation. To avoid this +large computational cost, the effects of the valves in the blood can be surrogated by relying on +reduced models for the valve dynamics [5, 34, 47, 71–74]. +Our computational model of the whole human heart encompasses the main features of the +cardiac hemodynamics: EM, cardiac valves, transition-to-turbulence effects, and interplay with +the external circulation. Indeed, our fluid model is driven by the four-chamber EM model recently +proposed in [58]. The multiphysics model is fully coupled to the external circulation described by +a lumped-parameter model, extending the computational framework we introduced in [26] to the +case of four-chamber CFD simulations. We carry out numerical simulations on an anatomically +accurate geometry of the heart, obtaining results that are quantitatively in agreement with data +from the medical literature and qualitative faithfully in terms of blood flow patterns. In this re- +spect, we analyze the role played by the highly biophysically detailed RDQ20 activation model [75] +on relevant hemodynamic quantities. Indeed, since the electromechanical displacement drives the +deformation of cardiac chambers for the CFD simulation, its calibration is fundamental towards +faithfully reproducing heart physiology. As shown in [58], the parameters of the activation model +play a significant role in determining the flow rates across cardiac valves, which have a dramatic +impact on the CFD simulation, both in terms of macroscopic indicators and overall flow distribu- +tion. Therefore, we present a detailed calibration of the active force generation model, validating +our results on several macroscopic heartbeat indicators such as stroke volumes, ejection fractions, +peak flowrates and, consequently, also in terms of blood velocities computed in the CFD simula- +tion. Our sensitivity analysis highlights that microscopic features of the RDQ20 model have a large +impact on the macroscopic characteristics of the heartbeat. Then, we show that our detailed com- +putational model can improve the understanding of the impact of the Left Bundle Branch Block +(LBBB) pathology [76] on CFD biomarkers, by capturing the effects that an electrophysiological +abnormality has on different physical processes behind the heart activity, henceforth allowing to +capture the intrinsic multiphysics nature of the cardiac function. +This paper represents one the +few examples in the literature on the modeling and simulation of the whole heart hemodynamics. +Moreover, to the best of our knowledge, this is the first work in which the 3D whole heart fluid +dynamics model is also coupled to a lumped-parameter closed-loop circulation model. +This paper is organized as follows: in Section 2, we introduce the mathematical models em- +ployed for the EM, fluid dynamics and circulation problems. Section 3 is devoted to the description +of the numerical methods for each subproblem and to the strategies used for their coupling. In +Section 4, we present numerical results on a realistic whole-heart geometry, in both physiological +and pathological conditions. Finally, limitations and conclusions follow in Section 5 and Section 6, +3 + +Figure 1: The whole-heart EM model: a) cardiac muscular fibers, (b) boundaries and impulse sites +(yellow spheres with bold labels), (c) coupling with circulation and we highlight the three main +regions of the EM model (atria, ventricles and non-conductive regions). +respectively. +2 +Mathematical model +In this section, we introduce the mathematical model. Specifically, the EM model is briefly de- +scribed in Section 2.1 and the whole-heart fluid domain is defined in Section 2.2. We present our +approach suitable to deal with the domain deformation in Section 2.3, the fluid dynamics model +in Section 2.4, and its coupling with the external circulation in Section 2.5. +2.1 +The whole-heart electromechanical model +The whole-heart EM is based on a comprehensive and biophysically detailed computational model +that we recently presented in [58]. More precisely, we consider a 3D description of cardiac EM in +all the four-chambers and a 0D representation of the complete circulatory system, including the +cardiac blood hemodynamics [59, 77], as we display in Figure 1c. +The whole-heart EM model includes a detailed myocardial fiber architecture built upon a total- +heart Laplace-Dirichlet Rule-Based Method [78], which couples together different methods for the +atria [79] and the ventricles [80], to properly reproduce the characteristic features of the cardiac +fiber bundles in all the four-chambers [79], see Figure 1a. +Cardiac electrophysiology is described by means of the monodomain equation equipped with +no-flux Neumann boundary conditions [81] and endowed with the following human ionic models: +ten Tusscher-Panfilov for the ventricles [82] and Courtemanche-Ramirez-Nattel for the atria [83]. +4 + +URADL.AFurthermore, the arterial vessels and the atrio-ventricular basal plane are assumed to be non- +conductive regions, whence electrically isolating the atria from the ventricles [58]. Finally, the +cardiac conduction system is substituted by a series of spherical electrical impulses, originating +from the sino-atrial node (SAN) and ending into the left and right ventricular endocardia which, +combined with a fast endocardial layer, surrogates the effect of the Purkinje network [58, 84], as +we show in Figure 1b. +The sarcomere mechanical activation is based on the biophysically detailed RDQ20 active +contraction model [75], properly calibrated for both atria [85] and ventricles [86]. The RDQ20 is +able to represent in detail the sophisticated microscopic active force generation mechanisms, taking +place at the scale of sarcomeres [86]. Moreover, to differentiate the active tension in left and right +ventricles, we consider a spatially heterogeneous active tension [77]. +The myocardial tissue mechanics is described by the momentum balance equation under the +hyperelasticity assumption [87]. We employ, for the active part, an orthotropic active stress formu- +lation [77], which surrogates the contraction caused by dispersed myofibers [88], and, for the passive +behavior, specific mechanical constitutive laws and model parameters for the different cardiac re- +gion: the Usyk constitutive law for both the atria and the ventricles [89] and a Neo-Hookean strain +energy density function for the atrio-ventricular basal plane and the vessels [87]. Finally, a nearly +incompressible formulation is enforced with a penalty method [58]. Concerning the mechanical +boundary conditions, we consider: i) generalized Robin boundary conditions on the epicardium, +surrogating both the presence of the pericardium and also the epicardial adipose tissue, crucial for +reproducing the correct downward and upward movement of the atrio-ventricular basal plane [58]; +ii) normal stress boundary conditions on the four-chamber endocardia and vessel endothelia to +account for the pressures exerted by the blood, where the endocardium and endothelium fluid +pressures are given by the coupling between the mechanical and the circulation problems [58, 59, +77]; iii) homogeneous Dirichlet boundary condition on all the artificial rings where we cut the +computational domain, since the arteries and atrial veins can be considered fixed here [58], as +displayed in Figure 1b. +The whole-heart 3D EM model is fully coupled with a 0D closed-loop lumped parameters model +for the blood hemodynamics through the entire cardiovascular network. Systemic and pulmonary +circulations are modeled using resistance-inductance-capacitance circuits (both for the arterial +and venous part) and non-ideal diodes stand for the heart valves +[59]. In Figure 1c we give a +graphical representation of the 3D-0D model. The coupling between the 0D and 3D EM models +is achieved by introducing volume-consistency coupling conditions, where the pressures of all the +four-chambers act as Lagrange multipliers associated with the introduced volume constraints [59, +77]. +The most relevant feedbacks, representing the interactions among electric signal propagation, +the cardiac tissue deformation and contraction, and the circulatory system, are modelled inside the +whole-heart EM model [58]. These include e.g. the mechano-electric feedback [90] (between elec- +trophysiology and mechanics) and the fibers-stretch and fibers-stretch-rate feedbacks [91] (between +mechanics and the activation model). +For the full set of equations of the whole-heart EM model, we refer to [58, 59, 77]. +In this paper, the whole-heart EM model serves as unidirectional input for the fluid dynamics +problem as we better detail in Section 2.3 and Section 2.4. +5 + +Figure 2: The whole-heart fluid domain: (a) subdomains composing the whole heart; (b) boundary +portions (the left and right part are separated for visualization purposes). +2.2 +The whole-heart fluid domain +Let Ωt be the fluid domain at time t > 0 bounded with a sufficiently regular boundary Γt ≡ ∂Ωt +and let (0, T) be the temporal domain, with T the final time. From a fluid dynamics view point, +the whole-heart fluid domain Ωt is topologically disjoint and split into left heart (LH, ΩLH +t ) and +right heart (RH, ΩRH +t +), as we show in Figure 2: Ωt = ΩRH +t +∪ΩLH +t , with Ω +RH +t +∩Ω +LH +t += ∅. Specifically, +Ω +RH +t += Ω +RA +t +∪ Ω +TV +t +∪ Ω +RV +t +∪ Ω +PV +t +∪ Ω +PT +t , +Ω +LH +t = Ω +LA +t +∪ Ω +MV +t +∪ Ω +LV +t +∪ Ω +AV +t +∪ Ω +AO +t +, +where ΩRA +t +, ΩRV +t , ΩPT +t +are the right atrium (RA), right ventricle (RV) and pulmonary trunk (PT) +subdomains, and ΩLA +t , ΩLV +t , ΩAO +t +the left atrium (LA), left ventricle (LV) and aorta (AO) subdo- +mains. Moreover, ΩTV +t +, ΩPV +t , ΩMV +t +, ΩAV +t +are the subdomains representing the rings of the tricuspid +valve (TV), pulmonary valve (PV), mitral valve (MV) and aortic valve (AV). Analogously, we +partition the boundary of the whole-heart domain as Γt = ΓRH +t +∪ ΓLH +t , with ΓRH +t +∩ ΓLH +t += ∅. In +particular, as displayed in Figure 2b, +ΓRH +t += Γout,RH ∪ Γin,RH ∪ Γw,RH +t +, +with Γin,RH the inlet sections of the superior and inferior venae cavae, Γout,RH the outlet section of +the pulmonary trunk, and Γw,RH +t +the endocardium of the RH. In an analogous fashion, on the left +part: +ΓLH +t += Γout,LH ∪ Γin,LH ∪ Γw,LH +t +, +with Γin,LH the five inlet sections of the four pulmonary veins, Γout,LH the outlet section of the +aorta, and Γw,LH +t +the LH endocardium. +6 + +Tin,LHTout,RHin.RH2.3 +The fluid domain displacement problem +To represent the deformation of the domain over time, we introduce a fixed reference configuration +ˆΩ ⊂ R3, such that the domain in current configuration Ωt is defined at any t ∈ (0, T) as +Ωt = {x ∈ R3 : x = �x + d(�x, t), �x ∈ �Ω}, +where d : �Ω × (0, T) is the displacement of the domain, and is obtained it by solving the following +harmonic extension problem: +� +−∇ · (s∇d) = 0 +in �Ω × (0, T), +(1a) +d = d∂Ω(x, t) +on ∂�Ω × (0, T). +(1b) +In Equation (1), d∂Ω : ∂�Ω × (0, T) is the boundary displacement, computed by restricting the +solution of the EM simulation to the endocardium and the endothelium. Furthermore, s : �Ω × +(0, T) → R is a space-dependent scalar field introduced to avoid distortion of mesh elements. +Specifically, we use the boundary-based stiffening approach proposed in [92]. We denote the fluid +domain displacement problem Equation (1) with the abridged notation +D(d, d∂Ω) = 0. +The EM simulation is solved with a significantly larger timestep than the CFD one. Therefore, +the boundary displacement d∂Ω is only available for some times tk, k = 0, 1, . . . , NEM, although +the domain displacement d is needed with a finer temporal resolution. Thus, problem (1) is solved +for all times tk, and then we construct a displacement field ˜d(t) : �Ω×(0, T) → R3 using smoothing +splines approximation in time [93]. +We compute the domain velocity by deriving the displacement in time as +uALE = ∂ �d +∂t in Ωt × (0, T). +(2) +2.4 +The Navier-Stokes equations in ALE framework with the RIIS +model of the valves +We model the blood in the cardiac cavities as an incompressible, viscous and Newtonian fluid +characterized by constant density ρ and constant dynamic viscosity µ. We therefore use the time- +dependent incompressible Navier-Stokes equations expressed in an Arbitrary Lagrangian Eulerian +(ALE) framework to account for the moving domain. We denote by u : Ωt × (0, T) → R3 and +p : Ωt × (0, T) → R the fluid velocity and pressure, respectively. Let σ be the Cauchy stress +tensor, defined for incompressible, Newtonian and viscous fluids as σ(u, p) = −pI + 2µϵ(u), with +ϵ(u) = 1 +2 +� +∇u + (∇u)T� +the strain-rate tensor. +We model the effects of cardiac valves in the fluid by means of the Resistive Immersed Im- +plicit Surface (RIIS) method [71]. +We consider four immersed surfaces Σk, with k ∈ Iv = +{MV, AV, TV, PV} the set of valves. Each valve is characterized by a resistance coefficient Rk +and a parameter εk representing the half thickness of the valve leaflets. The immersed surface is +implicitly described by a signed distance function ϕk : Ωt × (0, T) → R. With the RIIS method, +we introduce the following penalty term to the momentum balance of the Navier-Stokes equations +(expressed in ALE form): +R(u, uALE) = +� +k∈Iv +Rk +εk +δΣk,εk(ϕk) +� +u − uALE − uΣk +� +. +7 + +R penalizes the mismatch between the relative velocity u − uALE and the velocity of the valves’ +leaflets uΣk, weakly imposing a kinematic coupling condition only. The resistive term is only acting +on a tiny support around Σk, thanks to a smoothed Dirac delta function acting as multiplicative +factor. We refer to [71] for its definition. +By defining with +�∂u +∂t = ∂u +∂t + (uALE · ∇)u the ALE derivative, the 3D fluid dynamics model of +the whole heart reads: +� +� +� +� +� +� +� +� +� +� +� +� +� +� +� +� +� +� +� +� +� +� +� +� +� +� +� +� +� +� +� +� +� +ρ +�∂u +∂t + ((u − uALE) · ∇)u + ∇ · σ(u, p) + R(u, uALE) = 0 +in Ωt × (0, T), +(3a) +∇ · u = 0 +in Ωt × (0, T), +(3b) +σ(u, p)n = −pin +RAn +on Γin,RH × (0, T), +(3c) +σ(u, p)n = −pPUL +AR n +on Γout,RH × (0, T), +(3d) +σ(u, p)n = −pin +LAn +on Γin,LH × (0, T), +(3e) +σ(u, p)n = −pSYS +AR n +on Γout,LH × (0, T), +(3f) +u = uALE +on Γw,RH +t +∪ Γw,LH +t +× (0, T). (3g) +u = u0 +in Ω0 × {0}, +(3h) +where pin +RA, pPUL +AR , pin +LA, pSYS +AR are the pressures arising from the coupling with the circulation model, +as we detail in Section 2.5, and u0 is the initial velocity. We denote the 3D fluid dynamics model +of the whole heart in Equation (3) by +F(u, p, uALE) = 0. +2.5 +Coupling with circulation +To account for the interplay between the heart’s fluid dynamics and the hemodynamics of the +surrounding circulation, we couple the cardiac CFD model to a 0D closed-loop model of the whole +circulation proposed in [59]. Specifically, we extend to the whole heart the coupling strategy that +we devised in [26] for the case of the sole left heart. We consider a reduced (open) version of the +original circulation model in which we remove the equations for all the variables that are already +described by the 3D counterpart. The equations of the open system are reported in A. By denoting +with Q0D and p0D the vectors containing flowrates and pressures of the 0D model, respectively, we +refer to the open system with the notation +C (Q0D, p0D) = 0. +The coupling between the 3D CFD model and the open 0D model consists in the enforcement +of the continuity of pressures and flowrates on the artificially chopped boundaries ΓI of the fluid +domain, yielding the following conditions: +�pI +3D = pI +0D +on ΓI × (0, T), +(4a) +QI +3D = QI +0D +on ΓI × (0, T), +(4b) +which express dynamic and kinematic coupling, respectively, with1 +pI +3D = +1 +|ΓI| +� +ΓI p, +QI +3D = +� +ΓI(u − uALE) · n. +(5) +1We define the sign of the flowrate in accordance with the outward unit normal n. Thus, an inlet flowrate +(entering velocity) will be, by definition, negative. +8 + +Figure 3: The 3D-0D fluid dynamics model of the whole-heart coupled to the surrounding circu- +lation. +Considering the whole-heart fluid domain (see Figure 2b), the interface boundaries are ΓI = +Γin,RH ∪ Γout,RH ∪ Γin,LH ∪ Γout,LH. The 0D pressures (pI +0D) and flowrates (pI +0D) are [42, 94]: +pin,RH +0D += pin +RA, +Qin,RH +0D += −QSYS +VEN, +pout,RH +0D += pPUL +AR + RPUL +upstreamQPV, +Qout,RH +0D += QPV, +pin,LH +0D += pin +LA, +Qin,LH +0D += −QPUL +VEN, +pout,LH +0D += pSYS +AR + RSYS +upstreamQAV, +Qout,LH +0D += QAV. +From the point of view of the 3D CFD model, the conditions expressed by Equation (5) are +defective, since they prescribe the average pressure and the total flow rate over the entire section +ΓI, rather than pointwise stress and velocity distributions [50]. We choose to complete the pressure +condition as +σ(u, p)n = −pIn, on ΓI × (0, T). +(6) +The flowrate condition, conversely, is left in its defective form, since it is sufficient for the algorithm +we use for the 3D-0D coupling (see Section 3). +3 +Numerical methods +In this section, we describe the numerical methods we use to solve our multiphysics and mul- +tiscale system. The overall algorithm is presented in Algorithm 1 and graphically represented in +Figure 4. We can subdivide the overall procedure in a preliminary phase, in which we solve the +EM simulation [58] and the fluid domain displacement problem to lift the boundary displacement +to the fluid bulk domain, followed by the coupled 3D-0D CFD simulation. +9 + +Algorithm 1 Numerical scheme for the EM-driven CFD simulation of the whole heart +Solve whole-heart EM model +Pick solution in the last heartbeat: → dEM +i +, for i = 0, . . . , N +Restrict dEM +i +on ∂ΩS,endo +i +: d∂Ω +i , for i = 0, . . . , N +Solve fluid domain displacement problem: D(di, d∂Ω +i ) = 0, for i = 0, . . . , N +Build approximant: �d(t) +Initialization: n = 0, u0 = 0 +while n < Nt do +Update valves leaflet position +Compute ALE velocity: uALE +n+1 = +� +dn+1− � +dn +∆t +. +Solve circulation: C (Q0D +n+1, p0D +n+1) = 0 with data QI +0D,n +Compute interface data (0D → 3D): pI +0D,n+1 → pI +3D,n+1 +Solve fluid dynamics: F(un+1, pn+1, uALE +n+1 ) = 0, with Neumann data pI +3D,n+1 +Compute interface data (3D → 0D): QI +3D,n+1 → QI +0D,n+1 +n ← n + 1. +end while +Numerical methods for the EM model +For the numerical approximation of the whole-heart +EM model we employ the efficient Segregated-Staggered scheme [58, 59, 77]. In this numerical +scheme, the different cardiac physical models, contributing to both the 3D EM and the 0D blood +circulation, are sequentially solved in a segregated manner, using different resolutions in space +and time to properly account for the heterogeneous space and time scales characterizing different +physical processes [95]. +For the space discretization, we use the Finite Element (FE) method with continuous FE on a +tetrahedral mesh. We consider FE of order 2 (P2) for the electrophysiology to capture the traveling +wave dynamics and FE of order 1 (P1) for both the activation and the mechanics [58]. +For the time discretization, we use finite difference schemes [96]. Specifically, cardiac electro- +physiology is solved with Backward Differentiation Formula (BDF) of order 2, using an Implicit- +Explicit (IMEX) scheme where the diffusion term is treated implicitly, the ionic and reaction terms +explicitly. The ionic variables are advanced in time through an IMEX scheme [58, 59, 77]. We solve +the active contraction problem with an IMEX BDF1 method, and the mechanical problem with a +fully-implicit BDF1 scheme [77]. Finally, an IMEX scheme of the first order is used for the circu- +lation [58]. Moreover, two different time steps are used: a finer one for the electrophysiology and +a larger one for both the activation, the mechanics and the circulation [59]. Finally, we employ +recently developed stabilization methods – related to the circulation and the fibers-stretch-rate +feedback – that are crucial to obtain a stable solution in a four-chamber simulation scenario [97, +98]. Concerning the linear systems arising from the discretization of the whole-heart EM problem +we use: the conjugate gradient for the electrophysiology and the GMRES method for both the +mechanics and the activation, both empowered by an algebraic multigrid (AMG) preconditioner. +Finally, we solve the non-linear saddle-point problem arising from the coupling between the me- +chanics and the circulation by means of a Newton algorithm using, at the algebraic level, the Schur +complement reduction [59, 77]. +For further details about the numerical methods we use in the whole-heart EM model, we refer +to [58, 59, 77]. +10 + +Numerical methods for the fluid domain displacement problem +After simulating the +whole-heart EM and reaching a limit cycle in terms of pressure and volumes, we extract the +solution from the last simulated heartbeat. We restrict this solution to the heart endocardium +and the endothelium of outflow tracts, obtaining N + 1 solutions defined on the boundary of the +fluid domain (d∂Ω +i , with i = 0, . . . , N). We project d∂Ω +i +onto the CFD mesh with piecewise linear +interpolation, then solve the fluid domain displacement problem in Equation (1) to obtain the ALE +displacement d. We discretize the lifting problem in Equation (1) using FEs of order 1 (P1) and +the linear system arising from its discretization is preconditioned with an AMG preconditioner. +We solve the resulting linear system with the conjugate gradient method. The smoothing spline +approximation is computed independently for each mesh node, and the approximant is constructed +following the optimization procedure described in [99]. To compute the ALE velocity, we use BDF1 +to discretize in time Equation (2). +Numerical methods for the CFD cardiac model +We discretize Equation (3) in space using +FEs of order 1 for both velocity and pressure (P1 − P1). We employ the Variational Multiscale +- Large Eddy Simulation (VMS-LES) method to obtain a stable formulation of the NS-ALE- +RIIS equations discretized via equal order FE spaces. This also allows us to control instabilities +arising from the advection-dominated regime, and to model transition-to-turbulence in the LES +framework [16, 100, 101]. The VMS-LES formulation accounts for the ALE framework and the +RIIS modeling used for valves. For the complete formulation, we refer to [26]. +For the time discretization, we consider a uniform partition of the temporal domain in Nt +subintervals (tn, tn+1] of uniform size ∆t, with n = 0, . . . , Nt − 1. We denote from here quantities +approximated at time tn with the subscript n, e.g. un ≈ u(tn). We advance the problem in time +by means of BDF1. To reduce the computational burden of the numerical simulations, we use a +semi-implicit treatment of the nonlinearities, as done in [100]. The overall numerical scheme for +the fluid dynamics problem is detailed in [26]. +Since Neumann boundary conditions may give rise to instability phenomena in case of inflow, +we set backflow stabilization on all the Neumann boundaries in the inertial form presented in [102]. +The linear system arising from the discretization of Equation (3) is preconditioned with the +aSIMPLE preconditioner [103], and each of its blocks is preconditioned with an AMG precondi- +tioner. The linear system is then solved at each time step by the GMRES method. +Numerical method for the 0D circulation model +We solve the system of ODEs of the +circulation problem with an IMEX method of the first order. The time-step size ∆t employed for +its numerical discretization is the same used for the BDF advancing scheme in the 3D problem. +Numerical scheme for the coupled CFD problem +After initialization, for each temporal +step of the CFD problem, we update the position of valve leaflets and we compute the ALE velocity. +At every time step, we solve the 3D and 0D subproblems independently. First, we solve the 0D open +circulation problem using QI +n as input (i.e. the flowrates computed in the 3D model at previous +timestep, namely QSYS +VEN,n, QPV,n, QPUL +VEN,n, QAV,n). Then, from the solution of the circulation, we +compute the pressures pI +n+1 at the interfaces and solve the fluid dynamics problem providing those +pressures as Neumann boundary conditions at inlet and outlet sections. Finally, we compute the +interface data from the 3D to the 0D model, i.e. QI +n+1. This approach treats the coupling between +the 3D and 0D subproblems in a segregated and explicit way. +11 + +Figure 4: Graphical representation of the overall algorithm to simulate the whole-heart hemody- +namics driven by the EM model. +4 +Numerical results +In this section, we present the numerical results using the whole-heart fluid dynamics model. In +Section 4.1, we introduce the computational setting of the whole-heart EM and CFD simulations. +Section 4.2 is devoted to the calibration of the RDQ20 activation model to produce physiological +flowrates in the EM simulation. +The physiological results of the overall computational model +are presented in Section 4.3. +Finally, we apply the multiphysics computational model to the +pathological case of LBBB in Section 4.4. +4.1 +Computational setup +We consider a realistic whole-heart geometry provided by the Zygote solid 3D heart model [104], +an anatomically CAD model representing an average healthy human heart reconstructed from +high-resolution computer tomography scan data. We generate whole-heart tetrahedral meshes for +the EM and CFD problems that we report in Figure 5. Meshes are generated with vmtk [105] +using the methods and tools discussed in [26, 58, 106]. Details on the meshes for the EM and CFD +simulations are provided in Table 1. The valve leaflets are thin structures that we characterize, +in the context of the RIIS method, by small values of εk. To correctly capture the immersed +surfaces, we refine the CFD mesh close to the valve regions, as shown in Figure 5c. Specifically, +following [71], we choose hk such that εk ≥ 1.5hk, where hk is the minimum mesh size of the fluid +mesh in the valve region. +We carry out EM and CFD simulations in lifex [107, 108]2, a high-performance C++ FE library +developed within the iHEART project3, mainly focused on cardiac simulations and based on the +deal.II finite element core [109–111]. +Numerical simulations are run in parallel on the GALILEO100 supercomputer4 at the CINECA +2https://lifex.gitlab.io/ +3iHEART - An Integrated Heart model for the simulation of the cardiac function, European Research Council +(ERC) grant agreement No 740132, P.I. A. Quarteroni, 2017-2023 +4528 computing nodes each 2 x CPU Intel CascadeLake 8260, with 24 cores each, 2.4 GHz, 384GB RAM. See +https://wiki.u-gov.it/confluence/display/SCAIUS/UG3.3%3A+GALILEO100+UserGuide for technical specifi- +12 + +Figure 5: Tetrahedral meshes used in the computational model: a) mesh for the EM simulation, +b) mesh for the CFD simulation, c) mesh refinement on the valves region of the CFD mesh. +Simulation +Mesh size [mm] +Cells +Points +Physics +DOFs +∆t [s] +min +avg +max +EM +0.860 +2.97 +5.34 +180 472 +46 915 +Electrophysiology +310 505 +5 · 10−5 +Mechanics +140 745 +10−3 +Circulation +- +10−3 +CFD +0.210 +1.04 +3.82 +3 892 584 +652 204 +Fluid dynamics +2 608 816 +10−4 +Circulation +- +10−4 +Table 1: Setup of whole-heart EM and CFD simulations (mesh details and time step sizes). +supercomputing center, using 240 and 480 cores for the EM and CFD simulations, respectively. +The computational time to carry out a single heartbeat is about 1 hour and 20 minutes for the +EM simulation and 56 hours for the CFD simulation. +For the parameters of the EM model, we use the same values as in [58] with some minor +differences reported in B. As we better discuss in Section 4.2, a huge difference in terms of setup of +the EM simulation between [58] and the present work consists in the calibration of the activation +model to compute physiological blood flowrates. We simulate 20 heartbeats of the whole-heart +EM, and we report numerical results related to the last heartbeat, after verifying that the solution +is sufficiently close to a periodic limit cycle (in terms of pressure and volume transients). We +consider an heartbeat period of THB = 0.8 s. We pick the last simulated EM heartbeat as input +displacement for the CFD simulation. We set as initial condition for the velocity u0 = 0. The +cations. +13 + +Figure 6: Cardiac valves in their open and closed configurations coloured according to displacement +magnitude. Valves geometry are provided by Zygote [104], and we define displacement field aimed +at closing and opening the leaflets. +initial state of the circulation model (for the coupling with the fluid dynamics) is taken equal +to the values reached at the beginning of the last heartbeat in the EM simulation. Moreover, +as detailed in B, all the values of the parameters involved in the circulation model are the same +for the EM and the fluid dynamics simulations. The physical parameters for blood are density +ρ = 1.06 · 103 kg/m3 and dynamic viscosity µ = 3.5 · 10−3 kg/(m s). We simulate two heart cycles, +and we report the solution on the second cycle to remove the consequences of an unphysical null +initial condition. For the numerical results visualization (for both EM and CFD), we shift the time +domain in (0, THB). +The Zygote cardiac valves [104] are provided in their open configuration (TV, MV) and closed +configuration (PV, AV). Thus, we define displacement fields aimed at closing and opening their +leaflets, based on signed-distance functions and the solution of Laplace-Beltrami problems [26, +106]. In Figure 6, we report the valves in their open and closed configurations, colored according +to the leaflets’ displacement magnitude. We open and close the valves instantaneously (i.e. in one +time step) at the times reported in Table 2. These times are chosen by selecting the initial and +final times of the isovolumetric phases. The values we choose for εk, reported in Table 2, allow +to have a physiological representation of the valve leaflet. Indeed, we choose εk by averaging the +values of the leaflet thicknesses reported in [112]. Furthermore, the valve resistances values Rk are +reported in Table 2. We found that the condition number of the linear system associated to the +FE discretization of the fluid dynamics problem becomes larger as the ratio Rk/εk increases. Thus, +to keep contained the computational cost of the CFD simulation, we choose as Rk the minimum +value that guarantees impervious valves. +14 + +0 +22MV +AV +TV +PV +opening time +[s] +0.710 +0.262 +0.700 +0.279 +closing time +[s] +0.208 +0.666 +0.194 +0.677 +Rk +[kg/m/s] +104 +104 +104 +104 +εk +[mm] +0.68 +0.67 +0.77 +0.52 +Table 2: Setup of the RIIS method to model cardiac valves. +4.2 +Calibration of the RDQ20 activation model to achieve physiologi- +cal flows +Among the different components at the basis of cardiac EM simulations, the model describing the +active force generation at the microscale plays a pivotal role on the solution. Indeed, not only +the amount of force the muscle develops depends on it, but also its temporal distribution over the +heartbeat, i.e., the kinetics of contraction and relaxation. Muscle contraction, in turn, determines +the blood flow through the valves. +Moreover, since the electromechanical displacement drives +the fluid dynamics model, the same flows are then obtained in the CFD simulation. Therefore, +special care must be devoted here to the choice and calibration of the activation model, in order +to faithfully capture blood flowrates and, consequently, blood velocities. +For the above reasons, we chose to use the RDQ20 model [75], that is an active force model +with high biophysical fidelity and that is able to reproduce the main features of the experimentally +observed behaviors. The RDQ20 model is based on a detailed description of the calcium-driven +regulation of the thin filament, with explicit representation of end-to-end cooperative interactions, +and a description of the attachment-detachment process of crossbridges, at the basis of the force- +velocity relationship. Thereby, the model is able to reproduce the main mechanisms of contractility +regulation, mediated by calcium, fiber strain and fiber strain-rate. In particular, the fiber strain- +rate feedback, which is responsible for the well-known force-velocity relationship, plays a central +role in the regulation of hemodynamic flows, as demonstrated in [58] and confirmed in the present +study. +On this basis, we refine the calibration of the RDQ20 model, with a particular care on fluxes +through semilunar valves obtained by means of the 0D model in the EM simulation. We employ as a +starting point the calibration used in [58], suitable for the coupling with the TTP06 ionic model [82] +(see Table 3, setting A). In Table 4, column A, we report a list of biomarkers obtained by using the +calibration A in the EM simulation. Although the biomarkers characterizing the overall cardiac +function (i.e. end-systolic and end-diastolic volume, stroke volume and ejection fraction) are within +reference ranges, the maximum blood flux across valves is significantly above the physiological +range. In other terms, even if the total ejected blood is physiological, the instantaneous flow peak +is too large. This would clearly have a strong negative impact on the results of fluid dynamics +simulations. For instance, an excessively high velocity through the valve may result in high pressure +gradients, and an overall incorrect stress distribution over valve leaflets and cardiac walls [113, 114]. +To address this issue, we slow down the process of force generation, so that the tissue contrac- +tility is developed at a lower rate. More precisely, we reduce the association-dissociation rates of +troponin and tropomyosin of the RDQ20 model (i.e. Koff and Kbasic) by a factor 2. Moreover, +to compensate for the lower peak force caused by a slower kinetics, we increase the crossbridge +level contractility (i.e. aXB). We consider three different levels of contractility, as reported in +Table 3, respectively in columns B, C and D. However, as we show in Table 4 and Figure 7, on +15 + +Parameter +A +B +C +D +E +Regulatory units dynamics +Q +[−] +2 +2 +2 +2 +2 +kd +[µM] +0.36 +0.36 +0.36 +0.36 +0.36 +αkd +[µM/µm] +-0.2083 +-0.2083 +-0.2083 +-0.2083 +-0.2083 +µ +[−] +10 +10 +10 +10 +10 +γ +[−] +30 +30 +30 +30 +30 +Koff +[1/s] +8 +(*)4 +(*)4 +(*)4 +(*)4 +Kbasic +[1/s] +4 +(*)2 +(*)2 +(*)2 +(*)2 +Crossbridge dynamics (prescribed) +v0 +[1/s] +2 +2 +2 +2 +(*)0.5 +vmax +[1/s] +8 +8 +8 +8 +(*)2 +˜k2 +[−] +66 +66 +66 +66 +66 +µiso +0 +[−] +0.22 +0.22 +0.22 +0.22 +0.22 +Crossbridge dynamics (automatically calibrated) +r0 +[1/s] +134.31 +134.31 +134.31 +134.31 +33.24 +α +[−] +25.184 +25.184 +25.184 +25.184 +24.93 +µ0 +fP +[1/s] +32.225 +32.225 +32.225 +32.225 +7.98 +µ1 +fP +[1/s] +0.768 +0.768 +0.768 +0.768 +0.192 +Micro-macro upscaling +aXB +[MPa] +1500.0 +(*)1550.0 +(*)2925.0 +(*)5214.5 +(*)1550.0 +Table 3: Different calibrations of the RDQ20 activation model: A) calibration from [58]; B) C) +D) Reduced Koff, Kbasic and different levels of aXB; E) Reduced v0, vmax. Parameters that are +modified with respect to A are highlighted by the symbol (*). We remark that the parameters +r0, α, µ0 +fP and µ1 +fP are automatically calibrated from the four quantities v0, vmax, ˜k2 and µiso +0 +(for +furhter details, see [75]); therefore, the symbol (*) is not reported for these four parameters. For +the description of each parameter, we refer to the original paper of the RDQ20 model [75]. +16 + +Biomarker +Physiological values +A +B +C +D +E +ESVLV +[ml] +35 to 80 +[115] +53.8 +66.7 +53.5 +45.9 +66.4 +ESVRV +[ml] +69 ± 22 +[116] +58.0 +79.8 +65.3 +56.0 +72.3 +EDVLV +[ml] +126 to 208 +[115] +150 +128 +(↓)108 +(↓)87.9 +151 +EDVRV +[ml] +144 ± 23 +[117] +153 +152 +134 +119 +159 +SVLV +[ml] +81 to 137 +[115] +96.3 +(↓)61.5 +(↓)54.9 +(↓)42.0 +84.9 +SVRV +[ml] +94 ± 15 +[117] +95.4 +(↓)72.3 +(↓)69.1 +(↓)63.2 +87.1 +EFLV +[%] +49 to 73 +[118] +64.2 +(↓)48.0 +50.2 +(↓)47.8 +56.1 +EFRV +[%] +53 ± 6 +[119] +(↑)62.2 +47.5 +51.4 +53.0 +54.6 +Qmax +AV +[ml/s] +427 ± 129 +[120] +(↑)697 +327 +347 +304 +399 +Qmax +PV +[ml/s] +427 ± 129 +[120]† +(↑)756 +397 +427 +443 +478 +pmax +LV +[mmHg] +119 ± 13 +[121] +(↑)154 +(↓)99.5 +(↓)93.2 +(↓)80.7 +120 +pmax +RV +[mmHg] +35 ± 11 +[122] +37.2 +34.9 +38.2 +41.6 +32.2 +Table 4: Effects of different calibrations of the RDQ20 activation model on mechanics and hemo- +dynamics biomarkers. Biomarkers highlighted by the symbols (↑) and (↓) lie outside the reference +ranges, denoting values too large or too small, respectively, compared with reference ranges. †: +for the maximum PV flowrate, in absence of clinical ranges from literature, we consider the same +normal values of the AV flowrate. Each column corresponds to a different calibration as detailed +in Table 3. +Figure 7: pV loops of RV and LV obtained with different calibrations of the RDQ20 activation +model. Each line corresponds to a different calibration as detailed in Table 3. +17 + +RV pV loop +LV pV loop +45 +160 +A +A +40 +B +B +140 +C +C +35 +D +D +E +120 +E +30 +100 +80 +60 +15 +40 +10 +20 +5/ +0 L +0 L +40 +60 +80 +100 +120 +140 +160 +180 +200 +40 +60 +80 +100 +120 +140 +160 +180 +200 +V [ml] +V [ml](a) Force-velocity relation +(b) reduced v0 +(c) reduced vmax +Figure 8: Representation of the well-known force-velocity relationship of muscle cells. The nor- +malized active tension Ta/T iso +a , where T iso +a +denotes the force in isometric conditions, is a decreasing +function of the shortening velocity v. As shown in the figure, vmax is the shortening velocity for +which the active tension reaches zero, while v0 is the slope of the curve in correspondence of the +isometric conditions (i.e. v = 0). (a) generic force-velocity relationship, (b) effect of reducing v0 +(in grey, the curve from (a)), (c) effect of reducing vmax (in grey, the curve from (a)). +the one hand, we achieve the desired effect of reducing semilunar peak flows, thus bringing them +within the expected ranges. On the other hand, we compute significantly reduced stroke volume +and ejection fraction for both chambers, thus moving out of physiological ranges. Notice also that +this issue is also not resolved by adjusting the contractility. In fact, by raising aXB, not only the +state of contractility in systole is changed, but also in diastole, leading to a reduced end-diastolic +volume and, therefore, nullifying the effect of increased contractility, due to the Frank-Starling +mechanism. The three cases shown in Table 4 (B, C and D) are three illustrative cases out of the +many that we tested, but without being able to reduce peak flows within the expected ranges while +maintaining a physiological ejection fraction. The tests evidenced a paradigmatic short-blanket +problem, whereby just acting on kinetics and contractility it is not possible to lower the flows while +maintaining a regular ejection fraction. Evidently, another element must be taken into account. +Based on the results of [58], which showed that, by neglecting the fibers-stretch-rate feedback, +blood flows through the semilunar valves are significantly overestimated, we modified the calibra- +tion so as to, on the contrary, strengthen the effect of this feedback, but without changing either +the isometric force or the kinetics. Specifically, we acted in such a way as to steepen the force- +velocity relationship, the microscopic mechanism underlying the fibers-stretch-rate feedback, by +modifying the parameters governing cross-bridge dynamics. For this purpose, we took advantage +of the calibration technique illustrated in [75], by which the RDQ20 model can be tuned to achieve +a desired force-velocity relationship. Two features of the force-velocity relationship can be selected, +namely the maximum shortening velocity (vmax), that is the velocity corresponding to vanishing +active force, and the tangent to the curve under isometric conditions (v0). The geometric meaning +of the two quantities is illustrated in Figure 8. +Hence, starting from the B calibration, we modified the parameters to obtain a vmax and a v0 +equal to one-fourth of the original ones (see Table 3, column E). As evidenced in Table 4, with the +setting E all biomarkers fall within physiological ranges (see also Figure 7). We believe that this +result can be explained precisely by the mechanism of fibers-stretch-rate feedback, whereby regions +of the tissue undergoing rapid shortening experience a decrease in developed force, thus promoting +a more homogeneous shortening in space and without significant spikes in time, with a resulting +viscous-like effect. In conclusion, blood flow is redistributed more evenly over the duration of the +ejection phase. +Based on the very good match with the reference values of the different biomarkers, in this +18 + +work we use the calibration E as the baseline for EM simulations. +4.3 +Heart physiology and validation against clinical biomarkers +Figure 9 shows the whole-heart EM displacement for a single, representative heartbeat, where +we highlight six different phases: isovolumetric contraction, ejection (peak and mid-deceleration), +isovolumetric relaxation, ventricular passive filling, and atrial contraction. As pointed out in [58], +the whole-heart EM model can correctly reproduce the cardiac physiological motion. Moreover, +as shown in Section 4.2, we found that, after a thorough calibration of the activation model, +our numerical results are consistent with normal values found in literature in terms of several +volumetric biomarkers. +In Figure 10, we report the volumes of the heart chambers and large arteries versus time, and +we highlight time instants in which valves open and close. In Figure 13, we report the volume +rendering of the velocity magnitude obtained with our EM-driven CFD simulation. We start our +fluid dynamics simulation at the end of ventricular diastole. +During the active atrial contraction (Figure 13a), we observe the blood flowing from atria +to ventricles, producing two high-speed jets in the MV and TV. This moment corresponds to +the A-wave, as shown in Figure 12. In order to assess whether our numerical simulation is cor- +rectly reproducing the physiological heart function, we compare the peak velocities through valves +with physiological ranges available in literature and acquired in healthy subjects. We report this +comparison in Table 5. During diastole, we obtain lower velocities in the TV compared to MV, +consistently with clinical measurements available in literature [38, 123]. +During the isovolumetric contraction (Figure 13b), all valves are closed and the ventricular +volumes remain constant. +We measure lower velocity values compared to filling and ejection +phases. Moreover, we found that the intraventricular pressure is not well defined and is prone to +oscillations. As a matter of fact, since we are using EM as unidirectional input of the CFD model, +the dynamic balance between hemodynamics and tissue mechanics is neglected. Furthermore, since +we are modeling the cardiac valves with the RIIS method, weakly imposing a kinematic condition +only, the dynamic balance is not fulfilled even between blood and valves. Thus, our computational +model cannot correctly capture the physiological pressure transient during this phase, instead +producing nonphysical and large oscillations. In Figure 11, we report the pressure transients in +time and, for visualization purposes, we ignore the isovolumetric phases from the plot (grey boxes). +The ejection phase (Figure 13c and Figure 13d) is characterized by the opening of semilunar +valves, the contraction of ventricles, and the blood flowing from the LV to the AO and from the +RV to the PT. We measured peak flow rates equal to 398.74 mL/s and 478.16 mL/s, in the AV +and PV, respectively, consistently with physiological values [120]. Moreover, as shown in Table 5, +we found that also maximum velocities between AV and PV and peak ventricular pressures during +ejection are always in physiological ranges. +During the isovolumetric relaxation, both the atrioventricular and semilunar valves are closed. +The velocities measured are low compared to those of the other phases of the heartbeat. Further- +more, as for the isovolumetric contraction, the computational model cannot reproduce the typical +pressure decrease occurring in this phase. On the contrary, large pressure oscillations arise. +During the ventricular passive filling, the blood flows from the pulmonary veins and the venae +cavae into the LA and RA, respectively. Moreover, the atrioventricular valves are open and high- +speed jets form between their leaflets. This moment corresponds to the E-wave of diastole (see +Figure 12). Consistently with clinical measurements, the computational model is able to correctly +reproduce the formation of the clockwise jet in the LV, redirecting the blood towards the outflow +19 + +(a) t = 0.11 s +(b) t = 0.25 s +(c) t = 0.36 s +(d) t = 0.50 s +(e) t = 0.70 s +(f) t = 0.79 s +|dEM| [mm] +Figure 9: Whole heart deformed with EM displacement (with respect to the reference stress- +free configuration) and colored according to its magnitude for a single, representative heartbeat: +(a) active atrial contraction, (b) isovolumetric contraction, (c) ejection (peak), (d) ejection (mid- +deceleration), (e) isovolumetric relaxation, (f) passive ventricular filling. +20 + +0 +2 +4 +6 +8 +10 +12 +14 +16Figure 10: Volumes of RA, RV, PT (left) and LA, LV, AO (right) during a representative heartbeat. +Valves open and close, instantaneously, at the initial and final times of isovolumetric phases. We +report these times also in Table 2. +tract [124, 125]. Furthermore, from Table 5, we can observe that maximum velocity between MV +and TV leaflets are in the physiological ranges. Furthermore, we also report average atrial pressure +values and we found a general good agreement with reference data, even if left atrial pressure is +slightly larger than our reference. +21 + +180 +- -RA +180 +-.LA +RV +LV +160 +.PT +160 +·AA +TV closes + MV closes +140 +A PV opens +140 +AV opens +PV closes +AV closes + TVopens + MV opens +Volume +100 +Volume +100 +80 +80 +60 +60 +40 +40 +20 +20 +0 +0.2 +0.4 +0.6 +0.8 +0 +0.2 +0.4 +0.6 +0.8 +time [s] +time [s]Biomarker +In silico +Physiological values +Peak MV velocity +[m/s] +1.03 +0.89 ± 0.15 +[123] +Peak AV velocity +[m/s] +1.21 +1.07 ± 0.18 +[126] +Peak TV velocity +[m/s] +0.45 +0.48 ± 0.11 +[127] +Peak PV velocity +[m/s] +1.15 +0.80 to 1.20 +[128] +Mean LA pressure +[mmHg] +13.3 +2 to 12 +[129] +Peak LV pressure +[mmHg] +111 +119 ± 13 +[121] +Mean RA pressure +[mmHg] +6.33 +0 to 8 +[129] +Peak RV pressure +[mmHg] +37.0 +35 ± 11 +[122] +Table 5: Fluid dynamics biomarkers obtained with the whole-heart CFD simulation (normal ranges +or mean ± standard deviation). In silico values are computed by averaging fluid properties in +spherical control volumes located between the valve leaflets (velocities) and in the heart chambers +(pressures). +Figure 11: Pressure computed in control volumes located in RA, RV, PT (left) and LA, LV, +AA (right) during a representative heartbeat. Isovolumetric phases are not considered, since the +intraventricular pressure is not well defined when both valves are closed. +22 + +120 +--RA +120 +--LA +RV +-LV +...PT +100 +.AA +100 +80 +80 +60 +60 +Pressure +40 +40 +20 +20 +0 +-20 +-20 +0 +0.2 +0.4 +0.6 +0.8 +0 +0.2 +0.4 +0.6 +0.8 +time [s] +time [s]Figure 12: Velocity magnitudes computed in control volumes located between the valve leaflets: +TV, PV (left) and MV, AV (right). +23 + +1.2 +1.2 +-MV +PV +AV +[s / u] +0.8 +0.4 +0.4 +0.2 +0.2 +0 +0 +0 +0.2 +0.4 +0.6 +0.8 +0 +0.2 +0.4 +0.6 +0.8 +time [s] +time [s](a) t = 0.10 s +(b) t = 0.24 s +(c) t = 0.35 s +(d) t = 0.55 s +(e) t = 0.70 s +(f) t = 0.80 s +|u| [m/s] +Figure 13: Volume rendering of velocity magnitude in different frames during the cardiac cycle: (a) +diastolic a-wave peak, (b) isovolumetric contraction, (c) systolic peak, (d) mid systolic deceleration, +(e) isovolumetric relaxation, (f) diastolic a-wave peak. +24 + +0.0 +0.1 +0.2 +0.3 +0.4 +0.5 +0.6 +0.7 +0.8 +0.9 +1.0To better investigate blood flow patterns in the whole heart, we report the streamlines colored +according to velocity magnitude in different chambers in Figure 14. We compare our in silico +results with the MRI phase-velocity mapping visualization provided in Figure 1 of reference [125], +and we found a good accordance. Specifically, Figure 14a shows the rotation of the blood in the RA +as the chamber expands and the blood flows from the inferior and superior vena cava. Similarly, +on the left side, the blood flows from the pulmonary veins to the expanding LA producing collision +of blood jets and redirecting the flow towards the closed MV (see Figure 14b). During E-wave, as +we show in Figure 14c, asymmetric recirculation is observed: shear layers roll through MV leaflets +producing an O-vortex, as also seen in [1]. The counter-clockwise vortex under the posterior leaflet +quickly disappears, and the clockwise vortex becomes larger and larger producing a clockwise jet, +as described in [124], and clearly observed in Figure 14d. +25 + +(a) Right atrium t = 0.6 s +(b) Left atrium t = 0.6 s +|u| [m/s] +(c) Left ventricle t = 0.775 s +(d) Left heart t = 0.8 s +|u| [m/s] +Figure 14: Streamlines colored according to velocity magnitude at different locations in the heart +at different times: (a) right atrium during ventricular systole, (b) let atrium during ventricular +systole, (c) clockwise and counter-clockwise vortices in the left ventricle during early diastole, (d) +formation of clockwise jet in the left ventricle. +26 + +2个0.000.050.100.150.200.250.300.00 +0.20 +0.40 +0.60 +0.80 +1.004.4 +Application to a pathological scenario: Left Bundle Branch Block +effects on hemodynamics +In this section, we apply our multiphysics computational model to investigate the hemodynamic +consequences of the LBBB. This heart condition is commonly associated to an electrophysiological +abnormality; however, it implies a cascade of adverse events due to the interaction among different +physical processes. LBBB consists in a slow or even absent conduction through the left bundle +branch, causing a dyssynchronous contraction and relaxation of the left ventricle. Moreover, the +LV dyssynchrony may have profound consequences on the heart hemodynamics, influencing flow +patterns and, in turn, triggering heart remodeling [130, 131]. +To simulate LBBB with our EM model, we deactivate the impulse sites in the LV and in the +septum (see Figure 15), so that the signal is generated by the SAN and the RVm sites solely. Our +modeling choice is consistent with the work of [132], where a severe case of LBBB is accounted for +by activating only one site in the RV free wall. +Figure 16 displays volumes and their derivatives with respect to time for left and right ventricles, +in both physiological and LBBB conditions. The electrical dyssynchrony between left and right +parts produces a delay in the LV ejection and filling stages. Differently, no significant differences +are observed in the RV volumes. Furthermore, compared to the physiological case (see Table 4) +and consistently with [130], we measured reduced ejection fractions both in the right (53.87%) and +left (55.39%) ventricles. +By means of our whole-heart EM driven CFD simulation, we can quantify how the pathology +affects the endocardial wall stress. Let τ(u) = 2µϵ(u) be the viscous stress tensor, we compute +the wall shear stress vector as +WSS(u) = τ(u)n − (τ(u)n · n)n +on ∂Ω0 × (0, T). +The time averaged wall shear stress (TAWSS) is then defined as +TAWSS(u) = 1 +T +� T +0 +|WSS(u)| dt +on ∂Ω0. +Figure 17 shows the TAWSS in the right and left heart in physiological conditions and under LBBB. +We notice that the TAWSS distribution is almost unchanged for the right heart. Conversely, we +find that LBBB alters the wall shear stress in correspondence of the LV septum, suggesting the +potential occurrence of remodeling phenomena. In Figure 18, we report the minimum, maximum +and average WSS in the LV septum against time, for the physiological and LBBB simulations. +During the ejection, the WSS values are similar, while significant differences are present during +the filling phase. As a matter of fact, under LBBB, the space-averaged WSS peak is 27.4% higher +than the physiological case (see Figure 18, right). This is consistent with the work of Eriksson +et al. +(2017), where they observed, by means of 4Dflow MRI data, that LV dyssynchronous +motion influences blood flow patterns during diastole, contributing to the development of cardiac +remodeling [131]. +27 + +(a) physiological +(b) LBBB +Activation time [ms] +Figure 15: Activation maps and impulse sites (yellow spheres) in EM simulations; (a) physiological; +(b) LBBB. +Figure 16: Ventricular volumes and ventricular volume derivatives for physiological and LBBB +simulations. +28 + +0 +50 +100 +150 +200 +250 +320150 +150 +physiological +physiological +宜 +-LBBB +[ml] +-LBBB +100 +100 +50 +50 +0 +0.2 +0.4 +0.6 +0.8 +0 +0.2 +0.4 +0.6 +0.8 +t [s] +t [s] +500 +physiological +500 +physiological +LBBB +LBBB +1P/AAP +0 +P/ATAP +0 +-500 +-500 +0 +0.2 +0.4 +0.6 +0.8 +0 +0.2 +0.4 +0.6 +0.8 +t [s] +t [s](a) physiological, RH +(b) LBBB, RH +(c) physiological, LH +(d) LBBB, LH +TAWSS [Pa] +Figure 17: TAWSS of the whole heart in physiological and pathological conditions for the LH and +RH. Results obtained with whole-heart EM-driven CFD simulations, the left and right parts are +separated for visualization purposes. The largest differences are observed in the LV septum, as +highlighted by the black arrow. +Figure 18: WSS in the LV septum over time. Maximum, average and minimum in physiological +conditions (left); maximum, average and minimum in LBBB conditions (center); comparison be- +tween physiological and pathological conditions in terms of average values (right). The WSS is +computed in the red portion shown on the right. In terms of maximum values, the pathological +peak is 12.7% larger than the physiological case (cf. left and center figures). The average WSS +peak increases of 27.4% (right figure). +29 + +0.000.050.100.150.200.250.300.350.400.450.50MV closes + AV opens +AV closes +MV opensPhysiological +LBBB +Physiological vs LBBB +2 +2 +2 +max +-max +-physiological (avg) +avg +avg +-LBBB (avg) +min + septum +septum +septum +LV +LV +.8 +.8 +.8 +WSS +S +IsSI +0 +0 +0 +0.2 +0.4 +0.6 +0.8 +0 +0.2 +0.4 +0.6 +0.8 +0 +0.2 +0.4 +0.6 +0.8 +t [s] +t [s] +t [s]5 +Limitations and further developments +We discuss some limitations of our study. +The computational model introduced cannot fully +represent the isovolumetric phases in terms of pressure. +Indeed, when both valves are closed +and the ventricles are contracting/relaxing at constant volumes, the pressure is not well-defined, +and thus prone to spurious oscillations. +This is due to the fact that we are using kinematic +conditions only in all the ventricular boundaries. Indeed, we prescribe the EM displacement on +the endocardium and endothelium, and we model valves with the RIIS method using a penalty- +based kinematic condition. The oscillatory pressure during such phases does not influence the +velocity field. However, it prevents from using the simulated pressure values to choose when to +open and close the valves, forcing hence to prescribe a-priori opening and closing times. The use of +bidirectionally coupled FSI models for the blood-myocardium or blood-valve systems (or for both +of them) may allow to correctly capture the pressure transient during these phases, as seen for +instance in [42] where a fully-coupled electro-mechano-fluid of the heart is considered. +Moreover, we noticed that the ejection phase is too slow and the ventricular passive filling too +fast, if compared with medical literature values. Consequently, E-wave and A-wave are character- +ized by comparable amplitudes, whereas the EA ratio should be approximately equal to 1.30 ± +0.570 [123]. In this respect, we believe that the use of ionic models with a more realistic decrease +of calcium concentration is essential to better capture these phenomena. +Finally, we applied the computational model to a realistic, templated heart geometry. In order +to move towards the realization of whole-heart digital twins, further developments should involve +patient-specific cardiac simulations, accompanied with a stringent process of data assimilation, +model validation and uncertainty quantification. +6 +Conclusions +In this paper, we introduced a computational model for the hemodynamics simulation of the whole +human heart accounting for the main features affecting the intracardiac flows. We considered a +realistic whole-heart geometry, and we employed a four-chamber 3D-0D electromechanical model +to provide the displacement as input to the cardiac CFD model. We modelled the effect of cardiac +valves in the fluid via a resistive immersed method and we accounted for transition-to-turbulence +regime through the VMS-LES method. Moreover, for the first time, we coupled the 3D CFD +model of the whole heart to the surrounding closed-loop circulation, to get a geometric multiscale +3D-0D hemodynamic model of the entire cardiovascular system. We solved our multiphysics and +multiscale computational model using our in-house finite element library lifex. +We introduced a calibration of the activation model, driving the electromechanical simulation, +aimed at obtaining physiological realistic flowrates, and consequently blood velocities, in the CFD +simulation. Our calibration highlights the effect of the parameters of the active force generation +model, associated to the microscopic features of its kinematics and of the force-velocity relationship, +on the macroscopic heartbeat indicators. +We carried out EM-driven CFD simulation on a realistic whole-heart geometry and we showed +that the computational model can correctly reproduce blood velocities and pressure traces when +we compare the results with clinical ranges from medical literature. Furthermore, we found that +the computational model captures typical blood flow patterns observed in MRI phase-velocity +mapping visualizations. +Finally, we applied the whole-heart model to simulate the pathological scenario of Left Bundle +30 + +Branch Block: we correctly predicted the electrical delay, the consequent mechanical dyssynchrony, +a reduced ejection fraction, and an increasing wall shear stress in the left ventricular septum during +the filling stage. Overall, this study confirms that the interaction of different physics in a high- +fidelity integrated whole-heart model is essential for simulating the cardiac function, allowing to +faithfully capture pathological events occurring at different physical levels. +A +The open 0D circulation model +The closed-loop lumped-parameter (0D) circulation model that we employ was proposed in [59] +and inspired by [51, 133]. To couple the 0D model to the 3D model of the heart, we follow the +same steps presented in [26]. Specifically, the open 0D system we get reads as follows: for any +t ∈ (0, T), +dpSYS +AR (t) +dt += +1 +CSYS +AR +� +QAV(t) − QSYS +AR (t) +� +, +(7a) +dpSYS +VEN(t) +dt += +1 +CSYS +VEN +� +QSYS +AR (t) − QSYS +VEN(t) +� +, +(7b) +dpPUL +AR (t) +dt += +1 +CPUL +AR +� +QPV(t) − QPUL +AR (t) +� +, +(7c) +dpPUL +VEN(t) +dt += +1 +CPUL +VEN +� +QPUL +AR (t) − QVEN, 3D +PUL +(t) +� +, +(7d) +dQSYS +AR (t) +dt += RSYS +AR +LSYS +AR +� +−QSYS +AR (t) − pSYS +VEN(t) − pSYS +AR (t) +RSYS +AR +� +, +(7e) +dQPUL +AR (t) +dt += RPUL +AR +LPUL +AR +� +−QPUL +AR (t) − pPUL +VEN(t) − pPUL +AR (t) +RPUL +AR +� +, +(7f) +solved with suitable initial conditions, and +pin +LA(t) = pPUL +VEN(t) − RPUL +VENQPUL +VEN(t) − LPUL +VEN +dQPUL, 3D +VEN +(t) +dt +, +(8a) +pin +RA(t) = pSYS +VEN(t) − RSYS +VENQSYS +VEN(t) − LSYS +VEN +dQSYS, 3D +VEN +(t) +dt +. +(8b) +B +Setup of EM and CFD simulations +We report in this section the values of the parameters used in the EM and CFD simulations. +Table 6 reports the parameters for the circulation model used in the EM simulation. With +respect to the model presented in [58], we introduce two additional resistance elements (RSYS +upstream +and RPUL +upstream) and two inductance elements (LAV, LPV, see Figure 1c). They have the purpose +of making the 0D circulation model, which surrogates the fluid dynamics, as similar as possible +to the 3D CFD problem. For the same reason, the minimum resistances of the non-ideal diodes +representing the AV and PV are increased with respect to [58]. +31 + +The calibration of the RDQ20 activation model is extensively discussed in Section 4.2, and the +corresponding parameter values are reported in Table 3. As reference sarcomere length, we set +SL0 = 2.2 µm. For all other parameters in the EM model, we use the same values as those of the +baseline simulation of [58]. +Table 7 reports the values of the initial circulation states for the CFD simulation. They are +taken equal to the values reached at the beginning of the last heartbeat in the EM simulation. All +resistance, capacitance and inductance parameters are the same as in the EM model (see Table 6). +32 + +Parameter +Value +Systemic arteries +RSYS +AR +0.48 +mmHg s/ml +CSYS +AR +1.50 +ml/mmHg +LSYS +AR +0.005 +mmHg s2/ml +RSYS +upstream +0.048 +mmHg s/ml +pSYS +AR, 0 +83.9 +mmHg +QSYS +AR, 0 +0.0 +ml/s +Systemic veins +RSYS +VEN +0.26 +mmHg s/ml +CSYS +VEN +60 +ml/mmHg +LSYS +VEN +5 · 10−4 +mmHg s2/ml +pSYS +VEN, 0 +35.5 +mmHg +QSYS +VEN, 0 +0.0 +ml/s +Pulmonary arteries +RPUL +AR +0.032116 +mmHg s/ml +CPUL +AR +10 +ml/mmHg +LPUL +AR +0.0005 +mmHg s2/ml +RPUL +upstream +0.0032116 +mmHg s/ml +pPUL +AR, 0 +14.90 +mmHg +QPUL +AR, 0 +0.0 +ml/s +Pulmonary veins +RPUL +VEN +0.035684 +mmHg s/ml +CPUL +VEN +16 +ml/mmHg +LPUL +VEN +0.0005 +mmHg s2/ml +pPUL +VEN, 0 +13.58 +mmHg +QPUL +VEN, 0 +0.0 +ml/s +Valves +RMV +min +0.0075 +mmHg s/ml +RAV +min +0.0355 +mmHg s/ml +RTV +min +0.0075 +mmHg s/ml +RPV +min +0.0184 +mmHg s/ml +RMV +max, RAV +max, RTV +max, RPV +max +75006.2 +mmHg s/ml +LAV, LPV +5 · 10−4 +mmHg s2/ml +Table 6: Parameters and initial conditions of the circulation model used for the EM simulation. +33 + +Parameter +Value +Systemic arteries +pSYS +AR, 0 +86.3480 +mmHg +QSYS +AR, 0 +109.6429 +ml/s +Systemic veins +pSYS +VEN, 0 +34.4923 +mmHg +QSYS +VEN, 0 +112.9209 +ml/s +Pulmonary arteries +pPUL +AR, 0 +22.2310 +mmHg +QPUL +AR, 0 +83.2132 +ml/s +Pulmonary veins +pPUL +VEN, 0 +19.5813 +mmHg +QPUL +VEN, 0 +262.6397 +ml/s +Table 7: Initial states of the circulation model for the CFD simulation. The values are equal to +those of the circulation state variables at the beginning of the last heartbeat in the EM simulation. +All remaining circulation parameters (resistances, capacitances, inductances) are the same as in +the EM model (see Table 6). +34 + +Acknowledgments +AZ, LD and AQ received funding from the Italian Ministry of University and Research (MIUR) +within the PRIN (Research projects of relevant national interest) 2017 “Modeling the heart across +the scales: from cardiac cells to the whole organ” Grant Registration number 2017AXL54F). +MB, RP, FR, LD and AQ acknowledge the ERC Advanced Grant iHEART, “An Integrated +Heart Model for the simulation of the cardiac function”, 2017–2023, P.I. A. Quarteroni (ERC–2016– +ADG, project ID: 740132). +The authors of this work are members of the INdAM group GNCS “Gruppo Nazionale per il +Calcolo Scientifico” (National Group for Scientific Computing). +Finally, we acknowledge the CINECA award under the ISCRA initiative, for the availability +of high performance computing resources and support under the projects IsC87 MCH, P.I. A. +Zingaro, 2021-2022 and IsB25 MathBeat, P.I. A. Quarteroni, 2021-2022. +References +1C. Chnafa, S. Mendez, and F. Nicoud, “Image-based large-eddy simulation in a realistic left +heart”, Computers & Fluids 94, 173–187 (2014). +2A. This, L. Boilevin-Kayl, M. A. Fern´andez, and J.-F. Gerbeau, “Augmented resistive immersed +surfaces valve model for the simulation of cardiac hemodynamics with isovolumetric phases”, +International Journal for Numerical Methods in Biomedical Engineering 36, e3223 (2020). +3A. Tagliabue, L. Dede’, and A. Quarteroni, “Complex blood flow patterns in an idealized left +ventricle: a numerical study”, Chaos: An Interdisciplinary Journal of Nonlinear Science 27, +093939 (2017). +4A. Tagliabue, L. Dede’, and A. Quarteroni, “Fluid dynamics of an idealized left ventricle: the +extended nitsche’s method for the treatment of heart valves as mixed time varying boundary +conditions”, International Journal for Numerical Methods in Fluids 85, 135–164 (2017). +5I. Fumagalli, M. Fedele, C. Vergara, L. Dede’, S. Ippolito, F. Nicol`o, C. Antona, R. Scrofani, +and A. Quarteroni, “An image-based computational hemodynamics study of the systolic anterior +motion of the mitral valve”, Computers in Biology and Medicine 123, 103922 (2020). +6E. Karabelas, M. A. Gsell, C. M. Augustin, L. Marx, A. Neic, A. J. Prassl, L. Goubergrits, T. +Kuehne, and G. Plank, “Towards a computational framework for modeling the impact of aortic +coarctations upon left ventricular load”, Frontiers in Physiology 9, 538 (2018). +7A. This, H. G. Morales, O. Bonnefous, M. A. Fern´andez, and J.-F. Gerbeau, “A pipeline for +image based intracardiac CFD modeling and application to the evaluation of the PISA method”, +Computer Methods in Applied Mechanics and Engineering 358, 112627 (2020). +35 + +erc +HEART +erc +POLITECNICO +European Research Council +MILANO1863 +Established by the European Commission8A. Masci, M. Alessandrini, D. Forti, F. Menghini, L. Dede’, C. Tomasi, A. Quarteroni, and C. +Corsi, “A proof of concept for computational fluid dynamic analysis of the left atrium in atrial +fibrillation on a patient-specific basis”, Journal of Biomechanical Engineering 142 (2020). +9F. Viola, V. Meschini, and R. Verzicco, “Fluid–Structure-Electrophysiology interaction (FSEI) +in the left-heart: a multi-way coupled computational model”, European Journal of Mechanics- +B/Fluids 79, 212–232 (2020). +10J. O. Mangual, E. Kraigher-Krainer, A. De Luca, L. Toncelli, A. Shah, S. Solomon, G. Galanti, +F. Domenichini, and G. Pedrizzetti, “Comparative numerical study on left ventricular fluid +dynamics after dilated cardiomyopathy”, Journal of Biomechanics 46, 1611–1617 (2013). +11X. Zheng, J. Seo, V. Vedula, T. Abraham, and R. Mittal, “Computational modeling and analysis +of intracardiac flows in simple models of the left ventricle”, European Journal of Mechanics- +B/Fluids 35, 31–39 (2012). +12J. H. Seo, V. Vedula, T. Abraham, A. C. Lardo, F. Dawoud, H. Luo, and R. Mittal, “Effect of +the mitral valve on diastolic flow patterns”, Physics of Fluids 26, 121901 (2014). +13J. H. Seo and R. Mittal, “Effect of diastolic flow patterns on the function of the left ventricle”, +Physics of Fluids 25, 110801 (2013). +14A. Masci, M. Alessandrini, D. Forti, F. Menghini, L. Dede’, C. Tommasi, A. Quarteroni, and +C. Corsi, “A patient-specific computational fluid dynamics model of the left atrium in atrial fib- +rillation: development and initial evaluation”, in International conference on functional imaging +and modeling of the heart (Springer, 2017), pp. 392–400. +15A. Masci, L. Barone, L. Ded`e, M. Fedele, C. Tomasi, A. Quarteroni, and C. Corsi, “The im- +pact of left atrium appendage morphology on stroke risk assessment in atrial fibrillation: a +computational fluid dynamics study”, Frontiers in Physiology 9, 1938 (2019). +16A. Zingaro, L. Dede’, F. Menghini, and A. Quarteroni, “Hemodynamics of the heart’s left +atrium based on a variational multiscale-les numerical method”, European Journal of Mechanics- +B/Fluids 89, 380–400 (2021). +17M. Corti, L. Dede’, A. Zingaro, and A. Quarteroni, “Impact of atrial fibrillation on left atrium +haemodynamics: a computational fluid dynamics study”, Computers in Biology and Medicine, +106143 (2022). +18V. Vedula, R. George, L. Younes, and R. Mittal, “Hemodynamics in the left atrium and its +effect on ventricular flow patterns”, Journal of Biomechanical Engineering 137 (2015). +19R. Koizumi, K. Funamoto, T. Hayase, Y. Kanke, M. Shibata, Y. Shiraishi, and T. Yambe, “Nu- +merical analysis of hemodynamic changes in the left atrium due to atrial fibrillation”, Journal +of Biomechanics 48, 472–478 (2015). +20D. Dillon-Murphy, D. Marlevi, B. Ruijsink, A. Qureshi, H. Chubb, E. Kerfoot, M. O’Neill, D. +Nordsletten, O. Aslanidi, and A. De Vecchi, “Modeling left atrial flow, energy, blood heating +distribution in response to catheter ablation therapy”, Frontiers in Physiology 9, 1757 (2018). +21G. M. Bosi, A. Cook, R. Rai, L. J. Menezes, S. Schievano, R. Torii, and G. Burriesci, “Computa- +tional fluid dynamic analysis of the left atrial appendage to predict thrombosis risk”, Frontiers +in Cardiovascular Medicine 5, 34 (2018). +36 + +22O. Mazumder, S. Gupta, D. Roy, and A. Sinha, “Computational fluid dynamic model of left +atrium to analyze hemodynamic manifestation during atrial fibrillation”, in 2022 44th Annual +International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) +(IEEE, 2022), pp. 3967–3971. +23F. Viola, V. Meschini, and R. Verzicco, “Effects of stenotic aortic valve on the left heart hemody- +namics: a fluid-structure-electrophysiology approach”, arXiv preprint arXiv:2103.14680 (2021). +24F. Viola, V. Spandan, V. Meschini, J. Romero, M. Fatica, M. D. de Tullio, and R. Verzicco, +“FSEI-GPU: GPU accelerated simulations of the fluid–structure–electrophysiology interaction +in the left heart”, Computer Physics Communications 273, 108248 (2022). +25L. Dede’, F. Menghini, and A. Quarteroni, “Computational fluid dynamics of blood flow in +an idealized left human heart”, International Journal for Numerical Methods in Biomedical +Engineering 37, e3287 (2021). +26A. Zingaro, I. Fumagalli, L. Dede’, M. Fedele, P. C. Africa, A. F. Corno, and A. M. Quarteroni, +“A geometric multiscale model for the numerical simulation of blood flow in the human left +heart”, Discrete and Continous Dynamical System - S 15, 2391–2427 (2022). +27H. Wiputra, C. Q. Lai, G. L. Lim, J. J. W. Heng, L. Guo, S. M. Soomar, H. L. Leo, A. Biwas, +C. N. Z. Mattar, and C. H. Yap, “Fluid mechanics of human fetal right ventricles from image- +based computational fluid dynamics using 4d clinical ultrasound scans”, American Journal of +Physiology-Heart and Circulatory Physiology 311, H1498–H1508 (2016). +28D. Collia, L. Zovatto, G. Tonti, and G. Pedrizzetti, “Comparative analysis of right ventricle +fluid dynamics”, Frontiers in Bioengineering and Biotechnology 9 (2021). +29J. Mangual, F Domenichini, and G. Pedrizzetti, “Describing the highly three dimensional right +ventricle flow”, Annals of Biomedical Engineering 40, 1790–1801 (2012). +30V. Mihalef, R. I. Ionasec, P. Sharma, B. Georgescu, I. Voigt, M. Suehling, and D. Comaniciu, +“Patient-specific modelling of whole heart anatomy, dynamics and haemodynamics from four- +dimensional cardiac CT images”, Interface Focus 1, 286–296 (2011). +31J.-i. Okada, T. Washio, S. Sugiura, and T. Hisada, “Clinical and pharmacological application +of multiscale multiphysics heart simulator, UT-heart”, The Korean Journal of Physiology & +Pharmacology 23, 295–303 (2019). +32M. Peirlinck, F. S. Costabal, J. Yao, J. Guccione, S. Tripathy, Y. Wang, D. Ozturk, P. Segars, T. +Morrison, S. Levine, and E. Kuhl, “Precision medicine in human heart modeling”, Biomechanics +and modeling in mechanobiology 20, 803–831 (2021). +33J. Brenneisen, A. Daub, T. Gerach, E. Kovacheva, L. Huetter, B. Frohnapfel, O. D¨ossel, and A. +Loewe, “Sequential coupling shows minor effects of fluid dynamics on myocardial deformation +in a realistic whole-heart model”, Frontiers in Cardiovascular Medicine, 1967 (2021). +34E. Karabelas, S. Longobardi, J. Fuchsberger, O. Razeghi, C. Rodero, M. Strocchi, R. Rajani, +G. Haase, G. Plank, and S. Niederer, “Global sensitivity analysis of four chamber heart hemo- +dynamics using surrogate models”, IEEE Transactions on Biomedical Engineering (2022). +35F. Viola, G. Del Corso, R. De Paulis, and R. Verzicco, “GPU accelerated digital twins of the +human heart open new routes for cardiovascular research”, Research Square (2022). +36J. Fuchsberger, P. Aigner, S. Niederer, G. Plank, H. Schima, G. Haase, and E. Karabelas, “On the +incorporation of obstacles in a fluid flow problem using a navier–stokes–brinkman penalization +approach”, Journal of Computational Science 57, 101506 (2022). +37 + +37A. Quarteroni, L. Dede’, A. Manzoni, and C. Vergara, Mathematical modelling of the human +cardiovascular system: data, numerical approximation, clinical applications, Vol. 33 (Cambridge +University Press, 2019). +38Y. J. Choi, J. Constantino, V. Vedula, N. Trayanova, and R. Mittal, “A new mri-based model +of heart function with coupled hemodynamics and application to normal and diseased canine +left ventricles”, Frontiers in bioengineering and biotechnology 3, 140 (2015). +39A. Santiago, J. Aguado-Sierra, M. Zavala-Ak´e, R. Doste-Beltran, S. G´omez, R. Ar´ıs, J. C. Cajas, +E. Casoni, and M. V´azquez, “Fully coupled fluid-electro-mechanical model of the human heart +for supercomputers”, International Journal for Numerical Methods in Biomedical Engineering +34, e3140 (2018). +40A. Santiago, “Fluid-electro-mechanical model of the human heart for supercomputers”, PhD +thesis (Universitat Polit`ecnica de Catalunya, 2018). +41A. Gerbi, “Numerical approximation of cardiac electro-fluid-mechanical models: coupling strate- +gies for large-scale simulation”, PhD thesis (Ecole Polytechnique F´ed´erale de Lausanne, 2018). +42M. Bucelli, A. Zingaro, P. C. Africa, I. Fumagalli, L. Dede’, and A. M. Quarteroni, “A math- +ematical model that integrates cardiac electrophysiology, mechanics and fluid dynamics: appli- +cation to the human left heart”, International Journal for Numerical Methods in Biomedical +Engineering, e3678 (2022). +43F. Domenichini, G. Pedrizzetti, and B. Baccani, “Three-dimensional filling flow into a model +left ventricle”, Journal of fluid mechanics 539, 179–198 (2005). +44B. Baccani, F. Domenichini, and G. Pedrizzetti, “Vortex dynamics in a model left ventricle +during filling”, European Journal of Mechanics-B/Fluids 21, 527–543 (2002). +45A. Santiago, C. Butakoff, B. Eguzkitza, R. A. Gray, K. May-Newman, P. Pathmanathan, V. +Vu, and M. V´azquez, “Design and execution of a verification, validation, and uncertainty quan- +tification plan for a numerical model of left ventricular flow after lvad implantation”, PLoS +computational biology 18, e1010141 (2022). +46C. M. Augustin, A. Crozier, A. Neic, A. J. Prassl, E. Karabelas, T. Ferreira da Silva, J. F. +Fernandes, F. Campos, T. Kuehne, and G. Plank, “Patient-specific modeling of left ventricular +electromechanics as a driver for haemodynamic analysis”, EP Europace 18, iv121–iv129 (2016). +47A. Zingaro, M. Bucelli, I. Fumagalli, L. Dede’, and A. Quarteroni, “Modeling isovolumetric +phases in cardiac flows by an augmented resistive immersed implicit surface method”, arXiv +preprint arXiv:2208.09435 (2022). +48A. Zingaro, “Mathematical and numerical models for the fluid dynamics of the human heart”, +PhD thesis (Politecnico di Milano, 2022). +49A. This, “Image/model fusion for the quantification of mitral regurgitation severity”, PhD thesis +(Sorbonne universit´e, 2019). +50A. Quarteroni, A. Veneziani, and C. Vergara, “Geometric multiscale modeling of the cardio- +vascular system, between theory and practice”, Computer Methods in Applied Mechanics and +Engineering 302, 193–252 (2016). +51P. J. Blanco and R. A. Feij´oo, “A 3d-1d-0d computational model for the entire cardiovascular +system”, Mec´anica Computacional 29, 5887–5911 (2010). +38 + +52Y. Shi and T. Korakianitis, “Numerical simulation of cardiovascular dynamics with left heart +failure and in-series pulsatile ventricular assist device”, Artificial Organs 30, 929–948 (2006). +53V. Miliˇsi´c and A. Quarteroni, “Analysis of lumped parameter models for blood flow simulations +and their relation with 1d models”, ESAIM: Mathematical modelling and numerical analysis +38, 613–632 (2004). +54H. J. Kim, I. E. Vignon-Clementel, C. A. Figueroa, J. F. LaDisa, K. E. Jansen, J. A. Feinstein, +and C. A. Taylor, “On coupling a lumped parameter heart model and a three-dimensional finite +element aorta model”, Annals of biomedical engineering 37, 2153–2169 (2009). +55F. N. Van de Vosse and N. Stergiopulos, “Pulse wave propagation in the arterial tree”, Annual +Review of Fluid Mechanics 43, 467–499 (2011). +56L. Formaggia, D. Lamponi, and A. Quarteroni, “One-dimensional models for blood flow in +arteries”, Journal of engineering mathematics 47, 251–276 (2003). +57L. Formaggia, J.-F. Gerbeau, F. Nobile, and A. Quarteroni, “On the coupling of 3d and 1d +navier–stokes equations for flow problems in compliant vessels”, Computer Methods in Applied +Mechanics and Engineering 191, 561–582 (2001). +58M. Fedele, R. Piersanti, F. Regazzoni, M. Salvador, P. C. Africa, M. Bucelli, A. Zingaro, L. +Dede’, and A. Quarteroni, “A comprehensive and biophysically detailed computational model +of the whole human heart electromechanics”, arXiv preprint arXiv:2207.12460 (2022). +59F. Regazzoni, M. Salvador, P. Africa, M. Fedele, L. Ded`e, and A. Quarteroni, “A cardiac elec- +tromechanical model coupled with a lumped-parameter model for closed-loop blood circulation”, +Journal of Computational Physics 457, 111083 (2022). +60C. M. Augustin, M. A. Gsell, E. Karabelas, E. Willemen, F. W. Prinzen, J. Lumens, E. J. Vig- +mond, and G. Plank, “A computationally efficient physiologically comprehensive 3d–0d closed- +loop model of the heart and circulation”, Computer Methods in Applied Mechanics and Engi- +neering 386, 114092 (2021). +61F. Marcinno’, A. Zingaro, I. Fumagalli, L. Dede, and C. Vergara, “A computational study of +blood flow dynamics in the pulmonary arteries”, Vietnam Journal of Mathematics, 1–23 (2022). +62X. Ma, H. Gao, B. E. Griffith, C. Berry, and X. Luo, “Image-based fluid–structure interaction +model of the human mitral valve”, Computers & Fluids 71, 417–425 (2013). +63K. Kunzelman, D. R. Einstein, and R. Cochran, “Fluid–structure interaction models of the +mitral valve: function in normal and pathological states”, Philosophical Transactions of the +Royal Society B: Biological Sciences 362, 1393–1406 (2007). +64B. Su, L. Zhong, X.-K. Wang, J.-M. Zhang, R. San Tan, J. C. Allen, S. K. Tan, S. Kim, and +H. L. Leo, “Numerical simulation of patient-specific left ventricular model with both mitral and +aortic valves by FSI approach”, Computer methods and programs in biomedicine 113, 474–482 +(2014). +65H. Gao, L. Feng, N. Qi, C. Berry, B. E. Griffith, and X. Luo, “A coupled mitral valve—left +ventricle model with fluid–structure interaction”, Medical Engineering & Physics 47, 128–136 +(2017). +66J De Hart, G. Peters, P. Schreurs, and F. Baaijens, “A three-dimensional computational analysis +of fluid–structure interaction in the aortic valve”, Journal of Biomechanics 36, 103–112 (2003). +39 + +67C. Carmody, G Burriesci, I. Howard, and E. Patterson, “An approach to the simulation of +fluid–structure interaction in the aortic valve”, Journal of Biomechanics 39, 158–169 (2006). +68D. Oks, M. V´azquez, G. Houzeaux, C. Butakoff, and C. Samaniego, “Fluid-structure interaction +analysis of eccentricity and leaflet rigidity on thrombosis biomarkers in bioprosthetic aortic valve +replacements”, International Journal for Numerical Methods in Biomedical Engineering, e3649 +(2022). +69J. Hiromi Sp¨uhler and J. Hoffman, “An interface-tracking unified continuum model for fluid- +structure interaction with topology change and full-friction contact with application to aortic +valves”, International Journal for Numerical Methods in Engineering 122, 5258–5278 (2021). +70J. H. Sp¨uhler, J. Jansson, N. Jansson, and J. Hoffman, “3d fluid-structure interaction simulation +of aortic valves using a unified continuum ale fem model”, Frontiers in Physiology 9, 363 (2018). +71M. Fedele, E. Faggiano, L. Dede’, and A. Quarteroni, “A patient-specific aortic valve model based +on moving resistive immersed implicit surfaces”, Biomechanics and Modeling in Mechanobiology +16, 1779–1803 (2017). +72M. A. Fern´andez, J.-F. Gerbeau, and V. Martin, “Numerical simulation of blood flows through a +porous interface”, ESAIM: Mathematical Modelling and Numerical Analysis 42, 961–990 (2008). +73M. Astorino, J. Hamers, S. C. Shadden, and J.-F. Gerbeau, “A robust and efficient valve model +based on resistive immersed surfaces”, International Journal for Numerical Methods in Biomed- +ical Engineering 28, 937–959 (2012). +74A. This, L. Boilevin-Kayl, M. A. Fern´andez, and J.-F. Gerbeau, “Augmented resistive immersed +surfaces valve model for the simulation of cardiac hemodynamics with isovolumetric phases”, +International Journal for Numerical Methods in Biomedical Engineering 36, 10.1002/cnm.3223. +75F. Regazzoni, L. Ded`e, and A. Quarteroni, “Biophysically detailed mathematical models of +multiscale cardiac active mechanics”, PLOS Computational Biology 16, e1008294 (2020). +76N. Y. Tan, C. M. Witt, J. K. Oh, and Y.-M. Cha, “Left bundle branch block: current and future +perspectives”, Circulation: Arrhythmia and Electrophysiology 13, e008239 (2020). +77R. Piersanti, F. Regazzoni, M. Salvador, A. F. Corno, L. Dede’, C. Vergara, and A. Quar- +teroni, “3D–0D closed-loop model for the simulation of cardiac biventricular electromechanics”, +Computer Methods in Applied Mechanics and Engineering 391, 114607 (2022). +78R. Piersanti, “Mathematical and Numerical Modeling of Cardiac Fiber Generation and Elec- +tromechanical Function : Towards a Realistic Simulation of the Whole Heart”, PhD thesis +(Politecnico di Milano, 2021). +79R. Piersanti, P. C. Africa, M. Fedele, C. Vergara, L. Ded`e, A. F. Corno, and A. Quarteroni, +“Modeling cardiac muscle fibers in ventricular and atrial electrophysiology simulations”, Com- +puter Methods in Applied Mechanics and Engineering 373, 113468 (2021). +80R. Doste, D. Soto-Iglesias, G. Bernardino, A. Alcaine, R. Sebastian, S. Giffard-Roisin, M. Ser- +mesant, A. Berruezo, D. Sanchez-Quintana, and O. Camara, “A rule-based method to model +myocardial fiber orientation in cardiac biventricular geometries with outflow tracts”, Interna- +tional Journal for Numerical Methods in Biomedical Engineering 35, e3185 (2019). +81P. C. Franzone, L. F. Pavarino, and S. Scacchi, Mathematical cardiac electrophysiology, Vol. 13 +(Springer, 2014). +40 + +82K. H. ten Tusscher and A. V. Panfilov, “Alternans and spiral breakup in a human ventricular +tissue model”, American Journal of Physiology. Heart and Circulatory Physiology 291, 1088– +1100 (2006). +83M. Courtemanche, R. J. Ramirez, and S. Nattel, “Ionic mechanisms underlying human atrial ac- +tion potential properties: insights from a mathematical model”, American Journal of Physiology- +Heart and Circulatory Physiology 275, H301–H321 (1998). +84A. Lee, U. Nguyen, O. Razeghi, J. Gould, B. Sidhu, B. Sieniewicz, J. Behar, M. Mafi-Rad, G. +Plank, F. Prinzen, et al., “A rule-based method for predicting the electrical activation of the +heart with cardiac resynchronization therapy from non-invasive clinical data”, Medical Image +Analysis 57, 197–213 (2019). +85F. Mazhar, F. Regazzoni, C. Bartolucci, C. Corsi, L. Dede’, A. Quarteroni, and S. Severi, +“Electro-Mechanical Coupling in Human Atrial Cardiomyocytes: Model Development and Anal- +ysis of Inotropic Interventions”, in 2021 computing in cardiology (cinc), Vol. 2021-Septe (Sept. +2021), pp. 1–4. +86F. Regazzoni, L. Ded`e, and A. Quarteroni, “Machine learning of multiscale active force gen- +eration models for the efficient simulation of cardiac electromechanics”, Computer Methods in +Applied Mechanics and Engineering 370, 113268 (2020). +87R. Ogden, Non-linear elastic deformations (Dover Publications, 1997). +88D. Guan, J. Yao, X. Luo, and H. Gao, “Effect of myofibre architecture on ventricular pump +function by using a neonatal porcine heart model: from DT-MRI to rule-based methods”, Royal +Society Open Science 7, 191655 (2020). +89T. P. Usyk, I. J. LeGrice, and A. D. McCulloch, “Computational model of three-dimensional +cardiac electromechanics”, Computing and Visualization in Science 4, 249–257 (2002). +90M. Salvador, F. Regazzoni, S. Pagani, L. Dede’, N. Trayanova, and A. Quarteroni, “The role of +mechano-electric feedbacks and hemodynamic coupling in scar-related ventricular tachycardia”, +Computers in Biology and Medicine 142, 105203 (2022). +91F. Regazzoni, L. Ded`e, and A. Quarteroni, “Active force generation in cardiac muscle cells: +mathematical modeling and numerical simulation of the actin-myosin interaction”, Vietnam +Journal of Mathematics 49, 87–118 (2021). +92H. Jasak and Z. Tukovic, “Automatic mesh motion for the unstructured finite volume method”, +Transactions of FAMENA 30, 1–20 (2006). +93C. De Boor, A practical guide to splines, Vol. 27 (springer-verlag New York, 1978). +94J. Janela, A. Moura, and A. Sequeira, “Absorbing boundary conditions for a 3D non-Newtonian +fluid–structure interaction model for blood flow in arteries”, International Journal of Engineering +Science 48, 1332–1349 (2010). +95A. Quarteroni, T. Lassila, S. Rossi, and R. Ruiz-Baier, “Integrated heart—coupling multiscale +and multiphysics models for the simulation of the cardiac function”, Computer Methods in +Applied Mechanics and Engineering 314, 345–407 (2017). +96A. Quarteroni, Numerical models for differential problems, Vol. 16 (Springer, 2018). +97F. Regazzoni and A. Quarteroni, “An oscillation-free fully partitioned scheme for the numerical +modeling of cardiac active mechanics”, Computer Methods in Applied Mechanics and Engineer- +ing 373, 113506 (2021). +41 + +98F. Regazzoni, “Stabilization of staggered time discretization schemes for 0D-3D fluid-structure +interaction problems”, MOX report 17 (2022). +99G. Rodrıguez, “Smoothing and non-parametric regression”, Princeton University (2001). +100D. Forti and L. Ded`e, “Semi-implicit BDF time discretization of the Navier–Stokes equations +with VMS-LES modeling in a high performance computing framework”, Computers & Fluids +117, 168–182 (2015). +101Y Bazilevs, V. Calo, J. Cottrell, T. Hughes, A Reali, and G. Scovazzi, “Variational multiscale +residual-based turbulence modeling for large eddy simulation of incompressible flows”, Computer +Methods in Applied Mechanics and Engineering 197, 173–201 (2007). +102C. Bertoglio and A. Caiazzo, “A tangential regularization method for backflow stabilization in +hemodynamics”, Journal of Computational Physics 261, 162–171 (2014). +103S. Deparis, G. Grandperrin, and A. Quarteroni, “Parallel preconditioners for the unsteady +Navier–Stokes equations and applications to hemodynamics simulations”, Computers & Flu- +ids 92, 253–273 (2014). +104Z. M. G. Inc., “Zygote solid 3D heart generation ii developement report. tech. rep.”, (2014). +105L. Antiga, M. Piccinelli, L. Botti, B. Ene-Iordache, A. Remuzzi, and D. A. Steinman, “An +image-based modeling framework for patient-specific computational hemodynamics”, Medical +& Biological Engineering & Computing 46, 1097–1112 (2008). +106M. Fedele and A. M. Quarteroni, “Polygonal surface processing and mesh generation tools for +numerical simulations of the complete cardiac function”, International Journal for Numerical +Methods in Biomedical Engineering 37, e3435 (2021). +107P. C. Africa, “Lifex: a flexible, high performance library for the numerical solution of complex +finite element problems”, SoftwareX 20, 101252 (2022). +108P. C. Africa, R. Piersanti, M. Fedele, L. Dede’, and A. Quarteroni, “Lifex–heart module: a high- +performance simulator for the cardiac function package 1: fiber generation”, arXiv preprint +arXiv:2201.03303 (2022). +109D. Arndt, W. Bangerth, B. Blais, M. Fehling, R. Gassm¨oller, T. Heister, L. Heltai, U. K¨ocher, +M. Kronbichler, M. Maier, P. Munch, J.-P. Pelteret, S. Proell, K. Simon, B. Turcksin, D. Wells, +and J. Zhang, “The deal.ii library, version 9.3”, J. Numer. Math. 29 (2021). +110D. Arndt, W. Bangerth, D. Davydov, T. Heister, L. Heltai, M. Kronbichler, M. Maier, J. Pelteret, +B. Turcksin, and D. Wells, “The deal.II finite element library: design, features, and insights”, +Computers & Mathematics with Applications, 10.1016/j.camwa.2020.02.022 (2020). +111Pulmonary acceleration time to estimate pulmonary pressure, visited: October 2022. +112M. H. Crawford and C. A. Roldan, “Quantitative assessment of valve thickness in normal sub- +jects by transesophageal echocardiography”, The American Journal of Cardiology 87, 1419–1423 +(2001). +113P. van Ooij, M. Markl, J. D. Collins, J. C. Carr, C. Rigsby, R. O. Bonow, S. C. Malaisrie, P. M. +McCarthy, P. W. Fedak, and A. J. Barker, “Aortic valve stenosis alters expression of regional +aortic wall shear stress: new insights from a 4-dimensional flow magnetic resonance imaging +study of 571 subjects”, Journal of the American Heart Association 6, e005959 (2017). +114W. Kriz and M. Elger, “Renal anatomy”, Comprehensive clinical nephrology. 3rd ed. Philadel- +phia, 1–11 (2010). +42 + +115A. M. Maceira, S. K. Prasad, M. Khan, and D. J. Pennell, “Normalized left ventricular sys- +tolic and diastolic function by steady state free precession cardiovascular magnetic resonance”, +Journal of Cardiovascular Magnetic Resonance 8, 417–426 (2006). +116L. E. Hudsmith, S. E. Petersen, J. M. Francis, M. D. Robson, and S. Neubauer, “Normal human +left and right ventricular and left atrial dimensions using steady state free precession magnetic +resonance imaging”, Journal of Cardiovascular Magnetic Resonance 7, 775–782 (2005). +117A. M. Maceira, S. K. Prasad, M. Khan, and D. J. Pennell, “Reference right ventricular systolic +and diastolic function normalized to age, gender and body surface area from steady-state free +precession cardiovascular magnetic resonance”, European Heart Journal 27, 2879–2888 (2006). +118S. Clay, K. Alfakih, A. Radjenovic, T. Jones, and J. P. Ridgway, “Normal range of human +left ventricular volumes and mass using steady state free precession mri in the radial long axis +orientation”, Magnetic Resonance Materials in Physics, Biology and Medicine 19, 41–45 (2006). +119B. E. U. Burkhardt, C. J. Kellenberger, F. D. Franzoso, J. Geiger, A. Oxenius, and E. R. Valsan- +giacomo Buechel, “Right and left ventricular strain patterns after the atrial switch operation for +d-transposition of the great arteries—a magnetic resonance feature tracking study”, Frontiers +in Cardiovascular Medicine 6, 39 (2019). +120K. Hammermeister, R. Brooks, and J. Warbasse, “The rate of change of left ventricular volume +in man: i. validation and peak systolic ejection rate in health and disease”, Circulation 49, +729–738 (1974). +121T. Sugimoto, R. Dulgheru, A. Bernard, F. Ilardi, L. Contu, K. Addetia, L. Caballero, N. Akha- +ladze, G. D. Athanassopoulos, D. Barone, et al., “Echocardiographic reference ranges for nor- +mal left ventricular 2d strain: results from the eacvi norre study”, European Heart Journal- +Cardiovascular Imaging 18, 833–840 (2017). +122A. Bishop, P. White, P. Oldershaw, R. Chaturvedi, C. Brookes, and A. Redington, “Clinical +application of the conductance catheter technique in the adult human right ventricle”, Interna- +tional journal of cardiology 58, 211–221 (1997). +123L. Thomas, E. Foster, and N. B. Schiller, “Peak mitral inflow velocity predicts mitral regurgi- +tation severity”, Journal of the American College of Cardiology 31, 174–179 (1998). +124G. Di Labbio and L. Kadem, “Jet collisions and vortex reversal in the human left ventricle”, +Journal of Biomechanics 78, 155–160 (2018). +125P. J. Kilner, G.-Z. Yang, A. J. Wilkes, R. H. Mohiaddin, D. N. Firmin, and M. H. Yacoub, +“Asymmetric redirection of flow through the heart”, Nature 404, 759–761 (2000). +126F. C. Bueno, I. Rodr´ıguez-Bail´on, R. L´opez-Salguero, J. J. G´omez-Doblas, J. M. Garc´ıa-Pinilla, +and E. de Teresa Galv´an, “Effect of exercise on systolic left ventricular outflow velocity in +healthy adults”, Revista Espa˜nola de Cardiolog´ıa (English Edition) 59, 180–182 (2006). +127J.-O. Choi, M.-S. Shin, M.-J. Kim, H. O. Jung, J. R. Park, I. S. Sohn, H. Kim, S.-M. Park, +N. J. Yoo, J. H. Choi, et al., “Normal echocardiographic measurements in a korean population +study: part ii. doppler and tissue doppler imaging”, Journal of Cardiovascular Ultrasound 24, +144–152 (2016). +128Pulmonary acceleration time to estimate pulmonary pressure, visited: October 2022. +129Normal pressures in the heart and great vessels, (Accessed: October 2022) https : / / www . +msdmanuals.com/professional/multimedia/table/normal-pressures-in-the-heart- +and-great-vessels. +43 + +130L. Littmann and J. D. Symanski, “Hemodynamic implications of left bundle branch block”, +Journal of Electrocardiology 33, 115–121 (2000). +131J. Eriksson, J. Zajac, U. Alehagen, A. F. Bolger, T. Ebbers, and C.-J. Carlh¨all, “Left ventricular +hemodynamic forces as a marker of mechanical dyssynchrony in heart failure patients with left +bundle branch block”, Scientific Reports 7, 1–9 (2017). +132M. Alessandrini, M. De Craene, O. Bernard, S. Giffard-Roisin, P. Allain, I. Waechter-Stehle, +J. Weese, E. Saloux, H. Delingette, M. Sermesant, et al., “A pipeline for the generation of +realistic 3d synthetic echocardiographic sequences: methodology and open-access database”, +IEEE transactions on Medical Imaging 34, 1436–1451 (2015). +133M. Hirschvogel, M. Bassilious, L. Jagschies, S. M. Wildhirt, and M. W. Gee, “A monolithic 3d- +0d coupled closed-loop model of the heart and the vascular system: experiment-based parameter +estimation for patient-specific cardiac mechanics”, International Journal for Numerical Methods +in Biomedical Engineering 33, e2842 (2017). +44 + diff --git a/4dA0T4oBgHgl3EQfNf-w/content/tmp_files/load_file.txt b/4dA0T4oBgHgl3EQfNf-w/content/tmp_files/load_file.txt new file mode 100644 index 0000000000000000000000000000000000000000..75ac7f8e16725a61d818af8378edc8de248ee424 --- /dev/null +++ b/4dA0T4oBgHgl3EQfNf-w/content/tmp_files/load_file.txt @@ -0,0 +1,1762 @@ +filepath=/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf,len=1761 +page_content='An electromechanics-driven fluid dynamics model for the simulation of the whole human heart Alberto Zingaro1, Michele Bucelli1, Roberto Piersanti1, Francesco Regazzoni1, Luca Dede’1, and Alfio Quarteroni1, 2 1MOX, Laboratory of Modeling and Scientific Computing, Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milano, Italy 2Institute of Mathematics, ´Ecole Polytechnique F´ed´erale de Lausanne, Station 8, Av.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Piccard, CH-1015 Lausanne, Switzerland (Professor Emeritus).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Abstract We introduce a multiphysics and geometric multiscale computational model, suitable to describe the hemodynamics of the whole human heart, driven by a four-chamber electrome- chanical model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We first present a study on the calibration of the biophysically detailed RDQ20 activation model (Regazzoni et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=', 2020) that is able to reproduce the physiological range of hemodynamic biomarkers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Then, we demonstrate that the ability of the force gener- ation model to reproduce certain microscale mechanisms, such as the dependence of force on fiber shortening velocity, is crucial to capture the overall physiological mechanical and fluid dynamics macroscale behavior.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' This motivates the need for using multiscale models with high biophysical fidelity, even when the outputs of interest are relative to the macroscale.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We show that the use of a high-fidelity electromechanical model, combined with a detailed calibration process, allows us to achieve a remarkable biophysical fidelity in terms of both mechani- cal and hemodynamic quantities.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Indeed, our electromechanical-driven CFD simulations – carried out on an anatomically accurate geometry of the whole heart – provide results that match the cardiac physiology both qualitatively (in terms of flow patterns) and quantitatively (when comparing in silico results with biomarkers acquired in vivo).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Moreover, we consider the pathological case of left bundle branch block, and we investigate the consequences that an electrical abnormality has on cardiac hemodynamics thanks to our multiphysics integrated model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The computational model that we propose can faithfully predict a delay and an in- creasing wall shear stress in the left ventricle in the pathological condition.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The interaction of different physical processes in an integrated framework allows us to faithfully describe and model this pathology, by capturing and reproducing the intrinsic multiphysics nature of the human heart.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 1 Introduction The study of cardiac blood flows aims at enhancing the knowledge of heart physiology, assessing pathological conditions, and possibly improving clinical treatments and therapeutics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In the last decades, the role of mathematical models in the study of cardiac hemodynamics has increasingly gained relevance, for their non-invasiveness and flexibility with respect to geometries and flow 1 arXiv:2301.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='02148v1 [math.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='NA] 5 Jan 2023 conditions [1–9].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Computational Fluid Dynamics (CFD) is largely employed to provide a detailed description of cardiac flows and to estimate hemodynamic indicators, like e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=', the wall shear stress, that standard image-based techniques might not capture [1].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' A biophysically detailed mathematical model of cardiac hemodynamics entails the complex interplay among different processes such as the interaction with cardiac electromechanics (EM), valve dynamics, transition-to-turbulence effects, and coupling with the surrounding circulation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Furthermore, while the literature about CFD models of the left ventricle [3, 4, 10–13], left atrium [8, 14–22] and left heart [1, 9, 23–26] is relevant, the fluid dynamics simulation of the right heart is the subject of few works, and they focus on the sole right ventricle [27–29] , neglecting the right atrium description.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Conversely, whole-heart fluid dynamics modeling is a much more challenging topic and has been addressed only recently in few works [30–35].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' As a matter of fact, to the best of our knowledge, hemodynamics simulations of the whole heart are presented only in the following papers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' A four-chamber CFD model is proposed by the Siemens group in [30]: the displacement of cardiac walls is obtained from patient-specific images and, since the left and right sides are not connected to the surrounding circulation, they performed simulations separately on the two parts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In [31], the authors introduce a Fluid Structure Interaction (FSI) simulation of the whole heart based on the UT-Heart simulator developed at the University of Tokyo.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Moreover, FSI simulations of the whole heart have been performed in the context of the Living Heart Project [32].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' A sequentially-coupled FSI model of the whole heart is devised in [33], showing that few iterations of the solver are needed to reach convergence in terms of mechanical indicators.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Recently, a patient-specific whole-heart CFD model is introduced in [34], consisting of the Navier-Stokes-Brinkman equations [36] with prescribed boundary motion and, similarly to [30], simulations of the left and right parts are carried out separately.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Finally, a GPU-accelerated fully-coupled electro-mechano-fluid computational model of the whole heart, based on the immersed boundary method, is presented in [35].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In this paper, we propose a four-chamber electromechanical-driven fluid dynamics model, which is characterized by a remarkable biophysical fidelity and able to faithfully describe potential pathologies.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Moreover, to improve the virtual representation of the heart physiology enabled by our model, we present a meticulous calibration of the activation model (at the cellular level) to reproduce mechanical and hemodynamic biomarkers in the physiological range.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The blood flow in heart chambers is commonly modeled by Navier-Stokes equations for Newto- nian fluids [37].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' A crucial aspect in heart flows modeling is the treatment of boundary displacement, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' the way the deformation of cardiac walls is accounted for into the model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The boundary dis- placement can be the solution of a suitable mathematical model for the dynamics of the walls, fully coupled to the fluid dynamics model in an FSI framework, by imposing geometric, kinematic, and dynamic coupling conditions at the fluid-solid interface.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The motion of the myocardium is in turn driven by muscular EM, resulting in a coupled electro-mechano-fluid problem (see e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' [9, 23, 24, 38–42]).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' This approach, while being very comprehensive and physically motivated, entails a significant computational effort, due to the number of subsystems involved and to the non-linearity induced by the coupling.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' To mitigate this large computational cost, the boundary displacement can be prescribed as a datum, without any feedback from the fluid flow, in a CFD modeling framework.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The displacement may be prescribed by suitable analytical laws [3, 4, 11, 13, 16, 17, 25, 43–45], from patient-specific image-based reconstructions [1, 5, 7, 8, 14, 15], or else obtained from a previously performed EM simulation [2, 6, 26, 46–48].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The latter corresponds to a one-way coupled approach between EM and CFD, since only the kinematic coupling is enforced, without foreseeing any dynamic feedback from the fluid to the structure problem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' This approach is also referred to as “kinematic uncoupling” [2, 49].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' A critical issue in 3D cardiovascular hemodynamics modeling is the prescription of boundary 2 conditions at inlet and outlet sections, since boundary data are generally unavailable, but also because the circulatory system is a closed-loop network and the mathematical model should account for it.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' A possible approach is the so called geometric multiscale modeling [50]: the region of interest (in our case, the whole-heart) is described by a 3D model, while the remaining part of the circulation is addressed by means of lumped-parameter models, as 0D [50–54], or 1D [40, 50, 55–57].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The geometric multiscale modeling allows to account for the mutual interaction between the heart and the circulatory system, especially if the lumped parameter model provides a closed-loop description of the vascular network, as done in [26, 51, 58–61].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' An additional key aspect in cardiac CFD simulations is the modeling of the cardiac valves.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In principle, these can be treated by considering a structural model for the solid (leaflets of the valve and possibly its chordae tendinae) and a fluid dynamics model for the surrounding blood flow.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' This approach yields a coupled FSI model of the blood-valve system [24, 62–70], characterized by contact phenomena and fast dynamics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Thus, FSI valve models are commonly associated to a huge computational burden, to be added to the overall cost of the heart CFD simulation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' To avoid this large computational cost, the effects of the valves in the blood can be surrogated by relying on reduced models for the valve dynamics [5, 34, 47, 71–74].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Our computational model of the whole human heart encompasses the main features of the cardiac hemodynamics: EM, cardiac valves, transition-to-turbulence effects, and interplay with the external circulation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Indeed, our fluid model is driven by the four-chamber EM model recently proposed in [58].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The multiphysics model is fully coupled to the external circulation described by a lumped-parameter model, extending the computational framework we introduced in [26] to the case of four-chamber CFD simulations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We carry out numerical simulations on an anatomically accurate geometry of the heart, obtaining results that are quantitatively in agreement with data from the medical literature and qualitative faithfully in terms of blood flow patterns.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In this re- spect, we analyze the role played by the highly biophysically detailed RDQ20 activation model [75] on relevant hemodynamic quantities.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Indeed, since the electromechanical displacement drives the deformation of cardiac chambers for the CFD simulation, its calibration is fundamental towards faithfully reproducing heart physiology.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' As shown in [58], the parameters of the activation model play a significant role in determining the flow rates across cardiac valves, which have a dramatic impact on the CFD simulation, both in terms of macroscopic indicators and overall flow distribu- tion.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Therefore, we present a detailed calibration of the active force generation model, validating our results on several macroscopic heartbeat indicators such as stroke volumes, ejection fractions, peak flowrates and, consequently, also in terms of blood velocities computed in the CFD simula- tion.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Our sensitivity analysis highlights that microscopic features of the RDQ20 model have a large impact on the macroscopic characteristics of the heartbeat.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Then, we show that our detailed com- putational model can improve the understanding of the impact of the Left Bundle Branch Block (LBBB) pathology [76] on CFD biomarkers, by capturing the effects that an electrophysiological abnormality has on different physical processes behind the heart activity, henceforth allowing to capture the intrinsic multiphysics nature of the cardiac function.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' This paper represents one the few examples in the literature on the modeling and simulation of the whole heart hemodynamics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Moreover, to the best of our knowledge, this is the first work in which the 3D whole heart fluid dynamics model is also coupled to a lumped-parameter closed-loop circulation model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' This paper is organized as follows: in Section 2, we introduce the mathematical models em- ployed for the EM, fluid dynamics and circulation problems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Section 3 is devoted to the description of the numerical methods for each subproblem and to the strategies used for their coupling.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In Section 4, we present numerical results on a realistic whole-heart geometry, in both physiological and pathological conditions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Finally, limitations and conclusions follow in Section 5 and Section 6, 3 Figure 1: The whole-heart EM model: a) cardiac muscular fibers, (b) boundaries and impulse sites (yellow spheres with bold labels), (c) coupling with circulation and we highlight the three main regions of the EM model (atria, ventricles and non-conductive regions).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 2 Mathematical model In this section, we introduce the mathematical model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Specifically, the EM model is briefly de- scribed in Section 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='1 and the whole-heart fluid domain is defined in Section 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We present our approach suitable to deal with the domain deformation in Section 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='3, the fluid dynamics model in Section 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4, and its coupling with the external circulation in Section 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='1 The whole-heart electromechanical model The whole-heart EM is based on a comprehensive and biophysically detailed computational model that we recently presented in [58].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' More precisely, we consider a 3D description of cardiac EM in all the four-chambers and a 0D representation of the complete circulatory system, including the cardiac blood hemodynamics [59, 77], as we display in Figure 1c.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The whole-heart EM model includes a detailed myocardial fiber architecture built upon a total- heart Laplace-Dirichlet Rule-Based Method [78], which couples together different methods for the atria [79] and the ventricles [80], to properly reproduce the characteristic features of the cardiac fiber bundles in all the four-chambers [79], see Figure 1a.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Cardiac electrophysiology is described by means of the monodomain equation equipped with no-flux Neumann boundary conditions [81] and endowed with the following human ionic models: ten Tusscher-Panfilov for the ventricles [82] and Courtemanche-Ramirez-Nattel for the atria [83].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 4 URADL.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='AFurthermore, the arterial vessels and the atrio-ventricular basal plane are assumed to be non- conductive regions, whence electrically isolating the atria from the ventricles [58].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Finally, the cardiac conduction system is substituted by a series of spherical electrical impulses, originating from the sino-atrial node (SAN) and ending into the left and right ventricular endocardia which, combined with a fast endocardial layer, surrogates the effect of the Purkinje network [58, 84], as we show in Figure 1b.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The sarcomere mechanical activation is based on the biophysically detailed RDQ20 active contraction model [75], properly calibrated for both atria [85] and ventricles [86].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The RDQ20 is able to represent in detail the sophisticated microscopic active force generation mechanisms, taking place at the scale of sarcomeres [86].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Moreover, to differentiate the active tension in left and right ventricles, we consider a spatially heterogeneous active tension [77].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The myocardial tissue mechanics is described by the momentum balance equation under the hyperelasticity assumption [87].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We employ, for the active part, an orthotropic active stress formu- lation [77], which surrogates the contraction caused by dispersed myofibers [88], and, for the passive behavior, specific mechanical constitutive laws and model parameters for the different cardiac re- gion: the Usyk constitutive law for both the atria and the ventricles [89] and a Neo-Hookean strain energy density function for the atrio-ventricular basal plane and the vessels [87].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Finally, a nearly incompressible formulation is enforced with a penalty method [58].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Concerning the mechanical boundary conditions, we consider: i) generalized Robin boundary conditions on the epicardium, surrogating both the presence of the pericardium and also the epicardial adipose tissue, crucial for reproducing the correct downward and upward movement of the atrio-ventricular basal plane [58];' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' ii) normal stress boundary conditions on the four-chamber endocardia and vessel endothelia to account for the pressures exerted by the blood, where the endocardium and endothelium fluid pressures are given by the coupling between the mechanical and the circulation problems [58, 59, 77];' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' iii) homogeneous Dirichlet boundary condition on all the artificial rings where we cut the computational domain, since the arteries and atrial veins can be considered fixed here [58], as displayed in Figure 1b.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The whole-heart 3D EM model is fully coupled with a 0D closed-loop lumped parameters model for the blood hemodynamics through the entire cardiovascular network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Systemic and pulmonary circulations are modeled using resistance-inductance-capacitance circuits (both for the arterial and venous part) and non-ideal diodes stand for the heart valves [59].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In Figure 1c we give a graphical representation of the 3D-0D model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The coupling between the 0D and 3D EM models is achieved by introducing volume-consistency coupling conditions, where the pressures of all the four-chambers act as Lagrange multipliers associated with the introduced volume constraints [59, 77].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The most relevant feedbacks, representing the interactions among electric signal propagation, the cardiac tissue deformation and contraction, and the circulatory system, are modelled inside the whole-heart EM model [58].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' These include e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' the mechano-electric feedback [90] (between elec- trophysiology and mechanics) and the fibers-stretch and fibers-stretch-rate feedbacks [91] (between mechanics and the activation model).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' For the full set of equations of the whole-heart EM model, we refer to [58, 59, 77].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In this paper, the whole-heart EM model serves as unidirectional input for the fluid dynamics problem as we better detail in Section 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='3 and Section 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 5 Figure 2: The whole-heart fluid domain: (a) subdomains composing the whole heart;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' (b) boundary portions (the left and right part are separated for visualization purposes).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 The whole-heart fluid domain Let Ωt be the fluid domain at time t > 0 bounded with a sufficiently regular boundary Γt ≡ ∂Ωt and let (0, T) be the temporal domain, with T the final time.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' From a fluid dynamics view point, the whole-heart fluid domain Ωt is topologically disjoint and split into left heart (LH, ΩLH t ) and right heart (RH, ΩRH t ), as we show in Figure 2: Ωt = ΩRH t ∪ΩLH t , with Ω RH t ∩Ω LH t = ∅.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Specifically, Ω RH t = Ω RA t ∪ Ω TV t ∪ Ω RV t ∪ Ω PV t ∪ Ω PT t , Ω LH t = Ω LA t ∪ Ω MV t ∪ Ω LV t ∪ Ω AV t ∪ Ω AO t , where ΩRA t , ΩRV t , ΩPT t are the right atrium (RA), right ventricle (RV) and pulmonary trunk (PT) subdomains, and ΩLA t , ΩLV t , ΩAO t the left atrium (LA), left ventricle (LV) and aorta (AO) subdo- mains.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Moreover, ΩTV t , ΩPV t , ΩMV t , ΩAV t are the subdomains representing the rings of the tricuspid valve (TV), pulmonary valve (PV), mitral valve (MV) and aortic valve (AV).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Analogously, we partition the boundary of the whole-heart domain as Γt = ΓRH t ∪ ΓLH t , with ΓRH t ∩ ΓLH t = ∅.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In particular, as displayed in Figure 2b, ΓRH t = Γout,RH ∪ Γin,RH ∪ Γw,RH t , with Γin,RH the inlet sections of the superior and inferior venae cavae, Γout,RH the outlet section of the pulmonary trunk, and Γw,RH t the endocardium of the RH.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In an analogous fashion, on the left part: ΓLH t = Γout,LH ∪ Γin,LH ∪ Γw,LH t , with Γin,LH the five inlet sections of the four pulmonary veins, Γout,LH the outlet section of the aorta, and Γw,LH t the LH endocardium.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 6 Tin,LHTout,RHin.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='RH2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='3 The fluid domain displacement problem To represent the deformation of the domain over time, we introduce a fixed reference configuration ˆΩ ⊂ R3, such that the domain in current configuration Ωt is defined at any t ∈ (0, T) as Ωt = {x ∈ R3 : x = �x + d(�x, t), �x ∈ �Ω}, where d : �Ω × (0, T) is the displacement of the domain, and is obtained it by solving the following harmonic extension problem: � −∇ · (s∇d) = 0 in �Ω × (0, T), (1a) d = d∂Ω(x, t) on ∂�Ω × (0, T).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' (1b) In Equation (1), d∂Ω : ∂�Ω × (0, T) is the boundary displacement, computed by restricting the solution of the EM simulation to the endocardium and the endothelium.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Furthermore, s : �Ω × (0, T) → R is a space-dependent scalar field introduced to avoid distortion of mesh elements.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Specifically, we use the boundary-based stiffening approach proposed in [92].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We denote the fluid domain displacement problem Equation (1) with the abridged notation D(d, d∂Ω) = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The EM simulation is solved with a significantly larger timestep than the CFD one.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Therefore, the boundary displacement d∂Ω is only available for some times tk, k = 0, 1, .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' , NEM, although the domain displacement d is needed with a finer temporal resolution.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Thus, problem (1) is solved for all times tk, and then we construct a displacement field ˜d(t) : �Ω×(0, T) → R3 using smoothing splines approximation in time [93].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We compute the domain velocity by deriving the displacement in time as uALE = ∂ �d ∂t in Ωt × (0, T).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' (2) 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4 The Navier-Stokes equations in ALE framework with the RIIS model of the valves We model the blood in the cardiac cavities as an incompressible, viscous and Newtonian fluid characterized by constant density ρ and constant dynamic viscosity µ.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We therefore use the time- dependent incompressible Navier-Stokes equations expressed in an Arbitrary Lagrangian Eulerian (ALE) framework to account for the moving domain.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We denote by u : Ωt × (0, T) → R3 and p : Ωt × (0, T) → R the fluid velocity and pressure, respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Let σ be the Cauchy stress tensor, defined for incompressible, Newtonian and viscous fluids as σ(u, p) = −pI + 2µϵ(u), with ϵ(u) = 1 2 � ∇u + (∇u)T� the strain-rate tensor.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We model the effects of cardiac valves in the fluid by means of the Resistive Immersed Im- plicit Surface (RIIS) method [71].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We consider four immersed surfaces Σk, with k ∈ Iv = {MV, AV, TV, PV} the set of valves.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Each valve is characterized by a resistance coefficient Rk and a parameter εk representing the half thickness of the valve leaflets.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The immersed surface is implicitly described by a signed distance function ϕk : Ωt × (0, T) → R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' With the RIIS method, we introduce the following penalty term to the momentum balance of the Navier-Stokes equations (expressed in ALE form): R(u, uALE) = � k∈Iv Rk εk δΣk,εk(ϕk) � u − uALE − uΣk � .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 7 R penalizes the mismatch between the relative velocity u − uALE and the velocity of the valves’ leaflets uΣk, weakly imposing a kinematic coupling condition only.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The resistive term is only acting on a tiny support around Σk, thanks to a smoothed Dirac delta function acting as multiplicative factor.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We refer to [71] for its definition.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' By defining with �∂u ∂t = ∂u ∂t + (uALE · ∇)u the ALE derivative,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' the 3D fluid dynamics model of the whole heart reads: � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � ρ �∂u ∂t + ((u − uALE) · ∇)u + ∇ · σ(u,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' p) + R(u,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' uALE) = 0 in Ωt × (0,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' T),' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' (3a) ∇ · u = 0 in Ωt × (0,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' T),' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' (3b) σ(u,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' p)n = −pin RAn on Γin,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='RH × (0,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' T),' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' (3c) σ(u,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' p)n = −pPUL AR n on Γout,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='RH × (0,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' T),' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' (3d) σ(u,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' p)n = −pin LAn on Γin,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='LH × (0,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' T),' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' (3e) σ(u,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' p)n = −pSYS AR n on Γout,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='LH × (0,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' T),' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' (3f) u = uALE on Γw,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='RH t ∪ Γw,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='LH t × (0,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' T).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' (3g) u = u0 in Ω0 × {0}, (3h) where pin RA, pPUL AR , pin LA, pSYS AR are the pressures arising from the coupling with the circulation model, as we detail in Section 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='5, and u0 is the initial velocity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We denote the 3D fluid dynamics model of the whole heart in Equation (3) by F(u, p, uALE) = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='5 Coupling with circulation To account for the interplay between the heart’s fluid dynamics and the hemodynamics of the surrounding circulation, we couple the cardiac CFD model to a 0D closed-loop model of the whole circulation proposed in [59].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Specifically, we extend to the whole heart the coupling strategy that we devised in [26] for the case of the sole left heart.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We consider a reduced (open) version of the original circulation model in which we remove the equations for all the variables that are already described by the 3D counterpart.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The equations of the open system are reported in A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' By denoting with Q0D and p0D the vectors containing flowrates and pressures of the 0D model, respectively, we refer to the open system with the notation C (Q0D, p0D) = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The coupling between the 3D CFD model and the open 0D model consists in the enforcement of the continuity of pressures and flowrates on the artificially chopped boundaries ΓI of the fluid domain, yielding the following conditions: �pI 3D = pI 0D on ΓI × (0, T), (4a) QI 3D = QI 0D on ΓI × (0, T), (4b) which express dynamic and kinematic coupling, respectively, with1 pI 3D = 1 |ΓI| � ΓI p, QI 3D = � ΓI(u − uALE) · n.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' (5) 1We define the sign of the flowrate in accordance with the outward unit normal n.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Thus, an inlet flowrate (entering velocity) will be, by definition, negative.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 8 Figure 3: The 3D-0D fluid dynamics model of the whole-heart coupled to the surrounding circu- lation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Considering the whole-heart fluid domain (see Figure 2b), the interface boundaries are ΓI = Γin,RH ∪ Γout,RH ∪ Γin,LH ∪ Γout,LH.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The 0D pressures (pI 0D) and flowrates (pI 0D) are [42, 94]: pin,RH 0D = pin RA, Qin,RH 0D = −QSYS VEN, pout,RH 0D = pPUL AR + RPUL upstreamQPV, Qout,RH 0D = QPV, pin,LH 0D = pin LA, Qin,LH 0D = −QPUL VEN, pout,LH 0D = pSYS AR + RSYS upstreamQAV, Qout,LH 0D = QAV.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' From the point of view of the 3D CFD model, the conditions expressed by Equation (5) are defective, since they prescribe the average pressure and the total flow rate over the entire section ΓI, rather than pointwise stress and velocity distributions [50].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We choose to complete the pressure condition as σ(u, p)n = −pIn, on ΓI × (0, T).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' (6) The flowrate condition, conversely, is left in its defective form, since it is sufficient for the algorithm we use for the 3D-0D coupling (see Section 3).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 3 Numerical methods In this section, we describe the numerical methods we use to solve our multiphysics and mul- tiscale system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The overall algorithm is presented in Algorithm 1 and graphically represented in Figure 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We can subdivide the overall procedure in a preliminary phase, in which we solve the EM simulation [58] and the fluid domain displacement problem to lift the boundary displacement to the fluid bulk domain, followed by the coupled 3D-0D CFD simulation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 9 Algorithm 1 Numerical scheme for the EM-driven CFD simulation of the whole heart Solve whole-heart EM model Pick solution in the last heartbeat: → dEM i , for i = 0, .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' , N Restrict dEM i on ∂ΩS,endo i : d∂Ω i , for i = 0, .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' , N Solve fluid domain displacement problem: D(di, d∂Ω i ) = 0, for i = 0, .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' , N Build approximant: �d(t) Initialization: n = 0, u0 = 0 while n < Nt do Update valves leaflet position Compute ALE velocity: uALE n+1 = � dn+1− � dn ∆t .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Solve circulation: C (Q0D n+1, p0D n+1) = 0 with data QI 0D,n Compute interface data (0D → 3D): pI 0D,n+1 → pI 3D,n+1 Solve fluid dynamics: F(un+1, pn+1, uALE n+1 ) = 0, with Neumann data pI 3D,n+1 Compute interface data (3D → 0D): QI 3D,n+1 → QI 0D,n+1 n ← n + 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' end while Numerical methods for the EM model For the numerical approximation of the whole-heart EM model we employ the efficient Segregated-Staggered scheme [58, 59, 77].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In this numerical scheme, the different cardiac physical models, contributing to both the 3D EM and the 0D blood circulation, are sequentially solved in a segregated manner, using different resolutions in space and time to properly account for the heterogeneous space and time scales characterizing different physical processes [95].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' For the space discretization, we use the Finite Element (FE) method with continuous FE on a tetrahedral mesh.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We consider FE of order 2 (P2) for the electrophysiology to capture the traveling wave dynamics and FE of order 1 (P1) for both the activation and the mechanics [58].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' For the time discretization, we use finite difference schemes [96].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Specifically, cardiac electro- physiology is solved with Backward Differentiation Formula (BDF) of order 2, using an Implicit- Explicit (IMEX) scheme where the diffusion term is treated implicitly, the ionic and reaction terms explicitly.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The ionic variables are advanced in time through an IMEX scheme [58, 59, 77].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We solve the active contraction problem with an IMEX BDF1 method, and the mechanical problem with a fully-implicit BDF1 scheme [77].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Finally, an IMEX scheme of the first order is used for the circu- lation [58].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Moreover, two different time steps are used: a finer one for the electrophysiology and a larger one for both the activation, the mechanics and the circulation [59].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Finally, we employ recently developed stabilization methods – related to the circulation and the fibers-stretch-rate feedback – that are crucial to obtain a stable solution in a four-chamber simulation scenario [97, 98].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Concerning the linear systems arising from the discretization of the whole-heart EM problem we use: the conjugate gradient for the electrophysiology and the GMRES method for both the mechanics and the activation, both empowered by an algebraic multigrid (AMG) preconditioner.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Finally, we solve the non-linear saddle-point problem arising from the coupling between the me- chanics and the circulation by means of a Newton algorithm using, at the algebraic level, the Schur complement reduction [59, 77].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' For further details about the numerical methods we use in the whole-heart EM model, we refer to [58, 59, 77].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 10 Numerical methods for the fluid domain displacement problem After simulating the whole-heart EM and reaching a limit cycle in terms of pressure and volumes, we extract the solution from the last simulated heartbeat.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We restrict this solution to the heart endocardium and the endothelium of outflow tracts, obtaining N + 1 solutions defined on the boundary of the fluid domain (d∂Ω i , with i = 0, .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' , N).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We project d∂Ω i onto the CFD mesh with piecewise linear interpolation, then solve the fluid domain displacement problem in Equation (1) to obtain the ALE displacement d.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We discretize the lifting problem in Equation (1) using FEs of order 1 (P1) and the linear system arising from its discretization is preconditioned with an AMG preconditioner.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We solve the resulting linear system with the conjugate gradient method.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The smoothing spline approximation is computed independently for each mesh node, and the approximant is constructed following the optimization procedure described in [99].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' To compute the ALE velocity, we use BDF1 to discretize in time Equation (2).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Numerical methods for the CFD cardiac model We discretize Equation (3) in space using FEs of order 1 for both velocity and pressure (P1 − P1).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We employ the Variational Multiscale Large Eddy Simulation (VMS-LES) method to obtain a stable formulation of the NS-ALE- RIIS equations discretized via equal order FE spaces.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' This also allows us to control instabilities arising from the advection-dominated regime, and to model transition-to-turbulence in the LES framework [16, 100, 101].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The VMS-LES formulation accounts for the ALE framework and the RIIS modeling used for valves.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' For the complete formulation, we refer to [26].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' For the time discretization, we consider a uniform partition of the temporal domain in Nt subintervals (tn, tn+1] of uniform size ∆t, with n = 0, .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' , Nt − 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We denote from here quantities approximated at time tn with the subscript n, e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' un ≈ u(tn).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We advance the problem in time by means of BDF1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' To reduce the computational burden of the numerical simulations, we use a semi-implicit treatment of the nonlinearities, as done in [100].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The overall numerical scheme for the fluid dynamics problem is detailed in [26].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Since Neumann boundary conditions may give rise to instability phenomena in case of inflow, we set backflow stabilization on all the Neumann boundaries in the inertial form presented in [102].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The linear system arising from the discretization of Equation (3) is preconditioned with the aSIMPLE preconditioner [103], and each of its blocks is preconditioned with an AMG precondi- tioner.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The linear system is then solved at each time step by the GMRES method.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Numerical method for the 0D circulation model We solve the system of ODEs of the circulation problem with an IMEX method of the first order.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The time-step size ∆t employed for its numerical discretization is the same used for the BDF advancing scheme in the 3D problem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Numerical scheme for the coupled CFD problem After initialization, for each temporal step of the CFD problem, we update the position of valve leaflets and we compute the ALE velocity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' At every time step, we solve the 3D and 0D subproblems independently.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' First, we solve the 0D open circulation problem using QI n as input (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' the flowrates computed in the 3D model at previous timestep, namely QSYS VEN,n, QPV,n, QPUL VEN,n, QAV,n).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Then, from the solution of the circulation, we compute the pressures pI n+1 at the interfaces and solve the fluid dynamics problem providing those pressures as Neumann boundary conditions at inlet and outlet sections.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Finally, we compute the interface data from the 3D to the 0D model, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' QI n+1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' This approach treats the coupling between the 3D and 0D subproblems in a segregated and explicit way.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 11 Figure 4: Graphical representation of the overall algorithm to simulate the whole-heart hemody- namics driven by the EM model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 4 Numerical results In this section, we present the numerical results using the whole-heart fluid dynamics model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In Section 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='1, we introduce the computational setting of the whole-heart EM and CFD simulations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Section 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 is devoted to the calibration of the RDQ20 activation model to produce physiological flowrates in the EM simulation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The physiological results of the overall computational model are presented in Section 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Finally, we apply the multiphysics computational model to the pathological case of LBBB in Section 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='1 Computational setup We consider a realistic whole-heart geometry provided by the Zygote solid 3D heart model [104], an anatomically CAD model representing an average healthy human heart reconstructed from high-resolution computer tomography scan data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We generate whole-heart tetrahedral meshes for the EM and CFD problems that we report in Figure 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Meshes are generated with vmtk [105] using the methods and tools discussed in [26, 58, 106].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Details on the meshes for the EM and CFD simulations are provided in Table 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The valve leaflets are thin structures that we characterize, in the context of the RIIS method, by small values of εk.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' To correctly capture the immersed surfaces, we refine the CFD mesh close to the valve regions, as shown in Figure 5c.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Specifically, following [71], we choose hk such that εk ≥ 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='5hk, where hk is the minimum mesh size of the fluid mesh in the valve region.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We carry out EM and CFD simulations in lifex [107, 108]2, a high-performance C++ FE library developed within the iHEART project3, mainly focused on cardiac simulations and based on the deal.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='II finite element core [109–111].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Numerical simulations are run in parallel on the GALILEO100 supercomputer4 at the CINECA 2https://lifex.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='gitlab.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='io/ 3iHEART - An Integrated Heart model for the simulation of the cardiac function, European Research Council (ERC) grant agreement No 740132, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, 2017-2023 4528 computing nodes each 2 x CPU Intel CascadeLake 8260, with 24 cores each, 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4 GHz, 384GB RAM.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' See https://wiki.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='u-gov.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='it/confluence/display/SCAIUS/UG3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='3%3A+GALILEO100+UserGuide for technical specifi- 12 Figure 5: Tetrahedral meshes used in the computational model: a) mesh for the EM simulation, b) mesh for the CFD simulation, c) mesh refinement on the valves region of the CFD mesh.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Simulation Mesh size [mm] Cells Points Physics DOFs ∆t [s] min avg max EM 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='860 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='97 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='34 180 472 46 915 Electrophysiology 310 505 5 · 10−5 Mechanics 140 745 10−3 Circulation 10−3 CFD 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='210 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='04 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='82 3 892 584 652 204 Fluid dynamics 2 608 816 10−4 Circulation 10−4 Table 1: Setup of whole-heart EM and CFD simulations (mesh details and time step sizes).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' supercomputing center, using 240 and 480 cores for the EM and CFD simulations, respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The computational time to carry out a single heartbeat is about 1 hour and 20 minutes for the EM simulation and 56 hours for the CFD simulation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' For the parameters of the EM model, we use the same values as in [58] with some minor differences reported in B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' As we better discuss in Section 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2, a huge difference in terms of setup of the EM simulation between [58] and the present work consists in the calibration of the activation model to compute physiological blood flowrates.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We simulate 20 heartbeats of the whole-heart EM, and we report numerical results related to the last heartbeat, after verifying that the solution is sufficiently close to a periodic limit cycle (in terms of pressure and volume transients).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We consider an heartbeat period of THB = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='8 s.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We pick the last simulated EM heartbeat as input displacement for the CFD simulation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We set as initial condition for the velocity u0 = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The cations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 13 Figure 6: Cardiac valves in their open and closed configurations coloured according to displacement magnitude.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Valves geometry are provided by Zygote [104], and we define displacement field aimed at closing and opening the leaflets.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' initial state of the circulation model (for the coupling with the fluid dynamics) is taken equal to the values reached at the beginning of the last heartbeat in the EM simulation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Moreover, as detailed in B, all the values of the parameters involved in the circulation model are the same for the EM and the fluid dynamics simulations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The physical parameters for blood are density ρ = 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='06 · 103 kg/m3 and dynamic viscosity µ = 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='5 · 10−3 kg/(m s).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We simulate two heart cycles, and we report the solution on the second cycle to remove the consequences of an unphysical null initial condition.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' For the numerical results visualization (for both EM and CFD), we shift the time domain in (0, THB).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The Zygote cardiac valves [104] are provided in their open configuration (TV, MV) and closed configuration (PV, AV).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Thus, we define displacement fields aimed at closing and opening their leaflets, based on signed-distance functions and the solution of Laplace-Beltrami problems [26, 106].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In Figure 6, we report the valves in their open and closed configurations, colored according to the leaflets’ displacement magnitude.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We open and close the valves instantaneously (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' in one time step) at the times reported in Table 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' These times are chosen by selecting the initial and final times of the isovolumetric phases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The values we choose for εk, reported in Table 2, allow to have a physiological representation of the valve leaflet.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Indeed, we choose εk by averaging the values of the leaflet thicknesses reported in [112].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Furthermore, the valve resistances values Rk are reported in Table 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We found that the condition number of the linear system associated to the FE discretization of the fluid dynamics problem becomes larger as the ratio Rk/εk increases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Thus, to keep contained the computational cost of the CFD simulation, we choose as Rk the minimum value that guarantees impervious valves.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 14 0 22MV AV TV PV opening time [s] 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='710 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='262 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='700 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='279 closing time [s] 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='208 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='666 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='194 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='677 Rk [kg/m/s] 104 104 104 104 εk [mm] 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='68 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='67 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='77 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='52 Table 2: Setup of the RIIS method to model cardiac valves.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 Calibration of the RDQ20 activation model to achieve physiologi- cal flows Among the different components at the basis of cardiac EM simulations, the model describing the active force generation at the microscale plays a pivotal role on the solution.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Indeed, not only the amount of force the muscle develops depends on it, but also its temporal distribution over the heartbeat, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=', the kinetics of contraction and relaxation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Muscle contraction, in turn, determines the blood flow through the valves.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Moreover, since the electromechanical displacement drives the fluid dynamics model, the same flows are then obtained in the CFD simulation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Therefore, special care must be devoted here to the choice and calibration of the activation model, in order to faithfully capture blood flowrates and, consequently, blood velocities.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' For the above reasons, we chose to use the RDQ20 model [75], that is an active force model with high biophysical fidelity and that is able to reproduce the main features of the experimentally observed behaviors.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The RDQ20 model is based on a detailed description of the calcium-driven regulation of the thin filament, with explicit representation of end-to-end cooperative interactions, and a description of the attachment-detachment process of crossbridges, at the basis of the force- velocity relationship.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Thereby, the model is able to reproduce the main mechanisms of contractility regulation, mediated by calcium, fiber strain and fiber strain-rate.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In particular, the fiber strain- rate feedback, which is responsible for the well-known force-velocity relationship, plays a central role in the regulation of hemodynamic flows, as demonstrated in [58] and confirmed in the present study.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' On this basis, we refine the calibration of the RDQ20 model, with a particular care on fluxes through semilunar valves obtained by means of the 0D model in the EM simulation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We employ as a starting point the calibration used in [58], suitable for the coupling with the TTP06 ionic model [82] (see Table 3, setting A).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In Table 4, column A, we report a list of biomarkers obtained by using the calibration A in the EM simulation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Although the biomarkers characterizing the overall cardiac function (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' end-systolic and end-diastolic volume, stroke volume and ejection fraction) are within reference ranges, the maximum blood flux across valves is significantly above the physiological range.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In other terms, even if the total ejected blood is physiological, the instantaneous flow peak is too large.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' This would clearly have a strong negative impact on the results of fluid dynamics simulations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' For instance, an excessively high velocity through the valve may result in high pressure gradients, and an overall incorrect stress distribution over valve leaflets and cardiac walls [113, 114].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' To address this issue, we slow down the process of force generation, so that the tissue contrac- tility is developed at a lower rate.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' More precisely, we reduce the association-dissociation rates of troponin and tropomyosin of the RDQ20 model (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Koff and Kbasic) by a factor 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Moreover, to compensate for the lower peak force caused by a slower kinetics, we increase the crossbridge level contractility (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' aXB).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We consider three different levels of contractility, as reported in Table 3, respectively in columns B, C and D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' However, as we show in Table 4 and Figure 7, on 15 Parameter A B C D E Regulatory units dynamics Q [−] 2 2 2 2 2 kd [µM] 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='36 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='36 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='36 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='36 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='36 αkd [µM/µm] 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2083 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2083 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2083 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2083 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2083 µ [−] 10 10 10 10 10 γ [−] 30 30 30 30 30 Koff [1/s] 8 (*)4 (*)4 (*)4 (*)4 Kbasic [1/s] 4 (*)2 (*)2 (*)2 (*)2 Crossbridge dynamics (prescribed) v0 [1/s] 2 2 2 2 (*)0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='5 vmax [1/s] 8 8 8 8 (*)2 ˜k2 [−] 66 66 66 66 66 µiso 0 [−] 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='22 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='22 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='22 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='22 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='22 Crossbridge dynamics (automatically calibrated) r0 [1/s] 134.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='31 134.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='31 134.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='31 134.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='31 33.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='24 α [−] 25.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='184 25.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='184 25.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='184 25.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='184 24.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='93 µ0 fP [1/s] 32.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='225 32.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='225 32.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='225 32.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='225 7.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='98 µ1 fP [1/s] 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='768 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='768 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='768 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='768 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='192 Micro-macro upscaling aXB [MPa] 1500.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='0 (*)1550.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='0 (*)2925.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='0 (*)5214.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='5 (*)1550.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='0 Table 3: Different calibrations of the RDQ20 activation model: A) calibration from [58];' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' B) C) D) Reduced Koff, Kbasic and different levels of aXB;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' E) Reduced v0, vmax.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Parameters that are modified with respect to A are highlighted by the symbol (*).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We remark that the parameters r0, α, µ0 fP and µ1 fP are automatically calibrated from the four quantities v0, vmax, ˜k2 and µiso 0 (for furhter details, see [75]);' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' therefore, the symbol (*) is not reported for these four parameters.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' For the description of each parameter, we refer to the original paper of the RDQ20 model [75].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 16 Biomarker Physiological values A B C D E ESVLV [ml] 35 to 80 [115] 53.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='8 66.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='7 53.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='5 45.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='9 66.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4 ESVRV [ml] 69 ± 22 [116] 58.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='0 79.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='8 65.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='3 56.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='0 72.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='3 EDVLV [ml] 126 to 208 [115] 150 128 (↓)108 (↓)87.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='9 151 EDVRV [ml] 144 ± 23 [117] 153 152 134 119 159 SVLV [ml] 81 to 137 [115] 96.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='3 (↓)61.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='5 (↓)54.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='9 (↓)42.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='0 84.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='9 SVRV [ml] 94 ± 15 [117] 95.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4 (↓)72.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='3 (↓)69.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='1 (↓)63.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 87.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='1 EFLV [%] 49 to 73 [118] 64.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 (↓)48.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='0 50.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 (↓)47.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='8 56.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='1 EFRV [%] 53 ± 6 [119] (↑)62.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 47.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='5 51.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4 53.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='0 54.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='6 Qmax AV [ml/s] 427 ± 129 [120] (↑)697 327 347 304 399 Qmax PV [ml/s] 427 ± 129 [120]† (↑)756 397 427 443 478 pmax LV [mmHg] 119 ± 13 [121] (↑)154 (↓)99.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='5 (↓)93.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 (↓)80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='7 120 pmax RV [mmHg] 35 ± 11 [122] 37.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 34.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='9 38.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 41.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='6 32.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 Table 4: Effects of different calibrations of the RDQ20 activation model on mechanics and hemo- dynamics biomarkers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Biomarkers highlighted by the symbols (↑) and (↓) lie outside the reference ranges, denoting values too large or too small, respectively, compared with reference ranges.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' †: for the maximum PV flowrate, in absence of clinical ranges from literature, we consider the same normal values of the AV flowrate.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Each column corresponds to a different calibration as detailed in Table 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Figure 7: pV loops of RV and LV obtained with different calibrations of the RDQ20 activation model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Each line corresponds to a different calibration as detailed in Table 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 17 RV pV loop LV pV loop 45 160 A A 40 B B 140 C C 35 D D E 120 E 30 100 80 60 15 40 10 20 5/ 0 L 0 L 40 60 80 100 120 140 160 180 200 40 60 80 100 120 140 160 180 200 V [ml] V [ml](a) Force-velocity relation (b) reduced v0 (c) reduced vmax Figure 8: Representation of the well-known force-velocity relationship of muscle cells.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The nor- malized active tension Ta/T iso a , where T iso a denotes the force in isometric conditions, is a decreasing function of the shortening velocity v.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' As shown in the figure, vmax is the shortening velocity for which the active tension reaches zero, while v0 is the slope of the curve in correspondence of the isometric conditions (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' v = 0).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' (a) generic force-velocity relationship, (b) effect of reducing v0 (in grey, the curve from (a)), (c) effect of reducing vmax (in grey, the curve from (a)).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' the one hand, we achieve the desired effect of reducing semilunar peak flows, thus bringing them within the expected ranges.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' On the other hand, we compute significantly reduced stroke volume and ejection fraction for both chambers, thus moving out of physiological ranges.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Notice also that this issue is also not resolved by adjusting the contractility.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In fact, by raising aXB, not only the state of contractility in systole is changed, but also in diastole, leading to a reduced end-diastolic volume and, therefore, nullifying the effect of increased contractility, due to the Frank-Starling mechanism.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The three cases shown in Table 4 (B, C and D) are three illustrative cases out of the many that we tested, but without being able to reduce peak flows within the expected ranges while maintaining a physiological ejection fraction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The tests evidenced a paradigmatic short-blanket problem, whereby just acting on kinetics and contractility it is not possible to lower the flows while maintaining a regular ejection fraction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Evidently, another element must be taken into account.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Based on the results of [58], which showed that, by neglecting the fibers-stretch-rate feedback, blood flows through the semilunar valves are significantly overestimated, we modified the calibra- tion so as to, on the contrary, strengthen the effect of this feedback, but without changing either the isometric force or the kinetics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Specifically, we acted in such a way as to steepen the force- velocity relationship, the microscopic mechanism underlying the fibers-stretch-rate feedback, by modifying the parameters governing cross-bridge dynamics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' For this purpose, we took advantage of the calibration technique illustrated in [75], by which the RDQ20 model can be tuned to achieve a desired force-velocity relationship.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Two features of the force-velocity relationship can be selected, namely the maximum shortening velocity (vmax), that is the velocity corresponding to vanishing active force, and the tangent to the curve under isometric conditions (v0).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The geometric meaning of the two quantities is illustrated in Figure 8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Hence, starting from the B calibration, we modified the parameters to obtain a vmax and a v0 equal to one-fourth of the original ones (see Table 3, column E).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' As evidenced in Table 4, with the setting E all biomarkers fall within physiological ranges (see also Figure 7).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We believe that this result can be explained precisely by the mechanism of fibers-stretch-rate feedback, whereby regions of the tissue undergoing rapid shortening experience a decrease in developed force, thus promoting a more homogeneous shortening in space and without significant spikes in time, with a resulting viscous-like effect.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In conclusion, blood flow is redistributed more evenly over the duration of the ejection phase.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Based on the very good match with the reference values of the different biomarkers, in this 18 work we use the calibration E as the baseline for EM simulations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='3 Heart physiology and validation against clinical biomarkers Figure 9 shows the whole-heart EM displacement for a single, representative heartbeat, where we highlight six different phases: isovolumetric contraction, ejection (peak and mid-deceleration), isovolumetric relaxation, ventricular passive filling, and atrial contraction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' As pointed out in [58], the whole-heart EM model can correctly reproduce the cardiac physiological motion.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Moreover, as shown in Section 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2, we found that, after a thorough calibration of the activation model, our numerical results are consistent with normal values found in literature in terms of several volumetric biomarkers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In Figure 10, we report the volumes of the heart chambers and large arteries versus time, and we highlight time instants in which valves open and close.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In Figure 13, we report the volume rendering of the velocity magnitude obtained with our EM-driven CFD simulation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We start our fluid dynamics simulation at the end of ventricular diastole.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' During the active atrial contraction (Figure 13a), we observe the blood flowing from atria to ventricles, producing two high-speed jets in the MV and TV.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' This moment corresponds to the A-wave, as shown in Figure 12.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In order to assess whether our numerical simulation is cor- rectly reproducing the physiological heart function, we compare the peak velocities through valves with physiological ranges available in literature and acquired in healthy subjects.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We report this comparison in Table 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' During diastole, we obtain lower velocities in the TV compared to MV, consistently with clinical measurements available in literature [38, 123].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' During the isovolumetric contraction (Figure 13b), all valves are closed and the ventricular volumes remain constant.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We measure lower velocity values compared to filling and ejection phases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Moreover, we found that the intraventricular pressure is not well defined and is prone to oscillations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' As a matter of fact, since we are using EM as unidirectional input of the CFD model, the dynamic balance between hemodynamics and tissue mechanics is neglected.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Furthermore, since we are modeling the cardiac valves with the RIIS method, weakly imposing a kinematic condition only, the dynamic balance is not fulfilled even between blood and valves.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Thus, our computational model cannot correctly capture the physiological pressure transient during this phase, instead producing nonphysical and large oscillations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In Figure 11, we report the pressure transients in time and, for visualization purposes, we ignore the isovolumetric phases from the plot (grey boxes).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The ejection phase (Figure 13c and Figure 13d) is characterized by the opening of semilunar valves, the contraction of ventricles, and the blood flowing from the LV to the AO and from the RV to the PT.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We measured peak flow rates equal to 398.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='74 mL/s and 478.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='16 mL/s, in the AV and PV, respectively, consistently with physiological values [120].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Moreover, as shown in Table 5, we found that also maximum velocities between AV and PV and peak ventricular pressures during ejection are always in physiological ranges.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' During the isovolumetric relaxation, both the atrioventricular and semilunar valves are closed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The velocities measured are low compared to those of the other phases of the heartbeat.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Further- more, as for the isovolumetric contraction, the computational model cannot reproduce the typical pressure decrease occurring in this phase.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' On the contrary, large pressure oscillations arise.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' During the ventricular passive filling, the blood flows from the pulmonary veins and the venae cavae into the LA and RA, respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Moreover, the atrioventricular valves are open and high- speed jets form between their leaflets.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' This moment corresponds to the E-wave of diastole (see Figure 12).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Consistently with clinical measurements, the computational model is able to correctly reproduce the formation of the clockwise jet in the LV, redirecting the blood towards the outflow 19 (a) t = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='11 s (b) t = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='25 s (c) t = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='36 s (d) t = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='50 s (e) t = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='70 s (f) t = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='79 s |dEM| [mm] Figure 9: Whole heart deformed with EM displacement (with respect to the reference stress- free configuration) and colored according to its magnitude for a single, representative heartbeat: (a) active atrial contraction, (b) isovolumetric contraction, (c) ejection (peak), (d) ejection (mid- deceleration), (e) isovolumetric relaxation, (f) passive ventricular filling.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 20 0 2 4 6 8 10 12 14 16Figure 10: Volumes of RA, RV, PT (left) and LA, LV, AO (right) during a representative heartbeat.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Valves open and close, instantaneously, at the initial and final times of isovolumetric phases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We report these times also in Table 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' tract [124, 125].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Furthermore, from Table 5, we can observe that maximum velocity between MV and TV leaflets are in the physiological ranges.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Furthermore, we also report average atrial pressure values and we found a general good agreement with reference data, even if left atrial pressure is slightly larger than our reference.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 21 180 -RA 180 .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='LA RV LV 160 .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='PT 160 AA TV closes MV closes 140 A PV opens 140 AV opens PV closes AV closes TVopens MV opens Volume 100 Volume 100 80 80 60 60 40 40 20 20 0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='8 0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='8 time [s] time [s]Biomarker In silico Physiological values Peak MV velocity [m/s] 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='03 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='89 ± 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='15 [123] Peak AV velocity [m/s] 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='21 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='07 ± 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='18 [126] Peak TV velocity [m/s] 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='45 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='48 ± 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='11 [127] Peak PV velocity [m/s] 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='15 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='80 to 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='20 [128] Mean LA pressure [mmHg] 13.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='3 2 to 12 [129] Peak LV pressure [mmHg] 111 119 ± 13 [121] Mean RA pressure [mmHg] 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='33 0 to 8 [129] Peak RV pressure [mmHg] 37.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='0 35 ± 11 [122] Table 5: Fluid dynamics biomarkers obtained with the whole-heart CFD simulation (normal ranges or mean ± standard deviation).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In silico values are computed by averaging fluid properties in spherical control volumes located between the valve leaflets (velocities) and in the heart chambers (pressures).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Figure 11: Pressure computed in control volumes located in RA, RV, PT (left) and LA, LV, AA (right) during a representative heartbeat.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Isovolumetric phases are not considered, since the intraventricular pressure is not well defined when both valves are closed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 22 120 --RA 120 --LA RV LV .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='..' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='PT 100 .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='AA 100 80 80 60 60 Pressure 40 40 20 20 0 20 20 0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='8 0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='8 time [s] time [s]Figure 12: Velocity magnitudes computed in control volumes located between the valve leaflets: TV, PV (left) and MV, AV (right).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 23 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 MV PV AV [s / u] 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='8 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 0 0 0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='8 0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='8 time [s] time [s](a) t = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='10 s (b) t = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='24 s (c) t = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='35 s (d) t = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='55 s (e) t = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='70 s (f) t = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='80 s |u| [m/s] Figure 13: Volume rendering of velocity magnitude in different frames during the cardiac cycle: (a) diastolic a-wave peak, (b) isovolumetric contraction, (c) systolic peak, (d) mid systolic deceleration, (e) isovolumetric relaxation, (f) diastolic a-wave peak.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 24 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='1 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='3 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='5 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='7 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='8 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='9 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='0To better investigate blood flow patterns in the whole heart, we report the streamlines colored according to velocity magnitude in different chambers in Figure 14.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We compare our in silico results with the MRI phase-velocity mapping visualization provided in Figure 1 of reference [125], and we found a good accordance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Specifically, Figure 14a shows the rotation of the blood in the RA as the chamber expands and the blood flows from the inferior and superior vena cava.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Similarly, on the left side, the blood flows from the pulmonary veins to the expanding LA producing collision of blood jets and redirecting the flow towards the closed MV (see Figure 14b).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' During E-wave, as we show in Figure 14c, asymmetric recirculation is observed: shear layers roll through MV leaflets producing an O-vortex, as also seen in [1].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The counter-clockwise vortex under the posterior leaflet quickly disappears, and the clockwise vortex becomes larger and larger producing a clockwise jet, as described in [124], and clearly observed in Figure 14d.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 25 (a) Right atrium t = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='6 s (b) Left atrium t = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='6 s |u| [m/s] (c) Left ventricle t = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='775 s (d) Left heart t = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='8 s |u| [m/s] Figure 14: Streamlines colored according to velocity magnitude at different locations in the heart at different times: (a) right atrium during ventricular systole, (b) let atrium during ventricular systole, (c) clockwise and counter-clockwise vortices in the left ventricle during early diastole, (d) formation of clockwise jet in the left ventricle.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 26 2个0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='000.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='050.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='100.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='150.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='200.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='250.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='300.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='20 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='40 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='60 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='80 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='004.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4 Application to a pathological scenario: Left Bundle Branch Block effects on hemodynamics In this section, we apply our multiphysics computational model to investigate the hemodynamic consequences of the LBBB.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' This heart condition is commonly associated to an electrophysiological abnormality;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' however, it implies a cascade of adverse events due to the interaction among different physical processes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' LBBB consists in a slow or even absent conduction through the left bundle branch, causing a dyssynchronous contraction and relaxation of the left ventricle.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Moreover, the LV dyssynchrony may have profound consequences on the heart hemodynamics, influencing flow patterns and, in turn, triggering heart remodeling [130, 131].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' To simulate LBBB with our EM model, we deactivate the impulse sites in the LV and in the septum (see Figure 15), so that the signal is generated by the SAN and the RVm sites solely.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Our modeling choice is consistent with the work of [132], where a severe case of LBBB is accounted for by activating only one site in the RV free wall.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Figure 16 displays volumes and their derivatives with respect to time for left and right ventricles, in both physiological and LBBB conditions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The electrical dyssynchrony between left and right parts produces a delay in the LV ejection and filling stages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Differently, no significant differences are observed in the RV volumes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Furthermore, compared to the physiological case (see Table 4) and consistently with [130], we measured reduced ejection fractions both in the right (53.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='87%) and left (55.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='39%) ventricles.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' By means of our whole-heart EM driven CFD simulation, we can quantify how the pathology affects the endocardial wall stress.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Let τ(u) = 2µϵ(u) be the viscous stress tensor, we compute the wall shear stress vector as WSS(u) = τ(u)n − (τ(u)n · n)n on ∂Ω0 × (0, T).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The time averaged wall shear stress (TAWSS) is then defined as TAWSS(u) = 1 T � T 0 |WSS(u)| dt on ∂Ω0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Figure 17 shows the TAWSS in the right and left heart in physiological conditions and under LBBB.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We notice that the TAWSS distribution is almost unchanged for the right heart.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Conversely, we find that LBBB alters the wall shear stress in correspondence of the LV septum, suggesting the potential occurrence of remodeling phenomena.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In Figure 18, we report the minimum, maximum and average WSS in the LV septum against time, for the physiological and LBBB simulations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' During the ejection, the WSS values are similar, while significant differences are present during the filling phase.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' As a matter of fact, under LBBB, the space-averaged WSS peak is 27.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4% higher than the physiological case (see Figure 18, right).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' This is consistent with the work of Eriksson et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' (2017), where they observed, by means of 4Dflow MRI data, that LV dyssynchronous motion influences blood flow patterns during diastole, contributing to the development of cardiac remodeling [131].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 27 (a) physiological (b) LBBB Activation time [ms] Figure 15: Activation maps and impulse sites (yellow spheres) in EM simulations;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' (a) physiological;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' (b) LBBB.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Figure 16: Ventricular volumes and ventricular volume derivatives for physiological and LBBB simulations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 28 0 50 100 150 200 250 320150 150 physiological physiological 宜 LBBB [ml] LBBB 100 100 50 50 0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='8 0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='8 t [s] t [s] 500 physiological 500 physiological LBBB LBBB 1P/AAP 0 P/ATAP 0 500 500 0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='8 0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='8 t [s] t [s](a) physiological, RH (b) LBBB, RH (c) physiological, LH (d) LBBB, LH TAWSS [Pa] Figure 17: TAWSS of the whole heart in physiological and pathological conditions for the LH and RH.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Results obtained with whole-heart EM-driven CFD simulations, the left and right parts are separated for visualization purposes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The largest differences are observed in the LV septum, as highlighted by the black arrow.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Figure 18: WSS in the LV septum over time.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Maximum, average and minimum in physiological conditions (left);' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' maximum, average and minimum in LBBB conditions (center);' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' comparison be- tween physiological and pathological conditions in terms of average values (right).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The WSS is computed in the red portion shown on the right.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In terms of maximum values, the pathological peak is 12.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='7% larger than the physiological case (cf.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' left and center figures).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The average WSS peak increases of 27.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4% (right figure).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 29 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='000.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='050.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='100.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='150.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='200.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='250.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='300.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='350.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='400.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='450.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='50MV closes AV opens AV closes MV opensPhysiological LBBB Physiological vs LBBB 2 2 2 max max physiological (avg) avg avg LBBB (avg) min septum septum septum LV LV .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='8 .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='8 .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='8 WSS S IsSI 0 0 0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='8 0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='8 0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='8 t [s] t [s] t [s]5 Limitations and further developments We discuss some limitations of our study.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The computational model introduced cannot fully represent the isovolumetric phases in terms of pressure.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Indeed, when both valves are closed and the ventricles are contracting/relaxing at constant volumes, the pressure is not well-defined, and thus prone to spurious oscillations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' This is due to the fact that we are using kinematic conditions only in all the ventricular boundaries.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Indeed, we prescribe the EM displacement on the endocardium and endothelium, and we model valves with the RIIS method using a penalty- based kinematic condition.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The oscillatory pressure during such phases does not influence the velocity field.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' However, it prevents from using the simulated pressure values to choose when to open and close the valves, forcing hence to prescribe a-priori opening and closing times.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The use of bidirectionally coupled FSI models for the blood-myocardium or blood-valve systems (or for both of them) may allow to correctly capture the pressure transient during these phases, as seen for instance in [42] where a fully-coupled electro-mechano-fluid of the heart is considered.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Moreover, we noticed that the ejection phase is too slow and the ventricular passive filling too fast, if compared with medical literature values.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Consequently, E-wave and A-wave are character- ized by comparable amplitudes, whereas the EA ratio should be approximately equal to 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='30 ± 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='570 [123].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In this respect, we believe that the use of ionic models with a more realistic decrease of calcium concentration is essential to better capture these phenomena.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Finally, we applied the computational model to a realistic, templated heart geometry.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' In order to move towards the realization of whole-heart digital twins, further developments should involve patient-specific cardiac simulations, accompanied with a stringent process of data assimilation, model validation and uncertainty quantification.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 6 Conclusions In this paper, we introduced a computational model for the hemodynamics simulation of the whole human heart accounting for the main features affecting the intracardiac flows.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We considered a realistic whole-heart geometry, and we employed a four-chamber 3D-0D electromechanical model to provide the displacement as input to the cardiac CFD model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We modelled the effect of cardiac valves in the fluid via a resistive immersed method and we accounted for transition-to-turbulence regime through the VMS-LES method.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Moreover, for the first time, we coupled the 3D CFD model of the whole heart to the surrounding closed-loop circulation, to get a geometric multiscale 3D-0D hemodynamic model of the entire cardiovascular system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We solved our multiphysics and multiscale computational model using our in-house finite element library lifex.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We introduced a calibration of the activation model, driving the electromechanical simulation, aimed at obtaining physiological realistic flowrates, and consequently blood velocities, in the CFD simulation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Our calibration highlights the effect of the parameters of the active force generation model, associated to the microscopic features of its kinematics and of the force-velocity relationship, on the macroscopic heartbeat indicators.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' We carried out EM-driven CFD simulation on a realistic whole-heart geometry and we showed that the computational model can correctly reproduce blood velocities and pressure traces when we compare the results with clinical ranges from medical literature.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Furthermore, we found that the computational model captures typical blood flow patterns observed in MRI phase-velocity mapping visualizations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Finally, we applied the whole-heart model to simulate the pathological scenario of Left Bundle 30 Branch Block: we correctly predicted the electrical delay, the consequent mechanical dyssynchrony, a reduced ejection fraction, and an increasing wall shear stress in the left ventricular septum during the filling stage.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Overall, this study confirms that the interaction of different physics in a high- fidelity integrated whole-heart model is essential for simulating the cardiac function, allowing to faithfully capture pathological events occurring at different physical levels.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' A The open 0D circulation model The closed-loop lumped-parameter (0D) circulation model that we employ was proposed in [59] and inspired by [51, 133].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' To couple the 0D model to the 3D model of the heart, we follow the same steps presented in [26].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Specifically,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' the open 0D system we get reads as follows: for any t ∈ (0,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' T),' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' dpSYS AR (t) dt = 1 CSYS AR � QAV(t) − QSYS AR (t) � ,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' (7a) dpSYS VEN(t) dt = 1 CSYS VEN � QSYS AR (t) − QSYS VEN(t) � ,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' (7b) dpPUL AR (t) dt = 1 CPUL AR � QPV(t) − QPUL AR (t) � ,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' (7c) dpPUL VEN(t) dt = 1 CPUL VEN � QPUL AR (t) − QVEN,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 3D PUL (t) � ,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' (7d) dQSYS AR (t) dt = RSYS AR LSYS AR � −QSYS AR (t) − pSYS VEN(t) − pSYS AR (t) RSYS AR � ,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' (7e) dQPUL AR (t) dt = RPUL AR LPUL AR � −QPUL AR (t) − pPUL VEN(t) − pPUL AR (t) RPUL AR � ,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' (7f) solved with suitable initial conditions,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' and pin LA(t) = pPUL VEN(t) − RPUL VENQPUL VEN(t) − LPUL VEN dQPUL,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 3D VEN (t) dt ,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' (8a) pin RA(t) = pSYS VEN(t) − RSYS VENQSYS VEN(t) − LSYS VEN dQSYS,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 3D VEN (t) dt .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' (8b) B Setup of EM and CFD simulations We report in this section the values of the parameters used in the EM and CFD simulations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Table 6 reports the parameters for the circulation model used in the EM simulation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' With respect to the model presented in [58], we introduce two additional resistance elements (RSYS upstream and RPUL upstream) and two inductance elements (LAV, LPV, see Figure 1c).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' They have the purpose of making the 0D circulation model, which surrogates the fluid dynamics, as similar as possible to the 3D CFD problem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' For the same reason, the minimum resistances of the non-ideal diodes representing the AV and PV are increased with respect to [58].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 31 The calibration of the RDQ20 activation model is extensively discussed in Section 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2, and the corresponding parameter values are reported in Table 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' As reference sarcomere length, we set SL0 = 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 µm.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' For all other parameters in the EM model, we use the same values as those of the baseline simulation of [58].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Table 7 reports the values of the initial circulation states for the CFD simulation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' They are taken equal to the values reached at the beginning of the last heartbeat in the EM simulation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' All resistance, capacitance and inductance parameters are the same as in the EM model (see Table 6).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 32 Parameter Value Systemic arteries RSYS AR 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='48 mmHg s/ml CSYS AR 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='50 ml/mmHg LSYS AR 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='005 mmHg s2/ml RSYS upstream 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='048 mmHg s/ml pSYS AR, 0 83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='9 mmHg QSYS AR, 0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='0 ml/s Systemic veins RSYS VEN 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='26 mmHg s/ml CSYS VEN 60 ml/mmHg LSYS VEN 5 · 10−4 mmHg s2/ml pSYS VEN, 0 35.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='5 mmHg QSYS VEN, 0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='0 ml/s Pulmonary arteries RPUL AR 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='032116 mmHg s/ml CPUL AR 10 ml/mmHg LPUL AR 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='0005 mmHg s2/ml RPUL upstream 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='0032116 mmHg s/ml pPUL AR, 0 14.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='90 mmHg QPUL AR, 0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='0 ml/s Pulmonary veins RPUL VEN 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='035684 mmHg s/ml CPUL VEN 16 ml/mmHg LPUL VEN 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='0005 mmHg s2/ml pPUL VEN, 0 13.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='58 mmHg QPUL VEN, 0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='0 ml/s Valves RMV min 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='0075 mmHg s/ml RAV min 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='0355 mmHg s/ml RTV min 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='0075 mmHg s/ml RPV min 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='0184 mmHg s/ml RMV max, RAV max, RTV max, RPV max 75006.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2 mmHg s/ml LAV, LPV 5 · 10−4 mmHg s2/ml Table 6: Parameters and initial conditions of the circulation model used for the EM simulation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 33 Parameter Value Systemic arteries pSYS AR, 0 86.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='3480 mmHg QSYS AR, 0 109.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='6429 ml/s Systemic veins pSYS VEN, 0 34.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='4923 mmHg QSYS VEN, 0 112.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='9209 ml/s Pulmonary arteries pPUL AR, 0 22.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2310 mmHg QPUL AR, 0 83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2132 ml/s Pulmonary veins pPUL VEN, 0 19.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='5813 mmHg QPUL VEN, 0 262.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='6397 ml/s Table 7: Initial states of the circulation model for the CFD simulation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The values are equal to those of the circulation state variables at the beginning of the last heartbeat in the EM simulation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' All remaining circulation parameters (resistances, capacitances, inductances) are the same as in the EM model (see Table 6).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 34 Acknowledgments AZ, LD and AQ received funding from the Italian Ministry of University and Research (MIUR) within the PRIN (Research projects of relevant national interest) 2017 “Modeling the heart across the scales: from cardiac cells to the whole organ” Grant Registration number 2017AXL54F).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' MB, RP, FR, LD and AQ acknowledge the ERC Advanced Grant iHEART, “An Integrated Heart Model for the simulation of the cardiac function”, 2017–2023, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni (ERC–2016– ADG, project ID: 740132).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' The authors of this work are members of the INdAM group GNCS “Gruppo Nazionale per il Calcolo Scientifico” (National Group for Scientific Computing).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Finally, we acknowledge the CINECA award under the ISCRA initiative, for the availability of high performance computing resources and support under the projects IsC87 MCH, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Zingaro, 2021-2022 and IsB25 MathBeat, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, 2021-2022.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' References 1C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Chnafa, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Mendez, and F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Nicoud, “Image-based large-eddy simulation in a realistic left heart”, Computers & Fluids 94, 173–187 (2014).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 2A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' This, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Boilevin-Kayl, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Fern´andez, and J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='-F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Gerbeau, “Augmented resistive immersed surfaces valve model for the simulation of cardiac hemodynamics with isovolumetric phases”, International Journal for Numerical Methods in Biomedical Engineering 36, e3223 (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 3A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Tagliabue, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Dede’, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, “Complex blood flow patterns in an idealized left ventricle: a numerical study”, Chaos: An Interdisciplinary Journal of Nonlinear Science 27, 093939 (2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 4A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Tagliabue, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Dede’, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, “Fluid dynamics of an idealized left ventricle: the extended nitsche’s method for the treatment of heart valves as mixed time varying boundary conditions”, International Journal for Numerical Methods in Fluids 85, 135–164 (2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 5I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Fumagalli, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Fedele, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Vergara, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Dede’, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Ippolito, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Nicol`o, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Antona, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Scrofani, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, “An image-based computational hemodynamics study of the systolic anterior motion of the mitral valve”, Computers in Biology and Medicine 123, 103922 (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 6E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Karabelas, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Gsell, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Augustin, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Marx, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Neic, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Prassl, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Goubergrits, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Kuehne, and G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Plank, “Towards a computational framework for modeling the impact of aortic coarctations upon left ventricular load”, Frontiers in Physiology 9, 538 (2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 7A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' This, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Morales, O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Bonnefous, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Fern´andez, and J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='-F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Gerbeau, “A pipeline for image based intracardiac CFD modeling and application to the evaluation of the PISA method”, Computer Methods in Applied Mechanics and Engineering 358, 112627 (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 35 erc HEART erc POLITECNICO European Research Council MILANO1863 Established by the European Commission8A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Masci, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Alessandrini, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Forti, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Menghini, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Dede’, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Tomasi, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, and C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Corsi, “A proof of concept for computational fluid dynamic analysis of the left atrium in atrial fibrillation on a patient-specific basis”, Journal of Biomechanical Engineering 142 (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 9F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Viola, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Meschini, and R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Verzicco, “Fluid–Structure-Electrophysiology interaction (FSEI) in the left-heart: a multi-way coupled computational model”, European Journal of Mechanics- B/Fluids 79, 212–232 (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 10J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Mangual, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Kraigher-Krainer, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' De Luca, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Toncelli, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Shah, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Solomon, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Galanti, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Domenichini, and G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Pedrizzetti, “Comparative numerical study on left ventricular fluid dynamics after dilated cardiomyopathy”, Journal of Biomechanics 46, 1611–1617 (2013).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 11X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Zheng, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Seo, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Vedula, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Abraham, and R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Mittal, “Computational modeling and analysis of intracardiac flows in simple models of the left ventricle”, European Journal of Mechanics- B/Fluids 35, 31–39 (2012).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 12J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Seo, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Vedula, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Abraham, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Lardo, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Dawoud, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Luo, and R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Mittal, “Effect of the mitral valve on diastolic flow patterns”, Physics of Fluids 26, 121901 (2014).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 13J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Seo and R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Mittal, “Effect of diastolic flow patterns on the function of the left ventricle”, Physics of Fluids 25, 110801 (2013).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 14A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Masci, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Alessandrini, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Forti, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Menghini, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Dede’, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Tommasi, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, and C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Corsi, “A patient-specific computational fluid dynamics model of the left atrium in atrial fib- rillation: development and initial evaluation”, in International conference on functional imaging and modeling of the heart (Springer, 2017), pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 392–400.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 15A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Masci, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Barone, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Ded`e, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Fedele, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Tomasi, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, and C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Corsi, “The im- pact of left atrium appendage morphology on stroke risk assessment in atrial fibrillation: a computational fluid dynamics study”, Frontiers in Physiology 9, 1938 (2019).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 16A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Zingaro, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Dede’, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Menghini, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, “Hemodynamics of the heart’s left atrium based on a variational multiscale-les numerical method”, European Journal of Mechanics- B/Fluids 89, 380–400 (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 17M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Corti, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Dede’, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Zingaro, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, “Impact of atrial fibrillation on left atrium haemodynamics: a computational fluid dynamics study”, Computers in Biology and Medicine, 106143 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 18V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Vedula, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' George, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Younes, and R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Mittal, “Hemodynamics in the left atrium and its effect on ventricular flow patterns”, Journal of Biomechanical Engineering 137 (2015).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 19R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Koizumi, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Funamoto, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Hayase, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Kanke, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Shibata, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Shiraishi, and T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Yambe, “Nu- merical analysis of hemodynamic changes in the left atrium due to atrial fibrillation”, Journal of Biomechanics 48, 472–478 (2015).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 20D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Dillon-Murphy, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Marlevi, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Ruijsink, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Qureshi, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Chubb, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Kerfoot, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' O’Neill, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Nordsletten, O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Aslanidi, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' De Vecchi, “Modeling left atrial flow, energy, blood heating distribution in response to catheter ablation therapy”, Frontiers in Physiology 9, 1757 (2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 21G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Bosi, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Cook, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Rai, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Menezes, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Schievano, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Torii, and G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Burriesci, “Computa- tional fluid dynamic analysis of the left atrial appendage to predict thrombosis risk”, Frontiers in Cardiovascular Medicine 5, 34 (2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 36 22O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Mazumder, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Gupta, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Roy, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Sinha, “Computational fluid dynamic model of left atrium to analyze hemodynamic manifestation during atrial fibrillation”, in 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (IEEE, 2022), pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 3967–3971.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 23F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Viola, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Meschini, and R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Verzicco, “Effects of stenotic aortic valve on the left heart hemody- namics: a fluid-structure-electrophysiology approach”, arXiv preprint arXiv:2103.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='14680 (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 24F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Viola, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Spandan, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Meschini, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Romero, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Fatica, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' de Tullio, and R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Verzicco, “FSEI-GPU: GPU accelerated simulations of the fluid–structure–electrophysiology interaction in the left heart”, Computer Physics Communications 273, 108248 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 25L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Dede’, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Menghini, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, “Computational fluid dynamics of blood flow in an idealized left human heart”, International Journal for Numerical Methods in Biomedical Engineering 37, e3287 (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 26A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Zingaro, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Fumagalli, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Dede’, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Fedele, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Africa, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Corno, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, “A geometric multiscale model for the numerical simulation of blood flow in the human left heart”, Discrete and Continous Dynamical System - S 15, 2391–2427 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 27H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Wiputra, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Lai, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Lim, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Heng, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Guo, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Soomar, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Leo, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Biwas, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Mattar, and C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Yap, “Fluid mechanics of human fetal right ventricles from image- based computational fluid dynamics using 4d clinical ultrasound scans”, American Journal of Physiology-Heart and Circulatory Physiology 311, H1498–H1508 (2016).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 28D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Collia, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Zovatto, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Tonti, and G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Pedrizzetti, “Comparative analysis of right ventricle fluid dynamics”, Frontiers in Bioengineering and Biotechnology 9 (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 29J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Mangual, F Domenichini, and G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Pedrizzetti, “Describing the highly three dimensional right ventricle flow”, Annals of Biomedical Engineering 40, 1790–1801 (2012).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 30V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Mihalef, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Ionasec, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Sharma, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Georgescu, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Voigt, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Suehling, and D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Comaniciu, “Patient-specific modelling of whole heart anatomy, dynamics and haemodynamics from four- dimensional cardiac CT images”, Interface Focus 1, 286–296 (2011).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 31J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='-i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Okada, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Washio, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Sugiura, and T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Hisada, “Clinical and pharmacological application of multiscale multiphysics heart simulator, UT-heart”, The Korean Journal of Physiology & Pharmacology 23, 295–303 (2019).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 32M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Peirlinck, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Costabal, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Yao, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Guccione, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Tripathy, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Wang, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Ozturk, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Segars, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Morrison, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Levine, and E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Kuhl, “Precision medicine in human heart modeling”, Biomechanics and modeling in mechanobiology 20, 803–831 (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 33J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Brenneisen, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Daub, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Gerach, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Kovacheva, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Huetter, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Frohnapfel, O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' D¨ossel, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Loewe, “Sequential coupling shows minor effects of fluid dynamics on myocardial deformation in a realistic whole-heart model”, Frontiers in Cardiovascular Medicine, 1967 (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 34E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Karabelas, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Longobardi, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Fuchsberger, O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Razeghi, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Rodero, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Strocchi, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Rajani, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Haase, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Plank, and S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Niederer, “Global sensitivity analysis of four chamber heart hemo- dynamics using surrogate models”, IEEE Transactions on Biomedical Engineering (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 35F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Viola, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Del Corso, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' De Paulis, and R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Verzicco, “GPU accelerated digital twins of the human heart open new routes for cardiovascular research”, Research Square (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 36J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Fuchsberger, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Aigner, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Niederer, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Plank, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Schima, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Haase, and E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Karabelas, “On the incorporation of obstacles in a fluid flow problem using a navier–stokes–brinkman penalization approach”, Journal of Computational Science 57, 101506 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 37 37A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Dede’, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Manzoni, and C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Vergara, Mathematical modelling of the human cardiovascular system: data, numerical approximation, clinical applications, Vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 33 (Cambridge University Press, 2019).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 38Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Choi, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Constantino, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Vedula, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Trayanova, and R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Mittal, “A new mri-based model of heart function with coupled hemodynamics and application to normal and diseased canine left ventricles”, Frontiers in bioengineering and biotechnology 3, 140 (2015).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 39A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Santiago, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Aguado-Sierra, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Zavala-Ak´e, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Doste-Beltran, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' G´omez, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Ar´ıs, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Cajas, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Casoni, and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' V´azquez, “Fully coupled fluid-electro-mechanical model of the human heart for supercomputers”, International Journal for Numerical Methods in Biomedical Engineering 34, e3140 (2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 40A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Santiago, “Fluid-electro-mechanical model of the human heart for supercomputers”, PhD thesis (Universitat Polit`ecnica de Catalunya, 2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 41A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Gerbi, “Numerical approximation of cardiac electro-fluid-mechanical models: coupling strate- gies for large-scale simulation”, PhD thesis (Ecole Polytechnique F´ed´erale de Lausanne, 2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 42M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Bucelli, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Zingaro, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Africa, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Fumagalli, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Dede’, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, “A math- ematical model that integrates cardiac electrophysiology, mechanics and fluid dynamics: appli- cation to the human left heart”, International Journal for Numerical Methods in Biomedical Engineering, e3678 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 43F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Domenichini, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Pedrizzetti, and B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Baccani, “Three-dimensional filling flow into a model left ventricle”, Journal of fluid mechanics 539, 179–198 (2005).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 44B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Baccani, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Domenichini, and G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Pedrizzetti, “Vortex dynamics in a model left ventricle during filling”, European Journal of Mechanics-B/Fluids 21, 527–543 (2002).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 45A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Santiago, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Butakoff, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Eguzkitza, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Gray, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' May-Newman, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Pathmanathan, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Vu, and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' V´azquez, “Design and execution of a verification, validation, and uncertainty quan- tification plan for a numerical model of left ventricular flow after lvad implantation”, PLoS computational biology 18, e1010141 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 46C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Augustin, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Crozier, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Neic, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Prassl, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Karabelas, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Ferreira da Silva, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Fernandes, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Campos, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Kuehne, and G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Plank, “Patient-specific modeling of left ventricular electromechanics as a driver for haemodynamic analysis”, EP Europace 18, iv121–iv129 (2016).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 47A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Zingaro, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Bucelli, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Fumagalli, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Dede’, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, “Modeling isovolumetric phases in cardiac flows by an augmented resistive immersed implicit surface method”, arXiv preprint arXiv:2208.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='09435 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 48A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Zingaro, “Mathematical and numerical models for the fluid dynamics of the human heart”, PhD thesis (Politecnico di Milano, 2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 49A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' This, “Image/model fusion for the quantification of mitral regurgitation severity”, PhD thesis (Sorbonne universit´e, 2019).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 50A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Veneziani, and C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Vergara, “Geometric multiscale modeling of the cardio- vascular system, between theory and practice”, Computer Methods in Applied Mechanics and Engineering 302, 193–252 (2016).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 51P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Blanco and R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Feij´oo, “A 3d-1d-0d computational model for the entire cardiovascular system”, Mec´anica Computacional 29, 5887–5911 (2010).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 38 52Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Shi and T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Korakianitis, “Numerical simulation of cardiovascular dynamics with left heart failure and in-series pulsatile ventricular assist device”, Artificial Organs 30, 929–948 (2006).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 53V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Miliˇsi´c and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, “Analysis of lumped parameter models for blood flow simulations and their relation with 1d models”, ESAIM: Mathematical modelling and numerical analysis 38, 613–632 (2004).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 54H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Kim, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Vignon-Clementel, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Figueroa, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' LaDisa, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Jansen, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Feinstein, and C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Taylor, “On coupling a lumped parameter heart model and a three-dimensional finite element aorta model”, Annals of biomedical engineering 37, 2153–2169 (2009).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 55F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Van de Vosse and N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Stergiopulos, “Pulse wave propagation in the arterial tree”, Annual Review of Fluid Mechanics 43, 467–499 (2011).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 56L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Formaggia, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Lamponi, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, “One-dimensional models for blood flow in arteries”, Journal of engineering mathematics 47, 251–276 (2003).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 57L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Formaggia, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='-F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Gerbeau, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Nobile, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, “On the coupling of 3d and 1d navier–stokes equations for flow problems in compliant vessels”, Computer Methods in Applied Mechanics and Engineering 191, 561–582 (2001).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 58M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Fedele, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Piersanti, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Regazzoni, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Salvador, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Africa, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Bucelli, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Zingaro, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Dede’, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, “A comprehensive and biophysically detailed computational model of the whole human heart electromechanics”, arXiv preprint arXiv:2207.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='12460 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 59F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Regazzoni, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Salvador, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Africa, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Fedele, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Ded`e, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, “A cardiac elec- tromechanical model coupled with a lumped-parameter model for closed-loop blood circulation”, Journal of Computational Physics 457, 111083 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 60C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Augustin, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Gsell, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Karabelas, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Willemen, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Prinzen, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Lumens, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Vig- mond, and G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Plank, “A computationally efficient physiologically comprehensive 3d–0d closed- loop model of the heart and circulation”, Computer Methods in Applied Mechanics and Engi- neering 386, 114092 (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 61F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Marcinno’, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Zingaro, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Fumagalli, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Dede, and C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Vergara, “A computational study of blood flow dynamics in the pulmonary arteries”, Vietnam Journal of Mathematics, 1–23 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 62X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Ma, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Gao, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Griffith, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Berry, and X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Luo, “Image-based fluid–structure interaction model of the human mitral valve”, Computers & Fluids 71, 417–425 (2013).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 63K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Kunzelman, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Einstein, and R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Cochran, “Fluid–structure interaction models of the mitral valve: function in normal and pathological states”, Philosophical Transactions of the Royal Society B: Biological Sciences 362, 1393–1406 (2007).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 64B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Su, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Zhong, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='-K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Wang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='-M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Zhang, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' San Tan, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Allen, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Tan, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Kim, and H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Leo, “Numerical simulation of patient-specific left ventricular model with both mitral and aortic valves by FSI approach”, Computer methods and programs in biomedicine 113, 474–482 (2014).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 65H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Gao, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Feng, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Qi, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Berry, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Griffith, and X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Luo, “A coupled mitral valve—left ventricle model with fluid–structure interaction”, Medical Engineering & Physics 47, 128–136 (2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 66J De Hart, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Peters, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Schreurs, and F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Baaijens, “A three-dimensional computational analysis of fluid–structure interaction in the aortic valve”, Journal of Biomechanics 36, 103–112 (2003).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 39 67C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Carmody, G Burriesci, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Howard, and E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Patterson, “An approach to the simulation of fluid–structure interaction in the aortic valve”, Journal of Biomechanics 39, 158–169 (2006).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 68D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Oks, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' V´azquez, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Houzeaux, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Butakoff, and C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Samaniego, “Fluid-structure interaction analysis of eccentricity and leaflet rigidity on thrombosis biomarkers in bioprosthetic aortic valve replacements”, International Journal for Numerical Methods in Biomedical Engineering, e3649 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 69J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Hiromi Sp¨uhler and J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Hoffman, “An interface-tracking unified continuum model for fluid- structure interaction with topology change and full-friction contact with application to aortic valves”, International Journal for Numerical Methods in Engineering 122, 5258–5278 (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 70J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Sp¨uhler, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Jansson, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Jansson, and J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Hoffman, “3d fluid-structure interaction simulation of aortic valves using a unified continuum ale fem model”, Frontiers in Physiology 9, 363 (2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 71M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Fedele, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Faggiano, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Dede’, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, “A patient-specific aortic valve model based on moving resistive immersed implicit surfaces”, Biomechanics and Modeling in Mechanobiology 16, 1779–1803 (2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 72M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Fern´andez, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='-F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Gerbeau, and V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Martin, “Numerical simulation of blood flows through a porous interface”, ESAIM: Mathematical Modelling and Numerical Analysis 42, 961–990 (2008).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 73M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Astorino, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Hamers, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Shadden, and J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='-F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Gerbeau, “A robust and efficient valve model based on resistive immersed surfaces”, International Journal for Numerical Methods in Biomed- ical Engineering 28, 937–959 (2012).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 74A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' This, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Boilevin-Kayl, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Fern´andez, and J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='-F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Gerbeau, “Augmented resistive immersed surfaces valve model for the simulation of cardiac hemodynamics with isovolumetric phases”, International Journal for Numerical Methods in Biomedical Engineering 36, 10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='1002/cnm.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='3223.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 75F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Regazzoni, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Ded`e, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, “Biophysically detailed mathematical models of multiscale cardiac active mechanics”, PLOS Computational Biology 16, e1008294 (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 76N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Tan, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Witt, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Oh, and Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='-M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Cha, “Left bundle branch block: current and future perspectives”, Circulation: Arrhythmia and Electrophysiology 13, e008239 (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 77R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Piersanti, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Regazzoni, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Salvador, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Corno, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Dede’, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Vergara, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quar- teroni, “3D–0D closed-loop model for the simulation of cardiac biventricular electromechanics”, Computer Methods in Applied Mechanics and Engineering 391, 114607 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 78R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Piersanti, “Mathematical and Numerical Modeling of Cardiac Fiber Generation and Elec- tromechanical Function : Towards a Realistic Simulation of the Whole Heart”, PhD thesis (Politecnico di Milano, 2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 79R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Piersanti, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Africa, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Fedele, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Vergara, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Ded`e, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Corno, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, “Modeling cardiac muscle fibers in ventricular and atrial electrophysiology simulations”, Com- puter Methods in Applied Mechanics and Engineering 373, 113468 (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 80R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Doste, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Soto-Iglesias, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Bernardino, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Alcaine, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Sebastian, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Giffard-Roisin, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Ser- mesant, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Berruezo, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Sanchez-Quintana, and O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Camara, “A rule-based method to model myocardial fiber orientation in cardiac biventricular geometries with outflow tracts”, Interna- tional Journal for Numerical Methods in Biomedical Engineering 35, e3185 (2019).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 81P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Franzone, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Pavarino, and S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Scacchi, Mathematical cardiac electrophysiology, Vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 13 (Springer, 2014).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 40 82K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' ten Tusscher and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Panfilov, “Alternans and spiral breakup in a human ventricular tissue model”, American Journal of Physiology.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Heart and Circulatory Physiology 291, 1088– 1100 (2006).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 83M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Courtemanche, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Ramirez, and S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Nattel, “Ionic mechanisms underlying human atrial ac- tion potential properties: insights from a mathematical model”, American Journal of Physiology- Heart and Circulatory Physiology 275, H301–H321 (1998).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 84A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Lee, U.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Nguyen, O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Razeghi, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Gould, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Sidhu, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Sieniewicz, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Behar, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Mafi-Rad, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Plank, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Prinzen, et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=', “A rule-based method for predicting the electrical activation of the heart with cardiac resynchronization therapy from non-invasive clinical data”, Medical Image Analysis 57, 197–213 (2019).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 85F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Mazhar, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Regazzoni, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Bartolucci, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Corsi, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Dede’, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, and S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Severi, “Electro-Mechanical Coupling in Human Atrial Cardiomyocytes: Model Development and Anal- ysis of Inotropic Interventions”, in 2021 computing in cardiology (cinc), Vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 2021-Septe (Sept.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 2021), pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 1–4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 86F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Regazzoni, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Ded`e, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, “Machine learning of multiscale active force gen- eration models for the efficient simulation of cardiac electromechanics”, Computer Methods in Applied Mechanics and Engineering 370, 113268 (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 87R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Ogden, Non-linear elastic deformations (Dover Publications, 1997).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 88D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Guan, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Yao, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Luo, and H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Gao, “Effect of myofibre architecture on ventricular pump function by using a neonatal porcine heart model: from DT-MRI to rule-based methods”, Royal Society Open Science 7, 191655 (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 89T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Usyk, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' LeGrice, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' McCulloch, “Computational model of three-dimensional cardiac electromechanics”, Computing and Visualization in Science 4, 249–257 (2002).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 90M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Salvador, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Regazzoni, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Pagani, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Dede’, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Trayanova, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, “The role of mechano-electric feedbacks and hemodynamic coupling in scar-related ventricular tachycardia”, Computers in Biology and Medicine 142, 105203 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 91F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Regazzoni, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Ded`e, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, “Active force generation in cardiac muscle cells: mathematical modeling and numerical simulation of the actin-myosin interaction”, Vietnam Journal of Mathematics 49, 87–118 (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 92H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Jasak and Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Tukovic, “Automatic mesh motion for the unstructured finite volume method”, Transactions of FAMENA 30, 1–20 (2006).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 93C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' De Boor, A practical guide to splines, Vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 27 (springer-verlag New York, 1978).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 94J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Janela, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Moura, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Sequeira, “Absorbing boundary conditions for a 3D non-Newtonian fluid–structure interaction model for blood flow in arteries”, International Journal of Engineering Science 48, 1332–1349 (2010).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 95A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Lassila, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Rossi, and R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Ruiz-Baier, “Integrated heart—coupling multiscale and multiphysics models for the simulation of the cardiac function”, Computer Methods in Applied Mechanics and Engineering 314, 345–407 (2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 96A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, Numerical models for differential problems, Vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 16 (Springer, 2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 97F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Regazzoni and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, “An oscillation-free fully partitioned scheme for the numerical modeling of cardiac active mechanics”, Computer Methods in Applied Mechanics and Engineer- ing 373, 113506 (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 41 98F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Regazzoni, “Stabilization of staggered time discretization schemes for 0D-3D fluid-structure interaction problems”, MOX report 17 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 99G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Rodrıguez, “Smoothing and non-parametric regression”, Princeton University (2001).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 100D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Forti and L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Ded`e, “Semi-implicit BDF time discretization of the Navier–Stokes equations with VMS-LES modeling in a high performance computing framework”, Computers & Fluids 117, 168–182 (2015).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 101Y Bazilevs, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Calo, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Cottrell, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Hughes, A Reali, and G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Scovazzi, “Variational multiscale residual-based turbulence modeling for large eddy simulation of incompressible flows”, Computer Methods in Applied Mechanics and Engineering 197, 173–201 (2007).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 102C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Bertoglio and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Caiazzo, “A tangential regularization method for backflow stabilization in hemodynamics”, Journal of Computational Physics 261, 162–171 (2014).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 103S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Deparis, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Grandperrin, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, “Parallel preconditioners for the unsteady Navier–Stokes equations and applications to hemodynamics simulations”, Computers & Flu- ids 92, 253–273 (2014).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 104Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Inc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=', “Zygote solid 3D heart generation ii developement report.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' tech.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' rep.”, (2014).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 105L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Antiga, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Piccinelli, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Botti, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Ene-Iordache, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Remuzzi, and D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Steinman, “An image-based modeling framework for patient-specific computational hemodynamics”, Medical & Biological Engineering & Computing 46, 1097–1112 (2008).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 106M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Fedele and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, “Polygonal surface processing and mesh generation tools for numerical simulations of the complete cardiac function”, International Journal for Numerical Methods in Biomedical Engineering 37, e3435 (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 107P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Africa, “Lifex: a flexible, high performance library for the numerical solution of complex finite element problems”, SoftwareX 20, 101252 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 108P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Africa, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Piersanti, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Fedele, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Dede’, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Quarteroni, “Lifex–heart module: a high- performance simulator for the cardiac function package 1: fiber generation”, arXiv preprint arXiv:2201.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='03303 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 109D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Arndt, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Bangerth, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Blais, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Fehling, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Gassm¨oller, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Heister, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Heltai, U.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' K¨ocher, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Kronbichler, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Maier, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Munch, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='-P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Pelteret, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Proell, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Simon, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Turcksin, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Wells, and J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Zhang, “The deal.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='ii library, version 9.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='3”, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Numer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Math.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 29 (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 110D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Arndt, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Bangerth, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Davydov, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Heister, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Heltai, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Kronbichler, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Maier, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Pelteret, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Turcksin, and D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Wells, “The deal.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='II finite element library: design, features, and insights”, Computers & Mathematics with Applications, 10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='1016/j.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='camwa.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='02.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='022 (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 111Pulmonary acceleration time to estimate pulmonary pressure, visited: October 2022.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 112M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Crawford and C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Roldan, “Quantitative assessment of valve thickness in normal sub- jects by transesophageal echocardiography”, The American Journal of Cardiology 87, 1419–1423 (2001).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 113P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' van Ooij, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Markl, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Collins, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Carr, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Rigsby, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Bonow, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Malaisrie, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' McCarthy, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Fedak, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Barker, “Aortic valve stenosis alters expression of regional aortic wall shear stress: new insights from a 4-dimensional flow magnetic resonance imaging study of 571 subjects”, Journal of the American Heart Association 6, e005959 (2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 114W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Kriz and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Elger, “Renal anatomy”, Comprehensive clinical nephrology.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 3rd ed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Philadel- phia, 1–11 (2010).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 42 115A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Maceira, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Prasad, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Khan, and D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Pennell, “Normalized left ventricular sys- tolic and diastolic function by steady state free precession cardiovascular magnetic resonance”, Journal of Cardiovascular Magnetic Resonance 8, 417–426 (2006).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 116L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Hudsmith, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Petersen, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Francis, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Robson, and S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Neubauer, “Normal human left and right ventricular and left atrial dimensions using steady state free precession magnetic resonance imaging”, Journal of Cardiovascular Magnetic Resonance 7, 775–782 (2005).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 117A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Maceira, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Prasad, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Khan, and D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Pennell, “Reference right ventricular systolic and diastolic function normalized to age, gender and body surface area from steady-state free precession cardiovascular magnetic resonance”, European Heart Journal 27, 2879–2888 (2006).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 118S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Clay, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Alfakih, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Radjenovic, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Jones, and J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Ridgway, “Normal range of human left ventricular volumes and mass using steady state free precession mri in the radial long axis orientation”, Magnetic Resonance Materials in Physics, Biology and Medicine 19, 41–45 (2006).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 119B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' U.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Burkhardt, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Kellenberger, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Franzoso, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Geiger, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Oxenius, and E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Valsan- giacomo Buechel, “Right and left ventricular strain patterns after the atrial switch operation for d-transposition of the great arteries—a magnetic resonance feature tracking study”, Frontiers in Cardiovascular Medicine 6, 39 (2019).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 120K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Hammermeister, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Brooks, and J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Warbasse, “The rate of change of left ventricular volume in man: i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' validation and peak systolic ejection rate in health and disease”, Circulation 49, 729–738 (1974).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 121T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Sugimoto, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Dulgheru, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Bernard, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Ilardi, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Contu, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Addetia, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Caballero, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Akha- ladze, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Athanassopoulos, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Barone, et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=', “Echocardiographic reference ranges for nor- mal left ventricular 2d strain: results from the eacvi norre study”, European Heart Journal- Cardiovascular Imaging 18, 833–840 (2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 122A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Bishop, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' White, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Oldershaw, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Chaturvedi, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Brookes, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Redington, “Clinical application of the conductance catheter technique in the adult human right ventricle”, Interna- tional journal of cardiology 58, 211–221 (1997).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 123L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Thomas, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Foster, and N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Schiller, “Peak mitral inflow velocity predicts mitral regurgi- tation severity”, Journal of the American College of Cardiology 31, 174–179 (1998).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 124G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Di Labbio and L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Kadem, “Jet collisions and vortex reversal in the human left ventricle”, Journal of Biomechanics 78, 155–160 (2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 125P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Kilner, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='-Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Yang, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Wilkes, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Mohiaddin, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Firmin, and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Yacoub, “Asymmetric redirection of flow through the heart”, Nature 404, 759–761 (2000).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 126F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Bueno, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Rodr´ıguez-Bail´on, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' L´opez-Salguero, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' G´omez-Doblas, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Garc´ıa-Pinilla, and E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' de Teresa Galv´an, “Effect of exercise on systolic left ventricular outflow velocity in healthy adults”, Revista Espa˜nola de Cardiolog´ıa (English Edition) 59, 180–182 (2006).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 127J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='-O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Choi, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='-S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Shin, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='-J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Kim, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Jung, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Park, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Sohn, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Kim, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='-M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Park, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Yoo, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Choi, et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=', “Normal echocardiographic measurements in a korean population study: part ii.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' doppler and tissue doppler imaging”, Journal of Cardiovascular Ultrasound 24, 144–152 (2016).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 128Pulmonary acceleration time to estimate pulmonary pressure, visited: October 2022.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 129Normal pressures in the heart and great vessels, (Accessed: October 2022) https : / / www .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' msdmanuals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='com/professional/multimedia/table/normal-pressures-in-the-heart- and-great-vessels.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 43 130L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Littmann and J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Symanski, “Hemodynamic implications of left bundle branch block”, Journal of Electrocardiology 33, 115–121 (2000).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 131J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Eriksson, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Zajac, U.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Alehagen, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Bolger, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Ebbers, and C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content='-J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Carlh¨all, “Left ventricular hemodynamic forces as a marker of mechanical dyssynchrony in heart failure patients with left bundle branch block”, Scientific Reports 7, 1–9 (2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 132M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Alessandrini, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' De Craene, O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Bernard, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Giffard-Roisin, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Allain, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Waechter-Stehle, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Weese, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Saloux, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Delingette, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Sermesant, et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=', “A pipeline for the generation of realistic 3d synthetic echocardiographic sequences: methodology and open-access database”, IEEE transactions on Medical Imaging 34, 1436–1451 (2015).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 133M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Hirschvogel, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Bassilious, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Jagschies, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Wildhirt, and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' Gee, “A monolithic 3d- 0d coupled closed-loop model of the heart and the vascular system: experiment-based parameter estimation for patient-specific cardiac mechanics”, International Journal for Numerical Methods in Biomedical Engineering 33, e2842 (2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} +page_content=' 44' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/4dA0T4oBgHgl3EQfNf-w/content/2301.02148v1.pdf'} diff --git a/5dAzT4oBgHgl3EQf9_4T/content/tmp_files/2301.01926v1.pdf.txt b/5dAzT4oBgHgl3EQf9_4T/content/tmp_files/2301.01926v1.pdf.txt new file mode 100644 index 0000000000000000000000000000000000000000..10457e10dd76b1be96e539b2381cfff40ea3a001 --- /dev/null +++ b/5dAzT4oBgHgl3EQf9_4T/content/tmp_files/2301.01926v1.pdf.txt @@ -0,0 +1,1605 @@ +Springer Nature 2021 LATEX template +Auditing citation polarization during the +COVID-19 pandemic +Taekho You1, Jinseo Park2, June Young Lee2 and Jinhyuk +Yun3* +1Institute for Social Data Science, Pohang University of Science +and Technology, Cheongam-ro 77, Pohang, 37673, +Gyeongsangbukdo, Republic of Korea. +2Center for Global R&D Data Analysis, Korea Institute of +Science and Technology Information, Hoegi-ro 66, +Dongdaemun-gu, 02456 Seoul, Republic of Korea. +3*School of AI Convergence, Soongsil University, Sangdo-ro 369, +Dongjak-gu, 06978 Seoul, Republic of Korea. +*Corresponding author(s). E-mail(s): jinhyuk.yun@ssu.ac.kr; +Abstract +The recent pandemic stimulated scientists to publish a significant amount +of research that created a surge of citations of COVID-19-related papers +in a short time, leading to an abrupt inflation of the journal impact fac- +tor (IF). By auditing the complete set of COVID-19-related publications +in the Web of Science, we reveal here that COVID-19-related research +worsened the polarization of academic journals: the IF before the pan- +demic was proportional to the increment of IF, which had the effect of +increasing inequality while retaining the journal rankings. We also found +that the most highly cited studies related to COVID-19 were published +in prestigious journals at the onset of the epidemic, independent of their +innate importance or quality. Through the present quantitative investi- +gation, our findings caution against the belief that quantitative metrics, +particularly IF, can indicate the significance of individual papers. Rather, +such metrics reflect the social attention given to a particular study. +1 +arXiv:2301.01926v1 [cs.DL] 5 Jan 2023 + +Springer Nature 2021 LATEX template +2 +Auditing citation polarization during the COVID-19 pandemic +1 Introduction +The recent pandemic has boosted COVID-19-related research, which has led +to a growing number of researchers publishing COVID-19-related papers [1]. +During the pandemic, as of 2021 more than 4% of published research papers +focused on COVID-19 [1]. The availability of COVID-19-related research has +supported the public to overcome the current pandemic. +The expansion of this new research field has had a substantial impact +on the scholarly publishing ecosystem. COVID-19-related papers received a +large number of citations in a short period, causing a dramatic shift in +citation counts. Specifically, some journals benefited from publishing COVID- +19-related research because it significantly increased their mean citation rate. +As an illustrative example, the Lancet more than doubled its impact factor +(IF) from 79.323 to 202.731, according to the 2021 Journal Citation Reports +(JCR) released in June 2022. It has been contended that COVID-19-related +papers have inflated the citation-based metrics; indeed, some journals have +increased their IF by more than tenfold [2]. +Consequently, the long-lasting IF controversy has reemerged. Due to the +heavy-tailed nature of citation, which is sometimes referred to as the rich-get- +richer effect, many critics argue that IFs do not accurately reflect the impact +of scientific items because they rely solely upon mean citation counts [3, 4]. +In response, alternative metrics have been proposed [5, 6]. Moreover, although +the IF metric was designed to measure the performance of journals rather +than single papers [7], it is nevertheless frequently misunderstood to reflect +the quality of an individual paper [8]. The spreading of these misunderstand- +ings has increased unintended dynamics in the conduction and evaluation of +research [9, 10], even leading to cases of malpractice [11]. +Resolving this IF controversy from COVID-19-related papers necessitates +a deep comprehension of citation dynamics in academia, such as the extent to +which COVID-19 publications affect journal IFs and who benefits more from +publishing COVID-19-related papers. The Matthew effect [12], also known +as the rich-get-richer effect, gives valuable insight into the accumulation of +rewards in academia [13, 14, 15, 16, 17]. Previous studies demonstrated that a +little variation in early stages leads to a substantial difference in the productiv- +ity and citations of authors and journals in later stages [18, 14, 15]. Moreover, +a paper is more likely to receive citations when published in a prestigious jour- +nal that has a high IF [19]. Citation inequality results from the widening gaps +in return from such small, initial differences [20, 21, 17]. We believe that the +emergence of the COVID-19 research field presents an excellent opportunity +to comprehend scholarly dynamics in response to external societal influence. +In this study, we quantitatively exhibit the impact of COVID-19-related +papers on the citation ecosystem to aid in resolving the long-lasting debates on +the IF metric. For this purpose, we investigate the changes in IF by the publi- +cation of COVID-19-related papers considering prior journal IF. We find that +COVID-19-related papers received more citations than other papers, and we + +Springer Nature 2021 LATEX template +Auditing citation polarization during the COVID-19 pandemic +3 +100 +101 +102 +103 +104 +Number of citations received +10 +6 +10 +5 +10 +4 +10 +3 +10 +2 +10 +1 +100 +Cumulative density function +COVID-19-related papers (2020) +Non-COVID-19-related papers (2020) +COVID-19-related papers (2021) +Non-COVID-19-related papers (2021) +2019 +2020 +2021 +Year +0.0 +0.2 +0.4 +0.6 +0.8 +Citations between COVID-19-related papers +83.1 % +90.9 % +83.9 % +5.8 % +45.3 % +48.3 % +A +B +Citations received +References citing +Fig. +1 Difference +in +citation +distribution +between +COVID-19-related +and +non-COVID-19-related papers. A Citation distribution of COVID-19-related and non- +COVID-19-related papers that contribute to the annual IF calculation. For example, the +distribution of citations in 2021 includes citations received for papers published in 2019 and +2020 from the papers published in 2021. A distribution of the yearly citation pattern is +displayed in Fig. S2. B Citation origin of COVID-19-related papers. We display both the +percentage of citations received from other COVID-19-related papers and references citing +other COVID-19 publications. +also show that although most of the citations originated from other COVID- +19-related papers, the degree of benefit to the journals differs by the prestige of +the journals reflected in their prior IFs. We reveal that the number of COVID- +19-related papers and the extent of the increase in journal IF are nearly +uncorrelated, while the IFs of prestigious journals with high IFs increased more +than those of low-IF journals. Lastly, we find that the majority of highly cited +COVID-19 publications were published during the earliest stages of the pan- +demic, selected by prestige journals. Taken together, the results demonstrate +that the benefits of publishing COVID-19-related research were granted mainly +to the prestige journals, which may aggravate citation inequality in academia. +2 Results +2.1 Citation homophily between COVID-19-related +papers +During the pandemic, COVID-19-related papers have increased their share in +academia. In 2019, only 350 papers (0.013%) were related to COVID-19, many +of which were mainly focused on other coronaviruses, based on our search +query (see Methods for step-by-step details on gathering COVID-19-related +papers). As the virus spread, their share increased to 89,112 (2.004%) and +162,256 (4.194%) in 2020 and 2021, respectively. Moreover, COVID-19-related +research occupied a major fraction of all citations across academia. Such papers +published in 2020 received 2,654,613 citations until the end of 2021, which is + +Springer Nature 2021 LATEX template +4 +Auditing citation polarization during the COVID-19 pandemic +13.8% among the total 19,203,421 citations in 2020 and 2021. But not only +gaining a high share, COVID-19-related publications also received immediate +citations: those published in 2021 received 787,009 citations out of the total +6,457,473 citations in 2021 (12.2%). This same trend even extended down to +the monthly citation level, as displayed in Fig. S1. After publication, 31.8% of +the citations of COVID-19-related papers arose within 6 months, while 22.2% +did so for non-COVID-19 papers. Compared to the statistics indicating that +COVID-19-related papers produced just 4.1% and 6.9% of references within +the same time period (2020 and 2021, respectively), this proportion of received +citations is high. +The increased attention given to COVID-19 research resulted in a citation +distribution with a longer tail than other research. The two-year citation dis- +tribution shows that COVID-19-related papers received more citations than +non-COVID-19-related papers in a given year (see Fig. 1A for the merged +distribution along with Fig. S2 displaying separated distributions). When we +assume that the citation distribution follows a simple power law (y ∼ xk), +the COVID-19-related papers show k ≃ 1.9 and k ≃ 2.7 for 2020 and 2021, +respectively (see Methods for the detailed computation), while non-COVID-19- +related papers have an exponent of 3.2 and 3.3 for 2020 and 2021, respectively. +The lower exponents indicate that the proportion of COVID-19-related papers +with extremely high citation counts is greater than that of non-COVID-19- +related papers. Consequently, COVID-19-related papers also received more +citations on average. COVID-19-related research received an average of 22.6 +(2020) and 21.8 (2021) citations, while non-COVID-19-related papers received +4.9 (2020) and 5.2 (2021) citations. This result is consistent with a previous +observation using SCOPUS [1]. +We also find a homophily of citations, namely that the high citation counts +of COVID-19-related papers are attributable to other COVID-19-related +papers. We observed a high rate of citation exchange between publications +related to COVID-19, in which more than 40% of the references in these +papers cite other COVID-19-related papers, excluding 2019 (Fig. 1B). The +homophily is much stronger when we consider the received citations, where +90.9% of citations to COVID-19-related papers published in 2020 were from +other COVID-19-related papers. This finding indicates that the rising amount +of COVID-19-related research in 2020 and 2021 resulted in a number of such +papers receiving a substantial number of citations. +2.2 Contribution of COVID-19-related research to IF +inflation +Several highly cited COVID-19-related studies may bolster the publishing jour- +nals’ IF. To quantify this, we calculate the IF in terms of the existence and +number of COVID-19-related papers (see Methods for IF calculation). We mea- +sure two different types of IFs and compare them to estimate the advantage of +publishing COVID-19-related papers: IF excluding COVID-19-related papers +and IF including them. We observe that only 763 journals (16%) among those + +Springer Nature 2021 LATEX template +Auditing citation polarization during the COVID-19 pandemic +5 +10 +2 +10 +1 +100 +101 +102 +Impact factor (IF) +10 +5 +10 +3 +10 +1 +101 +Surplus IF by COVID-19-related papers +A +B +100 +101 +102 +103 +Number of COVID-19-related papers +10 +5 +10 +4 +10 +3 +10 +2 +10 +1 +100 +101 +Surplus IF by a single COVID-19-related paper +Fig. 2 Surplus impact factor (IF) by COVID-19-related publications. A Journal +impact factor increase by publishing COVID-19-related papers, where the simple superlinear +growth y ∼ x1.7 can characterize the growth pattern (dotted line). Here, we applied a simple +linear regression method to the logarithm of the values of interest to estimate the power-law +scaling relationship between the IF and its surplus by COVID-19-related papers, assuming +a simple power-law scaling of y = Cxk. B Increase in IF per COVID-19-related paper in +proportion to the number of COVID-19-related papers published in journals. The increase is +calculated by dividing the absolute difference in IF between papers including and excluding +COVID-19 by the number of COVID-19-related papers in the journals. In both A and B, +the red dots represent the average value of surplus IF and the error bars show the standard +deviation in log-scale. +publishing one or more COVID-19-related papers in 2019 and 2020 dropped in +IF in 2021, while the other 4,004 journals (84%) enhanced their IFs through +the publication of COVID-19-related papers in the same period. For the for- +mer, even though the journals decreased in IF by publishing COVID-19-related +papers, the amount of decrease was limited. Only one of these 763 journals +(CA-A CANCER JOURNAL FOR CLINICIANS) dropped in IF by more +than 1 (Fig. S3). +Individual scientists have a greater tendency to cite widespread, popular +journals than less popular journals due to psychological, sociological, and eco- +nomic factors, leading to the rich-get-richer phenomenon of citation [22]. For +the COVID-19-related papers, we find that the surplus IF is proportional to +the prior IF (Fig. 2). High correlation exists between IF and its surplus (Pear- +son r = 0.670, Fig. 2A), and their relationship is even superlinear (y = x1.7). +This pattern is also verified when we consider the relative advantage of IFs by +dividing the surplus IF by the prior journal IF, which also shows a positive +correlation (Fig. S4). +However, publishing numerous papers on COVID-19 did not necessar- +ily increase the journal IF. Instead, as more COVID-19-related papers were +published, the gain in IF per COVID-19-related paper decreased (Fig. 2B). +Journals that published only one COVID-19-related paper in 2019 and 2020 +increased their IF by 0.12 on average, whereas journals that published over + +Springer Nature 2021 LATEX template +6 +Auditing citation polarization during the COVID-19 pandemic +500 papers in the same period increased their IF by only 0.0009. For example, +the Lancet, the journal with the highest IF in JCR 2021, doubled its IF (from +93.04 to 189.25) while publishing only 46 COVID-19-related papers (9.4% of +all citable items) in 2019 and 2020. To take one more extreme example, one +journal that published only one COVID-19-related paper in 2019 and 2020 +increased its IF by 37, whereas the journal that published the largest num- +ber of COVID-19-related papers (730 papers) improved its IF by only 1.02. In +other words, while a single well-chosen paper published during the pandemic +could have potentially resulted in a significant increase in IF, publishing a large +number of COVID-19-related papers did not provide many benefits. Although +allocating more shares to COVID-19-related papers correlates positively with +the rise in the IFs of journals, the correlation is slight (Pearson r = 0.110; see +Fig. S5). +To confirm that COVID-19 research has legitimately increased journal +IFs, we examine the correlation between IFs across years taking into account +the existence of COVID-19-related papers. When excluding COVID-19-related +papers, the correlation between two consecutive years (Pearson r = 0.957 +between 2019 and 2020 and Pearson r = 0.925 between 2020 and 2021) is +significantly high. The correlation between the IF in 2021 excluding COVID- +19-related papers and the IF in 2020 with COVID-19-related papers is r = +0.926. Thus, the overall trend of journal IF without papers related to COVID- +19 did not change significantly, and this high correlation suggests the existence +of a linear relationship between the IFs for two consecutive years. Incorporat- +ing COVID-19-related papers, however, reduces the correlation between the +IFs for 2020 and 2021 (Pearson r = 0.850). Note that we also observe a lower +correlation between the IF for 2021 with COVID-19-related papers and the +IF for 2020 without COVID-19-related papers (Pearson r = 0.849), indicating +non-linear relationships between the IFs of the two consecutive years. Given +that journals with a higher prior IF received a greater increase in IF from +COVID-19-related papers (Fig. 2A), the publication of COVID-19 research +may contribute to the polarization of journal IFs. +2.3 The Matthew effect of IF polarization during the +pandemic +In the preceding sections, we demonstrated that the publication of COVID-19- +related research had a positive correlation with journal IFs, while the amount +of increment had a strong correlation with the prior IFs of the journals (Fig. 2). +One may wonder how much the overall journal landscape, i.e., the journal +rankings, has changed due to the surplus IFs, or conversely, the magnitude +of the change in IF based on the journal ranking [23]. To demonstrate the +influence of COVID-19-related publications on the landscape of JCR rankings, +we compare the ratio of surplus IF in 2021 considering the journal rank in their +research categories. On average, the publication of COVID-19-related papers +increased the journal IF by 15.2% (dotted line in Fig. 3). The IFs of the top +10% prestige journals increased by 39.4%, while the IFs of the bottom 10% + +Springer Nature 2021 LATEX template +Auditing citation polarization during the COVID-19 pandemic +7 +100 +101 +Increase ratio +Top 10% +10-20% +20-30% +30-40% +40-50% +50-60% +60-70% +70-80% +80-90% +90-100% +Journal Ranking +Fig. 3 Relative ratio of surplus IF from publishing COVID-19-related papers by +the 2021 journal rankings for JCR categories. The ratio was calculated by subtracting +the IF for 2021 excluding COVID-19-related papers from the IF for 2021 including COVID- +19-related papers and then dividing this value by the prior IF. The dotted line indicates +the average surplus IF from publishing COVID-19-related research (15.2%). Here, the boxes +represent the quartiles of the dataset except for points determined to be outliers. +journals increased by only 9.6% on average. Most journals increased their IF +by less than the average increase (15.2%) except for the top 20%. On average, +higher-ranked journals gained more citations, and this trend is robust across +all categories (Table S1). +The majority of journals with a significant increase in IF due to COVID- +19-related publications were already high-IF journals. Among all journals, 132 +increased their IF by greater than twofold. 55.3% of these 132 journals are +in the top 10% of at least one of their research categories. Only five journals +fall within the bottom 10%. In terms of research category, 102 of the 132 +journals (77.3%) are classified into Clinical Medicine since the majority of +COVID-19-related papers (70.9%) were published in this category. +This proportion of highly cited articles in prestigious journals has the +potential to exacerbate citation polarization. Indeed, we found that more +COVID-19-related articles were published in prestigious journals with a high + +Springer Nature 2021 LATEX template +8 +Auditing citation polarization during the COVID-19 pandemic +IF than in other journals (Fig. 4A). While the top 10% ranked journals pub- +lished 26.3% (51,976) of all COVID-19-related papers from 2019 to 2021, the +bottom 90% to 100% ranked journals published only 3.5% (6,977) in the same +period. We also observe that the share of COVID-19-related papers decreases +as the journal ranking falls (Fig. 4A). Moreover, the proportion of highly cited +papers exacerbates the disparity. Eighty-four percent (86) of the 102 papers +with over 1000 citations were published by the top 10% journals, while jour- +nals ranked 10 to 20 % published only eight of these studies. No papers with +over 1000 citations were published in the bottom 50% journals. +Citation is a stochastic multiplicative process, whereby papers with a higher +citation count are more likely to receive additional citations. Even with simi- +lar content between papers, those published in a more prominent location are +more likely to be cited [19]. In addition, earlier works may receive more cita- +tions because citations are cumulative. Indeed, we find that the majority of +highly cited COVID-19-related papers were published during the early stage +of the pandemic (early 2020), as shown in Fig. 4B. The number of COVID-19- +related publications gradually increased as the pandemic progressed (see the +blue line in Fig. 4B). Despite this, papers with higher numbers of citations were +generally published earlier. In conjunction with the finding that highly cited +studies were likely to be published in prestigious journals, we may conclude +that COVID-19-related studies were originally introduced in high IF journals, +and that lower IF journals then cited the previous publications from high IF +journals. +This growing pattern of citations could worsen the polarization of aca- +demic journals. Publication of COVID-19-related research gave a significantly +greater benefit to journals with higher IFs than to those with lower IFs. Con- +sequently, the relative position (rank) of the majority of journals shows only +minor changes, although the overall IF of all journals tended to increase dur- +ing the pandemic. Only 39.0% of journals that published COVID-19-related +papers moved to a higher rank by including COVID-19-related research in one +of their subject categories; among them, only 51.8% changed their IF quantile +to a higher one (Fig. S6). The other 61.0% of journals maintained or decreased +their ranking. In addition, significant increases or decreases in the ranks of +journals were rarely observed (Fig. S6). The majority (90%) of the top 10% +ranked journals maintained their position regardless of COVID-19 research, +while the other 10% fell into the 10% to 20% group. +In summary, i) the IF of journals increased overall by publishing COVID- +19-related research, ii) journals with higher IFs received greater benefits by +publishing COVID-19-related research, and iii) the relative ranks of jour- +nals did not change significantly from publishing COVID-19-related research. +These findings lead to an interesting question: Did the publication of COVID- +19-related research actually increase the polarization of journals? To answer +this, we applied the Gini coefficient [24], a well-known measure of income +inequality, to the distribution of journal IFs. In our investigation, the Gini +coefficient measures the distribution of citations across journals within a JCR + +Springer Nature 2021 LATEX template +Auditing citation polarization during the COVID-19 pandemic +9 +All +>10 +>100 +>1000 +Number of citations received +0.0 +0.2 +0.4 +0.6 +0.8 +Fraction of COVID-19-related papers +A +B +C +Top 10% +10-20% +20-30% +30-40% +40-50% +50-60% +60-70% +70-80% +80-90% +90-100% +2020 +2021 +2022 +Year +0.000 +0.025 +0.050 +0.075 +0.100 +0.125 +0.150 +0.175 +Probability density function +all +>10 +>100 +>1000 +0.0 +0.1 +0.2 +0.3 +0.4 +0.5 +0.6 +0.7 +Gini coefficient excluding COVID-19-related papers +0.0 +0.1 +0.2 +0.3 +0.4 +0.5 +0.6 +0.7 +Gini coefficient including COVID-19-related papers +Number of COVID-19 +-related papers +0 +800 +1600 +2400 +3200 +Gini coefficient changes +Increased +Decreased +Fig. 4 Distribution of COVID-19-related papers and their disparities. A Distribu- +tion of COVID-19-related research by journal ranking. As the number of citations increases, +the likelihood of papers belonging to high-IF journals increases. 26.4% of all papers were +published in the top 10% ranked journals, whereas 85.3% of papers with more than 1000 cita- +tions were published in the top 10% ranked journals. B Distribution of COVID-19-related +papers by publication date. From the beginning of the pandemic to mid-2020, the number of +papers increased. Approximately the same number of papers were published between then +and the end of 2021. Most of the highly cited papers (> 1000 citations) were published in +early 2020. C Plot of the Gini coefficient of the IF distribution by JCR category. Each dot +represents a JCR category. The Gini coefficient is computed using the IF distribution of +journals in a particular category including and excluding COVID-19-related papers. Blue +(orange) dots indicate an increase (decrease) in the Gini coefficient by publishing COVID- +19-related research. The size of the dots is proportional to the number of COVID-19-related +studies published in the category. +category, ranging from 0 for the lowest heterogeneity (when all journals receive +the same average number of citations) to 1 for the highest heterogeneity +(when only a single journal receives all citations). The trend illustrated by the +difference in the Gini coefficient as a function of the number of COVID-19- +related papers (see Figs. 4 and S7) implies that the disparity in the number +of citations between journals increases as the number of COVID-19-related +papers published increases. In conclusion, based on the present snapshot of +the Web of Science (WOS) dataset, we found that the general pattern of het- +erogeneity, or polarization, among journals rises as the number of published +COVID-19-related papers increases. +3 Discussion +From the outset of the global COVID-19 pandemic, many scholars pursued +the topic and published a massive number of studies in an unprecedentedly +short period. We discovered a trend that, as a result of the intensive publica- +tion, COVID-19-related papers acquired more citations than papers in other +domains, which reflects its considerable attention in academia. We uncovered +two significant consequences that may have led to a more severe polarization +of journals in terms of citations. First, 84% of journals that published COVID- +19-related papers in 2019 and 2020 increased their impact factors. Second, +prestigious journals were more likely to publish highly cited COVID-19-related +papers than other journals (Fig. 3). + +Springer Nature 2021 LATEX template +10 +Auditing citation polarization during the COVID-19 pandemic +Nonetheless, we demonstrated that publishing a large number of COVID- +19-related papers did not immediately boost a journal’s IF. Increasing numbers +of COVID-19-related papers published in a journal tended to diminish the +citation impact of a single COVID-19-related article. In addition, we found +that prestigious journals with a high prior IF gained more benefit (increased +IF) from publishing COVID-19-related research, and also that the publications +receiving the highest number of citations were predominantly published in +prestige journals during the early stages of the pandemic. Given that not all +COVID-19-related publications increased their journal’s IF, one may assume +that prestige journals simply have accepted and published more significant +research. However, considering that some papers published in prestige journals +were ultimately retracted [25, 26], the high number of citations given to these +journals is not only based on the significance of the works but also based in +part on the visibility of these journals, which can worsen the polarization of +academic publishing (Fig. 4). +As we could not explicitly assess the quality of each paper due to the scale +of the dataset, it is unclear which of the two aforementioned characteristics +(quality or visibility) has a larger impact on the current disparity in benefit +from publishing COVID-19-related research. We believe that a more in-depth +investigation of the relationship between research quality (or significance) and +citations may be necessary to increase the impact of our findings. Also, a more +detailed understanding of such correlation should form the basis of explaining +complex citation dynamics, yet we leave this for future research. +Despite its limitations, this study can provide important insights into +citation dynamics and its effects on global events. Because of the rich-get- +richer nature of citations, papers published in prestigious journals tend to +receive more citations. As the relative ranking of the journals did not change +significantly despite the increase in the overall IFs of journals publishing +COVID-19-related research, fluctuations in IF may not well reflect the actual +impact of academic publications. This effect predominantly benefited well- +established journals, while other journals did not experience benefits to the +same extent (Fig. 4). Our research indicates that IFs are vulnerable to exter- +nal events. The majority of the recent IF changes are attributable to citations +of COVID-19-related publications; consequently, after the pandemic is over, +the majority of the journals may revert to their pre-pandemic IF levels. It is +challenging to evaluate academic journals or other participants (researchers, +institutions, etc.) using basic statistics because doing so reflects only a portion +of actual scientific achievements. Therefore, the simplified metrics employed +by some governments [27] should be accompanied by a comprehensive and +qualitative analysis of journals and individual papers for assessment. +The use of quantitative indicators such as the IF metric has been under +debate. The San Francisco Declaration on Research Assessment (DORA), +which serves as the starting point for these discussions, explicitly states that +the use of journal-based measures (such as IFs) should be avoided to act as a + +Springer Nature 2021 LATEX template +Auditing citation polarization during the COVID-19 pandemic +11 +proxy for the quality of individual research publications, to evaluate the con- +tributions of an individual scientist, or to make hiring, promotion, or funding +choices. In practice, however, many funders and institutions employ journal- +based measures or the number of citations as markers for evaluation rather +than assessing the quality of individual papers. The polarization of citations +observed in this study demonstrates the inherent hazard of such indicators. +The IF metric is not a stable index against external shocks; it might fluctuate +temporarily and then revert following external factors. Along with the other +well-known limitations of IF, such as skewed citation distributions within jour- +nals [28, 29], the vulnerability of the IF metric as we found here indicates +that it is increasingly inappropriate to consider journal IF as a proxy for an +individual paper’s quality. +During the current pandemic, the rapid release of COVID-19-related works +resulted in less-qualified academic outputs to the public, leading to the retrac- +tion of many publications [30, 31]. Unfortunately, this issue happened not only +in journals with a reputation for a weak review process or low publishing dif- +ficulty but also in prestigious journals that are widely respected. Worse still, +these retracted works earned a substantial number of citations and extensive +media attention [32]. The general public may assume that papers published in +academic journals are trustworthy and may likewise trust secondary sources +such as scientific news reporting the results of academic findings. In the current +context of appraising science and technology, there is a chance that content +published in journals with strong indicators will be considered more reputable. +Scientists must inform the public that citation measures and journals are not +equivalent to the quality of individual research publications. In other words, +the number of citations should not be the defining characteristic of quality +research. The contemporary ecosystem of research and technology is seemingly +supported by scientists’ mutual trust and goodwill, and the public may view +the scientific community’s findings with a similar level of confidence. Combined +with the stability issue of the IF metric identified in this study, shouldn’t the +current practice of over-reliance on citation indices be discontinued so as not +to break this chain of trust? For this reason, we believe that responsible action +based on actual societal influence is essential for all members of academia, as +opposed to merely producing popular research to boost citation impact and +one’s professional reputation. +Methods +Data +We used publications and citation data from the XML dump of the Web of +Science Core Collection, which is dated back to 2017 and updated until the +26th week of 2022. The data includes complete copies of Science Citation +Index Expanded (SCIE), Social Sciences Citation Index (SSCI), and Arts & +Humanities Citation Index (AHCI), along with the Emerging Sources Citation + +Springer Nature 2021 LATEX template +12 +Auditing citation polarization during the COVID-19 pandemic +Index (ESCI). The data comprises 16,957,120 articles, 82,317 journals, and +116,086,223 references retrieved from papers published between 2017 and 2022. +COVID-19-related publications +COVID-19-related papers were retrieved from the Web of Science database +(WOS, +urlhttps://www.webofscience.com/) +using +the +following +search +queries +provided +by +Dimensions +(https://www.dimensions.ai/covid19/): +"2019-nCoV" OR "COVID-19" OR \SARS-CoV-2" OR "HCoV-2019" OR +"hcov" OR "NCOVID-19" OR "severe acute respiratory syndrome +coronavirus 2" OR "severe acute respiratory syndrome corona +virus 2" OR \coronavirus disease 2019" OR (("coronavirus" OR +"corona virus") AND (Wuhan OR China OR novel)). We limited the publi- +cations from 2019. A total of 251, 718 COVID-19-related papers were collected +on 4 July 2022. We consider all other papers in the WOS that were not +retrieved from the above searching process as non-COVID-19-related papers. +Estimation of the power-law exponent +In this study, the power-law exponents of the citation distribution in Fig. 1A +were estimated using the Python package named powerlaw [33]. Although +all the citation distributions in Fig. 1A seem to be heavy-tailed distribu- +tions, which are commonly referred to as the power law, we verified that +the distributions sincerely follow the power law via comparison with alterna- +tive distributions (e.g., log-normal or exponential). In the comparison with +the exponential distribution, all distributions were found to be more likely +to be power-law distributions rather than exponential (p < 0.001). However, +comparison with the log-normal distribution was unclear. Only non-COVID- +19-related papers published in 2021 better fit the power-law distribution in a +statistically significant manner, while the other three were inconclusive (p var- +ied 0.48–0.60). In this study, we estimated the power-law exponent with the +assumption of a simple power law (y ∼ xk) regardless of the best fit distribu- +tion, as we were more interested in comparing the thickness of the tails than +in determining the exact exponents. +Reproduction of the journal impact factors +Although we extracted the total number of publications in the WOS with +a complete copy of the WOS provided by Clarivate, minor differences can +be presented mainly because the WOS does not report detailed methods to +filter the dataset, e.g., dump dates and the coverage of citable items. Thus, +to reproduce and estimate the journal impact factors (IFs), we followed the +method used for the JCR impact factor [2] but with an in-house XML copy of +the Web of Science, as follows: + +Springer Nature 2021 LATEX template +Auditing citation polarization during the COVID-19 pandemic +13 +IF = citations received by items published in the past 2 years +number of citable items published in the past 2 years . +(1) +We limited the citable items to those belonging to the journals indexed in +SCI-Expanded, SSCI, and A&HCI. We also considered as citable items only +articles, review papers, and proceedings papers in terms of publication type; +however, publication types were not considered when computing the number +of citations. +Note that, as of 2020, Clarivate Inc. now considers early access publications +as regular publications and includes them in the calculation of IF. For instance, +if an article is published as early access in 2020 and officially published in +2021, then the article is counted as a citable item published in 2020, taking +into account the references as the citations occurred in 2020. The article is not +considered in 2021. +With this procedure, we successfully reproduced IF scores highly correlated +with the IFs provided by Clarivate JCR (Pearson r = 0.99; see Fig. S8). In +this study, we refer to the value computed from Eq. 1 as IF instead of the +impact factor provided by JCR unless otherwise specified. When computing +the IFs excluding COVID-19-related papers, we counted out the COVID-19- +related citable items and their references from the denominator and numerator +in Eq. 1, respectively. +Acknowledgement +This research was supported by the MSIT (Ministry of Science and ICT), +Republic of Korea, under the Innovative Human Resource Development +for Local Intellectualization support program (IITP-2022-RS-2022-00156360) +supervised by the IITP (Institute for Information & Communications Tech- +nology Planning & Evaluation). This work was also supported by the National +Research Foundation of Korea (NRF) funded by the Korean government (grant +No. NRF-2022R1C1C2004277 (T.Y.) and 2022R1A2C1091324 (J.Y.)). The +Korea Institute of Science and Technology Information (KISTI) also supported +this research with grant No. K-23-L03-C01 (J.Y.L., J.P.) and by providing +KREONET, a high-speed Internet connection. The funders had no role in the +study design, data collection and analysis, decision to publish, or preparation +of the manuscript. +Ethics declarations +Competing interests +The authors declare no competing interests. + +Springer Nature 2021 LATEX template +14 +Auditing citation polarization during the COVID-19 pandemic +References +[1] Ioannidis, J. P., Bendavid, E., Salholz-Hillel, M., Boyack, K. W. & Baas, +J. Massive covidization of research citations and the citation elite. Proceed- +ings of the National Academy of Sciences of the United States of America +119(28):e2204074,119 (2022). +[2] McVeigh, M. Journal citation reports 2022: A preview. (2022). https:// +clarivate.com/blog/journal-citation-reports-2022-a-preview/ +[3] Pendlebury, D. A. The use and misuse of journal metrics and other citation +indicators. Archivum immunologiae et therapiae experimentalis 57(1):1–11 +(2009). +[4] Larivi`ere, V., Kiermer, V., MacCallum, C. J., McNutt, M., Patterson, M., +Pulverer, B., Swaminathan, S., Taylor, S. & Curry, S. A simple proposal +for the publication of journal citation distributions. Preprint at https:// +www.biorxiv.org/content/10.1101/062109v2.abstract (2016). +[5] Moed, H. F. Measuring contextual citation impact of scientific journals. +Journal of Informetrics 4(3):265–277 (2010). +[6] Bradshaw, C. J. & Brook., B. W. How to rank journals. PLoS One +11(3):e0149,852 (2016). +[7] Garfield, E. Citation analysis as a tool in journal evaluation: Journals can +be ranked by frequency and impact of citations for science policy studies. +Science 178(4060):471–479 (1972). +[8] Lozano, G. A., Larivi`ere, V. & Gingras, Y. The weakening relation- +ship between the impact factor and papers’ citations in the digital age. +Journal of the American Society for Information Science and Technology +63(11):2140–2145 (2012). +[9] Calcagno, V., Demoinet, E., Gollner, K., Guidi, L., Ruths, D. & de Mazan- +court, C. Flows of research manuscripts among scientific journals reveal +hidden submission patterns. Science 338(6110):1065–1069 (2012). +[10] Rafols, I., Leydesdorff, L., O’Hare, A., Nightingale, P. & Stirling, A. How +journal rankings can suppress interdisciplinary research: A comparison +between innovation studies and business & management. Research Policy +41(7):1262–1282 (2012). +[11] You, T., Park. J., Lee, J. Y., Yun, J. & Jung, W.-S. Disturbance of ques- +tionable publishing to academia. Journal of Informetrics 16(2):101,294 +(2022). + +Springer Nature 2021 LATEX template +Auditing citation polarization during the COVID-19 pandemic +15 +[12] Merton, R. K.: The matthew effect in science: The reward and com- +munication systems of science are considered. Science 159(3810):56–63 +(1968). +[13] Perc, M. The matthew effect in empirical data. Journal of The Royal +Society Interface 11(98):20140,378 (2014). +[14] Bol, T., de Vaan, M. & van de Rijt, A. The matthew effect in science +funding. Proceedings of the National Academy of Sciences of the United +States of America 115(19):4887–4890 (2018). +[15] Nielsen, M. W. & Andersen, J.P. Global citation inequality is on the rise. +Proceedings of the National Academy of Sciences of the United States of +America 118(7):e2012208,118 (2021). +[16] Huang, J., Gates, A. J., Sinatra, R. & Barab´asi, A. L. Historical compari- +son of gender inequality in scientific careers across countries and disciplines. +Proceedings of the National Academy of Sciences of the United States of +America 117(9):4609–4616 (2020). +[17] Lawson, C., Geuna, A., Finardi, U.: The funding-productivity-gender +nexus in science, a multistage analysis. Research Policy 50(3):104,182 +(2021). +[18] Petersen, A. M., Jung, W.-S., Yang, J.-S. & Stanley, H.E. Quantitative +and empirical demonstration of the matthew effect in a study of career +longevity. Proceedings of the National Academy of Sciences of the United +States of America 108(1):18–23 (2011). +[19] Larivi`ere, V. & Gingras, Y. The impact factor’s matthew effect: A nat- +ural experiment in bibliometrics. Journal of the American Society for +Information Science and Technology 61(2):424–427 (2010). +[20] Allison, P. D., Long, J. S. & Krauze, T. K., Cumulative advantage and +inequality in science. American Sociological Review 615–62 (1982). +[21] Van de Rijt, A., Kang, S. M., Restivo, M. & Patil, A. Field experiments +of success-breeds-success dynamics. Proceedings of the National Academy +of Sciences of the United States of America 111(19):6934–6939 (2014). +[22] Wang, J. Unpacking the matthew effect in citations. Journal of Informet- +rics 8(2):329–339 (2014). +[23] Quaderi, +N. +Journal +citation +reports +2022: +Covid- +19 +research +continues +to +drive +increased +citation +impact. +Clarivate +(2022). +https://clarivate.com/blog/ +journal-citation-reports-2022-covid-19-research-continues-to-drive-increased-citation-impact/ + +Springer Nature 2021 LATEX template +16 +Auditing citation polarization during the COVID-19 pandemic +[24] Gini, C Variabilit`a e mutabilit`a: contributo allo studio delle distribuzioni +e delle relazioni statistiche.[Fasc. I.]. Tipogr. di P. Cuppini (1912) +[25] Mehra, +M. +R., +Ruschitzka, +F. +& +Patel, +A. +N. +Retrac- +tion—hydroxychloroquine or chloroquine with or without a macrolide for +treatment of covid-19: a multinational registry analysis (2020). +[26] Mehra, M. R., Desai, S. S., Kuy, S., Henry, T. D. & Patel, A. N. Retrac- +tion: cardiovascular disease, drug therapy, and mortality in covid-19. N +ENGL J MED. doi:10.1056/nejmoa2007621. (2020). +[27] Van Dalen, H. P. & Henkens, K. Intended and unintended consequences of +a publish-or-perish culture: A worldwide survey. Journal of the American +Society for Information Science and Technology 63(7):1282–1293 (2012). +[28] Van Leeuwen, T. N. & Moed, H.F. Characteristics of journal impact +factors: The effects of uncitedness and citation distribution on the under- +standing of journal impact factors. Scientometrics 63(2):357–371 (2005). +[29] Bornmann, L. & Leydesdorff, L. Skewness of citation impact data and +covariates of citation distributions: A large-scale empirical analysis based +on web of science data. Journal of Informetrics 11(1):164–175 (2017). +[30] Quinn, T. J., Burton, J. K., Carter, B., Cooper, N., Dwan, K., Field, R., +Freeman, S. C., Geue, C., Hsieh, P., McGill, K., Nevill, C. R., Rana, D., +Sutton, A., Rowan, M. T. & Xin, Y. Following the science? comparison of +methodological and reporting quality of covid-19 and other research from +the first wave of the pandemic. BMC Medicine 19(1):1–10 (2021). +[31] El-Menyar, A., Mekkodathil, A., Asim, M., Consunji, R., Rizoli, S., Abdel- +Aziz Bahey, A. & Al-Thani, H. Publications and retracted articles of +covid-19 pharmacotherapy-related research: A systematic review. Science +Progress 104(2):00368504211016,936 (2021). +[32] Khan, H., Gupta, P., Zimba, O. & Gupta, L. Bibliometric and altmet- +ric analysis of retracted articles on covid-19. Journal of Korean Medical +Science 37(6) (2022). +[33] Alstott, J., Bullmore, E. & Plenz., D. powerlaw: a python package for +analysis of heavy-tailed distributions. PloS One 9(1):e85,777 (2014). + +Springer Nature 2021 LATEX template +Auditing citation polarization during the COVID-19 pandemic +1 +Supplementary Information for +Auditing citation polarization during the COVID-19 +pandemic +Taekho You, Jinseo Park, June Young Lee, Jinhyuk Yun∗ +∗Corresponding author. Email: jinhyuk.yun@ssu.ac.kr +This PDF file includes: +Supplementary table S1 +Figures. S1 to S8 + +Springer Nature 2021 LATEX template +2 +Auditing citation polarization during the COVID-19 pandemic +Figure S1 Probability density function of the citation time difference between +the publication and the citation month of the papers. For the plot, the COVID-19- +related and non-COVID-19-related papers published in 2020 and 2021 were used. +Figure +S2 Citation +distribution +of +COVID-19-related +and +non-COVID-19- +related papers. The citation distribution that can contribute to the annual IF calculation +(left) is the same distribution as in Fig. 1A. The separated citation distributions in one- +year (middle) and two-year (right) time gaps show the same pattern. Both plots show that +COVID-19-related papers have a heavier-tailed distribution. + +Non-COviD-19-relatedpapers +COVID-19-relatedpapers +10'0 +Probability density function +0.06 +0.05 +0.04 +0.03 +乙O'0 +0.01 +0.00 +0 +5 +10 +15 +20 +Citationsafterpublication (month)100 +100 +100 +10-1 +10-1 +10-1, +Function +10-2, +10-2 +Density +10-3 +10-3, +10-3, +Cumulative +10-4 +10-4 +10-4 +Non-COVID-19 papers (2017) +10-5, +Non-COVID-19 papers (2017+2018) +10-5 +Non-COVID-19 papers (2018) +10-5 +cOVID-19 papers (2018+2019) +COVID-19 papers (2019) +Non-COVID-19 papers (2017) +Non-COVID-19 papers (2018+2019) +Non-COVID-19 papers (2019) +Non-COVID-19 papers (2018) +10-6 +COVID-19 papers (2019+2020) +COVID-19 papers (2020) +COVID-19 papers (2019) +10-6 +Non-COVID-19 papers (2019+2020) +Non-COVID-19 papers (2020) +Non-COVID-19 papers (2019) +100 +101 +102 +103 +104 +100 +101 +102 +103 +104 +100 +101 +102 +103 +104 +Number of Citations Received +Number of Citations Received +Number of Citations ReceivedSpringer Nature 2021 LATEX template +Auditing citation polarization during the COVID-19 pandemic +3 +Table S1 Relative ratio of surplus impact factor (IF) from publishing COVID-19-related papers by journal category classified by JCR. The bold +numbers represent the highest ratio of surplus IF in each category. +Journal Category +Top 10% +10–20% +20–30% +30–40% +40–50% +50–60% +60–70% +70–80% +80–90% +90–100% +Agricultural Science +1.664 +1.098 +1.111 +1.040 +1.002 +1.029 +1.078 +1.051 +1.039 +1.062 +Arts & Humanities, Interdisciplinary +1.209 +1.399 +1.146 +1.046 +0.999 +1.086 +1.165 +0.995 +0.990 +0.982 +Biology & Biochemistry +1.286 +1.103 +1.101 +1.090 +1.064 +1.067 +1.115 +1.062 +1.066 +1.081 +Chemistry +1.104 +1.043 +1.040 +1.042 +1.038 +1.055 +1.063 +1.026 +1.046 +1.109 +Clinical Medicine +1.508 +1.251 +1.189 +1.162 +1.152 +1.127 +1.142 +1.106 +1.102 +1.111 +Computer Science +1.112 +1.118 +1.125 +1.079 +1.061 +1.038 +1.090 +1.041 +1.071 +1.053 +Economics & Business +1.423 +1.161 +1.180 +1.115 +1.105 +1.088 +1.103 +1.075 +1.078 +1.024 +Engineering +1.044 +1.109 +1.041 +1.048 +1.029 +1.053 +1.027 +1.026 +1.037 +1.146 +Environment/Ecology +1.388 +1.209 +1.201 +1.135 +1.129 +1.112 +1.126 +1.106 +1.074 +1.121 +Geosciences +1.024 +1.012 +1.037 +1.018 +1.018 +1.002 +1.021 +1.033 +1.041 +1.001 +History & Archaeology +1.162 +1.165 +1.121 +1.072 +1.114 +0.994 +1.069 +1.003 +0.988 +0.969 +Literature & Language +1.106 +1.175 +1.111 +1.205 +1.026 +1.136 +1.110 +1.050 +1.019 +1.045 +Material Science +1.017 +1.018 +1.010 +1.024 +1.009 +1.010 +1.008 +1.011 +1.024 +1.054 +Mathematics +1.053 +1.046 +1.139 +1.039 +1.052 +1.062 +1.061 +1.016 +1.080 +1.013 +Multidisciplinary +1.102 +1.086 +1.081 +1.081 +1.056 +1.066 +1.057 +1.048 +1.061 +1.030 +Philosophy & Religion +1.232 +1.219 +1.221 +1.155 +1.067 +1.141 +1.131 +1.010 +1.066 +1.046 +Physics +1.058 +1.057 +1.039 +1.025 +1.018 +1.030 +1.030 +1.028 +1.014 +1.073 +Plant & Animal Science +1.102 +1.046 +1.055 +1.020 +1.068 +1.063 +1.096 +1.022 +1.039 +1.027 +Psychiatry/Psychology +1.666 +1.341 +1.183 +1.153 +1.121 +1.227 +1.137 +1.176 +1.137 +1.136 +Social Sciences, General +1.389 +1.218 +1.182 +1.147 +1.130 +1.073 +1.087 +1.070 +1.088 +1.033 +Visual & Performing Arts +1.088 +1.095 +1.069 +1.109 +1.049 +1.138 +1.038 +1.041 +1.025 +1.115 + +Springer Nature 2021 LATEX template +4 +Auditing citation polarization during the COVID-19 pandemic +Figure S3 Probability density function of journals with IFs that increased or +decreased by publishing COVID-19-related papers. The IFs of 763 journals (16%) +decreased while the IFs of 4004 journals (84%) increased. A The pdf for the absolute increase +in IF. B The pdf for the relative increase in IF divided by the IF excluding COVID-19-related +papers. +Figure S4 Relative IF increase by publishing COVID-19-related papers relative +to the journal’s IF. The extent of IF increase is divided by the IF excluding COVID-19- +related papers. The red squares and error bars respectively show the average value and the +standard deviation of the increased IF in log-scale. + +Increased +103 +Increased +Decreased +Decreased +102 +102 +Probability density function +101 +Probability density function +101 +100 +100 +10 +10-1 +10-2 +10-2, +10-3 +10-4 +10-3 +10-5 +10-3 +10-1 +100 +101 +102 +10-5 +10-3 +10-1 +100 +101 +Increase (decrease) of IF +Relative increase fdecreasej of IF101 +papers +100 +[9-related +by COVID-1! +10-2 +10-2 +10-1 +100 +101 +102 +Impact factor (IF)Springer Nature 2021 LATEX template +Auditing citation polarization during the COVID-19 pandemic +5 +Figure S5 Fraction of COVID-19-related papers published in journals by the +journal IFs. The red squares and error bars respectively show the average value and the +standard deviation of the fraction of COVID-19-related papers in log-scale. The Pearson +correlation is 0.110. +Figure S6 Change in journal ranking by publishing COVID-19-related papers. +The values show the change rate between ranking groups by publishing COVID-19-related +papers. Both Pearson and Spearman rank correlations of the IF ranks are 0.99. A Rank +change of all journals that published COVID-19-related papers in their category. B Rank +change of the journals that published COVID-19-related papers, where less than 10% of the +journals published COVID-19-related papers in their category. + +A +B + 1.0 +papers +Top 10% +0.90 +0.11 +EO0 +0.02 +0.01 +0.00 +0.00 +0.00 +0.01 +0.00 +Top 10% +1.00 +0.08 +0.00 +0.00 +0.00 +0.00 +0.00 +0.00 +0.00 +0.00 +paper +0.8 +10-20% +0.10 +0.75 +0.13 +0.03 +0.02 +0.01 +0.01 +0.00 +0.00 +0.00 +10-20% +0.00 +0.92 +0.00 +0.00 +0.00 +0.00 +0.00 +0.00 +0.00 +0.00 + COVID-19 + COVID-19 + 0.8 +20-30% +0.00 +0.14 +0.68 +0.15 +0.06 +0.02 +0.01 +1O'0 +0.01 +0.00 +20-30% +0.00 +0.00 +1.00 +0.12 +0.00 +0.08 +0.00 +0.00 +0.00 +0.00 +30-40% +0.00 +0.01 +0.02 +0.01 +0.00 +0.00 + 0.6 +0.14 +0.64 +0.14 +0.06 +30-40% +0.00 +0.00 +0.00 +0.88 +0.17 +0.00 +0.00 +0.00 +0.00 +0.00 +including +including + 0.6 +40-50% +0.00 +0.00 +0.00 +0.15 +0.61 +0.13 +0.05 +0.02 +0.01 +0.00 +40-50% +0.00 +0.00 +0.00 +0.00 +0.83 +0.00 +0.09 +0.00 +0.00 +0.00 +I ranking by IF i +50-60% +0.00 +0.00 +0.00 +0.01 +0.16 +0.63 +0.12 +0.05 +0.01 +0.00 +0.4 +50-60% +0.00 +0.00 +0.00 +0.00 +0.00 +0.92 +0.18 +0.00 +0.00 +0.00 +F +by + 0.4 +60-70% +0.00 +0.00 +0.00 +0.00 +0.01 +0.15 +0.64 +0.14 +0.05 +0.01 +60-70% +0.00 +0.00 +0.00 +0.00 +0.00 +0.00 +0.73 +0.09 +0.00 +0.00 +I ranking ! +70-80% +0.00 +0.00 +0.00 +0.00 +0.00 +0.00 +0.13 +0.65 +0.12 +0.02 +70-80% +0.00 +0.00 +0.00 +0.00 +0.00 +0.00 +0.00 +0.91 +0.09 +0.00 + 0.2 + 0.2 +80-90% +0.00 +0.00 +0.00 +0.00 +0.00 +0.00 +0.00 +0.11 +0.11 +euuno +0.71 +80-90% +0.00 +0.00 +0.00 +0.00 +0.00 +0.00 +0.00 +0.00 +0.82 +0.00 +0.00 +0.00 +0.00 +0.00 +0.00 +0.00 +0.00 +0.00 +0.07 +0.86 +90-100% +0.00 +0.00 +0.00 +0.00 +0.00 +0.00 +0.00 +0.00 +0.09 +1.00 +0.0 + 0.0 +oo +ol +ola +oo +ola +10 +lo +olo +olo +ofo +30° +20 +TOP +70 +-100° +ToF +30 +JournalrankingbyIF +excluding +COVID-19 +papers +Journal ranking +by IF +excluding +COVID-19 +papers100 +Fraction of COVID-19-related papers +10-4 +10-2 +10-1 +100 +101 +102 +Impact factorSpringer Nature 2021 LATEX template +6 +Auditing citation polarization during the COVID-19 pandemic +Figure S7 Change in the Gini coefficients of the JCR categories by the number +of published COVID-19-related papers. A positive correlation between the number +of COVID-19-related papers and the changes in the Gini coefficients of the categories is +observed (Pearson r = 0.545). +Figure S8 Correlation between the IFs provided by JCR and those calculated +in this paper. The Pearson correlation is high for both years 2020 and 2021. + +work +0.20 +19-related +COVID-1 +0.15 +Ag +0.10 +increased +coefficient +0.05 +00'0 +Gini +100 +101 +102 +103 +Number of COVID-19-related papers2020 +2021 +Pearson:0.997 +Pearson:0.998 +500 +250 +400 +200 +lated +150 +Icul +lcul +cal + 200 +F +100 +100 +50 +0 +0 +100 +200 +300 +400 +500 +0 +50 +100 +150 +200 +250 +300 +IF provided by JCR +IF provided by JCR \ No newline at end of file diff --git a/5dAzT4oBgHgl3EQf9_4T/content/tmp_files/load_file.txt b/5dAzT4oBgHgl3EQf9_4T/content/tmp_files/load_file.txt new file mode 100644 index 0000000000000000000000000000000000000000..c9d2402345978a4e759cbf22bf02febcc5b15a9a --- /dev/null +++ b/5dAzT4oBgHgl3EQf9_4T/content/tmp_files/load_file.txt @@ -0,0 +1,1122 @@ +filepath=/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf,len=1121 +page_content='Springer Nature 2021 LATEX template Auditing citation polarization during the COVID-19 pandemic Taekho You1, Jinseo Park2, June Young Lee2 and Jinhyuk Yun3* 1Institute for Social Data Science, Pohang University of Science and Technology, Cheongam-ro 77, Pohang, 37673, Gyeongsangbukdo, Republic of Korea.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 2Center for Global R&D Data Analysis, Korea Institute of Science and Technology Information, Hoegi-ro 66, Dongdaemun-gu, 02456 Seoul, Republic of Korea.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 3*School of AI Convergence, Soongsil University, Sangdo-ro 369, Dongjak-gu, 06978 Seoul, Republic of Korea.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Corresponding author(s).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' E-mail(s): jinhyuk.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='yun@ssu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='ac.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='kr;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Abstract The recent pandemic stimulated scientists to publish a significant amount of research that created a surge of citations of COVID-19-related papers in a short time, leading to an abrupt inflation of the journal impact fac- tor (IF).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' By auditing the complete set of COVID-19-related publications in the Web of Science, we reveal here that COVID-19-related research worsened the polarization of academic journals: the IF before the pan- demic was proportional to the increment of IF, which had the effect of increasing inequality while retaining the journal rankings.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' We also found that the most highly cited studies related to COVID-19 were published in prestigious journals at the onset of the epidemic, independent of their innate importance or quality.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Through the present quantitative investi- gation, our findings caution against the belief that quantitative metrics, particularly IF, can indicate the significance of individual papers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Rather, such metrics reflect the social attention given to a particular study.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 1 arXiv:2301.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='01926v1 [cs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='DL] 5 Jan 2023 Springer Nature 2021 LATEX template 2 Auditing citation polarization during the COVID-19 pandemic 1 Introduction The recent pandemic has boosted COVID-19-related research, which has led to a growing number of researchers publishing COVID-19-related papers [1].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' During the pandemic, as of 2021 more than 4% of published research papers focused on COVID-19 [1].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The availability of COVID-19-related research has supported the public to overcome the current pandemic.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The expansion of this new research field has had a substantial impact on the scholarly publishing ecosystem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' COVID-19-related papers received a large number of citations in a short period, causing a dramatic shift in citation counts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Specifically, some journals benefited from publishing COVID- 19-related research because it significantly increased their mean citation rate.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' As an illustrative example, the Lancet more than doubled its impact factor (IF) from 79.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='323 to 202.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='731, according to the 2021 Journal Citation Reports (JCR) released in June 2022.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' It has been contended that COVID-19-related papers have inflated the citation-based metrics;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' indeed, some journals have increased their IF by more than tenfold [2].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Consequently, the long-lasting IF controversy has reemerged.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Due to the heavy-tailed nature of citation, which is sometimes referred to as the rich-get- richer effect, many critics argue that IFs do not accurately reflect the impact of scientific items because they rely solely upon mean citation counts [3, 4].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' In response, alternative metrics have been proposed [5, 6].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Moreover, although the IF metric was designed to measure the performance of journals rather than single papers [7], it is nevertheless frequently misunderstood to reflect the quality of an individual paper [8].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The spreading of these misunderstand- ings has increased unintended dynamics in the conduction and evaluation of research [9, 10], even leading to cases of malpractice [11].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Resolving this IF controversy from COVID-19-related papers necessitates a deep comprehension of citation dynamics in academia, such as the extent to which COVID-19 publications affect journal IFs and who benefits more from publishing COVID-19-related papers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The Matthew effect [12], also known as the rich-get-richer effect, gives valuable insight into the accumulation of rewards in academia [13, 14, 15, 16, 17].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Previous studies demonstrated that a little variation in early stages leads to a substantial difference in the productiv- ity and citations of authors and journals in later stages [18, 14, 15].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Moreover, a paper is more likely to receive citations when published in a prestigious jour- nal that has a high IF [19].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Citation inequality results from the widening gaps in return from such small, initial differences [20, 21, 17].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' We believe that the emergence of the COVID-19 research field presents an excellent opportunity to comprehend scholarly dynamics in response to external societal influence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' In this study, we quantitatively exhibit the impact of COVID-19-related papers on the citation ecosystem to aid in resolving the long-lasting debates on the IF metric.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' For this purpose, we investigate the changes in IF by the publi- cation of COVID-19-related papers considering prior journal IF.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' We find that COVID-19-related papers received more citations than other papers, and we Springer Nature 2021 LATEX template Auditing citation polarization during the COVID-19 pandemic 3 100 101 102 103 104 Number of citations received 10 6 10 5 10 4 10 3 10 2 10 1 100 Cumulative density function COVID-19-related papers (2020) Non-COVID-19-related papers (2020) COVID-19-related papers (2021) Non-COVID-19-related papers (2021) 2019 2020 2021 Year 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='8 Citations between COVID-19-related papers 83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='1 % 90.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='9 % 83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='9 % 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='8 % 45.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='3 % 48.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='3 % A B Citations received References citing Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 1 Difference in citation distribution between COVID-19-related and non-COVID-19-related papers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' A Citation distribution of COVID-19-related and non- COVID-19-related papers that contribute to the annual IF calculation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' For example, the distribution of citations in 2021 includes citations received for papers published in 2019 and 2020 from the papers published in 2021.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' A distribution of the yearly citation pattern is displayed in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' S2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' B Citation origin of COVID-19-related papers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' We display both the percentage of citations received from other COVID-19-related papers and references citing other COVID-19 publications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' also show that although most of the citations originated from other COVID- 19-related papers, the degree of benefit to the journals differs by the prestige of the journals reflected in their prior IFs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' We reveal that the number of COVID- 19-related papers and the extent of the increase in journal IF are nearly uncorrelated, while the IFs of prestigious journals with high IFs increased more than those of low-IF journals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Lastly, we find that the majority of highly cited COVID-19 publications were published during the earliest stages of the pan- demic, selected by prestige journals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Taken together, the results demonstrate that the benefits of publishing COVID-19-related research were granted mainly to the prestige journals, which may aggravate citation inequality in academia.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 2 Results 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='1 Citation homophily between COVID-19-related papers During the pandemic, COVID-19-related papers have increased their share in academia.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' In 2019, only 350 papers (0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='013%) were related to COVID-19, many of which were mainly focused on other coronaviruses, based on our search query (see Methods for step-by-step details on gathering COVID-19-related papers).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' As the virus spread, their share increased to 89,112 (2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='004%) and 162,256 (4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='194%) in 2020 and 2021, respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Moreover, COVID-19-related research occupied a major fraction of all citations across academia.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Such papers published in 2020 received 2,654,613 citations until the end of 2021, which is Springer Nature 2021 LATEX template 4 Auditing citation polarization during the COVID-19 pandemic 13.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='8% among the total 19,203,421 citations in 2020 and 2021.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' But not only gaining a high share, COVID-19-related publications also received immediate citations: those published in 2021 received 787,009 citations out of the total 6,457,473 citations in 2021 (12.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='2%).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' This same trend even extended down to the monthly citation level, as displayed in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' S1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' After publication, 31.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='8% of the citations of COVID-19-related papers arose within 6 months, while 22.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='2% did so for non-COVID-19 papers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Compared to the statistics indicating that COVID-19-related papers produced just 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='1% and 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='9% of references within the same time period (2020 and 2021, respectively), this proportion of received citations is high.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The increased attention given to COVID-19 research resulted in a citation distribution with a longer tail than other research.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The two-year citation dis- tribution shows that COVID-19-related papers received more citations than non-COVID-19-related papers in a given year (see Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 1A for the merged distribution along with Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' S2 displaying separated distributions).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' When we assume that the citation distribution follows a simple power law (y ∼ xk), the COVID-19-related papers show k ≃ 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='9 and k ≃ 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='7 for 2020 and 2021, respectively (see Methods for the detailed computation), while non-COVID-19- related papers have an exponent of 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='2 and 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='3 for 2020 and 2021, respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The lower exponents indicate that the proportion of COVID-19-related papers with extremely high citation counts is greater than that of non-COVID-19- related papers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Consequently, COVID-19-related papers also received more citations on average.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' COVID-19-related research received an average of 22.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='6 (2020) and 21.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='8 (2021) citations, while non-COVID-19-related papers received 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='9 (2020) and 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='2 (2021) citations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' This result is consistent with a previous observation using SCOPUS [1].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' We also find a homophily of citations, namely that the high citation counts of COVID-19-related papers are attributable to other COVID-19-related papers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' We observed a high rate of citation exchange between publications related to COVID-19, in which more than 40% of the references in these papers cite other COVID-19-related papers, excluding 2019 (Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 1B).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The homophily is much stronger when we consider the received citations, where 90.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='9% of citations to COVID-19-related papers published in 2020 were from other COVID-19-related papers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' This finding indicates that the rising amount of COVID-19-related research in 2020 and 2021 resulted in a number of such papers receiving a substantial number of citations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='2 Contribution of COVID-19-related research to IF inflation Several highly cited COVID-19-related studies may bolster the publishing jour- nals’ IF.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' To quantify this, we calculate the IF in terms of the existence and number of COVID-19-related papers (see Methods for IF calculation).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' We mea- sure two different types of IFs and compare them to estimate the advantage of publishing COVID-19-related papers: IF excluding COVID-19-related papers and IF including them.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' We observe that only 763 journals (16%) among those Springer Nature 2021 LATEX template Auditing citation polarization during the COVID-19 pandemic 5 10 2 10 1 100 101 102 Impact factor (IF) 10 5 10 3 10 1 101 Surplus IF by COVID-19-related papers A B 100 101 102 103 Number of COVID-19-related papers 10 5 10 4 10 3 10 2 10 1 100 101 Surplus IF by a single COVID-19-related paper Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 2 Surplus impact factor (IF) by COVID-19-related publications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' A Journal impact factor increase by publishing COVID-19-related papers, where the simple superlinear growth y ∼ x1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='7 can characterize the growth pattern (dotted line).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Here, we applied a simple linear regression method to the logarithm of the values of interest to estimate the power-law scaling relationship between the IF and its surplus by COVID-19-related papers, assuming a simple power-law scaling of y = Cxk.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' B Increase in IF per COVID-19-related paper in proportion to the number of COVID-19-related papers published in journals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The increase is calculated by dividing the absolute difference in IF between papers including and excluding COVID-19 by the number of COVID-19-related papers in the journals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' In both A and B, the red dots represent the average value of surplus IF and the error bars show the standard deviation in log-scale.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' publishing one or more COVID-19-related papers in 2019 and 2020 dropped in IF in 2021, while the other 4,004 journals (84%) enhanced their IFs through the publication of COVID-19-related papers in the same period.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' For the for- mer, even though the journals decreased in IF by publishing COVID-19-related papers, the amount of decrease was limited.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Only one of these 763 journals (CA-A CANCER JOURNAL FOR CLINICIANS) dropped in IF by more than 1 (Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' S3).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Individual scientists have a greater tendency to cite widespread, popular journals than less popular journals due to psychological, sociological, and eco- nomic factors, leading to the rich-get-richer phenomenon of citation [22].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' For the COVID-19-related papers, we find that the surplus IF is proportional to the prior IF (Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 2).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' High correlation exists between IF and its surplus (Pear- son r = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='670, Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 2A), and their relationship is even superlinear (y = x1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='7).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' This pattern is also verified when we consider the relative advantage of IFs by dividing the surplus IF by the prior journal IF, which also shows a positive correlation (Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' S4).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' However, publishing numerous papers on COVID-19 did not necessar- ily increase the journal IF.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Instead, as more COVID-19-related papers were published, the gain in IF per COVID-19-related paper decreased (Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 2B).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Journals that published only one COVID-19-related paper in 2019 and 2020 increased their IF by 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='12 on average, whereas journals that published over Springer Nature 2021 LATEX template 6 Auditing citation polarization during the COVID-19 pandemic 500 papers in the same period increased their IF by only 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='0009.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' For example, the Lancet, the journal with the highest IF in JCR 2021, doubled its IF (from 93.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='04 to 189.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='25) while publishing only 46 COVID-19-related papers (9.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='4% of all citable items) in 2019 and 2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' To take one more extreme example, one journal that published only one COVID-19-related paper in 2019 and 2020 increased its IF by 37, whereas the journal that published the largest num- ber of COVID-19-related papers (730 papers) improved its IF by only 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='02.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' In other words, while a single well-chosen paper published during the pandemic could have potentially resulted in a significant increase in IF, publishing a large number of COVID-19-related papers did not provide many benefits.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Although allocating more shares to COVID-19-related papers correlates positively with the rise in the IFs of journals, the correlation is slight (Pearson r = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='110;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' see Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' S5).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' To confirm that COVID-19 research has legitimately increased journal IFs, we examine the correlation between IFs across years taking into account the existence of COVID-19-related papers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' When excluding COVID-19-related papers, the correlation between two consecutive years (Pearson r = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='957 between 2019 and 2020 and Pearson r = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='925 between 2020 and 2021) is significantly high.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The correlation between the IF in 2021 excluding COVID- 19-related papers and the IF in 2020 with COVID-19-related papers is r = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='926.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Thus, the overall trend of journal IF without papers related to COVID- 19 did not change significantly, and this high correlation suggests the existence of a linear relationship between the IFs for two consecutive years.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Incorporat- ing COVID-19-related papers, however, reduces the correlation between the IFs for 2020 and 2021 (Pearson r = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='850).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Note that we also observe a lower correlation between the IF for 2021 with COVID-19-related papers and the IF for 2020 without COVID-19-related papers (Pearson r = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='849), indicating non-linear relationships between the IFs of the two consecutive years.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Given that journals with a higher prior IF received a greater increase in IF from COVID-19-related papers (Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 2A), the publication of COVID-19 research may contribute to the polarization of journal IFs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='3 The Matthew effect of IF polarization during the pandemic In the preceding sections, we demonstrated that the publication of COVID-19- related research had a positive correlation with journal IFs, while the amount of increment had a strong correlation with the prior IFs of the journals (Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 2).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' One may wonder how much the overall journal landscape, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', the journal rankings, has changed due to the surplus IFs, or conversely, the magnitude of the change in IF based on the journal ranking [23].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' To demonstrate the influence of COVID-19-related publications on the landscape of JCR rankings, we compare the ratio of surplus IF in 2021 considering the journal rank in their research categories.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' On average, the publication of COVID-19-related papers increased the journal IF by 15.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='2% (dotted line in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 3).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The IFs of the top 10% prestige journals increased by 39.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='4%, while the IFs of the bottom 10% Springer Nature 2021 LATEX template Auditing citation polarization during the COVID-19 pandemic 7 100 101 Increase ratio Top 10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% 90-100% Journal Ranking Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 3 Relative ratio of surplus IF from publishing COVID-19-related papers by the 2021 journal rankings for JCR categories.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The ratio was calculated by subtracting the IF for 2021 excluding COVID-19-related papers from the IF for 2021 including COVID- 19-related papers and then dividing this value by the prior IF.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The dotted line indicates the average surplus IF from publishing COVID-19-related research (15.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='2%).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Here, the boxes represent the quartiles of the dataset except for points determined to be outliers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' journals increased by only 9.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='6% on average.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Most journals increased their IF by less than the average increase (15.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='2%) except for the top 20%.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' On average, higher-ranked journals gained more citations, and this trend is robust across all categories (Table S1).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The majority of journals with a significant increase in IF due to COVID- 19-related publications were already high-IF journals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Among all journals, 132 increased their IF by greater than twofold.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 55.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='3% of these 132 journals are in the top 10% of at least one of their research categories.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Only five journals fall within the bottom 10%.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' In terms of research category, 102 of the 132 journals (77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='3%) are classified into Clinical Medicine since the majority of COVID-19-related papers (70.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='9%) were published in this category.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' This proportion of highly cited articles in prestigious journals has the potential to exacerbate citation polarization.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Indeed, we found that more COVID-19-related articles were published in prestigious journals with a high Springer Nature 2021 LATEX template 8 Auditing citation polarization during the COVID-19 pandemic IF than in other journals (Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 4A).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' While the top 10% ranked journals pub- lished 26.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='3% (51,976) of all COVID-19-related papers from 2019 to 2021, the bottom 90% to 100% ranked journals published only 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='5% (6,977) in the same period.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' We also observe that the share of COVID-19-related papers decreases as the journal ranking falls (Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 4A).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Moreover, the proportion of highly cited papers exacerbates the disparity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Eighty-four percent (86) of the 102 papers with over 1000 citations were published by the top 10% journals, while jour- nals ranked 10 to 20 % published only eight of these studies.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' No papers with over 1000 citations were published in the bottom 50% journals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Citation is a stochastic multiplicative process, whereby papers with a higher citation count are more likely to receive additional citations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Even with simi- lar content between papers, those published in a more prominent location are more likely to be cited [19].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' In addition, earlier works may receive more cita- tions because citations are cumulative.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Indeed, we find that the majority of highly cited COVID-19-related papers were published during the early stage of the pandemic (early 2020), as shown in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 4B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The number of COVID-19- related publications gradually increased as the pandemic progressed (see the blue line in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 4B).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Despite this, papers with higher numbers of citations were generally published earlier.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' In conjunction with the finding that highly cited studies were likely to be published in prestigious journals, we may conclude that COVID-19-related studies were originally introduced in high IF journals, and that lower IF journals then cited the previous publications from high IF journals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' This growing pattern of citations could worsen the polarization of aca- demic journals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Publication of COVID-19-related research gave a significantly greater benefit to journals with higher IFs than to those with lower IFs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Con- sequently, the relative position (rank) of the majority of journals shows only minor changes, although the overall IF of all journals tended to increase dur- ing the pandemic.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Only 39.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='0% of journals that published COVID-19-related papers moved to a higher rank by including COVID-19-related research in one of their subject categories;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' among them, only 51.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='8% changed their IF quantile to a higher one (Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' S6).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The other 61.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='0% of journals maintained or decreased their ranking.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' In addition, significant increases or decreases in the ranks of journals were rarely observed (Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' S6).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The majority (90%) of the top 10% ranked journals maintained their position regardless of COVID-19 research, while the other 10% fell into the 10% to 20% group.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' In summary, i) the IF of journals increased overall by publishing COVID- 19-related research, ii) journals with higher IFs received greater benefits by publishing COVID-19-related research, and iii) the relative ranks of jour- nals did not change significantly from publishing COVID-19-related research.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' These findings lead to an interesting question: Did the publication of COVID- 19-related research actually increase the polarization of journals?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' To answer this, we applied the Gini coefficient [24], a well-known measure of income inequality, to the distribution of journal IFs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' In our investigation, the Gini coefficient measures the distribution of citations across journals within a JCR Springer Nature 2021 LATEX template Auditing citation polarization during the COVID-19 pandemic 9 All >10 >100 >1000 Number of citations received 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='8 Fraction of COVID-19-related papers A B C Top 10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% 90-100% 2020 2021 2022 Year 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='000 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='025 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='050 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='075 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='100 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='125 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='150 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='175 Probability density function all >10 >100 >1000 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='1 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='3 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='5 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='7 Gini coefficient excluding COVID-19-related papers 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='1 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='3 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='5 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='7 Gini coefficient including COVID-19-related papers Number of COVID-19 related papers 0 800 1600 2400 3200 Gini coefficient changes Increased Decreased Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 4 Distribution of COVID-19-related papers and their disparities.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' A Distribu- tion of COVID-19-related research by journal ranking.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' As the number of citations increases, the likelihood of papers belonging to high-IF journals increases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 26.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='4% of all papers were published in the top 10% ranked journals, whereas 85.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='3% of papers with more than 1000 cita- tions were published in the top 10% ranked journals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' B Distribution of COVID-19-related papers by publication date.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' From the beginning of the pandemic to mid-2020, the number of papers increased.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Approximately the same number of papers were published between then and the end of 2021.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Most of the highly cited papers (> 1000 citations) were published in early 2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' C Plot of the Gini coefficient of the IF distribution by JCR category.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Each dot represents a JCR category.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The Gini coefficient is computed using the IF distribution of journals in a particular category including and excluding COVID-19-related papers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Blue (orange) dots indicate an increase (decrease) in the Gini coefficient by publishing COVID- 19-related research.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The size of the dots is proportional to the number of COVID-19-related studies published in the category.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' category, ranging from 0 for the lowest heterogeneity (when all journals receive the same average number of citations) to 1 for the highest heterogeneity (when only a single journal receives all citations).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The trend illustrated by the difference in the Gini coefficient as a function of the number of COVID-19- related papers (see Figs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 4 and S7) implies that the disparity in the number of citations between journals increases as the number of COVID-19-related papers published increases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' In conclusion, based on the present snapshot of the Web of Science (WOS) dataset, we found that the general pattern of het- erogeneity, or polarization, among journals rises as the number of published COVID-19-related papers increases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 3 Discussion From the outset of the global COVID-19 pandemic, many scholars pursued the topic and published a massive number of studies in an unprecedentedly short period.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' We discovered a trend that, as a result of the intensive publica- tion, COVID-19-related papers acquired more citations than papers in other domains, which reflects its considerable attention in academia.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' We uncovered two significant consequences that may have led to a more severe polarization of journals in terms of citations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' First, 84% of journals that published COVID- 19-related papers in 2019 and 2020 increased their impact factors.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Second, prestigious journals were more likely to publish highly cited COVID-19-related papers than other journals (Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 3).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Springer Nature 2021 LATEX template 10 Auditing citation polarization during the COVID-19 pandemic Nonetheless, we demonstrated that publishing a large number of COVID- 19-related papers did not immediately boost a journal’s IF.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Increasing numbers of COVID-19-related papers published in a journal tended to diminish the citation impact of a single COVID-19-related article.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' In addition, we found that prestigious journals with a high prior IF gained more benefit (increased IF) from publishing COVID-19-related research, and also that the publications receiving the highest number of citations were predominantly published in prestige journals during the early stages of the pandemic.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Given that not all COVID-19-related publications increased their journal’s IF, one may assume that prestige journals simply have accepted and published more significant research.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' However, considering that some papers published in prestige journals were ultimately retracted [25, 26], the high number of citations given to these journals is not only based on the significance of the works but also based in part on the visibility of these journals, which can worsen the polarization of academic publishing (Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 4).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' As we could not explicitly assess the quality of each paper due to the scale of the dataset, it is unclear which of the two aforementioned characteristics (quality or visibility) has a larger impact on the current disparity in benefit from publishing COVID-19-related research.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' We believe that a more in-depth investigation of the relationship between research quality (or significance) and citations may be necessary to increase the impact of our findings.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Also, a more detailed understanding of such correlation should form the basis of explaining complex citation dynamics, yet we leave this for future research.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Despite its limitations, this study can provide important insights into citation dynamics and its effects on global events.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Because of the rich-get- richer nature of citations, papers published in prestigious journals tend to receive more citations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' As the relative ranking of the journals did not change significantly despite the increase in the overall IFs of journals publishing COVID-19-related research, fluctuations in IF may not well reflect the actual impact of academic publications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' This effect predominantly benefited well- established journals, while other journals did not experience benefits to the same extent (Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 4).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Our research indicates that IFs are vulnerable to exter- nal events.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The majority of the recent IF changes are attributable to citations of COVID-19-related publications;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' consequently, after the pandemic is over, the majority of the journals may revert to their pre-pandemic IF levels.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' It is challenging to evaluate academic journals or other participants (researchers, institutions, etc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=') using basic statistics because doing so reflects only a portion of actual scientific achievements.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Therefore, the simplified metrics employed by some governments [27] should be accompanied by a comprehensive and qualitative analysis of journals and individual papers for assessment.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The use of quantitative indicators such as the IF metric has been under debate.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The San Francisco Declaration on Research Assessment (DORA), which serves as the starting point for these discussions, explicitly states that the use of journal-based measures (such as IFs) should be avoided to act as a Springer Nature 2021 LATEX template Auditing citation polarization during the COVID-19 pandemic 11 proxy for the quality of individual research publications, to evaluate the con- tributions of an individual scientist, or to make hiring, promotion, or funding choices.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' In practice, however, many funders and institutions employ journal- based measures or the number of citations as markers for evaluation rather than assessing the quality of individual papers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The polarization of citations observed in this study demonstrates the inherent hazard of such indicators.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The IF metric is not a stable index against external shocks;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' it might fluctuate temporarily and then revert following external factors.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Along with the other well-known limitations of IF, such as skewed citation distributions within jour- nals [28, 29], the vulnerability of the IF metric as we found here indicates that it is increasingly inappropriate to consider journal IF as a proxy for an individual paper’s quality.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' During the current pandemic, the rapid release of COVID-19-related works resulted in less-qualified academic outputs to the public, leading to the retrac- tion of many publications [30, 31].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Unfortunately, this issue happened not only in journals with a reputation for a weak review process or low publishing dif- ficulty but also in prestigious journals that are widely respected.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Worse still, these retracted works earned a substantial number of citations and extensive media attention [32].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The general public may assume that papers published in academic journals are trustworthy and may likewise trust secondary sources such as scientific news reporting the results of academic findings.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' In the current context of appraising science and technology, there is a chance that content published in journals with strong indicators will be considered more reputable.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Scientists must inform the public that citation measures and journals are not equivalent to the quality of individual research publications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' In other words, the number of citations should not be the defining characteristic of quality research.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The contemporary ecosystem of research and technology is seemingly supported by scientists’ mutual trust and goodwill, and the public may view the scientific community’s findings with a similar level of confidence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Combined with the stability issue of the IF metric identified in this study, shouldn’t the current practice of over-reliance on citation indices be discontinued so as not to break this chain of trust?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' For this reason, we believe that responsible action based on actual societal influence is essential for all members of academia, as opposed to merely producing popular research to boost citation impact and one’s professional reputation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Methods Data We used publications and citation data from the XML dump of the Web of Science Core Collection, which is dated back to 2017 and updated until the 26th week of 2022.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The data includes complete copies of Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), and Arts & Humanities Citation Index (AHCI), along with the Emerging Sources Citation Springer Nature 2021 LATEX template 12 Auditing citation polarization during the COVID-19 pandemic Index (ESCI).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The data comprises 16,957,120 articles, 82,317 journals, and 116,086,223 references retrieved from papers published between 2017 and 2022.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' COVID-19-related publications COVID-19-related papers were retrieved from the Web of Science database (WOS, urlhttps://www.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='webofscience.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='com/) using the following search queries provided by Dimensions (https://www.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='dimensions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='ai/covid19/): "2019-nCoV" OR "COVID-19" OR \\SARS-CoV-2" OR "HCoV-2019" OR "hcov" OR "NCOVID-19" OR "severe acute respiratory syndrome coronavirus 2" OR "severe acute respiratory syndrome corona virus 2" OR \\coronavirus disease 2019" OR (("coronavirus" OR "corona virus") AND (Wuhan OR China OR novel)).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' We limited the publi- cations from 2019.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' A total of 251, 718 COVID-19-related papers were collected on 4 July 2022.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' We consider all other papers in the WOS that were not retrieved from the above searching process as non-COVID-19-related papers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Estimation of the power-law exponent In this study, the power-law exponents of the citation distribution in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 1A were estimated using the Python package named powerlaw [33].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Although all the citation distributions in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 1A seem to be heavy-tailed distribu- tions, which are commonly referred to as the power law, we verified that the distributions sincerely follow the power law via comparison with alterna- tive distributions (e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', log-normal or exponential).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' In the comparison with the exponential distribution, all distributions were found to be more likely to be power-law distributions rather than exponential (p < 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='001).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' However, comparison with the log-normal distribution was unclear.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Only non-COVID- 19-related papers published in 2021 better fit the power-law distribution in a statistically significant manner, while the other three were inconclusive (p var- ied 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='48–0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='60).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' In this study, we estimated the power-law exponent with the assumption of a simple power law (y ∼ xk) regardless of the best fit distribu- tion, as we were more interested in comparing the thickness of the tails than in determining the exact exponents.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Reproduction of the journal impact factors Although we extracted the total number of publications in the WOS with a complete copy of the WOS provided by Clarivate, minor differences can be presented mainly because the WOS does not report detailed methods to filter the dataset, e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', dump dates and the coverage of citable items.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Thus, to reproduce and estimate the journal impact factors (IFs), we followed the method used for the JCR impact factor [2] but with an in-house XML copy of the Web of Science, as follows: Springer Nature 2021 LATEX template Auditing citation polarization during the COVID-19 pandemic 13 IF = citations received by items published in the past 2 years number of citable items published in the past 2 years .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' (1) We limited the citable items to those belonging to the journals indexed in SCI-Expanded, SSCI, and A&HCI.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' We also considered as citable items only articles, review papers, and proceedings papers in terms of publication type;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' however, publication types were not considered when computing the number of citations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Note that, as of 2020, Clarivate Inc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' now considers early access publications as regular publications and includes them in the calculation of IF.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' For instance, if an article is published as early access in 2020 and officially published in 2021, then the article is counted as a citable item published in 2020, taking into account the references as the citations occurred in 2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The article is not considered in 2021.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' With this procedure, we successfully reproduced IF scores highly correlated with the IFs provided by Clarivate JCR (Pearson r = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='99;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' see Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' S8).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' In this study, we refer to the value computed from Eq.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 1 as IF instead of the impact factor provided by JCR unless otherwise specified.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' When computing the IFs excluding COVID-19-related papers, we counted out the COVID-19- related citable items and their references from the denominator and numerator in Eq.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 1, respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Acknowledgement This research was supported by the MSIT (Ministry of Science and ICT), Republic of Korea, under the Innovative Human Resource Development for Local Intellectualization support program (IITP-2022-RS-2022-00156360) supervised by the IITP (Institute for Information & Communications Tech- nology Planning & Evaluation).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' This work was also supported by the National Research Foundation of Korea (NRF) funded by the Korean government (grant No.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' NRF-2022R1C1C2004277 (T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=') and 2022R1A2C1091324 (J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=')).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The Korea Institute of Science and Technology Information (KISTI) also supported this research with grant No.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' K-23-L03-C01 (J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=') and by providing KREONET, a high-speed Internet connection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Ethics declarations Competing interests The authors declare no competing interests.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Springer Nature 2021 LATEX template 14 Auditing citation polarization during the COVID-19 pandemic References [1] Ioannidis, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Bendavid, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Salholz-Hillel, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Boyack, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' & Baas, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Massive covidization of research citations and the citation elite.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Proceed- ings of the National Academy of Sciences of the United States of America 119(28):e2204074,119 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [2] McVeigh, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Journal citation reports 2022: A preview.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' https:// clarivate.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='com/blog/journal-citation-reports-2022-a-preview/ [3] Pendlebury, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The use and misuse of journal metrics and other citation indicators.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Archivum immunologiae et therapiae experimentalis 57(1):1–11 (2009).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [4] Larivi`ere, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Kiermer, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', MacCallum, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', McNutt, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Patterson, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Pulverer, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Swaminathan, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Taylor, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' & Curry, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' A simple proposal for the publication of journal citation distributions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Preprint at https:// www.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='biorxiv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='org/content/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='1101/062109v2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='abstract (2016).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [5] Moed, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Measuring contextual citation impact of scientific journals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Journal of Informetrics 4(3):265–277 (2010).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [6] Bradshaw, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' & Brook.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' How to rank journals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' PLoS One 11(3):e0149,852 (2016).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [7] Garfield, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Citation analysis as a tool in journal evaluation: Journals can be ranked by frequency and impact of citations for science policy studies.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Science 178(4060):471–479 (1972).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [8] Lozano, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Larivi`ere, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' & Gingras, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The weakening relation- ship between the impact factor and papers’ citations in the digital age.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Journal of the American Society for Information Science and Technology 63(11):2140–2145 (2012).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [9] Calcagno, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Demoinet, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Gollner, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Guidi, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Ruths, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' & de Mazan- court, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Flows of research manuscripts among scientific journals reveal hidden submission patterns.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Science 338(6110):1065–1069 (2012).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [10] Rafols, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Leydesdorff, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', O’Hare, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Nightingale, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' & Stirling, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' How journal rankings can suppress interdisciplinary research: A comparison between innovation studies and business & management.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Research Policy 41(7):1262–1282 (2012).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [11] You, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Park.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Lee, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Yun, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' & Jung, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='-S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Disturbance of ques- tionable publishing to academia.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Journal of Informetrics 16(2):101,294 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Springer Nature 2021 LATEX template Auditing citation polarization during the COVID-19 pandemic 15 [12] Merton, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=': The matthew effect in science: The reward and com- munication systems of science are considered.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Science 159(3810):56–63 (1968).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [13] Perc, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The matthew effect in empirical data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Journal of The Royal Society Interface 11(98):20140,378 (2014).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [14] Bol, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', de Vaan, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' & van de Rijt, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The matthew effect in science funding.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Proceedings of the National Academy of Sciences of the United States of America 115(19):4887–4890 (2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [15] Nielsen, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' & Andersen, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Global citation inequality is on the rise.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Proceedings of the National Academy of Sciences of the United States of America 118(7):e2012208,118 (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [16] Huang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Gates, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Sinatra, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' & Barab´asi, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Historical compari- son of gender inequality in scientific careers across countries and disciplines.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Proceedings of the National Academy of Sciences of the United States of America 117(9):4609–4616 (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [17] Lawson, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Geuna, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Finardi, U.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=': The funding-productivity-gender nexus in science, a multistage analysis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Research Policy 50(3):104,182 (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [18] Petersen, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Jung, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='-S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Yang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='-S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' & Stanley, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Quantitative and empirical demonstration of the matthew effect in a study of career longevity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Proceedings of the National Academy of Sciences of the United States of America 108(1):18–23 (2011).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [19] Larivi`ere, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' & Gingras, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The impact factor’s matthew effect: A nat- ural experiment in bibliometrics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Journal of the American Society for Information Science and Technology 61(2):424–427 (2010).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [20] Allison, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Long, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' & Krauze, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Cumulative advantage and inequality in science.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' American Sociological Review 615–62 (1982).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [21] Van de Rijt, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Kang, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Restivo, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' & Patil, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Field experiments of success-breeds-success dynamics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Proceedings of the National Academy of Sciences of the United States of America 111(19):6934–6939 (2014).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [22] Wang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Unpacking the matthew effect in citations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Journal of Informet- rics 8(2):329–339 (2014).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [23] Quaderi, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Journal citation reports 2022: Covid- 19 research continues to drive increased citation impact.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Clarivate (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' https://clarivate.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='com/blog/ journal-citation-reports-2022-covid-19-research-continues-to-drive-increased-citation-impact/ Springer Nature 2021 LATEX template 16 Auditing citation polarization during the COVID-19 pandemic [24] Gini, C Variabilit`a e mutabilit`a: contributo allo studio delle distribuzioni e delle relazioni statistiche.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='[Fasc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Tipogr.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' di P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Cuppini (1912) [25] Mehra, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Ruschitzka, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' & Patel, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Retrac- tion—hydroxychloroquine or chloroquine with or without a macrolide for treatment of covid-19: a multinational registry analysis (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [26] Mehra, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Desai, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Kuy, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Henry, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' & Patel, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Retrac- tion: cardiovascular disease, drug therapy, and mortality in covid-19.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' N ENGL J MED.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' doi:10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='1056/nejmoa2007621.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [27] Van Dalen, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' & Henkens, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Intended and unintended consequences of a publish-or-perish culture: A worldwide survey.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Journal of the American Society for Information Science and Technology 63(7):1282–1293 (2012).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [28] Van Leeuwen, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' & Moed, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Characteristics of journal impact factors: The effects of uncitedness and citation distribution on the under- standing of journal impact factors.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Scientometrics 63(2):357–371 (2005).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [29] Bornmann, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' & Leydesdorff, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Skewness of citation impact data and covariates of citation distributions: A large-scale empirical analysis based on web of science data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Journal of Informetrics 11(1):164–175 (2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [30] Quinn, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Burton, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Carter, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Cooper, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Dwan, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Field, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Freeman, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Geue, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Hsieh, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', McGill, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Nevill, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Rana, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Sutton, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Rowan, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' & Xin, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Following the science?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' comparison of methodological and reporting quality of covid-19 and other research from the first wave of the pandemic.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' BMC Medicine 19(1):1–10 (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [31] El-Menyar, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Mekkodathil, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Asim, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Consunji, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Rizoli, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Abdel- Aziz Bahey, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' & Al-Thani, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Publications and retracted articles of covid-19 pharmacotherapy-related research: A systematic review.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Science Progress 104(2):00368504211016,936 (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [32] Khan, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Gupta, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Zimba, O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' & Gupta, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Bibliometric and altmet- ric analysis of retracted articles on covid-19.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Journal of Korean Medical Science 37(6) (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' [33] Alstott, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', Bullmore, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' & Plenz.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=', D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' powerlaw: a python package for analysis of heavy-tailed distributions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' PloS One 9(1):e85,777 (2014).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Springer Nature 2021 LATEX template Auditing citation polarization during the COVID-19 pandemic 1 Supplementary Information for Auditing citation polarization during the COVID-19 pandemic Taekho You, Jinseo Park, June Young Lee, Jinhyuk Yun∗ ∗Corresponding author.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Email: jinhyuk.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='yun@ssu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='ac.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='kr This PDF file includes: Supplementary table S1 Figures.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' S1 to S8 Springer Nature 2021 LATEX template 2 Auditing citation polarization during the COVID-19 pandemic Figure S1 Probability density function of the citation time difference between the publication and the citation month of the papers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' For the plot, the COVID-19- related and non-COVID-19-related papers published in 2020 and 2021 were used.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Figure S2 Citation distribution of COVID-19-related and non-COVID-19- related papers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The citation distribution that can contribute to the annual IF calculation (left) is the same distribution as in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 1A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The separated citation distributions in one- year (middle) and two-year (right) time gaps show the same pattern.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Both plots show that COVID-19-related papers have a heavier-tailed distribution.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=" Non-COviD-19-relatedpapers COVID-19-relatedpapers 10'0 Probability density function 0." metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='06 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='05 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='04 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content="03 乙O'0 0." metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='01 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0 5 10 15 20 Citationsafterpublication (month)100 100 100 10-1 10-1 10-1,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Function 10-2,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 10-2 Density 10-3 10-3,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 10-3,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Cumulative 10-4 10-4 10-4 Non-COVID-19 papers (2017) 10-5,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='Non-COVID-19 papers (2017+2018) ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='10-5 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='Non-COVID-19 papers (2018) ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='10-5 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='cOVID-19 papers (2018+2019) ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='COVID-19 papers (2019) ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='Non-COVID-19 papers (2017) ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='Non-COVID-19 papers (2018+2019) ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='Non-COVID-19 papers (2019) ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='Non-COVID-19 papers (2018) ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='10-6 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='COVID-19 papers (2019+2020) ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='COVID-19 papers (2020) ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='COVID-19 papers (2019) ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='10-6 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='Non-COVID-19 papers (2019+2020) ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='Non-COVID-19 papers (2020) ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='Non-COVID-19 papers (2019) ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='100 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='101 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='102 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='103 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='104 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='100 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='101 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='102 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='103 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='104 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='100 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='101 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='102 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='103 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='104 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='Number of Citations Received ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='Number of Citations Received ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='Number of Citations ReceivedSpringer Nature 2021 LATEX template ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='Auditing citation polarization during the COVID-19 pandemic ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='3 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='Table S1 Relative ratio of surplus impact factor (IF) from publishing COVID-19-related papers by journal category classified by JCR.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The bold numbers represent the highest ratio of surplus IF in each category.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Journal Category Top 10% 10–20% 20–30% 30–40% 40–50% 50–60% 60–70% 70–80% 80–90% 90–100% Agricultural Science 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='664 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='098 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='111 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='040 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='002 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='029 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='078 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='051 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='039 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='062 Arts & Humanities, Interdisciplinary 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='209 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='399 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='146 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='046 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='999 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='086 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='165 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='995 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='990 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='982 Biology & Biochemistry 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='286 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='103 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='101 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='090 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='064 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='067 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='115 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='062 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='066 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='081 Chemistry 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='104 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='043 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='040 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='042 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='038 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='055 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='063 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='026 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='046 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='109 Clinical Medicine 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='508 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='251 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='189 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='162 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='152 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='127 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='142 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='106 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='102 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='111 Computer Science 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='112 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='118 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='125 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='079 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='061 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='038 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='090 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='041 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='071 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='053 Economics & Business 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='423 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='161 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='180 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='115 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='105 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='088 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='103 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='075 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='078 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='024 Engineering 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='044 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='109 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='041 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='048 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='029 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='053 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='027 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='026 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='037 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='146 Environment/Ecology 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='388 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='209 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='201 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='135 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='129 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='112 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='126 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='106 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='074 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='121 Geosciences 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='024 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='012 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='037 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='018 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='018 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='002 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='021 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='033 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='041 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='001 History & Archaeology 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='162 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='165 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='121 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='072 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='114 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='994 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='069 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='003 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='988 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='969 Literature & Language 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='106 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='175 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='111 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='205 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='026 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='136 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='110 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='050 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='019 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='045 Material Science 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='017 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='018 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='010 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='024 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='009 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='010 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='008 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='011 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='024 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='054 Mathematics 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='053 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='046 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='139 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='039 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='052 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='062 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='061 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='016 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='080 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='013 Multidisciplinary 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='102 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='086 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='081 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='081 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='056 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='066 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='057 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='048 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='061 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='030 Philosophy & Religion 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='232 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='219 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='221 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='155 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='067 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='141 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='131 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='010 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='066 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='046 Physics 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='058 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='057 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='039 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='025 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='018 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='030 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='030 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='028 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='014 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='073 Plant & Animal Science 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='102 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='046 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='055 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='020 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='068 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='063 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='096 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='022 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='039 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='027 Psychiatry/Psychology 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='666 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='341 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='183 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='153 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='121 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='227 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='137 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='176 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='137 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='136 Social Sciences, General 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='389 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='218 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='182 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='147 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='130 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='073 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='087 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='070 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='088 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='033 Visual & Performing Arts 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='088 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='095 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='069 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='109 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='049 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='138 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='038 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='041 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='025 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='115 Springer Nature 2021 LATEX template 4 Auditing citation polarization during the COVID-19 pandemic Figure S3 Probability density function of journals with IFs that increased or decreased by publishing COVID-19-related papers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The IFs of 763 journals (16%) decreased while the IFs of 4004 journals (84%) increased.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' A The pdf for the absolute increase in IF.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' B The pdf for the relative increase in IF divided by the IF excluding COVID-19-related papers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Figure S4 Relative IF increase by publishing COVID-19-related papers relative to the journal’s IF.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The extent of IF increase is divided by the IF excluding COVID-19- related papers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The red squares and error bars respectively show the average value and the standard deviation of the increased IF in log-scale.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Increased 103 Increased Decreased Decreased 102 102 Probability density function 101 Probability density function 101 100 100 10 10-1 10-2 10-2, 10-3 10-4 10-3 10-5 10-3 10-1 100 101 102 10-5 10-3 10-1 100 101 Increase (decrease) of IF Relative increase fdecreasej of IF101 papers 100 [9-related by COVID-1!' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 10-2 10-2 10-1 100 101 102 Impact factor (IF)Springer Nature 2021 LATEX template Auditing citation polarization during the COVID-19 pandemic 5 Figure S5 Fraction of COVID-19-related papers published in journals by the journal IFs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The red squares and error bars respectively show the average value and the standard deviation of the fraction of COVID-19-related papers in log-scale.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The Pearson correlation is 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='110.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Figure S6 Change in journal ranking by publishing COVID-19-related papers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The values show the change rate between ranking groups by publishing COVID-19-related papers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Both Pearson and Spearman rank correlations of the IF ranks are 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='99.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' A Rank change of all journals that published COVID-19-related papers in their category.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' B Rank change of the journals that published COVID-19-related papers, where less than 10% of the journals published COVID-19-related papers in their category.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' A B 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='0 papers Top 10% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='90 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='11 EO0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='02 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='01 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='01 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 Top 10% 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='08 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 paper 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='8 10-20% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='10 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='75 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='13 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='03 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='02 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='01 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='01 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 10-20% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='92 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 COVID-19 COVID-19 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='8 20-30% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='14 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='68 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='15 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='06 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='02 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content="01 1O'0 0." metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='01 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 20-30% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='12 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='08 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 30-40% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='01 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='02 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='01 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='14 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='64 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='14 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='06 30-40% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='88 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='17 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 including including 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='6 40-50% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='15 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='61 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='13 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='05 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='02 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='01 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 40-50% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='83 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='09 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 I ranking by IF i 50-60% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='01 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='16 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='63 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='12 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='05 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='01 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='4 50-60% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='92 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='18 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 F by 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='4 60-70% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='01 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='15 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='64 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='14 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='05 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='01 60-70% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='73 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='09 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 I ranking !' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' 70-80% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='13 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='65 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='12 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='02 70-80% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='91 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='09 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='2 80-90% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='11 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='11 euuno 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='71 80-90% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='82 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='07 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='86 90-100% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='09 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='00 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='0 oo ol ola oo ola 10 lo olo olo ofo 30° 20 TOP 70 100° ToF 30 JournalrankingbyIF excluding COVID-19 papers Journal ranking by IF excluding COVID-19 papers100 Fraction of COVID-19-related papers 10-4 10-2 10-1 100 101 102 Impact factorSpringer Nature 2021 LATEX template 6 Auditing citation polarization during the COVID-19 pandemic Figure S7 Change in the Gini coefficients of the JCR categories by the number of published COVID-19-related papers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' A positive correlation between the number of COVID-19-related papers and the changes in the Gini coefficients of the categories is observed (Pearson r = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='545).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' Figure S8 Correlation between the IFs provided by JCR and those calculated in this paper.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' The Pearson correlation is high for both years 2020 and 2021.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content=' work 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='20 19-related COVID-1 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='15 Ag 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='10 increased coefficient 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content="05 00'0 Gini 100 101 102 103 Number of COVID-19-related papers2020 2021 Pearson:0." metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='997 Pearson:0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} +page_content='998 500 250 400 200 lated 150 Icul lcul cal 200 F 100 100 50 0 0 100 200 300 400 500 0 50 100 150 200 250 300 IF provided by JCR IF provided by JCR' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5dAzT4oBgHgl3EQf9_4T/content/2301.01926v1.pdf'} diff --git a/5tAzT4oBgHgl3EQf9_5e/content/tmp_files/2301.01927v1.pdf.txt b/5tAzT4oBgHgl3EQf9_5e/content/tmp_files/2301.01927v1.pdf.txt new file mode 100644 index 0000000000000000000000000000000000000000..fc4b7823212960b22417689fa1bc314e5454ae51 --- /dev/null +++ b/5tAzT4oBgHgl3EQf9_5e/content/tmp_files/2301.01927v1.pdf.txt @@ -0,0 +1,1066 @@ +Towards simultaneous coherent radiation in the +visible and microwave bands with doped +molecular crystals +Hao Wu,†,‡,§ Tong Li,†,‡,§ Zhang-Qi Yin,† Jiyang Ma,∗,†,‡ Xu-Ri Yao,†,‡ Bo +Zhang,†,‡ Mark Oxborrow,¶ and Qing Zhao†,‡ +†Center for Quantum Technology Research and Key Laboratory of Advanced Optoelectronic +Quantum Architecture and Measurements (MOE), School of Physics, Beijing Institute of +Technology, Beijing 100081, China +‡Beijing Academy of Quantum Information Sciences, Beijing 100193, China +¶Department of Materials, Imperial College London, South Kensington, SW7 2AZ London, +United Kingdom +§These authors contributed equally: Hao Wu, Tong Li +E-mail: mjy@bit.edu.cn +Abstract +Coherent sources exploiting the stimulated emission of non-equilibrium quantum +systems, i.e. gain media, have proven indispensable for advancing fundamental research +and engineering. The operating electromagnetic bands of such coherent sources have +been continuously enriched for increasing demands. Nevertheless, for a single bench- +top coherent source, simultaneous generation of radiation in multiple bands, especially +when the bands are widely separated, present formidable challenges with a single gain +medium. Here, we propose a mechanism of simultaneously realizing the stimulated +1 +arXiv:2301.01927v1 [physics.optics] 5 Jan 2023 + +emission of radiation in the visible and microwave bands, i.e. lasing and masing ac- +tions, at ambient conditions by utilizing photoexcited singlet and triplet states of the +pentacene molecules that are doped in p-terphenyl. The possibility is validated by the +observed amplified spontaneous emission (ASE) at 645 nm with a narrow linewidth +around 1 nm from the pentacene-doped p-terphenyl crystal used for masing at 1.45 +GHz and consolidated by a 20-fold-lower threshold of ASE compared to the reported +masing threshold. The overall threshold of the pentacene-based multiband coherent +source can be optimized by appropriate alignment of the pump-light polarization with +the pentacene’s transition dipole moment. Our work not only shows a great promise on +immediate realization of multiband coherent sources but also establishes an intriguing +solid-state platform for fundamental research of quantum optics in multiple frequency +domains. +Introduction +Coherent sources that capable of generating coherent electromagnetic radiation have served +as crucial ingredients enabling numerous breakthroughs in the fields of physical, environmen- +tal, and biological sciences. A well-established approach to achieve coherent electromagnetic +radiation is to exploit the stimulated emission process1 induced by interactions between elec- +tromagnetic fields and matters. Stemming from the discovery of the stimulated emission, +optical lasers spanning from the ultraviolet to the near-infrared region, together with their +forerunners and microwave analogs, masers, have become indispensable coherent sources for +applications in telecommunication,2 metrology,3,4 sensing,5–7 machining8 and quantum infor- +mation.9,10 In addition, the recent development of terahertz11 and X-ray12 lasers has offered +alluring promise of shedding light on astronomical observations, spectroscopy and structural +biology. The rich variety of applications require distinct coherent sources operational across +the extremely wide electromagnetic spectrum from microwave to X-rays. Even though the +underlying mechanism of those coherent sources is the same (i.e. the stimulated emission), +2 + +their practical realizations are completely different in terms of the components, structures +and scale, rendering simultaneous generation of coherent radiation in widely separated spec- +trum bands with a single source hitherto unattainable on the bench top. +Gain media, constituted by the quantum systems with non-degenerate energy levels, are +the core determining radiation wavelengths of the stimulated emission, therefore, vital for +realizing multiband coherent sources. It is not scarce for quantum systems to possess multiple +transitions across different spectrum bands by their intrinsic properties (e.g. large quantum +numbers13) and/or external manipulations with electric/magnetic fields.14 Ruby (chromium +ions-doped aluminum oxide) is a representative gain medium which can be employed for +both solid-state masers15 and lasers,16 i.e. capable of generating coherent radiation in the +microwave and visible regions, while the vastly different experimental setups, especially the +cryogenic and magnetic-field requirements for ruby masers, have resulted in no demonstration +of simultaneous maser and laser actions of ruby to date. More recently, the negatively charged +nitrogen-vacancy defects (NV−) in diamond have been demonstrated to be promising solid- +state maser17 and laser18 gain media at ambient conditions. However, the preparations and +material properties of the NV− diamonds employed for the maser and laser applications +are rather different. The diamonds were synthesized via the routes of high pressure high +temperature (HPHT) and chemical vapor deposition (CVD) for the NV− laser and maser, +respectively, which gives rise to the different concentrations of the doped nitrogen atoms and +NV−. The concentration of the gain media, i.e. NV−, required for the laser action is 1.4 +p.p.m. which is about four folds higher than that (0.36 p.p.m.) used for the NV− maser. +With respect to the performance of the NV− diamond based coherent sources, the relatively +weak output (∼1 pW) of the NV− maser17 and the broad spectral linewidth (20 nm) of the +NV− laser18 still need to be substantially improved for practical considerations. In addition +to the solid-state systems, due to the rich energy structures, the stimulated emission of +radiation ranging from the microwave to infrared (IR) domain have been observed in vapor +of Rydberg atoms19,20 but their ability of simultaneous generation of coherent radiation in +3 + +multiple wavelength domains is still not evident. Moreover, the limited number of atoms +has also restricted the power of such coherent sources. The output power of the Rydberg +masers has been reported to be up to 10 pW21 and the pulse energy of a Rydberg IR laser +was approximately 1 µJ.20 +Compared to the gain media mentioned above, doped molecular crystals combines the key +advantages of the inorganic solid-state systems (e.g. robustness and ease of integration) with +rich opportunities of tuning the energy levels of the quantum systems, like Rydberg atoms, +through bottom-up engineering of the guests and host matrices.22,23 While sustaining the +satisfying optical, magnetic and electronic properties, the doping concentrations of molecular +crystals are tunable up to tens of thousands p.p.m.24 implying the great potential of realizing +powerful coherent sources with such gain media. Among numerous doped molecular crystals, +pentacene-doped p-terphenyl (Pc:Ptp) has attracted extensive attention especially in the +last decade. +Pc:Ptp is a low-cost and easy-fabricated single organic crystal, which can +be produced in bulk with a high quality.25 Due to the doping structure, the functional +dopants, i.e. pentacene molecules, are well protected by the matrix of p-terphenyl, which +not only possess great steadiness and durability but also emerge appealing photophysical +properties (which are lacking in neat pentacene) well suited for multidisciplinary applications, +such as photovoltaics,24 dynamic nuclear polarization (DNP)26 and quantum information +processing.10,27,28 In particular, Pc:Ptp is the first, and so far the only doped molecular crystal +capable of masing at room temperature in Earth’s field29 and the superior maser output at +a level of milliwatt (i.e. 109 times higher than that of NV− masers) has yet to be surpassed +by other room-temperature maser gain media. The maser application as well as the recent +applications mentioned above mainly exploits the photoexcited triplet states of pentacene in +p-terphenyl. It is worth noting that the singlet states of pentacene are also intriguing due to +their photoluminescent properties, owing to which the stimulated emission of radiation in the +visible region manifesting as amplified spontaneous emission (ASE) have been observed30,31 +implying the potential of the pentacene molecules to be suitable gain media for lasing as +4 + +well. However, since the ASE phenomena were observed with pentacene doped in trans-1,4- +distyrylbenzene (trans-DSB)30 and 1,4-bis(2-cyano styryl)benzene (2-CSB),31 respectively, +instead of p-terphenyl, and the dynamics of pentacene’s triplet spins can be modulated by +the host effects,32 the suitability of these two host matrices for the maser action of pentacene +remains elusive. +In this work, we investigate the optical emission properties of the Pc:Ptp crystals em- +ployed in the previous maser studies6,27,33 and first demonstrate the ASE process in Pc:Ptp at +645 nm, where molecular crystals rarely reached, under the experimental conditions identical +to those required for the maser action. Combining spectral analysis with nanosecond time- +resolved characterizations, the ASE properties of Pc:Ptp are systematically studied at room +temperature. The obtained ASE spectrum shows an extremely narrow linewidth around 1 +nm that is the narrowest among the reported molecular crystals revealing ASE behaviors. +Since ASE is a prerequisite for lasing, our results provide solid evidence that Pc:Ptp can +serve as a solid-state multiband gain medium for emitting coherent microwave and visible +light simultaneously at ambient conditions. +Results +Structural and optical properties of Pc:Ptp +Throughout the study, pink Pc:Ptp single crystals, shown in Fig. 1a, with a doping concen- +tration of 1000 p.p.m. was used, which is the same as that employed for room-temperature +masers.6,27,33 The relatively high doping concentration allowing sufficient pentacene molecules +for microwave and optical gains arises from the similar molecular packing coefficients of pen- +tacene and p-terphenyl which favors the formation of solid solutions with these two organic +substances.34,35 The molecular packing coefficient k can be expressed as k = (z × V0)/V , +where z is the number of molecules in the unit cell, V0 and V are the volumes of the molecule +and unit cell, respectively. The k values of pentacene and p-terphenyl have been determined +5 + +b +c +400 μm +ab plane +a +d +e +S0 +S1 +510 nm +550 nm +590 nm +475 nm + +λ = 645 nm +T2 +T1 +Tx +Ty +Tz + Intersystem crossing +Non- +radiative +transition +Radiative +transition +Maser transition + +fxz = 1.45 GHz +Internal +conversion +Site 1 +Site 2 +b +a +c +x +z +y +500 +600 +700 +800 +0.0 +0.5 +1.0 +Normalized intensity (a.u.) +Wavelength (nm) + Fluorescence + Fitting +27.6 + 0.3 nm +400 +500 +600 +700 +0.0 +0.5 +1.0 +Normalized absorbance (a.u.) +Wavelength (nm) + Absorbance +Laser transition +0-1 +} +475 nm +510 nm +550 nm +590 nm +645 nm +599 nm +701 nm +± +1 mm +Figure 1: Material characterizations of Pc:Ptp and mechanism of simultaneous +coherent radiation. a Optical microscopic image of a Pc:Ptp crystal under white-light +illumination. The cleavage plane, i.e. ab plane, of the crystal is labelled. b Crystal struc- +ture of the host matrix, p-terphenyl (blue) with substitutionally doped pentacene molecules +(pink). The two inequivalent doping sites as well as the molecular axes of pentacene are +labelled. c UV/vis absorption and d Fluorescence spectra of Pc:Ptp with all characteristic +peaks labelled. The linewidth, i.e. FWHM of the strongest fluorescent peak is determined +by a Lorentzian fitting. Inset: optical microscopic image of a fluorescent Pc:Ptp with the +excitation light filtered. e Proposed mechanism of simultaneous lasing and masing actions +in Pc:Ptp exploiting the transitions of stimulated emission highlighted in the pentacene’s +singlet (pink) and triplet (orange) manifolds. +6 + +to be 0.743 and 0.751 that brings them adjacent to the closest packing condition where k= +0.74.34 As shown in Fig. 1b, at room temperature, pentacene molecules are substitutionally +doped in the monoclinic unit cell of p-terphenyl with two inequivalent doping sites.36 The +structural properties of Pc:Ptp crystals are dominated by the host matrix, i.e. p-terphenyl. +Therefore, Pc:Ptp also possesses an intrinsic laminar structure with a cleavage (001) (i.e. +ab) plane25 where the molecules stand (see Fig. 1b). The ‘head-to-head’ packing against +the ab plane results in the weaker intermolecular interactions compared to the π − π inter- +actions along the molecular xy plane where x and y denote the long and short axes of the +molecules, respectively. The cleavage property facilitates the fabrication of Pc:Ptp crystals +with distinguishable crystal planes. As exhibited in Fig. 1a, the large crystal facet can be +straightforwardly determined to be the (001) plane due to the obvious delamination shown +on the edge. +The optical properties of Pc:Ptp were investigated by measuring its absorption and flu- +orescence spectra as illustrated in Fig. 1c and 1d. It can be found that the doped crystal +reveals explicit absorption at wavelengths of 475, 510, 550 and 590 nm, which correspond to +the characteristic transitions between pentacene’s excited and ground singlet states25,37 as +depicted in Fig. 1e. According to the absorption spectrum, the highest absorbance locates +at 590 nm was referred to determine the optimal wavelength for optically pumping Pc:Ptp in +the following measurements. In terms of the photoluminescent property of Pc:Ptp, Fig. 1d +indicates that the crystal can generate intense fluorescence at wavelengths of 599 and 645 +nm, corresponding to the 0-0 and 0-1 transitions,30 respectively, as well as a weak emission +band central at 701 nm under illumination of a green light-emitting diode (LED). The small +peak near 550 nm is attributed to the emission of LED which was not completed filtered +during the fluorescence measurements. The full width at half maximum (FWHM) of the +strongest fluorescence peak at 645 nm is measured to be 27.6±0.3 nm. +7 + +Mechanism of the simultaneous lasing and masing actions in Pc:Ptp +Combining the optical properties obtained above with the reported properties of Pc:Ptp +masers,29,38 we propose the mechanism of realizing simultaneous lasing and masing actions +by exploiting both the photoexcited singlet and triplet states of Pc:Ptp crystals. As schemat- +ically demonstrated in Fig. 1e, the pentacene molecules in the ground singlet state can be +efficiently promoted to the excited singlet state with an optical pumping at 590 nm, by which +the population inversion is achieved in the singlet manifold for the stimulated emission of ra- +diation in the visible region, e.g. 599 or 645 nm where the strong fluorescence was observed. +In the meantime, due to the spin-orbit coupling, pentacene molecules can also transfer to +the excited triplet state (T2) via the intersystem crossing with a yield of 62.5% at room +temperature39 and rapidly decay to the lowest triplet state (T1) via the internal conversion. +Since the triplet state is metastable, the pentacene molecules will eventually decay back to +the ground singlet state by either the radiative or non-radiative T1 →S0 transition.40,41 In +Earth’s field, T1 is non-degenerate and constituted by three sublevels Tx, Ty and Tz due to +the dipolar interactions of pentacene’s triplet electron spins. The resonance frequencies of +the triplet sublevels governed by the zero-field-splitting (ZFS) parameters have been deter- +mined by electron paramagnetic resonance (EPR) measurements42,43 to be 1.45 GHz, 1.344 +GHz and 106.5 MHz for Tx ↔Tz, Ty ↔Tz and Tx ↔Ty transitions, respectively. An alluring +property of the pentacene’s lowest triplet state is that, upon its generation, the populations +of the triplet electrons follow a non-Boltzmann distribution in the three sublevels with a ratio +of Px : Py : Pz= 0.76:0.16:0.0844 at room temperature. The strong population inversion and +relatively slow spin-lattice relaxation43 between the Tx and Tz sublevels can be exploited for +realizing the stimulated emission of radiation in the microwave region, i.e. masing. There- +fore, the optical pumping of Pc:Ptp can simultaneously introduce population inversions in +both singlet and triplet manifolds fulfilling the prerequisites of the lasing and masing actions +at ambient conditions. +8 + +Spectral analysis of the ASE of Pc:Ptp +As the masing action has been successfully demonstrated with the Pc:Ptp crystal,27,33 we +verify the mechanism of multiband coherent radiation proposed above by investigating the +feasibility of the stimulated emission in the pentacene’s singlet states under the experimental +conditions similar to that for the maser studies. We measured the emission spectra of Pc:Ptp +under the optical pumping of a nanosecond pulsed laser which has proven to be sufficiently +powerful for achieving the threshold of Pc:Ptp masers.38 The pump laser was focused onto +the cleavage plane of the crystal with a beam diameter of about 5 mm (see Supplementary +Fig. 2 for the detailed experimental setup). At different pump intensities, the emission +spectra shown in Fig. 2a were recorded by collecting the emitted light from the edge of the +sample (see inset in Fig. 2a). It can be seen that at the peak wavelength (i.e. 645 nm) of +the Pc:Ptp’s emission spectra, the intensity gradually increases while the spectral linewidth +equal to the FWHM gets narrower with the increment of pump energies implying the ASE +process occurs. The incomplete emission peaks at the wavelength of 600 nm is due to the +long pass filter with a cut-on wavelength of 600 nm used to filter out the pump light at 590 +nm. In contrast, the filter used in the fluorescence measurements has a cut-on wavelength +of 550 nm leading to a complete peak at 600 nm, as shown in Fig. 2a. +To characterize the ASE process of Pc:Ptp, the intensity as well as the FWHM of the +strongest emission peak at 645 nm was plotted as a function of the pump intensity as shown +in Fig. 2b. There are two distinct areas in Fig. 2b, which were respectively fitted by linear +equations, resulting in a kink behavior of laser-like thresholds. The difference between ASE +and lasing processes is that in general, there is an optical cavity in the composition of a +laser, while ASE is the stimulated emission that occurs without a cavity.74 We define the +kink intensity as the ASE threshold of Pc:Ptp, which is 1.47 mJ cm−2. From the slopes of +the two fitted lines, it is evident that below the threshold, with the increment of the pump +intensity, the emission intensity increases slightly while the FWHM narrows rapidly, whereas +the situation changes oppositely above the threshold. This is because once the pump intensity +9 + +a +b +d +c +e +Optical pumping +Emission +1.2 +Pump intensity +0.6 +1.8 +2.4 +0.0 +0.5 +1.0 + Emission intensity + FWHM + Fitting +mJ cm-2 +Normalized intensity (a.u.) +0 +4 +8 +12 + FWHM (nm) +( +) +640 +642 +644 +646 +648 +650 +0.0 +4.0 +8.0 +Emission intensity (a.u.) +Wavelength (nm) + Experiment + Fitting +1.33 nm +400 +500 +600 +700 +2 +4 +6 +8 +10 +67 +31 +72 +69 +49 +71 +70 +69 +68 +66 +65 +49 +65 +64 +63 +62 +61 +60 +59 +58 +57 +49 +4950 +54 +53 +52 +51 +56 +56 +55 +48 +47 +46 +FWHM (nm) +Wavelength (nm) +Pc:Ptp +45 +550 +600 +650 +700 +750 +0.0 +0.5 +1.0 +1.5 +Emission intensity (a.u.) +Wavelength (nm) + Fluorescence spectrum + 0.58 mJ cm-2 + 0.80 mJ cm-2 + 1.09 mJ cm-2 + 1.43 mJ cm-2 +( +400 +500 +600 +700 +800 +900 +0.0 +2.0 +4.0 +6.0 +Emission intensity (a.u.) +Wavelength (nm) + 2.20 mJ cm-2 +Figure 2: Spectral analysis of Pc:Ptp’s ASE process. a Effect of pump intensity in +the emission spectrum of Pc:Ptp. Inset: schematic of the experimental setup. b Dependence +of the intensity (pink dots) and FWHM (orange triangles) of the emission peak at 645 nm +on pump intensity. The kink behavior of the dependence reveals the ASE process whose +threshold is determined by the point of intersection between the two linear fitting lines +(black dotted). +c Emission spectrum of Pc:Ptp measured above the ASE threshold. +d +Zoomed-in view of the 645-nm ASE peak (highlighted by a pink dotted box in c) whose +FWHM is determined by a Lorentzian fitting (black dotted curve). e Comparison of the +wavelength and FWHM of Pc:Ptp’s narrowest ASE peak with those of the reported organic +single crystals31,45–72 reviewed in ref.73 Details of the reported organic single crystals are +summarized in Supplementary Information. The regime of ASE wavelength rarely reached +by organic single crystals is highlighted with a black dotted box. +10 + +exceeds the threshold, the stimulated radiation near the wavelength of 645 nm is substantially +enhanced, resulting in a rapid increase in the emission intensity in the vicinity of 645 nm +manifesting a narrowing of the entire emission spectrum while the reduction of FWHM is +limited by the optical inhomogeneous broadening of pentacene molecules. In Fig. 2c and +2d, the narrowest emission spectrum arising from Pc:Ptp’s ASE process was obtained with +a pump intensity of 2.2 mJ cm−2. Compared with the normal fluorescence spectrum of the +Pc:Ptp crystal in Fig. 2a, the ASE spectra in Fig. 2c and 2d show a substantial narrowing +of the linewidth from 25 to 1.33 nm, which clearly reveals the feasibility of Pc:Ptp for +achieving the stimulated emission of radiation in the visible region. Most importantly, the +ASE threshold determined here, i.e. 1.47 mJ cm−2, is much lower than the maser threshold38 +of Pc:Ptp, 26.3 mJ cm−2 measured with the similar optical pump source, which indicates the +simultaneous lasing and masing actions can be realized once the maser threshold is fulfilled. +In addition, we have also compared the ASE performance of Pc:Ptp with various organic +single crystals in terms of the ASE wavelength and linewidth.73 In Fig. 2e, we summarized +the reported organic single crystals with evident ASE behaviors of which the measured +emission linewidths are below 10 nm. The types of the referred crystals can be found in +the Supplementary Information. As demonstrated in Fig. 2e, the ASE wavelengths of these +organic single crystals are distributed in various visible bands, especially in the wavelength +range of 400-600 nm, but leave the regime between 600 and 700 nm (i.e. red-color regime) +rarely reached. Thus, the ASE wavelength of Pc: Ptp at 645 nm is a good supplement to fill +in this almost blank regime. Moreover, among the listed crystals, Pc:Ptp has the narrowest +ASE linewidth of 1.33 nm, as per our knowledge, which is even comparable to some organic +crystal lasers.75–79 The outstanding monochromaticity reveals the potential of Pc:Ptp to be +a novel organic solid-state laser gain media. +11 + +a +c +b +d +Exp. +Sim. +τem= 6.7 ns +T1 +|1> +|3> +|2> +|4> +|5> +P +W32 +W23 +A32 +k43 +k35 +k21 +k51 +τem= 3.4 ns +S1 +S0 +2 +4 +6 +8 +3 +4 +5 +6 +7 +Pump intensity mJ cm-2 +Emission lifetime (ns) +4 +8 +12 +16 +20 + W32 ( × 107 s-1) +τem, cal= 6.7 ns +τem, cal= 3.4 ns +τem, cal = A32+W32+k35 +1 +0.0 +0.5 +1.0 +Normalized intensity (a.u.) + 1.13 mJ cm-2 + 2.20 mJ cm-2 + 3.16 mJ cm-2 + 4.09 mJ cm-2 + 5.12 mJ cm-2 + 6.14 mJ cm-2 + 7.16 mJ cm-2 +Pump enhancement +0 +10 +20 +30 +40 +0.0 +0.5 +1.0 + 1.13 mJ cm-2 + 2.20 mJ cm-2 + 3.16 mJ cm-2 + 4.09 mJ cm-2 + 5.12 mJ cm-2 + 6.14 mJ cm-2 + 7.16 mJ cm-2 +Pump enhancement +Normalized intensity (a.u.) +Time (ns) +(k52) +( +) +Figure 3: Kinetic analysis of Pc:Ptp’s ASE process. a Pump-intensity-dependent +emission decays of Pc:Ptp measured at 645 nm. The emission lifetimes are obtained with an +exponential fitting. b Five-level kinetic model accounting for the pump-intensity-dependent +emission decays of Pc:Ptp. c Simulation results of the pump-intensity-dependent emission +decay of Pc:Ptp on the basis of the five-level model in b. d Simulated rates of stimulated +emission W32 (pink) as a function of pump intensity. The pump-intensity-dependent emission +lifetimes (orange) are calculated according to the equation embedded. +12 + +Kinetic analysis of the ASE of Pc:Ptp +Emission lifetimes are important parameters that can be employed to interpret the kinetic +processes involved in the electronic states upon photoexcitation. We therefore further an- +alyze the kinetic behaviors of the ASE process of Pc:Ptp based on the emission lifetime +measurements. The associated experimental setup can be found in the Supplementary In- +formation Fig. 3. As the fluorescence lifetime of Pc:Ptp has been estimated to be around +9 ns39 at room temperature, a photodetector with a time resolution of 1 ns resolution was +employed in our setup for capturing the kinetic process accurately. Fig. 3a shows the emis- +sion decays obtained with different pump intensities. By exponential fittings of the decay +curves, we found the emission lifetime was decreased from 6.7 to 3.4 ns (as indicated by +the black arrow in Fig. 3a) with enhanced optical pumping. The emission lifetime of 6.7 ns +obtained with the relative weak pumping is close to the reported value of 9 ns.39 The faster +decays observed with the stronger optical pumping implies a pump-intensity-dependent ki- +netic process that is included in the emission process. This behavior is consistent with the +characteristic of an ASE process that the higher pump intensity will lead to enhanced stim- +ulated emission induced by the increased photons generated from spontaneous emission. To +fully characterize the observed pump-intensity-dependent emission decays, we constructed a +five-level kinetic model comprising both singlet and triplet states of the pentacene molecules +as demonstrated in Fig. 3b. The origins of the photoexcited singlet and triplet states are +similar to that demonstrated in Fig. 1c. To reduce the complexity of the kinetic model, +the numbers of the vibrational levels included in the ground (S0) and first excited singlet +states (S1) were decreased to two, as illustrated by |1⟩ and |2⟩ of S0, and |3⟩ and |4⟩ of S1 +in Fig. 3b. In addition, due to the extremely fast internal conversion between T2 and T1 in +a time scale of femtosecond to picosecond,80 the model was further simplified by assuming +the direct intersystem crossing from S1 to T1, i.e. from the lowest vibrational level of S1, +|3⟩ to |5⟩ shown in Fig. 3b. Thus, the kinetic processes involved in the five-level model are +the optical pumping (|1⟩ → |4⟩), the relaxation between the vibrational levels in the singlet +13 + +manifold (|4⟩ → |3⟩ and |2⟩ → |1⟩), the spontaneous emission (|3⟩ → |2⟩), the simulated +emission (|3⟩ → |2⟩) and absorption (|2⟩ → |3⟩) and the intersystem crossing (|3⟩ → |5⟩ and +|5⟩ → |1⟩ (|2⟩)). +Based on the kinetic processes, we derived a set of coupled rate equations to simulate +the observed emission decays as a function of the pump intensity (see Supplementary In- +formation). We found the simulated decay curves shown in Fig. 3c can well reproduce the +measured emission decays as well as the dependence of the emission lifetimes with the pump +intensity by a set of stimulated transition rates, W32 and W23 (see Fig. 3b and 3d). The +stimulated emission rate, W32, obtained from the simulation shows an almost linear increase +from 4×107 to 1.8×108 s−1 (exceeding the spontaneous emission rate,39 A32 = 4.2×107 s−1) +with the enhanced pump intensity that reveals the transition of the dominant kinetic process +in the emission decay from the spontaneous emission to the stimulated emission, i.e. ASE +occurs. The emission lifetimes τem,cal in Fig. 3d were calculated with τem,cal = +1 +A32+W32+k35 +where k35 = 6.9 × 107 s−1 is the rate of the intersystem crossing.39 +Optimization of the ASE efficiency +It is known that the efficiency of the transition of molecules in the ground singlet state to the +excited singlet state can be maximized by aligning the polarization of pump light with the +molecules’ transition dipole moments.81 For the pentacene molecules doped in p-terphenyl, +the pentacene’s short axis (i.e. the y axis in Fig. 1b), almost parallel to the ab cleavage +plane of the crystal,25 coincides with the transition dipole moment of the lowest spin allowed +transition of pentacene.82 Therefore, we further attempted to optimize the ASE efficiency +by enhancing the singlet transition probability of the pentacene’s molecules which would +benefit the realization of a low-threshold Pc:Ptp laser in the future. +Fig. 4a schematically illustrates the experimental setup (see Methods and Supplementary +Information for more details) where a horizontally polarized laser beam was focused by a +combination of a reversely placed beam expander and a convex lens and propagated perpen- +14 + +0 +100 +200 +300 +1.2 +1.4 +1.6 +1.8 +ASE threshold (mJ cm ) +-2 +Angle (°) +1.2 +1.8 +2.4 +3.0 + Experiment + Fitting + 95% Confidence interval +Emission intensity (a.u.) +a +b +c +Beam expander +(reversely placed) +Convex lens +Rotational stage +Pump laser +Pc:Ptp +Horizontal polarization +33.7° +33.7° +θ +Light polarization +Transition dipole moment +PLmax +PLmin +b axis ++ +- +ab +1 +D +ab +2 +D +ab plane +d +0° +Figure 4: Dependence of ASE performance on the alignment of crystal orien- +tation with pump-light polarization. a Schematic of the experimental setup. b The +emission intensity (upper subplot) and ASE threshold (lower subplot) of Pc:Ptp’s ASE peak +at 645 nm as a function of the rotated angle of the crystal. The measured data sets (squares +and triangles) are fitted by sinusoidal functions (solid curves) with a period of 180◦ and 95% +confidence interval bands (shadowed areas). Error bars denote the standard errors of the +data. c Schematic diagram illustrating the orientations of pentacene’s short molecular axes +(purple sticks), i.e., the transition dipole moments (dashed lines) projected into the Pc:Ptp +crystal’s ab plane. The angle between pentacene’s short axes and light polarization (solid +lines with arrows) is defined by θ. The maximum and minimum emission intensities obtained +respectively with θ = 33.7◦ and −56.3◦ are indicated in the left bottom corner. +15 + +dicular with respect to the cleavage plane (i.e. ab plane) of the Pc:Ptp crystal fixed on a +rotational sample disk. Under the excitation of a fixed pump intensity exceeding the ASE +threshold, the strong emission intensity resulting from the ASE process shown in Fig. 4b was +measured when the crystal was rotated within the plane where the cleavage facet locates. +It can be seen that the emission intensity shows an angular dependence, and the periodic +behavior can be fitted by a sinusoidal function with a period of 180◦, i.e. the maximum +and minimum emission intensities occur with a 90◦ interval. The orthogonal correlation +can be explained by the convolution effect of the alignments between the light polarization +and the transition dipole moments of the pentacene molecules doped in two inequivalent +sites. As shown in Fig. 4c, the projections of the pentacene’s transition dipole moments +into the ab plane, Dab +i +(i = 1 and 2) are parallel to the short molecular axes of the two +groups of pentacene molecules, and thus, symmetrical about the crystal b-axis according to +the room-temperature crystal structure of p-terphenyl.83 The angles of the two transition +dipole moments with respect to the b-axis are both 33.7◦. By assuming the transition dipole +moments of the two groups of pentacene molecules only vary in terms of the orientation, +the obtained emission intensity is proportional to |Dab +1 · E|2 + |Dab +2 · E|2 where E is the +electric field vector of the laser light in the ab plane.84 By denoting the angle between the +light polarization and one of the transition dipole moments to be θ (−90◦ < θ ≤ 90◦), +the emission intensity is found to be proportional to 1 + cos[( 2θ +180π) − 67.4 +180 π] cos ( 67.4 +180 π) which +implies a modulation of the emission intensity with a period of 180◦ and matches with our +measurements. It can also be found that the maximum and minimum emission intensities +should be obtained when θ = 33.7◦ and −56.3◦ which correspond to the scenarios where the +light polarization is parallel and perpendicular to the b axis, respectively. +Moreover, the ASE threshold was measured as a function of the rotation angle which +reveals a similar periodic trend and orthogonal relationship but with a ∼ 90◦ offset of the +extreme points with respect to those in Fig. 4d. This offset is due to that the highest singlet +transition probability indicated by the strongest emission intensity in Fig. 4d facilitates the +16 + +buildup of the population inversion in the singlet manifold and thus reduces the threshold +of achieving the ASE process, and vice versa. +Therefore, by adjusting the angle of the light polarization with respect to the pentacene’s +transition dipole moment, the highest singlet transition probability can be achieved, offering +the advantages of a two-fold enhancement of the emission intensity and a reduction of the +ASE threshold by around 30% (see Fig. 4b and 4d) compared to those measured at an +orthogonal position. This strategy will be beneficial for lowering not only the lasing threshold +of Pc:Ptp, but also its masing threshold, because the facilitated transition to the excited +singlet state can also lead to the more efficient generation of the pentacene’s triplet states +via the intersystem crossing. +Discussion +In summary, our study reveals the unexplored potential of Pc:Ptp crystals as room-temperature +laser gain media which has been overlooked in previous fluorescent and magnetic-resonance +spectroscopic studies44,81,85 on Pc:Ptp’s photoexcited spin states. Even without an optical +cavity, the stimulated emission observed at 645 nm with a narrow linewidth around 1 nm +shows a great promise of Pc:Ptp lasers to fill the wavelength gap of the existing organic +solid-state lasers. Most importantly, since Pc:Ptp masers have been realized with the identi- +cal crystals and optical-pumping conditions,38 our findings prove the feasibility of achieving +simultaneous lasing and masing actions with Pc:Ptp crystals at room temperature. +The next step will be to fabricate a multiband coherent device by incorporating a Pc:Ptp +crystal with a hybrid cavity architecture supporting both resonances at 645 nm and 1.45 GHz. +Considering the volumes of the three-dimensional (3D) dielectric microwave cavities29,86 and +the Pc:Ptp crystals employed in the Pc:Ptp masers, a Fabry-P´erot optical cavity could +be a compatible choice for promoting the lasing action while not perturbing the microwave +electromagnetic modes in the 3D dielectric cavities. The pumping threshold of the multiband +17 + +coherent radiations can be minimized by appropriate alignments of the pentacene’s short +molecular axis with the polarization of the optical pumping, as well as the magnetic field +of the electromagnetic mode in the microwave cavity.86 We envision that the correlation +and manipulation of the optical and microwave photons simultaneously generated by the +proposed multiband coherent source are worth being investigated for fundamental tests of +quantum optics, the possibility of phase locking for development of self-referenced frequency +combs and optimization of the solid-state quantum sensors exploiting the nonlinear behaviors +of the stimulated emission in either the microwave6 or visible5 band. +Methods +Sample preparation +A Pc:Ptp single crystal with a doping concentration of 1000 p.p.m. was grown with the +Bridgman method as reported in ref.29 The as-grown Pc:Ptp crystal was cut to obtain a +cleavage facet which was successively polished by abrasive papers, 0.1-µm cerium oxide +powder and 0.05-µm aluminum oxide powder. The surface parallel to the finished facet was +polished by repeating the above procedures. +Optical characterizations +The UV/vis absorption spectrum of Pc:Ptp was collected using a UV-visible-near infrared +spectrophotometer (Lambda 1050+, PerkinElmer). The fluorescence spectrum of Pc:Ptp +was collected using a home-built setup whose block diagram is shown in Supplementary +Information Fig.1. A green LED source was used to illuminate the sample, and the fluo- +rescence spectrum was collected by an optical spectrum analyzer (Maya 2000 Pro, Ocean +Optics, resolution 1 nm). The optical microscopic images were taken by a Complementary +Metal-Oxide-Semiconductor Transistor (CMOS) camera (AP-MV-UH1080, Apico). +18 + +ASE measurements +The ASE properties of Pc:Ptp were determined via a home-built setup illustrated in Supple- +mentary Information Fig.2. An optical parametric oscillator (OPO) (BBOPO-Vis, Deyang +Tech. +Inc., pulse duration 7 ns) pumped by an Nd:YAG Q-switched laser (Nimma-900, +Beamtech, repetition rate 10 Hz) with horizontal polarized output at 590 nm was used for +the ASE measurements. The OPO output beam was focused on the sample surface by a +reversely placed beam expander (2x) and a convex lens with a focal length of 20 cm. The +beam diameter was 5 mm, which completely covered the sample surface. A 50/50 beam split- +ter was used to divert the pump light for measuring the pump energy with a energy meter +(BGS6321, Beijing Institute of Optoelectronic Technology). A long-pass filter with cut-on +wavelength of 600 nm was used to eliminate the pump light. The ASE signals were collected +by an optical fiber connected to a high-resolution spectrometer (SpectraPro HRS-750, Pro +EM 512B, Teledyne Princeton Instruments). +Emission lifetime measurements +The experimental setup was similar to that used for the ASE measurements except the +spectrometer was replaced by a photodetector (DET10A2, Thorlabs, resolution 1 ns). The +time-domain emission signals under several different pump energies were collected by an +oscilloscope (WAVERUNNER 6KA, LeCroy). +Orientation-dependent emission measurements +The setup was the same as the ASE measurements. The Pc:Ptp crystal was fixed on the +center of a rotational disk (HRSP40-L, Heng Yang Optics, 0.1◦ resolution) so that the in- +cident light can propagate perpendicular to the cleavage plane. The crystal was rotated +with an interval of 30◦ between each measurement. The emission spectra of Pc:Ptp were +measured at different rotation angles under the same pump intensity of 1.53 mJ cm−2. The +19 + +ASE thresholds were measured by varying the pump intensity at different rotation angles +with an interval of 30◦. +Acknowledgement +The authors sincerely thank Shamil Mirkhanov for stimulating discussions and Tan Wang and +FORTEC Technology (HK) Co.Ltd. for providing us with the monochromator SpectraPro +HRS-750. H.W. acknowledges financial support from the National Science Foundation of +China (NSFC) (12204040) and the China Postdoctoral Science Foundation (YJ20210035 and +2021M700439). Jiyang Ma acknowledge financial support from China National Postdoctral +Program for Innovative Talents (BX20200057). +References +(1) Einstein, A. Zur quantentheorie der strahlung. First published in 1916, 121–128. +(2) Nagourney, W. Quantum electronics for atomic physics and telecommunication; OUP +Oxford, 2014. +(3) Hall, J. L.; Ye, J.; Diddams, S. A.; Ma, L.-S.; Cundiff, S. T.; Jones, D. J. Ultrasensitive +spectroscopy, the ultrastable lasers, the ultrafast lasers, and the seriously nonlinear +fiber: a new alliance for physics and metrology. IEEE Journal of Quantum Electronics +2001, 37, 1482–1492. +(4) Arias, E. F. The metrology of time. Philosophical Transactions of the Royal Society A: +Mathematical, Physical and Engineering Sciences 2005, 363, 2289–2305. +(5) Hahl, F. A.; Lindner, L.; Vidal, X.; Luo, T.; Ohshima, T.; Onoda, S.; Ishii, S.; Zait- +sev, A. M.; Capelli, M.; Gibson, B. C., et al. Magnetic-field-dependent stimulated emis- +sion from nitrogen-vacancy centers in diamond. Science Advances 2022, 8, eabn7192. +20 + +(6) Wu, H.; Yang, S.; Oxborrow, M.; Zhao, Q.; Zhang, B.; Du, J. Enhanced quantum +sensing with room-temperature solid-state masers. arXiv preprint arXiv:2201.04298 +2022, +(7) Jiang, M.; Su, H.; Wu, Z.; Peng, X.; Budker, D. Floquet maser. Science Advances 2021, +7, eabe0719. +(8) Dubey, A. K.; Yadava, V. Laser beam machining—A review. International Journal of +Machine Tools and Manufacture 2008, 48, 609–628. +(9) Lamas-Linares, A.; Howell, J. C.; Bouwmeester, D. Stimulated emission of polarization- +entangled photons. Nature 2001, 412, 887–890. +(10) Breeze, J. D.; Salvadori, E.; Sathian, J.; Alford, N. M.; Kay, C. W. Room-temperature +cavity quantum electrodynamics with strongly coupled Dicke states. npj Quantum In- +formation 2017, 3, 1–5. +(11) Chevalier, P.; Amirzhan, A.; Wang, F.; Piccardo, M.; Johnson, S. G.; Capasso, F.; +Everitt, H. O. Widely tunable compact terahertz gas lasers. Science 2019, 366, 856– +860. +(12) Bostedt, C.; Boutet, S.; Fritz, D. M.; Huang, Z.; Lee, H. J.; Lemke, H. T.; Robert, A.; +Schlotter, W. F.; Turner, J. J.; Williams, G. J. Linac coherent light source: The first +five years. Reviews of Modern Physics 2016, 88, 015007. +(13) Gallagher, T. Springer Handbook of Atomic, Molecular, and Optical Physics; Springer +New York: New York, NY, 2006; pp 235–245. +(14) Condon, E. GH Shortley The Theory of Atomic Spectra. 1935. +(15) Strayer, D.; Dick, G.; Mercereau, J. Performance of a superconducting cavity stabilized +ruby maser oscillator. IEEE Transactions on Magnetics 1987, 23, 1624–1628. +21 + +(16) MAIMAN, T. Stimulated Optical Radiation in Ruby. Nature 1960, 187, 493–494. +(17) Breeze, J. D.; Salvadori, E.; Sathian, J.; Alford, N. M.; Kay, C. W. Continuous-wave +room-temperature diamond maser. Nature 2018, 555, 493–496. +(18) Savvin, A.; Dormidonov, A.; Smetanina, E.; Mitrokhin, V.; Lipatov, E.; Genin, D.; +Potanin, S.; Yelisseyev, A.; Vins, V. NV–diamond laser. Nature Communications 2021, +12, 1–8. +(19) Moi, L.; Goy, P.; Gross, M.; Raimond, J.; Fabre, C.; Haroche, S. Rydberg-atom masers. +I. A theoretical and experimental study of super-radiant systems in the millimeter-wave +domain. Physical Review A 1983, 27, 2043. +(20) Grischkowsky, D.; Lankard, J.; Sorokin, P. An atomic Rydberg state 16-µ laser. IEEE +Journal of Quantum Electronics 1977, 13, 392–396. +(21) Moi, L.; Fabre, C.; Goy, P.; Gross, M.; Haroche, S.; Encrenaz, P.; Beaudin, G.; +Lazareff, B. Heterodyne detection of Rydberg atom maser emission. Optics Communi- +cations 1980, 33, 47–50. +(22) Bayliss, S.; Laorenza, D.; Mintun, P.; Kovos, B.; Freedman, D.; Awschalom, D. Opti- +cally addressable molecular spins for quantum information processing. Science 2020, +370, 1309–1312. +(23) Bayliss, S. L.; Deb, P.; Laorenza, D. W.; Onizhuk, M.; Galli, G.; Freedman, D. E.; +Awschalom, D. D. Enhancing Spin Coherence in Optically Addressable Molecular +Qubits through Host-Matrix Control. Phys. Rev. X 2022, 12, 031028. +(24) Lubert-Perquel, D.; Salvadori, E.; Dyson, M.; Stavrinou, P. N.; Montis, R.; Na- +gashima, H.; Kobori, Y.; Heutz, S.; Kay, C. W. Identifying triplet pathways in dilute +pentacene films. Nature communications 2018, 9, 1–10. +22 + +(25) Cui, S.; Liu, Y.; Li, G.; Han, Q.; Ge, C.; Zhang, L.; Guo, Q.; Ye, X.; Tao, X. Growth reg- +ulation of pentacene-doped p-terphenyl crystals on their physical properties for promis- +ing maser gain medium. Crystal Growth & Design 2020, 20, 783–792. +(26) Takeda, K. Studies on dynamic nuclear polarization using photo-excited triplet electron +spins. Ph.D. thesis, Kyoto University, 2003. +(27) Wu, H.; Mirkhanov, S.; Ng, W.; Oxborrow, M. Bench-Top Cooling of a Microwave +Mode Using an Optically Pumped Spin Refrigerator. Physical Review Letters 2021, +127, 053604. +(28) Kothe, G.; Lukaschek, M.; Yago, T.; Link, G.; Ivanov, K. L.; Lin, T.-S. Initializing 214 +Pure 14-Qubit Entangled Nuclear Spin States in a Hyperpolarized Molecular Solid. The +Journal of Physical Chemistry Letters 2021, 12, 3647–3654. +(29) Oxborrow, M.; Breeze, J. D.; Alford, N. M. Room-temperature solid-state maser. Na- +ture 2012, 488, 353–356. +(30) Wang, H.; Li, F.; Gao, B.; Xie, Z.; Liu, S.; Wang, C.; Hu, D.; Shen, F.; Xu, Y.; +Shang, H., et al. Doped organic crystals with high efficiency, color-tunable emission +toward laser application. Crystal growth & design 2009, 9, 4945–4950. +(31) Zhao, Y.; Wang, L.; Chen, X.; Zhang, B.; Shen, F.; Song, H.; Wang, H. A high-mobility, +high-luminescence and low-threshold pentacene-doped cyano-substituted distyrylben- +zene crystal. Journal of Materials Chemistry C 2019, 7, 13447–13453. +(32) Ong, J.-L.; Sloop, D.; Lin, T.-S. Peculiar spin dynamics of the photoexcited triplet +state of pentacene in a benzoic acid crystal: an ESE study. Applied Magnetic Resonance +1994, 6, 359–371. +(33) Wu, H.; Xie, X.; Ng, W.; Mehanna, S.; Li, Y.; Attwood, M.; Oxborrow, M. Room- +23 + +temperature quasi-continuous-wave pentacene maser pumped by an invasive Ce: YAG +luminescent concentrator. Physical Review Applied 2020, 14, 064017. +(34) Yu, H.-L. Electron spin echo studies of photoexcited triplet states of polyacenes in +mixed crystals. Ph.D. thesis, Washington University in St. Louis, 1982. +(35) Kitaigorodsky, A. Molecular crystals and molecules; Elsevier, 2012; Vol. 29. +(36) Lang, J.; Sloop, D. J.; Lin, T.-S. Dynamics of p-terphenyl crystals at the phase transi- +tion temperature: a zero-field EPR study of the photoexcited triplet state of pentacene +in p-terphenyl crystals. The Journal of Physical Chemistry A 2007, 111, 4731–4736. +(37) Bogatko, S.; Haynes, P. D.; Sathian, J.; Wade, J.; Kim, J.-S.; Tan, K.-J.; Breeze, J.; +Salvadori, E.; Horsfield, A.; Oxborrow, M. Molecular design of a room-temperature +maser. The Journal of Physical Chemistry C 2016, 120, 8251–8260. +(38) Salvadori, E.; Breeze, J. D.; Tan, K.-J.; Sathian, J.; Richards, B.; Fung, M. W.; Wol- +fowicz, G.; Oxborrow, M.; Alford, N. M.; Kay, C. W. Nanosecond time-resolved char- +acterization of a pentacene-based room-temperature MASER. Scientific reports 2017, +7, 1–8. +(39) Takeda, K.; Takegoshi, K.; Terao, T. Zero-field electron spin resonance and theoretical +studies of light penetration into single crystal and polycrystalline material doped with +molecules photoexcitable to the triplet state via intersystem crossing. The Journal of +chemical physics 2002, 117, 4940–4946. +(40) Siebrand, W. Mechanisms of intersystem crossing in aromatic hydrocarbons. Chemical +Physics Letters 1970, 6, 192–194. +(41) Henry, B. R.; Siebrand, W. Spin–orbit coupling in aromatic hydrocarbons. Analysis of +nonradiative transitions between singlet and triplet states in benzene and naphthalene. +The Journal of Chemical Physics 1971, 54, 1072–1085. +24 + +(42) Yang, T.-C.; Sloop, D. J.; Weissman, S.; Lin, T.-S. Zero-field magnetic resonance of the +photo-excited triplet state of pentacene at room temperature. The Journal of Chemical +Physics 2000, 113, 11194–11201. +(43) Wu, H.; Ng, W.; Mirkhanov, S.; Amirzhan, A.; Nitnara, S.; Oxborrow, M. Unraveling +the room-temperature spin dynamics of photoexcited pentacene in its lowest triplet +state at zero field. The Journal of Physical Chemistry C 2019, 123, 24275–24279. +(44) Sloop, D. J.; Yu, H.-L.; Lin, T.-S.; Weissman, S. Electron spin echoes of a photoexcited +triplet: pentacene in p-terphenyl crystals. The Journal of Chemical Physics 1981, 75, +3746–3757. +(45) Qu, S.; Lu, Q.; Wu, S.; Wang, L.; Liu, X. Two dimensional directed π–π interactions +in a linear shaped bi-1, 3, 4-oxadiazole derivative to achieve organic single crystal with +highly polarized fluorescence and amplified spontaneous emissions. Journal of Materials +Chemistry 2012, 22, 24605–24609. +(46) Losio, P. A.; Hunziker, C.; G¨unter, P. Amplified spontaneous emission in para- +sexiphenyl bulk single crystals. Applied physics letters 2007, 90, 241103. +(47) Hibino, R.; Nagawa, M.; Hotta, S.; Ichikawa, M.; Koyama, T.; Taniguchi, Y. Emission +Gain-Narrowing from Melt-Recrystallized Organic Semiconductors. Advanced Materials +2002, 14, 119–122. +(48) Baronas, P.; Kreiza, G.; Adom˙enas, P.; Adom˙enien˙e, O.; Kazlauskas, K.; Ribierre, J.-C.; +Adachi, C.; Jurˇs˙enas, S. Low-threshold light amplification in bifluorene single crystals: +Role of the trap states. ACS applied materials & interfaces 2018, 10, 2768–2775. +(49) Ichikawa, M.; Hibino, R.; Inoue, M.; Haritani, T.; Hotta, S.; Koyama, T.; Taniguchi, Y. +Improved crystal-growth and emission gain-narrowing of thiophene/phenylene co- +oligomers. Advanced Materials 2003, 15, 213–217. +25 + +(50) Xu, Z.; Liao, Q.; Shi, Q.; Zhang, H.; Yao, J.; Fu, H. Low-Threshold Nanolasers Based +on Slab-Nanocrystals of H-Aggregated Organic Semiconductors. Advanced Materials +2012, 24, OP216–OP220. +(51) Xie, W.; Li, Y.; Li, F.; Shen, F.; Ma, Y. Amplified spontaneous emission from cyano +substituted oligo (p-phenylene vinylene) single crystal with very high photoluminescent +efficiency. Applied physics letters 2007, 90, 141110. +(52) Park, S.; Kwon, J. E.; Park, S.-Y.; Kwon, O.-H.; Kim, J. K.; Yoon, S.-J.; Chung, J. W.; +Whang, D. R.; Park, S. K.; Lee, D. K., et al. Crystallization-Induced Emission +Enhancement and Amplified Spontaneous Emission from a CF3-Containing Excited- +State Intramolecular-Proton-Transfer Molecule. Advanced Optical Materials 2017, 5, +1700353. +(53) Wu, C.; DeLong, M.; Vardeny, Z.; Ferraris, J. Structual and optical studies of distyryl- +benzene single crystals. Synthetic metals 2003, 137, 939–941. +(54) Xu, Y.; Zhang, H.; Li, F.; Shen, F.; Wang, H.; Li, X.; Yu, Y.; Ma, Y. Supramolecular +interaction-induced self-assembly of organic molecules into ultra-long tubular crystals +with wave guiding and amplified spontaneous emission. Journal of Materials Chemistry +2012, 22, 1592–1597. +(55) Xie, W.; Li, F.; Wang, H.; Xie, Z.; Shen, F.; Ma, Y.; Lu, W.; Zhang, D.; Ma, D. +Stimulated emission from distyrylbenzene derivative crystals grown by vapor deposition. +Applied optics 2007, 46, 4431–4433. +(56) Kabe, R.; Nakanotani, H.; Sakanoue, T.; Yahiro, M.; Adachi, C. Effect of molecular +morphology on amplified spontaneous emission of bis-styrylbenzene derivatives. Ad- +vanced Materials 2009, 21, 4034–4038. +(57) Nakanotani, H.; Saito, M.; Nakamura, H.; Adachi, C. Highly balanced ambipolar mobil- +26 + +ities with intense electroluminescence in field-effect transistors based on organic single +crystal oligo (p-phenylenevinylene) derivatives. Applied Physics Letters 2009, 95, 197. +(58) Mizuno, H.; Haku, U.; Marutani, Y.; Ishizumi, A.; Yanagi, H.; Sasaki, F.; Hotta, S. Sin- +gle Crystals of 5, 5’-Bis (4’-methoxybiphenyl-4-yl)-2, 2’-bithiophene for Organic Laser +Media. Advanced Materials 2012, 24, 5744–5749. +(59) Xia, H.; Yang, J.; Fang, H.-H.; Chen, Q.-D.; Wang, H.-Y.; Yu, X.-Q.; Ma, Y.-G.; +Jiang, M.-H.; Sun, H.-B. Efficient Two-Photon Excited Amplified Spontaneous Emis- +sion from Organic Single Crystals. ChemPhysChem 2010, 11, 1871–1875. +(60) Chen, Q.-D.; Fang, H.-H.; Xu, B.; Yang, J.; Xia, H.; Chen, F.-P.; Tian, W.-J.; Sun, H.- +B. Two-photon induced amplified spontaneous emission from needlelike triphenylamine- +containing derivative crystals with low threshold. Applied Physics Letters 2009, 94, +201113. +(61) Gu, X.; Yao, J.; Zhang, G.; Yan, Y.; Zhang, C.; Peng, Q.; Liao, Q.; Wu, Y.; Xu, Z.; +Zhao, Y., et al. Polymorphism-Dependent Emission for Di (p-methoxylphenyl) diben- +zofulvene and Analogues: Optical Waveguide/Amplified Spontaneous Emission Behav- +iors. Advanced Functional Materials 2012, 22, 4862–4872. +(62) Chen, J.; Ma, S.; Zhang, J.; Li, B.; Xu, B.; Tian, W. Low-loss optical waveguide +and highly polarized emission in a uniaxially oriented molecular crystal based on 9, +10-distyrylanthracene derivatives. Acs Photonics 2015, 2, 313–318. +(63) Wang, K.; Zhang, H.; Chen, S.; Yang, G.; Zhang, J.; Tian, W.; Su, Z.; Wang, Y. +Organic Polymorphs: One-Compound-Based Crystals with Molecular-Conformation- +and Packing-Dependent Luminescent Properties. Advanced Materials 2014, 26, 6168– +6173. +(64) Xu, B.; Fang, H.; Dong, Y.; Chen, F.; Chen, Q.; Sun, H.; Tian, W. Solid state emis- +27 + +sion enhancement of 9, 10-distyrylanthracene derivatives and amplified spontaneous +emission from a large single crystal. New Journal of Chemistry 2010, 34, 1838–1842. +(65) Sakurai, Y.; Yamamoto, J.; Yamao, T.; Hotta, S. Emission and charge transport proper- +ties of thiophene-terminated thiophene/phenylene co-oligomer crystals. Displays 2013, +34, 406–412. +(66) Fang, H.-H.; Chen, Q.-D.; Yang, J.; Xia, H.; Gao, B.-R.; Feng, J.; Ma, Y.-G.; Sun, H.- +B. Two-photon pumped amplified spontaneous emission from cyano-substituted oligo +(p-phenylenevinylene) crystals with aggregation-induced emission enhancement. The +Journal of Physical Chemistry C 2010, 114, 11958–11961. +(67) Varghese, S.; Yoon, S.-J.; Calzado, E. M.; Casado, S.; Boj, P. G.; D´ıaz-Garc´ıa, M. A.; +Resel, R.; Fischer, R.; Mili´an-Medina, B.; Wannemacher, R., et al. Stimulated Res- +onance Raman Scattering and Laser Oscillation in Highly Emissive Distyrylbenzene- +Based Molecular Crystals. Advanced Materials 2012, 24, 6473–6478. +(68) Ichikawa, M.; Nakamura, K.; Inoue, M.; Mishima, H.; Haritani, T.; Hibino, R.; +Koyama, T.; Taniguchi, Y. Photopumped laser oscillation and charge-injected lumines- +cence from organic semiconductor single crystals of a thiophene/phenylene co-oligomer. +Applied Physics Letters 2005, 87, 221113. +(69) Cheng, X.; Wang, K.; Huang, S.; Zhang, H.; Zhang, H.; Wang, Y. Organic crystals with +near-infrared amplified spontaneous emissions based on 2’-hydroxychalcone derivatives: +subtle structure modification but great property change. Angewandte Chemie 2015, +127, 8489–8493. +(70) Fichou, D.; Delysse, S.; Nunzi, J.-M. First evidence of stimulated emission from a +monolithic organic single crystal: α-Octithiophene. Advanced Materials 1997, 9, 1178– +1181. +28 + +(71) Ichikawa, M.; Hibino, R.; Inoue, M.; Haritani, T.; Hotta, S.; Araki, K.-i.; Koyama, T.; +Taniguchi, Y. Laser oscillation in monolithic molecular single crystals. Advanced mate- +rials 2005, 17, 2073–2077. +(72) Wang, X.; Liao, Q.; Lu, X.; Li, H.; Xu, Z.; Fu, H. Shape-engineering of self-assembled +organic single microcrystal as optical microresonator for laser applications. Scientific +reports 2014, 4, 1–8. +(73) Jiang, Y.; Liu, Y.-Y.; Liu, X.; Lin, H.; Gao, K.; Lai, W.-Y.; Huang, W. Organic solid- +state lasers: A materials view and future development. Chemical Society Reviews 2020, +49, 5885–5944. +(74) Allen, L.; Peters, G. Superradiance, coherence brightening and amplified spontaneous +emission. Physics Letters A 1970, 31, 95–96. +(75) Li, Y.; Wang, K.; Liao, Q.; Fu, L.; Gu, C.; Yu, Z.; Fu, H. Tunable triplet-mediated +multicolor lasing from nondoped organic TADF microcrystals. Nano Letters 2021, 21, +3287–3294. +(76) Huang, H.; Yu, Z.; Zhou, D.; Li, S.; Fu, L.; Wu, Y.; Gu, C.; Liao, Q.; Fu, H. Wavelength- +Turnable organic microring laser arrays from thermally activated delayed fluorescent +emitters. Acs Photonics 2019, 6, 3208–3214. +(77) Zhou, Z.; Qiao, C.; Wang, K.; Wang, L.; Liang, J.; Peng, Q.; Wei, Z.; Dong, H.; +Zhang, C.; Shuai, Z., et al. Experimentally Observed Reverse Intersystem Crossing- +Boosted Lasing. Angewandte Chemie International Edition 2020, 59, 21677–21682. +(78) Zhang, T.; Zhou, Z.; Liu, X.; Wang, K.; Fan, Y.; Zhang, C.; Yao, J.; Yan, Y.; Zhao, Y. S. +Thermally Activated Lasing in Organic Microcrystals toward Laser Displays. Journal +of the American Chemical Society 2021, 143, 20249–20255. +29 + +(79) Cai, Y.; Zhang, S.; Wu, C.; Wang, Z.; Xu, W.; Chen, X.; Wang, Y. Octagonal Qua- +sicrystal Defect Mode Laser-Based PVK: Ir (ppy)3 Polymer Driven by Optical Pumping. +Nanomaterials 2022, 12, 1386. +(80) Iinuma, M. Dynamic nuclear polarization at high temperature for polarized proton +target. Ph.D. thesis, Kyoto University, 1997. +(81) G¨uttler, F.; Croci, M.; Renn, A.; Wild, U. P. Single molecule polarization spectroscopy: +pentacene in p-terphenyl. Chemical physics 1996, 211, 421–430. +(82) Pariser, R. Theory of the electronic spectra and structure of the polyacenes and of +alternant hydrocarbons. The Journal of Chemical Physics 1956, 24, 250–268. +(83) Rietveld, H.; Maslen, E.; Clews, C. An X-ray and neutron diffraction refinement of the +structure of p-terphenyl. Acta Crystallographica Section B: Structural Crystallography +and Crystal Chemistry 1970, 26, 693–706. +(84) G¨uttler, F.; Sepiol, J.; Plakhotnik, T.; Mitterdorfer, A.; Renn, A.; Wild, U. P. Single +molecule spectroscopy: fluorescence excitation spectra with polarized light. Journal of +luminescence 1993, 56, 29–38. +(85) K¨ohler, J. Magnetic resonance of a single molecular spin. Physics reports 1999, 310, +261–339. +(86) Breeze, J.; Tan, K.-J.; Richards, B.; Sathian, J.; Oxborrow, M.; Alford, N. M. Enhanced +magnetic Purcell effect in room-temperature masers. Nature Communications 2015, 6, +1–6. +30 + diff --git a/5tAzT4oBgHgl3EQf9_5e/content/tmp_files/load_file.txt b/5tAzT4oBgHgl3EQf9_5e/content/tmp_files/load_file.txt new file mode 100644 index 0000000000000000000000000000000000000000..f864b4ace3ff9f214e4b18bc45c5f190dab1853b --- /dev/null +++ b/5tAzT4oBgHgl3EQf9_5e/content/tmp_files/load_file.txt @@ -0,0 +1,1597 @@ +filepath=/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf,len=1596 +page_content='Towards simultaneous coherent radiation in the visible and microwave bands with doped molecular crystals Hao Wu,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='†,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='‡,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='§ Tong Li,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='†,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='‡,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='§ Zhang-Qi Yin,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='† Jiyang Ma,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='∗,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='†,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='‡ Xu-Ri Yao,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='†,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='‡ Bo Zhang,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='†,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='‡ Mark Oxborrow,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='¶ and Qing Zhao†,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='‡ †Center for Quantum Technology Research and Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurements (MOE),' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' School of Physics,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Beijing Institute of Technology,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Beijing 100081,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' China ‡Beijing Academy of Quantum Information Sciences,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Beijing 100193,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' China ¶Department of Materials,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Imperial College London,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' South Kensington,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' SW7 2AZ London,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' United Kingdom §These authors contributed equally: Hao Wu,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Tong Li E-mail: mjy@bit.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='edu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='cn Abstract Coherent sources exploiting the stimulated emission of non-equilibrium quantum systems, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' gain media, have proven indispensable for advancing fundamental research and engineering.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The operating electromagnetic bands of such coherent sources have been continuously enriched for increasing demands.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Nevertheless, for a single bench- top coherent source, simultaneous generation of radiation in multiple bands, especially when the bands are widely separated, present formidable challenges with a single gain medium.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Here, we propose a mechanism of simultaneously realizing the stimulated 1 arXiv:2301.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='01927v1 [physics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='optics] 5 Jan 2023 emission of radiation in the visible and microwave bands, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' lasing and masing ac- tions, at ambient conditions by utilizing photoexcited singlet and triplet states of the pentacene molecules that are doped in p-terphenyl.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The possibility is validated by the observed amplified spontaneous emission (ASE) at 645 nm with a narrow linewidth around 1 nm from the pentacene-doped p-terphenyl crystal used for masing at 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='45 GHz and consolidated by a 20-fold-lower threshold of ASE compared to the reported masing threshold.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The overall threshold of the pentacene-based multiband coherent source can be optimized by appropriate alignment of the pump-light polarization with the pentacene’s transition dipole moment.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Our work not only shows a great promise on immediate realization of multiband coherent sources but also establishes an intriguing solid-state platform for fundamental research of quantum optics in multiple frequency domains.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Introduction Coherent sources that capable of generating coherent electromagnetic radiation have served as crucial ingredients enabling numerous breakthroughs in the fields of physical, environmen- tal, and biological sciences.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' A well-established approach to achieve coherent electromagnetic radiation is to exploit the stimulated emission process1 induced by interactions between elec- tromagnetic fields and matters.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Stemming from the discovery of the stimulated emission, optical lasers spanning from the ultraviolet to the near-infrared region, together with their forerunners and microwave analogs, masers, have become indispensable coherent sources for applications in telecommunication,2 metrology,3,4 sensing,5–7 machining8 and quantum infor- mation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='9,10 In addition, the recent development of terahertz11 and X-ray12 lasers has offered alluring promise of shedding light on astronomical observations, spectroscopy and structural biology.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The rich variety of applications require distinct coherent sources operational across the extremely wide electromagnetic spectrum from microwave to X-rays.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Even though the underlying mechanism of those coherent sources is the same (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' the stimulated emission), 2 their practical realizations are completely different in terms of the components, structures and scale, rendering simultaneous generation of coherent radiation in widely separated spec- trum bands with a single source hitherto unattainable on the bench top.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Gain media, constituted by the quantum systems with non-degenerate energy levels, are the core determining radiation wavelengths of the stimulated emission, therefore, vital for realizing multiband coherent sources.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' It is not scarce for quantum systems to possess multiple transitions across different spectrum bands by their intrinsic properties (e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' large quantum numbers13) and/or external manipulations with electric/magnetic fields.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='14 Ruby (chromium ions-doped aluminum oxide) is a representative gain medium which can be employed for both solid-state masers15 and lasers,16 i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' capable of generating coherent radiation in the microwave and visible regions, while the vastly different experimental setups, especially the cryogenic and magnetic-field requirements for ruby masers, have resulted in no demonstration of simultaneous maser and laser actions of ruby to date.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' More recently, the negatively charged nitrogen-vacancy defects (NV−) in diamond have been demonstrated to be promising solid- state maser17 and laser18 gain media at ambient conditions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' However, the preparations and material properties of the NV− diamonds employed for the maser and laser applications are rather different.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The diamonds were synthesized via the routes of high pressure high temperature (HPHT) and chemical vapor deposition (CVD) for the NV− laser and maser, respectively, which gives rise to the different concentrations of the doped nitrogen atoms and NV−.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The concentration of the gain media, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' NV−, required for the laser action is 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='4 p.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='p.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='m.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' which is about four folds higher than that (0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='36 p.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='p.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='m.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=') used for the NV− maser.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' With respect to the performance of the NV− diamond based coherent sources, the relatively weak output (∼1 pW) of the NV− maser17 and the broad spectral linewidth (20 nm) of the NV− laser18 still need to be substantially improved for practical considerations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' In addition to the solid-state systems, due to the rich energy structures, the stimulated emission of radiation ranging from the microwave to infrared (IR) domain have been observed in vapor of Rydberg atoms19,20 but their ability of simultaneous generation of coherent radiation in 3 multiple wavelength domains is still not evident.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Moreover, the limited number of atoms has also restricted the power of such coherent sources.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The output power of the Rydberg masers has been reported to be up to 10 pW21 and the pulse energy of a Rydberg IR laser was approximately 1 µJ.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='20 Compared to the gain media mentioned above, doped molecular crystals combines the key advantages of the inorganic solid-state systems (e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' robustness and ease of integration) with rich opportunities of tuning the energy levels of the quantum systems, like Rydberg atoms, through bottom-up engineering of the guests and host matrices.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='22,23 While sustaining the satisfying optical, magnetic and electronic properties, the doping concentrations of molecular crystals are tunable up to tens of thousands p.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='p.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='m.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='24 implying the great potential of realizing powerful coherent sources with such gain media.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Among numerous doped molecular crystals, pentacene-doped p-terphenyl (Pc:Ptp) has attracted extensive attention especially in the last decade.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Pc:Ptp is a low-cost and easy-fabricated single organic crystal, which can be produced in bulk with a high quality.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='25 Due to the doping structure, the functional dopants, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' pentacene molecules, are well protected by the matrix of p-terphenyl, which not only possess great steadiness and durability but also emerge appealing photophysical properties (which are lacking in neat pentacene) well suited for multidisciplinary applications, such as photovoltaics,24 dynamic nuclear polarization (DNP)26 and quantum information processing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='10,27,28 In particular, Pc:Ptp is the first, and so far the only doped molecular crystal capable of masing at room temperature in Earth’s field29 and the superior maser output at a level of milliwatt (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 109 times higher than that of NV− masers) has yet to be surpassed by other room-temperature maser gain media.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The maser application as well as the recent applications mentioned above mainly exploits the photoexcited triplet states of pentacene in p-terphenyl.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' It is worth noting that the singlet states of pentacene are also intriguing due to their photoluminescent properties, owing to which the stimulated emission of radiation in the visible region manifesting as amplified spontaneous emission (ASE) have been observed30,31 implying the potential of the pentacene molecules to be suitable gain media for lasing as 4 well.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' However, since the ASE phenomena were observed with pentacene doped in trans-1,4- distyrylbenzene (trans-DSB)30 and 1,4-bis(2-cyano styryl)benzene (2-CSB),31 respectively, instead of p-terphenyl, and the dynamics of pentacene’s triplet spins can be modulated by the host effects,32 the suitability of these two host matrices for the maser action of pentacene remains elusive.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' In this work, we investigate the optical emission properties of the Pc:Ptp crystals em- ployed in the previous maser studies6,27,33 and first demonstrate the ASE process in Pc:Ptp at 645 nm, where molecular crystals rarely reached, under the experimental conditions identical to those required for the maser action.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Combining spectral analysis with nanosecond time- resolved characterizations, the ASE properties of Pc:Ptp are systematically studied at room temperature.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The obtained ASE spectrum shows an extremely narrow linewidth around 1 nm that is the narrowest among the reported molecular crystals revealing ASE behaviors.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Since ASE is a prerequisite for lasing, our results provide solid evidence that Pc:Ptp can serve as a solid-state multiband gain medium for emitting coherent microwave and visible light simultaneously at ambient conditions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Results Structural and optical properties of Pc:Ptp Throughout the study, pink Pc:Ptp single crystals, shown in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 1a, with a doping concen- tration of 1000 p.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='p.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='m.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' was used, which is the same as that employed for room-temperature masers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='6,27,33 The relatively high doping concentration allowing sufficient pentacene molecules for microwave and optical gains arises from the similar molecular packing coefficients of pen- tacene and p-terphenyl which favors the formation of solid solutions with these two organic substances.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='34,35 The molecular packing coefficient k can be expressed as k = (z × V0)/V , where z is the number of molecules in the unit cell, V0 and V are the volumes of the molecule and unit cell, respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The k values of pentacene and p-terphenyl have been determined 5 b c 400 μm ab plane a d e S0 S1 510 nm 550 nm 590 nm 475 nm λ = 645 nm T2 T1 Tx Ty Tz Intersystem crossing Non- radiative transition Radiative transition Maser transition fxz = 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='45 GHz Internal conversion Site 1 Site 2 b a c x z y 500 600 700 800 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='5 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='0 Normalized intensity (a.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='u.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=') Wavelength (nm) Fluorescence Fitting 27.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='3 nm 400 500 600 700 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='5 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='0 Normalized absorbance (a.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='u.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=') Wavelength (nm) Absorbance Laser transition 0-1 } 475 nm 510 nm 550 nm 590 nm 645 nm 599 nm 701 nm ± 1 mm Figure 1: Material characterizations of Pc:Ptp and mechanism of simultaneous coherent radiation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' a Optical microscopic image of a Pc:Ptp crystal under white-light illumination.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The cleavage plane, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' ab plane, of the crystal is labelled.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' b Crystal struc- ture of the host matrix, p-terphenyl (blue) with substitutionally doped pentacene molecules (pink).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The two inequivalent doping sites as well as the molecular axes of pentacene are labelled.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' c UV/vis absorption and d Fluorescence spectra of Pc:Ptp with all characteristic peaks labelled.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The linewidth, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' FWHM of the strongest fluorescent peak is determined by a Lorentzian fitting.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Inset: optical microscopic image of a fluorescent Pc:Ptp with the excitation light filtered.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' e Proposed mechanism of simultaneous lasing and masing actions in Pc:Ptp exploiting the transitions of stimulated emission highlighted in the pentacene’s singlet (pink) and triplet (orange) manifolds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 6 to be 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='743 and 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='751 that brings them adjacent to the closest packing condition where k= 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='74.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='34 As shown in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 1b, at room temperature, pentacene molecules are substitutionally doped in the monoclinic unit cell of p-terphenyl with two inequivalent doping sites.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='36 The structural properties of Pc:Ptp crystals are dominated by the host matrix, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' p-terphenyl.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Therefore, Pc:Ptp also possesses an intrinsic laminar structure with a cleavage (001) (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' ab) plane25 where the molecules stand (see Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 1b).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The ‘head-to-head’ packing against the ab plane results in the weaker intermolecular interactions compared to the π − π inter- actions along the molecular xy plane where x and y denote the long and short axes of the molecules, respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The cleavage property facilitates the fabrication of Pc:Ptp crystals with distinguishable crystal planes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' As exhibited in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 1a, the large crystal facet can be straightforwardly determined to be the (001) plane due to the obvious delamination shown on the edge.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The optical properties of Pc:Ptp were investigated by measuring its absorption and flu- orescence spectra as illustrated in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 1c and 1d.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' It can be found that the doped crystal reveals explicit absorption at wavelengths of 475, 510, 550 and 590 nm, which correspond to the characteristic transitions between pentacene’s excited and ground singlet states25,37 as depicted in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 1e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' According to the absorption spectrum, the highest absorbance locates at 590 nm was referred to determine the optimal wavelength for optically pumping Pc:Ptp in the following measurements.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' In terms of the photoluminescent property of Pc:Ptp, Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 1d indicates that the crystal can generate intense fluorescence at wavelengths of 599 and 645 nm, corresponding to the 0-0 and 0-1 transitions,30 respectively, as well as a weak emission band central at 701 nm under illumination of a green light-emitting diode (LED).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The small peak near 550 nm is attributed to the emission of LED which was not completed filtered during the fluorescence measurements.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The full width at half maximum (FWHM) of the strongest fluorescence peak at 645 nm is measured to be 27.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='6±0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='3 nm.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 7 Mechanism of the simultaneous lasing and masing actions in Pc:Ptp Combining the optical properties obtained above with the reported properties of Pc:Ptp masers,29,38 we propose the mechanism of realizing simultaneous lasing and masing actions by exploiting both the photoexcited singlet and triplet states of Pc:Ptp crystals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' As schemat- ically demonstrated in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 1e, the pentacene molecules in the ground singlet state can be efficiently promoted to the excited singlet state with an optical pumping at 590 nm, by which the population inversion is achieved in the singlet manifold for the stimulated emission of ra- diation in the visible region, e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 599 or 645 nm where the strong fluorescence was observed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' In the meantime, due to the spin-orbit coupling, pentacene molecules can also transfer to the excited triplet state (T2) via the intersystem crossing with a yield of 62.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='5% at room temperature39 and rapidly decay to the lowest triplet state (T1) via the internal conversion.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Since the triplet state is metastable, the pentacene molecules will eventually decay back to the ground singlet state by either the radiative or non-radiative T1 →S0 transition.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='40,41 In Earth’s field, T1 is non-degenerate and constituted by three sublevels Tx, Ty and Tz due to the dipolar interactions of pentacene’s triplet electron spins.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The resonance frequencies of the triplet sublevels governed by the zero-field-splitting (ZFS) parameters have been deter- mined by electron paramagnetic resonance (EPR) measurements42,43 to be 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='45 GHz, 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='344 GHz and 106.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='5 MHz for Tx ↔Tz, Ty ↔Tz and Tx ↔Ty transitions, respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' An alluring property of the pentacene’s lowest triplet state is that, upon its generation, the populations of the triplet electrons follow a non-Boltzmann distribution in the three sublevels with a ratio of Px : Py : Pz= 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='76:0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='16:0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='0844 at room temperature.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The strong population inversion and relatively slow spin-lattice relaxation43 between the Tx and Tz sublevels can be exploited for realizing the stimulated emission of radiation in the microwave region, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' masing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' There- fore, the optical pumping of Pc:Ptp can simultaneously introduce population inversions in both singlet and triplet manifolds fulfilling the prerequisites of the lasing and masing actions at ambient conditions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 8 Spectral analysis of the ASE of Pc:Ptp As the masing action has been successfully demonstrated with the Pc:Ptp crystal,27,33 we verify the mechanism of multiband coherent radiation proposed above by investigating the feasibility of the stimulated emission in the pentacene’s singlet states under the experimental conditions similar to that for the maser studies.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' We measured the emission spectra of Pc:Ptp under the optical pumping of a nanosecond pulsed laser which has proven to be sufficiently powerful for achieving the threshold of Pc:Ptp masers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='38 The pump laser was focused onto the cleavage plane of the crystal with a beam diameter of about 5 mm (see Supplementary Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 2 for the detailed experimental setup).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' At different pump intensities, the emission spectra shown in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 2a were recorded by collecting the emitted light from the edge of the sample (see inset in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 2a).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' It can be seen that at the peak wavelength (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 645 nm) of the Pc:Ptp’s emission spectra, the intensity gradually increases while the spectral linewidth equal to the FWHM gets narrower with the increment of pump energies implying the ASE process occurs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The incomplete emission peaks at the wavelength of 600 nm is due to the long pass filter with a cut-on wavelength of 600 nm used to filter out the pump light at 590 nm.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' In contrast, the filter used in the fluorescence measurements has a cut-on wavelength of 550 nm leading to a complete peak at 600 nm, as shown in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 2a.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' To characterize the ASE process of Pc:Ptp, the intensity as well as the FWHM of the strongest emission peak at 645 nm was plotted as a function of the pump intensity as shown in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 2b.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' There are two distinct areas in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 2b, which were respectively fitted by linear equations, resulting in a kink behavior of laser-like thresholds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The difference between ASE and lasing processes is that in general, there is an optical cavity in the composition of a laser, while ASE is the stimulated emission that occurs without a cavity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='74 We define the kink intensity as the ASE threshold of Pc:Ptp, which is 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='47 mJ cm−2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' From the slopes of the two fitted lines, it is evident that below the threshold, with the increment of the pump intensity, the emission intensity increases slightly while the FWHM narrows rapidly, whereas the situation changes oppositely above the threshold.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' This is because once the pump intensity 9 a b d c e Optical pumping Emission 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='2 Pump intensity 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='6 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='8 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='5 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='0 Emission intensity FWHM Fitting mJ cm-2 Normalized intensity (a.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='u.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=') 0 4 8 12 FWHM (nm) ( ) 640 642 644 646 648 650 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='0 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='0 8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='0 Emission intensity (a.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='u.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=') Wavelength (nm) Experiment Fitting 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='33 nm 400 500 600 700 2 4 6 8 10 67 31 72 69 49 71 70 69 68 66 65 49 65 64 63 62 61 60 59 58 57 49 4950 54 53 52 51 56 56 55 48 47 46 FWHM (nm) Wavelength (nm) Pc:Ptp 45 550 600 650 700 750 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='5 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='0 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='5 Emission intensity (a.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='u.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=') Wavelength (nm) Fluorescence spectrum 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='58 mJ cm-2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='80 mJ cm-2 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='09 mJ cm-2 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='43 mJ cm-2 ( 400 500 600 700 800 900 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='0 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='0 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='0 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='0 Emission intensity (a.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='u.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=') Wavelength (nm) 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='20 mJ cm-2 Figure 2: Spectral analysis of Pc:Ptp’s ASE process.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' a Effect of pump intensity in the emission spectrum of Pc:Ptp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Inset: schematic of the experimental setup.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' b Dependence of the intensity (pink dots) and FWHM (orange triangles) of the emission peak at 645 nm on pump intensity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The kink behavior of the dependence reveals the ASE process whose threshold is determined by the point of intersection between the two linear fitting lines (black dotted).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' c Emission spectrum of Pc:Ptp measured above the ASE threshold.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' d Zoomed-in view of the 645-nm ASE peak (highlighted by a pink dotted box in c) whose FWHM is determined by a Lorentzian fitting (black dotted curve).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' e Comparison of the wavelength and FWHM of Pc:Ptp’s narrowest ASE peak with those of the reported organic single crystals31,45–72 reviewed in ref.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='73 Details of the reported organic single crystals are summarized in Supplementary Information.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The regime of ASE wavelength rarely reached by organic single crystals is highlighted with a black dotted box.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 10 exceeds the threshold, the stimulated radiation near the wavelength of 645 nm is substantially enhanced, resulting in a rapid increase in the emission intensity in the vicinity of 645 nm manifesting a narrowing of the entire emission spectrum while the reduction of FWHM is limited by the optical inhomogeneous broadening of pentacene molecules.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' In Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 2c and 2d, the narrowest emission spectrum arising from Pc:Ptp’s ASE process was obtained with a pump intensity of 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='2 mJ cm−2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Compared with the normal fluorescence spectrum of the Pc:Ptp crystal in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 2a, the ASE spectra in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 2c and 2d show a substantial narrowing of the linewidth from 25 to 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='33 nm, which clearly reveals the feasibility of Pc:Ptp for achieving the stimulated emission of radiation in the visible region.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Most importantly, the ASE threshold determined here, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='47 mJ cm−2, is much lower than the maser threshold38 of Pc:Ptp, 26.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='3 mJ cm−2 measured with the similar optical pump source, which indicates the simultaneous lasing and masing actions can be realized once the maser threshold is fulfilled.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' In addition, we have also compared the ASE performance of Pc:Ptp with various organic single crystals in terms of the ASE wavelength and linewidth.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='73 In Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 2e, we summarized the reported organic single crystals with evident ASE behaviors of which the measured emission linewidths are below 10 nm.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The types of the referred crystals can be found in the Supplementary Information.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' As demonstrated in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 2e, the ASE wavelengths of these organic single crystals are distributed in various visible bands, especially in the wavelength range of 400-600 nm, but leave the regime between 600 and 700 nm (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' red-color regime) rarely reached.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Thus, the ASE wavelength of Pc: Ptp at 645 nm is a good supplement to fill in this almost blank regime.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Moreover, among the listed crystals, Pc:Ptp has the narrowest ASE linewidth of 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='33 nm, as per our knowledge, which is even comparable to some organic crystal lasers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='75–79 The outstanding monochromaticity reveals the potential of Pc:Ptp to be a novel organic solid-state laser gain media.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 11 a c b d Exp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Sim.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' τem= 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='7 ns T1 |1> |3> |2> |4> |5> P W32 W23 A32 k43 k35 k21 k51 τem= 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='4 ns S1 S0 2 4 6 8 3 4 5 6 7 Pump intensity mJ cm-2 Emission lifetime (ns) 4 8 12 16 20 W32 ( × 107 s-1) τem, cal= 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='7 ns τem, cal= 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='4 ns τem, cal = A32+W32+k35 1 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='5 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='0 Normalized intensity (a.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='u.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=') 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='13 mJ cm-2 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='20 mJ cm-2 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='16 mJ cm-2 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='09 mJ cm-2 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='12 mJ cm-2 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='14 mJ cm-2 7.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='16 mJ cm-2 Pump enhancement 0 10 20 30 40 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='5 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='0 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='13 mJ cm-2 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='20 mJ cm-2 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='16 mJ cm-2 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='09 mJ cm-2 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='12 mJ cm-2 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='14 mJ cm-2 7.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='16 mJ cm-2 Pump enhancement Normalized intensity (a.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='u.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=') Time (ns) (k52) ( ) Figure 3: Kinetic analysis of Pc:Ptp’s ASE process.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' a Pump-intensity-dependent emission decays of Pc:Ptp measured at 645 nm.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The emission lifetimes are obtained with an exponential fitting.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' b Five-level kinetic model accounting for the pump-intensity-dependent emission decays of Pc:Ptp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' c Simulation results of the pump-intensity-dependent emission decay of Pc:Ptp on the basis of the five-level model in b.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' d Simulated rates of stimulated emission W32 (pink) as a function of pump intensity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The pump-intensity-dependent emission lifetimes (orange) are calculated according to the equation embedded.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 12 Kinetic analysis of the ASE of Pc:Ptp Emission lifetimes are important parameters that can be employed to interpret the kinetic processes involved in the electronic states upon photoexcitation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' We therefore further an- alyze the kinetic behaviors of the ASE process of Pc:Ptp based on the emission lifetime measurements.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The associated experimental setup can be found in the Supplementary In- formation Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' As the fluorescence lifetime of Pc:Ptp has been estimated to be around 9 ns39 at room temperature, a photodetector with a time resolution of 1 ns resolution was employed in our setup for capturing the kinetic process accurately.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 3a shows the emis- sion decays obtained with different pump intensities.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' By exponential fittings of the decay curves, we found the emission lifetime was decreased from 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='7 to 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='4 ns (as indicated by the black arrow in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 3a) with enhanced optical pumping.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The emission lifetime of 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='7 ns obtained with the relative weak pumping is close to the reported value of 9 ns.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='39 The faster decays observed with the stronger optical pumping implies a pump-intensity-dependent ki- netic process that is included in the emission process.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' This behavior is consistent with the characteristic of an ASE process that the higher pump intensity will lead to enhanced stim- ulated emission induced by the increased photons generated from spontaneous emission.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' To fully characterize the observed pump-intensity-dependent emission decays, we constructed a five-level kinetic model comprising both singlet and triplet states of the pentacene molecules as demonstrated in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 3b.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The origins of the photoexcited singlet and triplet states are similar to that demonstrated in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 1c.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' To reduce the complexity of the kinetic model, the numbers of the vibrational levels included in the ground (S0) and first excited singlet states (S1) were decreased to two, as illustrated by |1⟩ and |2⟩ of S0, and |3⟩ and |4⟩ of S1 in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 3b.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' In addition, due to the extremely fast internal conversion between T2 and T1 in a time scale of femtosecond to picosecond,80 the model was further simplified by assuming the direct intersystem crossing from S1 to T1, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' from the lowest vibrational level of S1, |3⟩ to |5⟩ shown in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 3b.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Thus, the kinetic processes involved in the five-level model are the optical pumping (|1⟩ → |4⟩), the relaxation between the vibrational levels in the singlet 13 manifold (|4⟩ → |3⟩ and |2⟩ → |1⟩), the spontaneous emission (|3⟩ → |2⟩), the simulated emission (|3⟩ → |2⟩) and absorption (|2⟩ → |3⟩) and the intersystem crossing (|3⟩ → |5⟩ and |5⟩ → |1⟩ (|2⟩)).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Based on the kinetic processes, we derived a set of coupled rate equations to simulate the observed emission decays as a function of the pump intensity (see Supplementary In- formation).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' We found the simulated decay curves shown in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 3c can well reproduce the measured emission decays as well as the dependence of the emission lifetimes with the pump intensity by a set of stimulated transition rates, W32 and W23 (see Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 3b and 3d).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The stimulated emission rate, W32, obtained from the simulation shows an almost linear increase from 4×107 to 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='8×108 s−1 (exceeding the spontaneous emission rate,39 A32 = 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='2×107 s−1) with the enhanced pump intensity that reveals the transition of the dominant kinetic process in the emission decay from the spontaneous emission to the stimulated emission, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' ASE occurs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The emission lifetimes τem,cal in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 3d were calculated with τem,cal = 1 A32+W32+k35 where k35 = 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='9 × 107 s−1 is the rate of the intersystem crossing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='39 Optimization of the ASE efficiency It is known that the efficiency of the transition of molecules in the ground singlet state to the excited singlet state can be maximized by aligning the polarization of pump light with the molecules’ transition dipole moments.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='81 For the pentacene molecules doped in p-terphenyl, the pentacene’s short axis (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' the y axis in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 1b), almost parallel to the ab cleavage plane of the crystal,25 coincides with the transition dipole moment of the lowest spin allowed transition of pentacene.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='82 Therefore, we further attempted to optimize the ASE efficiency by enhancing the singlet transition probability of the pentacene’s molecules which would benefit the realization of a low-threshold Pc:Ptp laser in the future.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 4a schematically illustrates the experimental setup (see Methods and Supplementary Information for more details) where a horizontally polarized laser beam was focused by a combination of a reversely placed beam expander and a convex lens and propagated perpen- 14 0 100 200 300 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='2 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='4 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='6 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='8 ASE threshold (mJ cm ) 2 Angle (°) 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='2 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='8 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='4 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='0 Experiment Fitting 95% Confidence interval Emission intensity (a.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='u.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=') a b c Beam expander (reversely placed) Convex lens Rotational stage Pump laser Pc:Ptp Horizontal polarization 33.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='7° 33.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='7° θ Light polarization Transition dipole moment PLmax PLmin b axis + ab 1 D ab 2 D ab plane d 0° Figure 4: Dependence of ASE performance on the alignment of crystal orien- tation with pump-light polarization.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' a Schematic of the experimental setup.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' b The emission intensity (upper subplot) and ASE threshold (lower subplot) of Pc:Ptp’s ASE peak at 645 nm as a function of the rotated angle of the crystal.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The measured data sets (squares and triangles) are fitted by sinusoidal functions (solid curves) with a period of 180◦ and 95% confidence interval bands (shadowed areas).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Error bars denote the standard errors of the data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' c Schematic diagram illustrating the orientations of pentacene’s short molecular axes (purple sticks), i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=', the transition dipole moments (dashed lines) projected into the Pc:Ptp crystal’s ab plane.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The angle between pentacene’s short axes and light polarization (solid lines with arrows) is defined by θ.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The maximum and minimum emission intensities obtained respectively with θ = 33.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='7◦ and −56.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='3◦ are indicated in the left bottom corner.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 15 dicular with respect to the cleavage plane (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' ab plane) of the Pc:Ptp crystal fixed on a rotational sample disk.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Under the excitation of a fixed pump intensity exceeding the ASE threshold, the strong emission intensity resulting from the ASE process shown in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 4b was measured when the crystal was rotated within the plane where the cleavage facet locates.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' It can be seen that the emission intensity shows an angular dependence, and the periodic behavior can be fitted by a sinusoidal function with a period of 180◦, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' the maximum and minimum emission intensities occur with a 90◦ interval.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The orthogonal correlation can be explained by the convolution effect of the alignments between the light polarization and the transition dipole moments of the pentacene molecules doped in two inequivalent sites.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' As shown in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 4c, the projections of the pentacene’s transition dipole moments into the ab plane, Dab i (i = 1 and 2) are parallel to the short molecular axes of the two groups of pentacene molecules, and thus, symmetrical about the crystal b-axis according to the room-temperature crystal structure of p-terphenyl.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='83 The angles of the two transition dipole moments with respect to the b-axis are both 33.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='7◦.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' By assuming the transition dipole moments of the two groups of pentacene molecules only vary in terms of the orientation, the obtained emission intensity is proportional to |Dab 1 · E|2 + |Dab 2 · E|2 where E is the electric field vector of the laser light in the ab plane.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='84 By denoting the angle between the light polarization and one of the transition dipole moments to be θ (−90◦ < θ ≤ 90◦), the emission intensity is found to be proportional to 1 + cos[( 2θ 180π) − 67.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='4 180 π] cos ( 67.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='4 180 π) which implies a modulation of the emission intensity with a period of 180◦ and matches with our measurements.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' It can also be found that the maximum and minimum emission intensities should be obtained when θ = 33.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='7◦ and −56.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='3◦ which correspond to the scenarios where the light polarization is parallel and perpendicular to the b axis, respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Moreover, the ASE threshold was measured as a function of the rotation angle which reveals a similar periodic trend and orthogonal relationship but with a ∼ 90◦ offset of the extreme points with respect to those in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 4d.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' This offset is due to that the highest singlet transition probability indicated by the strongest emission intensity in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 4d facilitates the 16 buildup of the population inversion in the singlet manifold and thus reduces the threshold of achieving the ASE process, and vice versa.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Therefore, by adjusting the angle of the light polarization with respect to the pentacene’s transition dipole moment, the highest singlet transition probability can be achieved, offering the advantages of a two-fold enhancement of the emission intensity and a reduction of the ASE threshold by around 30% (see Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 4b and 4d) compared to those measured at an orthogonal position.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' This strategy will be beneficial for lowering not only the lasing threshold of Pc:Ptp, but also its masing threshold, because the facilitated transition to the excited singlet state can also lead to the more efficient generation of the pentacene’s triplet states via the intersystem crossing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Discussion In summary, our study reveals the unexplored potential of Pc:Ptp crystals as room-temperature laser gain media which has been overlooked in previous fluorescent and magnetic-resonance spectroscopic studies44,81,85 on Pc:Ptp’s photoexcited spin states.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Even without an optical cavity, the stimulated emission observed at 645 nm with a narrow linewidth around 1 nm shows a great promise of Pc:Ptp lasers to fill the wavelength gap of the existing organic solid-state lasers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Most importantly, since Pc:Ptp masers have been realized with the identi- cal crystals and optical-pumping conditions,38 our findings prove the feasibility of achieving simultaneous lasing and masing actions with Pc:Ptp crystals at room temperature.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The next step will be to fabricate a multiband coherent device by incorporating a Pc:Ptp crystal with a hybrid cavity architecture supporting both resonances at 645 nm and 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='45 GHz.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Considering the volumes of the three-dimensional (3D) dielectric microwave cavities29,86 and the Pc:Ptp crystals employed in the Pc:Ptp masers, a Fabry-P´erot optical cavity could be a compatible choice for promoting the lasing action while not perturbing the microwave electromagnetic modes in the 3D dielectric cavities.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The pumping threshold of the multiband 17 coherent radiations can be minimized by appropriate alignments of the pentacene’s short molecular axis with the polarization of the optical pumping, as well as the magnetic field of the electromagnetic mode in the microwave cavity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='86 We envision that the correlation and manipulation of the optical and microwave photons simultaneously generated by the proposed multiband coherent source are worth being investigated for fundamental tests of quantum optics, the possibility of phase locking for development of self-referenced frequency combs and optimization of the solid-state quantum sensors exploiting the nonlinear behaviors of the stimulated emission in either the microwave6 or visible5 band.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Methods Sample preparation A Pc:Ptp single crystal with a doping concentration of 1000 p.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='p.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='m.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' was grown with the Bridgman method as reported in ref.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='29 The as-grown Pc:Ptp crystal was cut to obtain a cleavage facet which was successively polished by abrasive papers, 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='1-µm cerium oxide powder and 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='05-µm aluminum oxide powder.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The surface parallel to the finished facet was polished by repeating the above procedures.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Optical characterizations The UV/vis absorption spectrum of Pc:Ptp was collected using a UV-visible-near infrared spectrophotometer (Lambda 1050+, PerkinElmer).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The fluorescence spectrum of Pc:Ptp was collected using a home-built setup whose block diagram is shown in Supplementary Information Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' A green LED source was used to illuminate the sample, and the fluo- rescence spectrum was collected by an optical spectrum analyzer (Maya 2000 Pro, Ocean Optics, resolution 1 nm).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The optical microscopic images were taken by a Complementary Metal-Oxide-Semiconductor Transistor (CMOS) camera (AP-MV-UH1080, Apico).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 18 ASE measurements The ASE properties of Pc:Ptp were determined via a home-built setup illustrated in Supple- mentary Information Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' An optical parametric oscillator (OPO) (BBOPO-Vis, Deyang Tech.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Inc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=', pulse duration 7 ns) pumped by an Nd:YAG Q-switched laser (Nimma-900, Beamtech, repetition rate 10 Hz) with horizontal polarized output at 590 nm was used for the ASE measurements.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The OPO output beam was focused on the sample surface by a reversely placed beam expander (2x) and a convex lens with a focal length of 20 cm.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The beam diameter was 5 mm, which completely covered the sample surface.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' A 50/50 beam split- ter was used to divert the pump light for measuring the pump energy with a energy meter (BGS6321, Beijing Institute of Optoelectronic Technology).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' A long-pass filter with cut-on wavelength of 600 nm was used to eliminate the pump light.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The ASE signals were collected by an optical fiber connected to a high-resolution spectrometer (SpectraPro HRS-750, Pro EM 512B, Teledyne Princeton Instruments).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Emission lifetime measurements The experimental setup was similar to that used for the ASE measurements except the spectrometer was replaced by a photodetector (DET10A2, Thorlabs, resolution 1 ns).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The time-domain emission signals under several different pump energies were collected by an oscilloscope (WAVERUNNER 6KA, LeCroy).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Orientation-dependent emission measurements The setup was the same as the ASE measurements.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The Pc:Ptp crystal was fixed on the center of a rotational disk (HRSP40-L, Heng Yang Optics, 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='1◦ resolution) so that the in- cident light can propagate perpendicular to the cleavage plane.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The crystal was rotated with an interval of 30◦ between each measurement.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The emission spectra of Pc:Ptp were measured at different rotation angles under the same pump intensity of 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='53 mJ cm−2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The 19 ASE thresholds were measured by varying the pump intensity at different rotation angles with an interval of 30◦.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Acknowledgement The authors sincerely thank Shamil Mirkhanov for stimulating discussions and Tan Wang and FORTEC Technology (HK) Co.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='Ltd.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' for providing us with the monochromator SpectraPro HRS-750.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' acknowledges financial support from the National Science Foundation of China (NSFC) (12204040) and the China Postdoctoral Science Foundation (YJ20210035 and 2021M700439).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Jiyang Ma acknowledge financial support from China National Postdoctral Program for Innovative Talents (BX20200057).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' References (1) Einstein, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Zur quantentheorie der strahlung.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' First published in 1916, 121–128.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (2) Nagourney, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Quantum electronics for atomic physics and telecommunication;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' OUP Oxford, 2014.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (3) Hall, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ye, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Diddams, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ma, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Cundiff, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Jones, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ultrasensitive spectroscopy, the ultrastable lasers, the ultrafast lasers, and the seriously nonlinear fiber: a new alliance for physics and metrology.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' IEEE Journal of Quantum Electronics 2001, 37, 1482–1492.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (4) Arias, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The metrology of time.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 2005, 363, 2289–2305.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (5) Hahl, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Lindner, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Vidal, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Luo, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ohshima, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Onoda, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ishii, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Zait- sev, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Capelli, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Gibson, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Magnetic-field-dependent stimulated emis- sion from nitrogen-vacancy centers in diamond.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Science Advances 2022, 8, eabn7192.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 20 (6) Wu, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Yang, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Oxborrow, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Zhao, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Zhang, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Du, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Enhanced quantum sensing with room-temperature solid-state masers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' arXiv preprint arXiv:2201.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='04298 2022, (7) Jiang, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Su, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wu, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Peng, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Budker, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Floquet maser.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Science Advances 2021, 7, eabe0719.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (8) Dubey, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Yadava, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Laser beam machining—A review.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' International Journal of Machine Tools and Manufacture 2008, 48, 609–628.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (9) Lamas-Linares, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Howell, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Bouwmeester, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Stimulated emission of polarization- entangled photons.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Nature 2001, 412, 887–890.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (10) Breeze, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Salvadori, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Sathian, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Alford, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Kay, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Room-temperature cavity quantum electrodynamics with strongly coupled Dicke states.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' npj Quantum In- formation 2017, 3, 1–5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (11) Chevalier, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Amirzhan, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wang, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Piccardo, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Johnson, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Capasso, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Everitt, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Widely tunable compact terahertz gas lasers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Science 2019, 366, 856– 860.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (12) Bostedt, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Boutet, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Fritz, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Huang, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Lee, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Lemke, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Robert, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Schlotter, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Turner, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Williams, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Linac coherent light source: The first five years.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Reviews of Modern Physics 2016, 88, 015007.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (13) Gallagher, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Springer Handbook of Atomic, Molecular, and Optical Physics;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Springer New York: New York, NY, 2006;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' pp 235–245.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (14) Condon, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' GH Shortley The Theory of Atomic Spectra.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 1935.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (15) Strayer, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Dick, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Mercereau, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Performance of a superconducting cavity stabilized ruby maser oscillator.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' IEEE Transactions on Magnetics 1987, 23, 1624–1628.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 21 (16) MAIMAN, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Stimulated Optical Radiation in Ruby.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Nature 1960, 187, 493–494.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (17) Breeze, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Salvadori, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Sathian, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Alford, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Kay, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Continuous-wave room-temperature diamond maser.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Nature 2018, 555, 493–496.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (18) Savvin, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Dormidonov, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Smetanina, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Mitrokhin, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Lipatov, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Genin, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Potanin, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Yelisseyev, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Vins, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' NV–diamond laser.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Nature Communications 2021, 12, 1–8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (19) Moi, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Goy, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Gross, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Raimond, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Fabre, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Haroche, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Rydberg-atom masers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' A theoretical and experimental study of super-radiant systems in the millimeter-wave domain.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Physical Review A 1983, 27, 2043.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (20) Grischkowsky, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Lankard, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Sorokin, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' An atomic Rydberg state 16-µ laser.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' IEEE Journal of Quantum Electronics 1977, 13, 392–396.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (21) Moi, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Fabre, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Goy, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Gross, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Haroche, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Encrenaz, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Beaudin, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Lazareff, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Heterodyne detection of Rydberg atom maser emission.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Optics Communi- cations 1980, 33, 47–50.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (22) Bayliss, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Laorenza, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Mintun, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Kovos, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Freedman, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Awschalom, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Opti- cally addressable molecular spins for quantum information processing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Science 2020, 370, 1309–1312.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (23) Bayliss, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Deb, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Laorenza, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Onizhuk, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Galli, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Freedman, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Awschalom, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Enhancing Spin Coherence in Optically Addressable Molecular Qubits through Host-Matrix Control.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Phys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Rev.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' X 2022, 12, 031028.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (24) Lubert-Perquel, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Salvadori, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Dyson, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Stavrinou, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Montis, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Na- gashima, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Kobori, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Heutz, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Kay, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Identifying triplet pathways in dilute pentacene films.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Nature communications 2018, 9, 1–10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 22 (25) Cui, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Liu, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Li, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Han, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ge, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Zhang, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Guo, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ye, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Tao, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Growth reg- ulation of pentacene-doped p-terphenyl crystals on their physical properties for promis- ing maser gain medium.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Crystal Growth & Design 2020, 20, 783–792.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (26) Takeda, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Studies on dynamic nuclear polarization using photo-excited triplet electron spins.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ph.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' thesis, Kyoto University, 2003.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (27) Wu, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Mirkhanov, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ng, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Oxborrow, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Bench-Top Cooling of a Microwave Mode Using an Optically Pumped Spin Refrigerator.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Physical Review Letters 2021, 127, 053604.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (28) Kothe, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Lukaschek, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Yago, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Link, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ivanov, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Lin, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Initializing 214 Pure 14-Qubit Entangled Nuclear Spin States in a Hyperpolarized Molecular Solid.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The Journal of Physical Chemistry Letters 2021, 12, 3647–3654.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (29) Oxborrow, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Breeze, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Alford, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Room-temperature solid-state maser.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Na- ture 2012, 488, 353–356.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (30) Wang, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Li, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Gao, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Xie, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Liu, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wang, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Hu, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Shen, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Xu, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Shang, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Doped organic crystals with high efficiency, color-tunable emission toward laser application.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Crystal growth & design 2009, 9, 4945–4950.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (31) Zhao, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wang, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Chen, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Zhang, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Shen, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Song, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wang, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' A high-mobility, high-luminescence and low-threshold pentacene-doped cyano-substituted distyrylben- zene crystal.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Journal of Materials Chemistry C 2019, 7, 13447–13453.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (32) Ong, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Sloop, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Lin, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Peculiar spin dynamics of the photoexcited triplet state of pentacene in a benzoic acid crystal: an ESE study.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Applied Magnetic Resonance 1994, 6, 359–371.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (33) Wu, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Xie, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ng, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Mehanna, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Li, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Attwood, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Oxborrow, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Room- 23 temperature quasi-continuous-wave pentacene maser pumped by an invasive Ce: YAG luminescent concentrator.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Physical Review Applied 2020, 14, 064017.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (34) Yu, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Electron spin echo studies of photoexcited triplet states of polyacenes in mixed crystals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ph.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' thesis, Washington University in St.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Louis, 1982.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (35) Kitaigorodsky, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Molecular crystals and molecules;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Elsevier, 2012;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 29.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (36) Lang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Sloop, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Lin, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Dynamics of p-terphenyl crystals at the phase transi- tion temperature: a zero-field EPR study of the photoexcited triplet state of pentacene in p-terphenyl crystals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The Journal of Physical Chemistry A 2007, 111, 4731–4736.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (37) Bogatko, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Haynes, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Sathian, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wade, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Kim, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Tan, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Breeze, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Salvadori, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Horsfield, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Oxborrow, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Molecular design of a room-temperature maser.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The Journal of Physical Chemistry C 2016, 120, 8251–8260.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (38) Salvadori, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Breeze, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Tan, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Sathian, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Richards, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Fung, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wol- fowicz, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Oxborrow, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Alford, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Kay, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Nanosecond time-resolved char- acterization of a pentacene-based room-temperature MASER.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Scientific reports 2017, 7, 1–8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (39) Takeda, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Takegoshi, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Terao, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Zero-field electron spin resonance and theoretical studies of light penetration into single crystal and polycrystalline material doped with molecules photoexcitable to the triplet state via intersystem crossing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The Journal of chemical physics 2002, 117, 4940–4946.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (40) Siebrand, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Mechanisms of intersystem crossing in aromatic hydrocarbons.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Chemical Physics Letters 1970, 6, 192–194.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (41) Henry, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Siebrand, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Spin–orbit coupling in aromatic hydrocarbons.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Analysis of nonradiative transitions between singlet and triplet states in benzene and naphthalene.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The Journal of Chemical Physics 1971, 54, 1072–1085.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 24 (42) Yang, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Sloop, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Weissman, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Lin, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Zero-field magnetic resonance of the photo-excited triplet state of pentacene at room temperature.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The Journal of Chemical Physics 2000, 113, 11194–11201.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (43) Wu, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ng, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Mirkhanov, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Amirzhan, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Nitnara, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Oxborrow, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Unraveling the room-temperature spin dynamics of photoexcited pentacene in its lowest triplet state at zero field.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The Journal of Physical Chemistry C 2019, 123, 24275–24279.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (44) Sloop, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Yu, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Lin, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Weissman, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Electron spin echoes of a photoexcited triplet: pentacene in p-terphenyl crystals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The Journal of Chemical Physics 1981, 75, 3746–3757.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (45) Qu, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Lu, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wu, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wang, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Liu, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Two dimensional directed π–π interactions in a linear shaped bi-1, 3, 4-oxadiazole derivative to achieve organic single crystal with highly polarized fluorescence and amplified spontaneous emissions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Journal of Materials Chemistry 2012, 22, 24605–24609.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (46) Losio, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Hunziker, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' G¨unter, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Amplified spontaneous emission in para- sexiphenyl bulk single crystals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Applied physics letters 2007, 90, 241103.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (47) Hibino, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Nagawa, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Hotta, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ichikawa, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Koyama, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Taniguchi, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Emission Gain-Narrowing from Melt-Recrystallized Organic Semiconductors.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Advanced Materials 2002, 14, 119–122.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (48) Baronas, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Kreiza, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Adom˙enas, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Adom˙enien˙e, O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Kazlauskas, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ribierre, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Adachi, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Jurˇs˙enas, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Low-threshold light amplification in bifluorene single crystals: Role of the trap states.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' ACS applied materials & interfaces 2018, 10, 2768–2775.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (49) Ichikawa, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Hibino, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Inoue, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Haritani, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Hotta, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Koyama, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Taniguchi, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Improved crystal-growth and emission gain-narrowing of thiophene/phenylene co- oligomers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Advanced Materials 2003, 15, 213–217.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 25 (50) Xu, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Liao, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Shi, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Zhang, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Yao, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Fu, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Low-Threshold Nanolasers Based on Slab-Nanocrystals of H-Aggregated Organic Semiconductors.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Advanced Materials 2012, 24, OP216–OP220.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (51) Xie, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Li, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Li, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Shen, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ma, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Amplified spontaneous emission from cyano substituted oligo (p-phenylene vinylene) single crystal with very high photoluminescent efficiency.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Applied physics letters 2007, 90, 141110.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (52) Park, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Kwon, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Park, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Kwon, O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Kim, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Yoon, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Chung, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Whang, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Park, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Lee, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Crystallization-Induced Emission Enhancement and Amplified Spontaneous Emission from a CF3-Containing Excited- State Intramolecular-Proton-Transfer Molecule.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Advanced Optical Materials 2017, 5, 1700353.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (53) Wu, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' DeLong, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Vardeny, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ferraris, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Structual and optical studies of distyryl- benzene single crystals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Synthetic metals 2003, 137, 939–941.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (54) Xu, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Zhang, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Li, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Shen, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wang, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Li, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Yu, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ma, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Supramolecular interaction-induced self-assembly of organic molecules into ultra-long tubular crystals with wave guiding and amplified spontaneous emission.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Journal of Materials Chemistry 2012, 22, 1592–1597.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (55) Xie, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Li, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wang, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Xie, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Shen, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ma, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Lu, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Zhang, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ma, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Stimulated emission from distyrylbenzene derivative crystals grown by vapor deposition.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Applied optics 2007, 46, 4431–4433.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (56) Kabe, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Nakanotani, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Sakanoue, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Yahiro, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Adachi, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Effect of molecular morphology on amplified spontaneous emission of bis-styrylbenzene derivatives.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ad- vanced Materials 2009, 21, 4034–4038.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (57) Nakanotani, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Saito, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Nakamura, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Adachi, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Highly balanced ambipolar mobil- 26 ities with intense electroluminescence in field-effect transistors based on organic single crystal oligo (p-phenylenevinylene) derivatives.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Applied Physics Letters 2009, 95, 197.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (58) Mizuno, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Haku, U.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Marutani, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ishizumi, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Yanagi, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Sasaki, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Hotta, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Sin- gle Crystals of 5, 5’-Bis (4’-methoxybiphenyl-4-yl)-2, 2’-bithiophene for Organic Laser Media.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Advanced Materials 2012, 24, 5744–5749.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (59) Xia, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Yang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Fang, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Chen, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wang, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Yu, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ma, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Jiang, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Sun, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Efficient Two-Photon Excited Amplified Spontaneous Emis- sion from Organic Single Crystals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' ChemPhysChem 2010, 11, 1871–1875.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (60) Chen, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Fang, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Xu, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Yang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Xia, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Chen, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Tian, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Sun, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='- B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Two-photon induced amplified spontaneous emission from needlelike triphenylamine- containing derivative crystals with low threshold.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Applied Physics Letters 2009, 94, 201113.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (61) Gu, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Yao, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Zhang, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Yan, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Zhang, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Peng, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Liao, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wu, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Xu, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Zhao, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Polymorphism-Dependent Emission for Di (p-methoxylphenyl) diben- zofulvene and Analogues: Optical Waveguide/Amplified Spontaneous Emission Behav- iors.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Advanced Functional Materials 2012, 22, 4862–4872.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (62) Chen, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ma, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Zhang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Li, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Xu, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Tian, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Low-loss optical waveguide and highly polarized emission in a uniaxially oriented molecular crystal based on 9, 10-distyrylanthracene derivatives.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Acs Photonics 2015, 2, 313–318.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (63) Wang, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Zhang, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Chen, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Yang, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Zhang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Tian, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Su, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wang, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Organic Polymorphs: One-Compound-Based Crystals with Molecular-Conformation- and Packing-Dependent Luminescent Properties.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Advanced Materials 2014, 26, 6168– 6173.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (64) Xu, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Fang, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Dong, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Chen, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Chen, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Sun, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Tian, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Solid state emis- 27 sion enhancement of 9, 10-distyrylanthracene derivatives and amplified spontaneous emission from a large single crystal.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' New Journal of Chemistry 2010, 34, 1838–1842.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (65) Sakurai, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Yamamoto, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Yamao, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Hotta, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Emission and charge transport proper- ties of thiophene-terminated thiophene/phenylene co-oligomer crystals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Displays 2013, 34, 406–412.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (66) Fang, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Chen, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Yang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Xia, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Gao, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Feng, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ma, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Sun, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='- B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Two-photon pumped amplified spontaneous emission from cyano-substituted oligo (p-phenylenevinylene) crystals with aggregation-induced emission enhancement.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The Journal of Physical Chemistry C 2010, 114, 11958–11961.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (67) Varghese, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Yoon, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Calzado, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Casado, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Boj, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' D´ıaz-Garc´ıa, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Resel, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Fischer, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Mili´an-Medina, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wannemacher, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Stimulated Res- onance Raman Scattering and Laser Oscillation in Highly Emissive Distyrylbenzene- Based Molecular Crystals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Advanced Materials 2012, 24, 6473–6478.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (68) Ichikawa, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Nakamura, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Inoue, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Mishima, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Haritani, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Hibino, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Koyama, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Taniguchi, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Photopumped laser oscillation and charge-injected lumines- cence from organic semiconductor single crystals of a thiophene/phenylene co-oligomer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Applied Physics Letters 2005, 87, 221113.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (69) Cheng, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wang, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Huang, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Zhang, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Zhang, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wang, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Organic crystals with near-infrared amplified spontaneous emissions based on 2’-hydroxychalcone derivatives: subtle structure modification but great property change.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Angewandte Chemie 2015, 127, 8489–8493.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (70) Fichou, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Delysse, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Nunzi, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' First evidence of stimulated emission from a monolithic organic single crystal: α-Octithiophene.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Advanced Materials 1997, 9, 1178– 1181.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 28 (71) Ichikawa, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Hibino, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Inoue, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Haritani, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Hotta, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Araki, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Koyama, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Taniguchi, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Laser oscillation in monolithic molecular single crystals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Advanced mate- rials 2005, 17, 2073–2077.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (72) Wang, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Liao, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Lu, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Li, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Xu, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Fu, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Shape-engineering of self-assembled organic single microcrystal as optical microresonator for laser applications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Scientific reports 2014, 4, 1–8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (73) Jiang, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Liu, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Liu, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Lin, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Gao, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Lai, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Huang, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Organic solid- state lasers: A materials view and future development.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Chemical Society Reviews 2020, 49, 5885–5944.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (74) Allen, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Peters, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Superradiance, coherence brightening and amplified spontaneous emission.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Physics Letters A 1970, 31, 95–96.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (75) Li, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wang, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Liao, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Fu, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Gu, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Yu, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Fu, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Tunable triplet-mediated multicolor lasing from nondoped organic TADF microcrystals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Nano Letters 2021, 21, 3287–3294.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (76) Huang, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Yu, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Zhou, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Li, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Fu, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wu, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Gu, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Liao, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Fu, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wavelength- Turnable organic microring laser arrays from thermally activated delayed fluorescent emitters.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Acs Photonics 2019, 6, 3208–3214.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (77) Zhou, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Qiao, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wang, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wang, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Liang, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Peng, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wei, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Dong, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Zhang, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Shuai, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Experimentally Observed Reverse Intersystem Crossing- Boosted Lasing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Angewandte Chemie International Edition 2020, 59, 21677–21682.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (78) Zhang, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Zhou, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Liu, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wang, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Fan, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Zhang, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Yao, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Yan, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Zhao, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Thermally Activated Lasing in Organic Microcrystals toward Laser Displays.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Journal of the American Chemical Society 2021, 143, 20249–20255.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 29 (79) Cai, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Zhang, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wu, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wang, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Xu, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Chen, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wang, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Octagonal Qua- sicrystal Defect Mode Laser-Based PVK: Ir (ppy)3 Polymer Driven by Optical Pumping.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Nanomaterials 2022, 12, 1386.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (80) Iinuma, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Dynamic nuclear polarization at high temperature for polarized proton target.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Ph.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' thesis, Kyoto University, 1997.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (81) G¨uttler, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Croci, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Renn, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wild, U.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Single molecule polarization spectroscopy: pentacene in p-terphenyl.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Chemical physics 1996, 211, 421–430.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (82) Pariser, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Theory of the electronic spectra and structure of the polyacenes and of alternant hydrocarbons.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' The Journal of Chemical Physics 1956, 24, 250–268.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (83) Rietveld, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Maslen, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Clews, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' An X-ray and neutron diffraction refinement of the structure of p-terphenyl.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Acta Crystallographica Section B: Structural Crystallography and Crystal Chemistry 1970, 26, 693–706.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (84) G¨uttler, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Sepiol, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Plakhotnik, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Mitterdorfer, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Renn, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Wild, U.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Single molecule spectroscopy: fluorescence excitation spectra with polarized light.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Journal of luminescence 1993, 56, 29–38.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (85) K¨ohler, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Magnetic resonance of a single molecular spin.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Physics reports 1999, 310, 261–339.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' (86) Breeze, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Tan, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content='-J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Richards, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Sathian, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Oxborrow, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Alford, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Enhanced magnetic Purcell effect in room-temperature masers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' Nature Communications 2015, 6, 1–6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} +page_content=' 30' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/5tAzT4oBgHgl3EQf9_5e/content/2301.01927v1.pdf'} diff --git a/6NAyT4oBgHgl3EQfQfY1/content/tmp_files/2301.00045v1.pdf.txt b/6NAyT4oBgHgl3EQfQfY1/content/tmp_files/2301.00045v1.pdf.txt new file mode 100644 index 0000000000000000000000000000000000000000..4bfd41ce51011a18a31394110b4ffe8c1becc693 --- /dev/null +++ b/6NAyT4oBgHgl3EQfQfY1/content/tmp_files/2301.00045v1.pdf.txt @@ -0,0 +1,1492 @@ +1 + +An Analysis of Honeypots and their Impact as a +Cyber Deception Tactic +Daniel Zielinski, Hisham A. Kholidy +State University of New York (SUNY) Polytechnic Institute, +College of Engineering, Network and Computer Security Department, Utica, NY USA +zielind@sunypoly.edu, hisham.kholidy@sunypoly.edu + +Abstract— This paper explores deploying a cyber +honeypot system to learn how cyber defenders can use a +honeypot system as a deception mechanism to gather +intelligence. Defenders can gather intelligence about an +attacker such as the autonomous system that the +attacker’s IP is allocated from, the way the attacker is +trying to penetrate the system, what different types of +attacks are being used, the commands the attacker is +running once they are inside the honeypot, and what +malware the attacker is downloading to the deployed +system. We demonstrate an experiment to implement a +honeypot system that can lure in attackers and gather all +the information mentioned above. The data collected is +then thoroughly analyzed and explained to understand +all this information. This experiment can be recreated +and makes use of many open-source tools to successfully +create a honeypot system. +Keywords—honeypots, deception mechanism, autonomous +system, deceptive honeypot system, open-source tools +I. +INTRODUCTION +Cyber honeypots can be very useful tools when trying to +lure in attackers and gain a defensive advantage by learning +how attackers are operating and what information they are +seeking. Honeypots are very common systems that many +enterprise organizations use to improve their security +program and gain insight into the common attacks they face. +When using a honeypot, defenders can gain various +amounts of information about the attackers targeting them. +Defenders can then use this information to better protect +their “real” environments. Defenders can gain insight into +the most popular types of attacks that are being used to +break into their honeypot systems and then make sure that +their actual systems are properly protected against these +attacks. Defenders can also learn what data the attackers are +trying to reach, or what exactly the motive of the attackers +is. In addition, defenders will be able to learn the different +types of malware that attackers are installing onto the +honeypot systems. +Modern honeypots are not able to look or be configured +the same way that they have in the past. This is +because over time attackers started learning different ways +to detect if what they were attacking is a honeypot. Once an +attacker figures out that what they are attacking is a +honeypot, they will no longer use their time or resources on +the honeypot. This will then make it where the defender will +not be able to gain the information they are seeking about +the attacker, and the attacker will not use up a great number +of resources on the honeypot. Therefore, modern honeypot +systems must be deceptive. They need to look, run, and +operate like the “real” system. An effective way of doing so +is by looking at your actual system and then placing data +that looks the same but is fake into your honeypot. This will +make attackers spend a lot of time trying to exploit your +honeypot and make them spend time searching for artifacts +such as valid credentials. This will also help dry up a lot of +the attackers’ resources and make them waste a lot of +money. By the time the attackers realize that they have been +caught in a honeypot trap, they may become extremely +frustrated and decide to use their remaining resources on a +different target to prevent them from falling into another +honeytrap that you or your organization may have set up. +In the next sections, We will be explaining the details of a +honeypot and the architecture of a modern deceptive +honeypot system. We will also go into the different types of +information a defender can gain from using a honeypot and +how this information can be leveraged. We then conduct an +experiment in which we use a Virtual Private Server to +deploy an open-source honeypot system that utilizes many +different honeypot containers to create an effective system. +This system also utilizes many other features such as an +IPS/IDS and a data visualization dashboard to make it easier +to analyze data. +II. +BACKGROUND +In this section, we will discuss the network architecture +of where to place a honeypot, different types of honeypots, +types of data honeypots can collect, and techniques to +achieve deception within the honeypot itself. +A. Honeypot Network Architecture +When deciding where to place a honeypot on a network +you must be very careful. Placing a honeypot inside an +internal network can be a major security design flaw. This is +because it would allow an attacker to easily gain access to +your internal network by exploiting the honeypot. An + +2 + +attacker would then be able to move laterally throughout the +network to find real systems and servers. In essence, this +would make it much easier for the attacker because you +would be “inviting” them into your internal local area +network (LAN). Instead, a honeypot should be placed inside +a network’s Demilitarized Zone (DMZ). + +Figure 1: Architecture of a network with a honeypot deployed [1]. + +A DMZ is an isolated part of the network that is connected +to the internet and is typically where public-facing services +are placed. In this example, a web server, DNS server, and +FTP server are all placed inside the DMZ along with the +honeypot [1]. The DMZ is separated on both sides with a +firewall. The firewall between the DMZ and Internet is +typically a weaker firewall that allows traffic to outside +users to do tasks like visiting a website hosted or interacting +with available files. This firewall will not allow in all traffic +but will have the necessary ports open to allow +functionality. The firewall between the DMZ and the +production network is a much more hardened firewall. This +firewall will not let outside, or unauthorized users bypass it +easily. In an enterprise setting, most organizations will have +an Intrusion Detection System (IDS), or Intrusion +Prevention System (IPS) configured to alert on attacks being +made against this firewall [2]. This is to keep cyber analysts +alert of any attacks that might be going on against their +network. The production network may have sensitive data +flowing through it and may have systems that store +important data, so it is important to keep the honeypot +isolated from it. +B. Honeypot Characteristics +There are different types of modern honeypots that one +can deploy into their environments. However, all the +honeypots do have some things in common such as being +low-maintenance, low cost, and easy to deploy [3]. The +reason honeypots need to need to be low maintenance is +that, from an organizational perspective, engineers cannot +be using up large amounts of their time manually making +changes and fixes to the honeypot. They have many other +duties to take care of and should be able to look at and +analyze data collected from the honeypot easily. This is also +connected to why a honeypot is easy to deploy. You should +take your time deploying a honeypot to ensure that +everything is working properly, but honeypots are easy to +deploy because more time and focus should be spent on +securing the real environment. If anything goes wrong with +your honeypot you should be able to just reboot it and most +of the time it will fix itself. Furthermore, a honeypot is often +relatively low cost compared to the number of resources an +individual or organization may have. Now, one can make a +massive honeypot that is extremely expensive, but it would +not make any sense from a financial perspective or provide +a great amount of additional benefit. Most individuals and +organizations deploy honeypots that are not very expensive +since they can provide great benefits at a low cost. This also +then allows for a great number of financial resources to be +spent on other things like employees, software, and other +systems. +C. Types of Honeypots Based on their Design +Many different types of Honeypots can be deployed with +various complexities. + +Figure 2: Types of Honeypots [Based on Design] [4]. + +A pure honeypot is meant to be a full-scale replica of a +production environment that contains fake data that is meant +to pose as real data. A high-interactive honeypot is like a +pure honeypot because it runs many different real-like +services, but it does not hold as much data and is not a +replica of a full-scale production environment. A mild- +interaction honeypot is different than a high-interaction +honeypot because it just emulates aspects of the application +layer but does not have its own OS. This can be used to stall +or confuse attackers who are trying to attack your systems +[5]. Finally, a low-interaction honeypot is very lightweight +and can be used to match a small number of services and +applications that are in use. This type of honeypot can be +used to keep track of UDP, TCP, and ICMP ports and + +TYPESOFHONEYPOT +[Based on the design] +Low-interaction Honeypots + Medium-interaction Honeypots +High-interaction Honeypots +Pure HoneypotsDMZ +Webserver +DNSserver +Internet +Firewall +Firewall +Production +Honeypot +network +FTPserver3 + +services. In which we make can make use of things like fake +databases, data, and files as bait to trap attackers to +understand the attacks that happen in real-time [4]. +D. Types of Honeypots Based on their Technologies +There are different types of honeypots based on the +deception technologies that they utilize. + + +Figure 3: Types of Honeypots [Based on their deception technology] [4]. + +A malware honeypot is a type of honeypot that is meant +to identify and trap malware inside a network or system. A +sophisticated malware honeypot will recognize the +malware’s signature and alert based on its Common +Vulnerabilities and Exposures (CVE) ID. It will also keep a +count of the CVEs to help recognize the most used malware +that is being installed onto a system. A database honeypot is +exactly what it sounds like – a honeypot that appears to be a +vulnerable database. Often it will be something like an SQL +database that will allow things like injection attacks to occur +to attract attackers looking to gain sensitive information that +can be stored in a database such as credit card numbers. A +spider honeypot is installed to trap web crawlers that target +web applications with the intent of stealing data. An email +honeypot is a fake email server that utilizes hoax email +addresses and emails to attract attackers to interact with it. +Any suspicious email received by attackers can be scanned, +and their email addresses can be then blacklisted on the real +email servers. A spam honeypot is like an email honeypot +but is meant to attract spammers to exploit vulnerable email +elements and give details about their activities. Finally, a +honeynet is a honeypot system that can contain various +types of honeypots mentioned. It will often aggregate data +from all the honeypots into a central location to make +alerting and monitoring easier [4]. +E. Data that Honeypots Collect +Honeypots can collect a large amount of data that can be +very useful in establishing a more secure security program +as well as serve to be very useful for research purposes. It is +often useful to have a service configured that aggregates this +data into a dashboard that makes it easier to look at and +understand. +One of the most basic artifacts that a honeypot can +collect is the attacker’s IP address. This is a useful artifact +because from the IP address we can see things like the +general location of the attacker to help identify if potentially +many attacks are coming from a specific area. Additionally, +we can see if this attacker is launching many attacks on our +systems, and we can see how long they have been attacking +our systems [6]. We can also block this IP address from +accessing our real network. Finally, from the IP address, we +can find the attacker’s Autonomous System Number (ASN) +to see what Autonomous System the attacker’s IP is +allocated from. +Another artifact we can gather about the attacker is what +Operating System (OS) they are using on their host. We +may not be able to figure out the exact version of what OS +they are using, but we will be able to figure out a general +version of the OS they are using. For instance, we can see if +the attacker is running Windows 7 or if the attacker is +running a Linux version from 2.2.x-3.x. +Many attackers utilize automation tools to try to break +into a server. + +Figure 4: A sample Dictionary Attack trying common passwords on an +SSH server [7]. +They often run dictionary attacks, a type of attack that +tries to log in to a server using a list of commonly used or +default usernames and passwords, to crack into your system. +We can use a honeypot to see what passwords and +usernames the attackers are trying to use to log in and even +take it a step further by making a count of each username +and password attempted to find the most popular credentials +attackers are trying to use. +Furthermore, when an attacker gains access to a +honeypot system we can see what commands they are +running once they break in. This can help us figure out what +their motives may be and what they are looking for. We can +also see what they are downloading onto the system. They + +ACCoUNTCHECK:[ssh)Host:192.168.18.132(1of1,complete)User:owmedb(1of1,complete +Password:123456(1of3546complete +ICcoUNTCHECK:[ssh)Host:192.168.18.132(1of1,complete)User:ownedb(1of1,ecomplete +Password:12345(2of3546complete) +AccoUNTCHECK:[ssh)Host:192.168.18.132(1of1,0complete)User:Ounedb(1of1,0complete +Password:password(3of3546complete) +ICcoUNTCHECK:[ssh]Host:192.168.18.132(1of1,0complete)User:ownedb(1of1,0complete +Password:password1（4of3546complete） +ACCouNTCHECK:ssh)Host:192.168.18.132(1of1,0complete)User:ownedb(1of1,0complete +Pas5word:123456789（5of3546complete +ACCoUNTCHECK:[ssh)Host:192.168.18.132(1of1,complete)User:ownedb(1of1,complete +as5word:12345678(6of3546complete +CCoUNTCHECK:[ssh)Host:192.168.18.132(1of1,ecomplete)User:ownedb(1of1,ecomplete +assword:1234567890(7of3546complete) +ACCoUNTCHECK:[ssh)Host:192.168.18.132(1of1,complete)User:ownedb(1of1,ecomplete +assword:abc123(8of3546.complete +ACcoUNTCHECK:[ssh)Host:192.168.18.132(1of1,0complete)User:ownedb(1of1,@complete +Password:computer(9of3546complete) +ICoUNTCHECK:[ssh)Host:192.168.18.132(1of1,0complete)User:ownedb(1of1,0complete +Password:Th3Basics(10of3546complete +ACCOUNTFOUND:[ssh]Host:192.168.18.132User:OwnedbPassword:Th3B@sics[SUCCESS] +rootebt:~#TypesofHoneypot +Based on theirdeception technology +MalwareHoneypots +Database Honeypots +Spam Honeypots +Email Honeypots +SpiderHoneypots +Honeynet Honeypots4 + +can download worms or viruses onto the system, or maybe +even agents that will try to clean up log files to keep them +undetected so they can persist more. They even might install +things like rootkits to help with this persistence. In essence, +any time the attacker attempts to interact with the honeypot +system, you can access and track that information. +F. Honeypot Deception Techniques +Many different techniques can be utilized to achieve +deception within a honeypot system to trick attackers. To +start, a commonly used technique is to not allow open +access to a server. If attackers can connect to your server on +the first try, it is often a sign that you are inviting them in, +and it may cause them to be very suspicious. To solve this, +make it that after many failed attempts they will finally be +able to gain access and login into your system. This will +make them think that their brute-forcing or dictionary attack +worked, but it still took a while for it to be a success. This +will cause them to have less suspicion. +Another more complex technique is to utilize honey +credentials. + +Figure 5: Honey Credentials being stored in memory [8]. + +Honey credentials help catch malicious actors by +injecting fake credentials into a system’s memory. When an +attacker gains access to your network and finds the honey +credentials, they will attempt to use them. Since these +credentials don’t exist, any attempt to use them can trigger +an alert and notify you immediately. In a targeted attack, the +attacker will be able to dump recovered honey credentials +from the system’s memory through privilege escalation or a +system flaw. The attacker will then attempt to perform +lateral movement into the fake objects, resulting in their +exposure and making it easy to trace them [9]. +Finally, the most important deception technique is to +make the honeypot blend in and look realistic. Earlier we +mentioned the different types of honeypots based on their +deception technology. It is important to be able to look at +these honeypots from the attacker’s perspective. For +instance, an SSH server must look like an actual SSH server +that you or your organization would utilize. If it looks fake +no one will take the bait and they will be able to easily detect +what they are attacking is a honeypot. You should be +configuring and storing data on your honeypot that you +would store on your actual systems but just make sure it is +fake information [10]. You can install real services you +would normally use onto your honeypot, but just make sure +they do not contain sensitive information. By having your +honeypot blend in, it will keep your attacker occupied and +cause them to reveal more information about themselves and +what their motives are. +III. T-POT HONEYPOT SYSTEM FRAMEWORK +In this section, we detail the honeypot system we will be +deploying to gather data and intelligence about attackers. +A. System Fundamentals +T-Pot is an open-source all-in-one honeypot platform +that runs on Debian Linux. The honeypot daemons as well +as other support components are dockered. This allows T- +Pot to run multiple honeypot daemons and tools on the same +network interface while maintaining a small footprint and +while constraining each honeypot within its own +environment [11]. Documentation to the platform can be +found at https://github.com/telekom-security/tpotce. T-Pot +uses docker images for 25 different honeypots to create a +massive honeypot system. All the honeypots each represent +different systems. For instance, cowrie is a medium-to-high +interaction SSH and Telnet honeypot designed to log brute +force attacks and the shell interaction performed by the +attacker [12]. In addition, another example is the honeytrap +honeypot is a network security tool written to observe +attacks against TCP or UDP services [13]. You can read +about all 25 different honeypots deployed in the system on +the documentation page for T-Pot. For the most part, +understanding each honeypot will not be very important +because we will mainly focus on the aggregated data +collected for the whole system. +B. Tools Utilized by T-Pot +T-Pot uses a variety of different tools to aggregate, alert, +monitor, and analyze data collected. + +Figure 6: Description of tools used by T-Pot [11]. + +mitrikate20alptax6foeto) +msy. +[0000003] +Primary +Username +adninistrator +Donain +NTLM +microsoft +- +76d202631 +SHAI +bc059168c2d8d1200c +tspkg: +KO +wdigest +Username +adninistrator +Donain +Passuord +microsoft.con +(nuil) +kerberos : +Csername +adninistrator +Donain +microsoft.con +Password +ssp: +superpass +credman : +AuthenticationIdt +Q91755333（00000090：05781345) +UOTSSOC +NewCredentials fron 0 +Jser Name +HindouaBMarkB +nark +Domsin +SID +S-1-5-21-1469176257-2007698836-3967804884-1001 +mSY. +(00800003] +Primary +Username +root +Donain +: +NTLM +211394dc229g20 +200018a69ac04 +SHA1 +Sa71afbecd987668b917e4425c82ac29a0e39b93 +tspkg: +KO +wdsgest +Username +root +Donain +Iinux,org +Password +: +(ltnu) +livessp +kerberos : +Username +root +Donain +linux.org +Password +notreallythepassword +ssp: +credman : +2 +HACyberchefawebappforencryption,encoding,compressionanddataanalysis +ELK stack to beautifulyvisualizeallthe events captured byT-Pot +ElasticsearchHeadawebfrontendforbrowsingandinteractingwithanElasticSearchcluster +Fattapysharkbasedscriptforextractingnetworkmetadataandfingerprintsfrompcapfilesandlivenetwork +traffic. +Spiderfoot a open source intelligenceautomation tool +·Suricata a Network Security Monitoring engine5 + +All the tools mentioned in Figure 6 can be further +analyzed by examining the information found on the T-Pot +documentation page. For our experiment, we will focus on +the ELK stack and Suricata tools. ELK stack has a tool +named Kibana, which we will utilize to visualize all the data +collected by the different honeypots. We can use Kibana to +analyze the data for each individual honeypot as well as +look at an aggregated view of information collected from all +the honeypots deployed in one dashboard [14]. Suricata is a +network security monitoring engine that we can use to +monitor and alert us about suspicious activity occurring +within the system [15]. The information from Suricata will +be viewable and aggregated into the Kibana Dashboard. As +mentioned previously and displayed in Figure 6 there are +many other tools pre-built into T-Pot, but we will not be +using them as they are not necessary to understand and +analyze the data collected. +C. Deploying the T-Pot Honeypot System +The system requirements for deploying the system are as +follows: 8 GB Ram, 128 GB SSD, Network via DHCP, and +a non-proxied internet connection. You can download the +Pre-built ISO Image (~50 MB), or you can create your own +ISO Image that allows you to customize the system to fit +your needs better. Since we are doing this for research +purposes to see what information we can learn about +attackers and not using the system in an enterprise +environment, xutilized the Pre-built ISO Image. +To deploy the honeypot system, I used a Virtual Private +Server (VPS). I uploaded the ISO file to the VPS provider’s +website and then began installation. The reason I used a +VPS is to avoid large amounts of unwanted traffic coming +towards my personal network. I also did not want to reveal +my actual public IP address to attackers. +Once the ISO image finished installing in my VM the +honeypot system was fully functional and ready to lure in +attackers. To reach the T-Pot landing page to gain access to +all the tools deployed in the system I had to go into a web +browser and enter “https://:64297”. I then logged in +with the credentials created during installation. + +Figure 7: T-Pot Landing Page. + +Now that the honeypot is fully deployed attackers will +begin to attack the honeypot. We can view data collected +from the attacks in the Kibana dashboard. I will wait +approximately 3 weeks before analyzing the data just to let a +good amount of data accumulate. In the next section, we +will test some of the functionality on the honeypot by +running some attacks to make sure the honeypot is +configured properly before waiting the 3 weeks to analyze +the data collected. +IV. TESTING HONEYPOT SYSTEM +FUNCTIONALITY +In this section, we will test some of the functionalities of +the honeypot by running a Nmap scan and brute force attack +against the honeypot system. We will target Cowrie, the +SSH honeypot container. +A. Port Scanning the Honeypot System +I used a Kali Linux Virtual Machine to simulate an +attack on the honeypot system. Information on how to +install +Kali +Linux +can +be +found +at +https://www.kali.org/docs/. Kali Linux comes with Nmap, a +port scanning tool, preinstalled on the system. To use this +tool, I opened a terminal session and ran the command +“nmap -p 22 140.82.3.147” as seen in Figure 8. + +Figure 8: Nmap scan ran against the Honeypot System. +This command runs a port scan only checking to see if port +22 is open on the IP address specified. From the results +returned from the Nmap scan, we can see that port 22 is open +to be used by the SSH Service. This is because as mentioned +earlier, Cowrie is a high interaction SSH honeypot designed +to log brute force attacks and the shell interaction performed +by the attacker once they gain entry. +A. Brute-Forcing the System +For simplicity of the experiment and to test the +functionality of the honeypot system, I logged into the +honeypot SSH server and made the root account accessible +over SSH. Also, I changed the password to 12345. I then +went to my Kali Linux terminal and used the hydra tool to +crack the password for the root account to the honeypot. I +ran +the +command +“hydra +-l +root +-P +/usr/share/wordlists/Metasploit/unix_password.txt +-T +6 +ssh://140.82.3.147” as seen below in Figure 9. + +Elasticsearch +Cockpit +Cyberchef +Head +Kibana +SecurityMeter +Spiderfoot +T-Pot@GitHub(kalikali)-[~ +nmap-p22140.82.3.147 +Nmapscanreportfor 140.82.3.147.vultr.com（140.82.3.147) +Hostisup(0.20slatency). +PORT +STATESERVICE +22/tcp openssh +Nmap done:1IPaddress(1hostup)scanned in 0.o9 seconds +(kalikali)-[~]6 + + +Figure 9: Running the Hydra Brute-Force Attack. +The -l option in the command tells hydra to try to log in +with the root user. The -P option tells hydra to use the +password list specified in the command and the -t option +tells hydra how many threads to use. So, in this attack +scenario, we used 6 threads. Also, we told it to attack the +SSH service on the IP specified. As we can see in Figure 9, +we got the exact results that we expected. The password +that was cracked for the root user was 12345. Since the test +has concluded, I logged back into the SSH server and +made it not possible to login to the root user through SSH +like how it was prior to me modifying it. This is because +this is a good security practice and if it was possible to +login to the root user over SSH it would make the attacker +suspicious of the honeypot. I then changed the password +back to what it was prior. The original password was not +that difficult to crack either, but more difficult to crack +than 12345. This is because we do still want the attacker to +be able to gain access so we can gain more information on +what they are looking to do once they are inside the server. +I will now let the honeypot system run for a few weeks so it +can collect a large amount of data to come back and +analyze. +V. +ANALYZING +DATA +COLLECTED +FROM +ATTACKS +ON +THE +SYSTEM +In this section, we will analyze the data collected by +the honeypot system in the Kibana Dashboard. We will +look at the different categories of information collected to +see what information we can learn about the attackers. For +the most part, we will look at the aggregated data collected +by all the containers, but in some cases, we will look at +data collected by a specific container. +A. Accessing the Data +To access the data, we need to first go back to the T-Pot +Landing Page. This can be reached by entering +“https://:64297” into a web browser and logging in +with the credentials created. I then clicked on “Kibana” to +bring me to the Kibana app dashboards page. + +Figure 10: Kibana App Main Dashboards Page. + +As seen in Figure 10, a page opens that displays a list of +the different honeypot containers deployed. You can reach +an individual dashboard by just clicking on the name of +that honeypot. You will also notice that the first two titles +are +“T-Pot” and “T-Pot Live Attack Map”. T-Pot Live Attack +Map just shows a global map of where attacks within the +T- Pot Network are occurring in the current time. This is +not very useful to us. However, the T-Pot Dashboard is the +dashboard we will be mainly using because it displays the +visuals and data collected by all the honeypots aggregated +into one dashboard. Therefore, we will go ahead and click +on “T-Pot” to display this dashboard. + +Figure 11: Kibana T-Pot Dashboard. + +The dashboard displayed is shown in Figure 11. As +mentioned, this dashboard allows us to visualize and view + +-(kalickali)-[] +Shydraroot-P/usr/share/wordlists/netasploit/unixpasswords.txt-t6ssh://140.82.3.147 +ydrav9.2(c)2021byvanHauser/THC6DavidMaciejakPleasedonotuseinmilitaryorsecretsel +-bindnhe*gawsndaay +ydra（https://github.com/vanhauser-thc/thc-hydra)starting at2022-03-0518:36:55 +WARNINGRestorefile(youhave10secondstoabort...(useoption-Itoskipwaiting))fromaprev +ore +DATAmax6tasksper1serveroveralu6tasks,1009 Logintries(L:1/p:1009),-169 triespertas +DATAlattacking ssh://140.82.3.147:22/ +22[ssh] host:140.82.3.147login:rootpasword12345 +1 of 1target successfully completed,1 valid password found +ydra(https://github.com/vanhauser-thc/thc-hydra)finishedat2022-03-0518:37:06Dashboards +Search.. +Title +Description +>T-Pot +T-PotDashboard +>T-PotLiveAttackMap +T-PotLiveAttackMap +Adbhoney +AdbhoneyDashboard +Ciscoasa +CiscoasaDashboard +CitrixHoneypot +CitrixHoneypotDashboard +Conpot +ConpotDashboard +Cowrie +CowrieDashboard +Ddospot +Ddospot Dashboard +Dicompot +DicompotDashboard +Dionaea +Dionaea Dashboardceetineis-toto +330.008 +198.915 +132.032 +18.613 +4.300 +3.661 +1,015 +640 +623 +520 +Covte-ltxde +Ooge-As +wd-Aade +Aehony-Atste +opy-tci +-Ad +Rixis +--7 + +the data collected from all the honeypots in one place. Next, +we will be looking at and analyzing some of the charts and +data lists that are relevant to gathering intelligence about the +attackers. +B. Most Attacked Honeypots +At the top of the dashboard, we can see the ten +honeypots that experienced the most attacks ranked in +sequential order from the most attacked honeypot to the +least attacked honeypot. + +Figure 12: Top 10 attacked honeypots + +As we can see in Figure 12, the most attacked honeypot +was Cowrie, the SSH honeypot. This is not very surprising +since SSH is a very well-understood protocol and can be +used to gain remote shell access to a server. If the attacker +can abuse the SSH service and gain access to your server, +they will be able to obtain a lot of information and have +access to your system. From a defender’s point of view, +this is important information to understand because since +SSH is the most attacked protocol, in a live production +environment gaining remote access should not be very +easy. On top of utilizing a very secure password and SSH +keys, MFA should also be configured to gain remote shell +access. We can also see that the Dionaea is the second most +attacked honeypot, which uses the FTP service to capture +attack payloads and malware. This tells us that any way +that an attacker can either gain remote access to a server +(SSH) or be able to upload files to a server remotely (FTP) +is going to attract attackers. This is because the concept of +being able to remotely impact a server can have high +consequences if abused. As a defender, we can learn from +this to make sure that all servers that have remote protocol +ports open must be highly secure. +C. Attacks by Country +The next graphic we will examine is the countries where +the most attacks are from. I will also discuss why that +information may and may not be useful. + +Figure 13: Attacks by Country Pie Chart. +The legend on the right of Figure 13 shows the list of +where the most attacks came from in descending order. As +we can see, the top 3 countries where the most attacks +came from are the United States, China, and Russia. These +countries tend to have more sophisticated cyber programs +and larger populations, so it is not very surprising. This +information can be useful to us because if a specific threat +actor is targeting an individual company, we may be able +to track where that threat actor is located. However, any +decent attacker is most likely using a VPS where they can +choose the location of where their IP address is located, or +they are using a VPN to hide their true location. That is +why focusing on this graphic is not very useful, but it is +more important to focus on the IP addresses themselves. +We will look at this next. +D. Attacker Source IP Addresses and their ASNs +Looking at the Source IP Addresses of where most of the +attacks are coming from is very useful to cyber defenders. +This is because most attackers have a finite number of +resources to work with, so their IP Space is not unlimited. + + +Figure 14: Top 10 Attacker Source IP Addresses. + +In Figure 14, we can see the IP addresses where most of +the attacks came from. This information is very useful to +cyber defenders because these IP addresses can be +blacklisted from the production network. This will not allow + +HoneypotAttacks-Top10 +334,592 +197,072 +132,574 +18,613 +4,308 +Cowrie-Attacks +Dionaea-Attacks +Honeytrap-Attacks +Heralding-Attacks +Adbhoney-Attacks +3,668 +1,020 +640 +627 +526 +Rdpy-Attacks +Tanner-Attacks +CitrixHoneypot-Attacks +Mailoney-Attacks +ConPot-AttacksAttacks byCountry +UnitedStates +China +Russia +Vietnam +India +Canada +Japan +Singapore +Hong Kong +TurkeyAttackersourcelp-Top1o +Source Ip +Count +165.22.234.121 +26,552 +2.56.56.14 +18,204 +69.171.13.237 +11,027 +140.82.156.72 +9,094 +85.100.124.175 +3,158 +109.94.179.81 +3,154 +110.227.249.142 +3,152 +190.75.220.137 +3,151 +83.52.23.252 +3,151 +103.43.77.175 +3,1498 + +these attackers to use these IP addresses on the actual +network. The attackers will now have to use other IP +addresses to gain access to your resources, therefore making +them exhaust their resources. If you continuously blacklist +the IP addresses where a lot of the attacks are coming from +you can deter attacks from continuing to attack your +network. This will help prevent a lot of the noisier attackers +from being able to exploit your systems, but you also must +be aware that more stealthy attackers will be able to remain +under the radar. +From the attacker’s IP address, we can figure out the +Autonomous System Number (ASN) to learn what +organizations are allocating the attacker’s IP address. + + +Figure 15: Top 10 ASNs used by attackers attacking the system. + +In Figure 15 we can see that the most popular ASN +organization used to attack our honeypot system is +Digital Ocean, one of the most popular VPS providers. +This shows that a lot of people are abusing the benefits +they offer to launch cyber-attacks. There also does +appear to be a good number of China-based +organizations on the list, helping to support that there +might be a lot of attacks launched on the honeypot +from China. This information can be useful to us +because when we see a user utilizing an IP allocated +from Digital Ocean, for example, we can be more +cautious and possibly even raise an alert since we +know it is commonly used by attackers to attack our +systems. +E. Most Used OS by Attackers +From our honeypot system, we can gather information +about the most popular Operating Systems used by +attackers to attack the honeypot system. Kibana displays a +pie chart of the operating systems and displays the legend +in descending order from most to least used OS. + +Figure 16: Pie Chart displaying Attacker OS Distributions. + +From this pie chart, we can see that Windows 7/8 was +the most popular used OS by attackers followed by Linux +2.2.x-3.x. We do not get an exact version number for the +most part, but still, get a good idea of the OS the attacker +is using. I found it particularly unusual that attackers are +running older versions of Windows and Linux, but it can +be valuable information. Most common users run +Windows 10 as of now, so users running Windows 7/8 +can be something to look out for when investigating +attackers. A lot of basic users do not run Linux, so +anytime there is suspicious activity coming from a user +running Linux it can be a red flag. +F. Most Common Credentials +In this section, we will look at the most common +credentials used by attackers to try to brute-force login to +our systems. Many of these usernames and passwords +come from dictionary lists of commonly used passwords. + +Figure 17: Most Common Usernames attempted by attackers. + +In Figure 17, we see the most common usernames +attackers tried to use to log in to our honeypot system. As +we can see, root and admin were among the most popular. +This makes sense because root and admin accounts tend to +have higher privileges. This shows us from a defensive +perspective why it is so important to disable remote root +login because it can be very dangerous if attackers manage +to log in. + + +AttackerAs/N-Top10 +AS +ASN +Count +14061 +DigitalOcean,LLC +83.743 +45899 +VNPTCorp +25,944 +395800 +GreybeardTechnologyLLC +18.324 +45090 +Shenzhen Tencent Computer... +16,955 +38365 +BeijingBaiduNetcom Scienc... +11,744 +4134 +No.31,Jin-rongStreet +11,167 +29944 +Latisys-Ashburn,LLC +11,027 +12389 +Rostelecom +10,625 +174 +CogentCommunications +9,895 +135377 +UCloud (Hk) Holdings Group... +8,074PofoSDistribution +Windows7or8 +Linux 2.2.x-3.x +WindowsNTkernel +Linux3.11andnewer +Linux3.1-3.10 +Linux 2.2.x-3.x (barebon.. +WindowsNTkernel6.x +WindowsNTkernel5.x +Linux 2.2.x-3.x (no time.. +Linux 2.4.xes +student +zabbix +user +admin1 +666666 +jenkins +minecraft service +postgres +Hoddns +888888 +default +test +tomcat +wwwroot +ftp +sa +ubuntu +ubnt +root +www-data +administrator +user +phil +Admin +user123 +ftpuser +testuser +admin +nproc +(empty) +mysql +guest +hadoop +supervisor +git +oracle +www +server +anonymous +deploy +Administrator +web +tech +mother +data +pi +dev9 + + +Figure 18: Most Common Passwords attempted by attackers. + +In Figure 18, we see the most common passwords +attackers tried to use to log in to our honeypot system. Many +of these are weak passwords that a basic user might make or +default passwords. We can learn from this that it is +extremely important to change default passwords and to +replace them with a very strong password to prevent +attackers from gaining easy access. Passwords should be +relatively long and utilize a combination of letters, +numbers, and special characters. +G. Analyzing Suricata Alerts +In this section, we will analyze the alert raised by +Suricata, an open-source IDS/IPS that feeds data into our +Kibana dashboard. + +Figure 19: Suricata Alerts. + +In Figure 19, we can see the 10 most common alerts +detected by Suricata. From this, we can tell that a lot of +attackers try to abuse TCP and manipulate the Three-Way +TCP Handshake. We can also tell that a lot of users were +using a port scanning tool like Nmap because there were a +lot of detections of incomplete connections. Finally, we +also detected some alerts that SSH and SMB sessions were +established. This means that some attackers did gain entry +inside our honeypot system, as we expected. + +Figure 20: Suricata CVEs Detected. + +In Figure 20, we can see the top 3 CVEs detected by +Suricata. As mentioned before, a CVE is a common +vulnerability and exposure that is known by the public and +is detected by a unique signature. CVE-2019-12263 is a +high vulnerability with a CVSS score of 8.1. This +vulnerability refers to a Buffer Overflow in the TCP +component of Wind River VxWorks 6.9.4 and vx7 [16]. +The next most common CVE detected is CVE-2019-0708 +which has a CVSS score of 9.8, also very high. This +vulnerability is a “Remote Desktop Services Remote Code +Execution Vulnerability.” This means that an attacker can +remotely execute code on another user’s system [17]. +Finally, the third most popular CVE is CVE-2020-11910 +which has a CVSS score of 5.3, a medium level severity. +This vulnerability is caused by the Treck TCP/IP stack +before version 6.0.1.66 having an ICMPv4 Out-of-bounds +Read [18]. This means that the system is reading packets +that are not in bounds. From all these CVEs detected, it can +teach us to make sure that our systems are patched against +these popular vulnerabilities. It also shows us how +important it is to regularly patch our systems when there +are security updates because attackers will try to exploit +our systems if they are not patched. +H. Cowrie Top Shell Commands Executed +In this section, we will look at the top commands +executed inside of the Cowrie honeypot. To look at this, we +will go back to the main T-Pot Dashboard page and select +“Cowrie Dashboard” to only see the Dashboard for this +specific honeypot. As mentioned earlier, Cowrie is a high +interaction SSH honeypot that tracks commands entered by +attackers into the command line and that data gets sent into +Kibana to analyze. + +Figure 20: Top 10 Commands Executed by attackers. + +In Figure 20, we can see the top 10 commands run by + +1q2w3e4r admin1234 abc123 +1234qwer +fucker +666666 +54321 +111111 +1111 test +default +111111234567 +1 root 1234 +admin +aquario user1 +888888 +guest +1001chin +(empty)123456 +system +12345 +Win1doWs +tech +ivdev +123123 +123 nproc password +friend +password123 +pass +ubnt +1234567890 +07ujMkoadmin +00000000 +XC3511 +5up123456789 +alpine +12345678 +vizxV +qwerty +ZIxx. +1qaz2wsxSuricataAlertSignature-Top10 +Description +Count +SURICATASTREAMreassemblysequenceGAP--missingpacket(s) +65,799 +ETPOLiCYSSHsessioninprogressonExpectedPort +13,043 +SURICATASTREAMPacketwithbrokenack +12,053 +SURICATATCPy4invalidchecksum +5,008 +SURICATASTREAMPacketwithinvalidtimestamp +3,609 +SURlCATAApplayerDetectprotocolonlyonedirection +1,553 +SURICATASTREAMFINrecvbutnosession +1,034 +ETSCANZmapUser-Agent(inbound) +998 +SURICATASTREAMRSTrecvbutnoSession +831 +ETINFOPotentiallyunsafeSMBv1protocolinuse +740SuricatacVE-Top10 +CVEID +Count +CVE-2019-12263... +62 +CVE-2019-0708 +14 +CVE-2020-11910 +6CowrieInput-Top1o +CommandLineInput +Count +uname -a +126 +cat/proc/cpuinfo/grepmodel/grepname +117 +cat/proc/cpuinfo|grepname|head-n1 +a... +117 +cat/proc/cpuinfogrepname +WC- +117 +crontab -l +117 +free-m|grepMem|awk'(print$2,$3,$4,$... +117 +Is-lh$(whichIs) +117 +top +117 +uname +117 +uname -m +11710 + +attackers inside the honeypot. As we can see, the most run +command is the “uname” command with the -a flag. We +also see many different variations of this command in the +top 10 list. This command reveals a lot about the system +such as what Linux distro is being run on the system and the +version of the distro. This is useful to attackers because if +the system is out of date there may be vulnerabilities that +the attacker can exploit. Therefore, it is important to update +and patch your systems. The next most popular command +that we see being run is the attacker looking for information +about the system’s CPU. An attacker may be interested in +the CPU info to see how much processing power your +system has. The reason they care about this is that crypto +mining is currently extremely popular but requires many +resources to drive a profit. So, if attackers can create a +botnet of devices that mines for crypto in the background of +victims’ computers, it will have no cost to the attackers, and +they will receive all the benefits. +I. +CONCLUSION +A honeypot system can be very beneficial to an +organization, but it must be properly implemented and +deployed. Honeypots need to be deceptive enough to trick +the attacker into thinking that they are in a real system. +This will cause the attackers to use up time and resources +when attacking the honeypot system, but it will also reveal +information about how the attackers are penetrating the +system and what they are looking for once they gain access +to the system. This information can be useful to the +defenders because we can learn how attackers operate and +can make sure our actual systems are secure against the +various attacks that are being used by attackers. We can +also make sure that the information attackers are looking to +seek is properly protected. +For future work, we will extend our current +cybersecurity framework [19-53] by integrating the +Honeypot system as a Cyber Deception Tactic to deceive +the attacker. +REFERENCS +[1] B. Lutkevich, “How to build a honeypot to increase network +security,” +WhatIs.com, +31-Mar-2021. +[Online]. +Available: +https://whatis.techtarget.com/feature/How-to-build-a-honeypot-to-increase- +network-security. [Accessed: 10-Mar-2022]. +[2] J. Xi, "A Design and Implement of IPS Based on Snort," 2011 +Seventh International Conference on Computational Intelligence and +Security, 2011, pp. 771-773, doithat: 10.1109/CIS.2011.175. +[3] R. Bhardwaj, “Understanding types and benefits of honeypot in +network security,” IP With Ease, 22-May-2020. [Online]. Available: +https://ipwithease.com/understanding-types-and-benefits-of-honeypot-in- +network-security/. [Accessed: 10-Mar-2022]. +[4] R. Chandel, “Comprehensive guide on honeypots,” Hacking +Articles, +13-Jan-2022. +[Online]. +Available: +https://www.hackingarticles.in/comprehensive-guide-on-honeypots/. +[Accessed: 10-Mar-2022]. +[5] “What is a honeypot? how it increases security,” Rapid7. [Online]. +Available: +https://www.rapid7.com/fundamentals/honeypots/. +[Accessed: 10-Mar-2022]. +[6] Taylor, Jamie, Joseph Devlin, and Kevin Curran. "Bringing location +to +IP Addresses with IP Geolocation." Journal of Emerging +Technologies in Web Intelligence 4.3 (2012). +[7] P. Engebretson and D. Kennedy, The basics of hacking and +penetration testing ethical hacking and penetration testing Made Easy. +Amsterdam: Syngress, an imprint of Elsevier, 2013. +[8] S. R. Northcutt, “Improve detection using Honeycreds,” +Improve Detection using HoneyCreds, 01-Jan-1970. [Online]. +Available: +https://securitywa.blogspot.com/2016/04/improve- +detection-using- honeycreds.html. [Accessed: 10-Mar-2022]. +[9] “Using honey credentials to make pivoting detectable,” LogRhythm, +26-Mar-2020. [Online]. Available: https://logrhythm.com/blog/using- +honeywords-to-make-password-cracking-detectable/. [Accessed: 10- +Mar- 2022]. +[10] N. C. Rowe, “ Honeypot Deception Tactics,” U.S. Naval +Postgraduate +School. +[Online]. +Available: +http://faculty.nps.edu/ncrowe/honeypot_deception_tactics.htm. +[Accessed: 10-Mar-2022]. +[11] +Telekom-Security, “Telekom-security/TPOTCE: T-pot - the +all in one honeypot platform ,” GitHub. [Online]. Available: +https://github.com/telekom-security/tpotce. [Accessed: 10-Mar-2022]. +[12] +Cowrie, “Cowrie/Cowrie: Cowrie SSH/telnet honeypot +https://cowrie.readthedocs.io,” +GitHub. +[Online]. +Available: +https://github.com/cowrie/cowrie. [Accessed: 10-Mar-2022]. +[13] +T. Werner, “Armedpot/Honeytrap,” Honeytrap. [Online]. +Available: https://github.com/armedpot/honeytrap/. [Accessed: 10-Mar- +2022]. +[14] +Kibana, “Kibana Guide,” Elastic. [Online]. Available: +https://www.elastic.co/guide/en/kibana/current/index.html. [Accessed: +10- Mar-2022]. +[15] +Suricata, “Documentation - Suricata,” Suricata, 02-Jun-2021. +[Online]. Available: https://suricata.io/documentation/. [Accessed: 10- +Mar-2022]. +[16] +NIST, “CVE-2019-12263 Detail,” National Vulnerability +Database, +09-Aug-2019. +[Online]. +Available: +https://nvd.nist.gov/vuln/detail/CVE- 2019-12263. [Accessed: 10-Mar- +2022]. +[17] +“CVE-2019-0708 Detail,” National Vulnerability Database, +16-May- +2019. +[Online]. +Available: +https://nvd.nist.gov/vuln/detail/CVE-2019-0708. [Accessed: 10-Mar- +2022]. +[18] +“CVE-2020-11910 Detail,” National Vulnerability Database, +17-Jun- +2020. +[Online]. +Available: +https://nvd.nist.gov/vuln/detail/CVE-2020- 11910. [Accessed: 10-Mar- +2022]. +[19] Hisham A. Kholidy, “Multi-Layer Attack Graph Analysis in the +5G Edge Network Using a Dynamic Hexagonal Fuzzy Method”,. +Sensors 2022, 22, 9. +[20] Hisham A. Kholidy, “Multi-Layer Attack Graph Analysis in the +5G Edge Network Using a Dynamic Hexagonal Fuzzy Method”, Sensor +Journal. Sensors 2022, 22, 9. https://doi.org/10.3390/s22010009. +[21] Hisham A. Kholidy, Andrew Karam, James L. Sidoran, +Mohammad A. Rahman, "5G Core Security in Edge Networks: A +Vulnerability Assessment Approach", the 26th IEEE Symposium on +Computers and Communications (IEEE ISCC 2021), Athens, Greece, +September 5-8, 2021. https://ieeexplore.ieee.org/document/9631531 +[22] Hisham A. Kholidy, “A Triangular Fuzzy based Multicriteria +Decision Making Approach for Assessing Security Risks in 5G +Networks”, December 2021, Preprint={2112.13072}, arXiv. +[23] Kholidy, H.A., Fabrizio Baiardi, "CIDS: A framework for +Intrusion Detection in Cloud Systems", in the 9th Int. Conf. on +Information Technology: New Generations ITNG 2012, April 16-18, +Las +Vegas, +Nevada, +USA. +http://www.di.unipi.it/~hkholidy/projects/cids/ +[24] Kholidy, H.A. (2020), "Autonomous mitigation of cyber risks in +the Cyber–Physical Systems", doi:10.1016/j.future.2020.09.002, Future +Generation Computer Systems,Volume 115, 2021, Pages 171-187, +ISSN 0167-739X, https://doi.org/10.1016/j.future.2020.09.002. +[25] Hisham A. Kholidy, Abdelkarim Erradi, Sherif Abdelwahed, + +11 + +Fabrizio Baiardi, "A risk mitigation approach for autonomous cloud +intrusion response system", Computing Journal, Springer, DOI: +10.1007/s00607-016-0495-8, June 2016. (Impact factor: 2.220). +https://link.springer.com/article/10.1007/s00607-016-0495-8 +[26] Hisham A. Kholidy, “Detecting impersonation attacks in cloud +computing environments using a centric user profiling approach”, +Future Generation Computer Systems, Vol 115, 17, December 13, +2020, ISSN 0167-739X. +[27] Kholidy, H.A., Baiardi, F., Hariri, S., et al.: “A hierarchical +cloud intrusion detection system: design and evaluation”, Int. J. Cloud +Comput., Serv. Archit. (IJCCSA), 2012, 2, pp. 1–24. +[28] Kholidy, H.A., “Detecting impersonation attacks in cloud +computing environments using a centric user profiling approach”, +Future Generation Computer Systems, Volume 115, issue 17, +December +13, +2020, +Pages +171-187, +ISSN +0167-739X, +https://doi.org/10.1016/j.future.2020.12. +[29] Kholidy, Hisham A.: 'Correlation-based sequence alignment +models for detecting masquerades in cloud computing', IET +Information Security, 2020, 14, (1), p.39-50, DOI: 10.1049/iet- +ifs.2019.0409. +[30] Kholidy, H.A., Abdelkarim Erradi, “A Cost-Aware Model for +Risk Mitigation in Cloud Computing SystemsSuccessful accepted in +12th ACS/IEEE International Conference on Computer Systems and +Applications (AICCSA), Marrakech, Morocco, November, 2015. +[31] A H M Jakaria, Mohammad A. Rahman, Alvi A. Khalil, Hisham +A. Kholidy, Matthew Anderson et al “Trajectory Synthesis for a UAV +Swarm Based on Resilient Data Collection Objectives", IEEE +Transactions on Network and Service Management, November, 2022 +doi: 10.1109/TNSM.2022.3216804. +[32] Hisham A. Kholidy, Andrew Karam, James Sidoran, et al. +“Toward Zero Trust Security in 5G Open Architecture Network Slices”, +the 40th IEEE Military Conference (MILCOM), San Diego, CA, USA, +November 29, 2022. +[33] Hisham A. Kholidy, Riaad Kamaludeen “An Innovative +Hashgraph-based Federated Learning Approach for Multi Domain 5G +Network Protection”, IEEE Future Networks (5G World Forum), +Montreal, Canada, October 2022. +[34] Hisham A. Kholidy, Andrew Karam, Jeffrey H. Reed, Yusuf +Elazzazi, "An Experimental 5G Testbed for Secure Network Slicing +Evaluation", IEEE Future Networks (5G World Forum), Montreal, +Canada, October 2022. +[35] Hisham A. Kholidy, Salim Hariri, Pratik Satam, Safwan Ahmed +Almadani “Toward an Experimental Federated 6G Testbed: A +Federated learning Approach”, the 13th Int. Conf. on Information and +Communication Technology Convergence (ICTC), Jeju Island, Korea, +October 9, 2022. +[36] NI Haque, MA Rahman, D Chen, Hisham Kholidy, “BIoTA: +Control-Aware Attack Analytics for Building Internet of Things”, 2021 +18th +Annual +IEEE +International +Conference +on +Sensing, +Communication +[37] Kholidy, H.A., Ali T., Stefano I., et al, “Attacks Detection in +SCADA Systems Using an Improved Non-Nested Generalized +Exemplars Algorithm", the 12th IEEE International Conference on +Computer Engineering and Systems (ICCES 2017), December 19-20, +2017. +[38] Qian Chen, Kholidy, H.A., Sherif Abdelwahed, John Hamilton, +"Towards Realizing a Distributed Event and Intrusion Detection +System", the Int. Conf. on Future Network Systems and Security, +Florida, USA, Aug 2017. +[39] Hisham A. Kholidy, Abdelkarim Erradi, Sherif Abdelwahed, +Abdulrahman Azab, “A Finite State Hidden Markov Model for +Predicting Multistage Attacks in Cloud Systems", in the 12th IEEE Int. +Conf. on Dependable, Autonomic and Secure Computing, China, +August 2014. +[40] Ferrucci, R., & Kholidy, H. A. (2020, May). A Wireless +Intrusion Detection for the Next Generation (5G) Networks”, Master’s +Thesis, SUNY poly. +[41] Rahman, A., Mahmud, M., Iqbal, T., Saraireh, L., Hisham A. +Kholidy., et. al. (2022). Network anomaly detection in 5G networks. +Mathematical Modelling of Engineering Problems, Vol. 9, No. 2, pp. +397-404. https://doi.org/10.18280/mmep.090213 +[42] Hisham Kholidy, +“State +Compression +and +Quantitative +Assessment Model for Assessing Security Risks in the Oil and Gas +Transmission +Systems”, +doi +: +10.48550/ARXIV.2112.14137, +https://arxiv.org/abs/2112.14137}, December 2021. +[43] Hisham A. Kholidy, “Correlation Based Sequence Alignment +Models For Detecting Masquerades in Cloud Computing”, IET +Information Security Journal, DOI: 10.1049/iet-ifs.2019.0409, Sept. +2019 +(ISI +Impact +Factor(IF): +1.51) +https://digital- +library.theiet.org/content/journals/10.1049/iet-ifs.2019.0409 +[44] Hisham A. Kholidy, “An Intelligent Swarm based Prediction +Approach for Predicting Cloud Computing User Resource Needs”, the +Computer Communications Journal, December 19 (ISI IF: 2.766). +https://www.sciencedirect.com/science/article/abs/pii/S0140366419303 +329 +[45] Hisham A. Kholidy, Abdelkarim Erradi, “VHDRA: A Vertical +and Horizontal Dataset Reduction Approach for Cyber-Physical Power- +Aware +Intrusion +Detection +Systems”, +SECURITY +AND +COMMUNICATION NETWORKS Journal (ISI IF: 1.376), March 7, +2019. +vol. +2019, +Article +ID +6816943, +15 +pages. https://doi.org/10.1155/2019/6816943. +[46] Hisham A. Kholidy, Hala Hassan, Amany Sarhan, Abdelkarim +Erradi, Sherif Abdelwahed, "QoS Optimization for Cloud Service +Composition Based on Economic Model", Book Chapter in the Internet +of Things. User-Centric IoT, Volume 150 of the series Lecture Notes of +the Institute for Computer Sciences, Social Informatics and +Telecommunications Engineering pp 355-366, June 2015. +[47] Hisham A. Kholidy, Alghathbar Khaled s., “Adapting and +accelerating the Stream Cipher Algorithm RC4 using Ultra Gridsec and +HIMAN and use it to secure HIMAN Data”, Journal of Information +Assurance and Security (JIAS), vol. 4 (2009)/ issue 4, pp 274-283, +2009. (Indexed by INSPEC, Scopus, Pubzone, Computer Information +System Abstracts, MathSci). +[48] Hisham A. Kholidy, “Towards A Scalable Symmetric Key +Cryptographic +Scheme: +Performance +Evaluation +and +Security +Analysis”, IEEE International Conference on Computer Applications & +Information Security (ICCAIS), Riyadh, Saudi Arabia, May 1-3, 2019. +https://ieeexplore.ieee.org/document/8769482 +[49] Samar SH. Haytamy, Hisham A. Kholidy, Fatma A. “ICSD: +Integrated Cloud Services Dataset”, Springer, Lecture Note in +Computer +Science, +ISBN +978-3-319-94471-5, +https://doi.org/10.1007/978-3-319-94472-2. +[50] Stefano Iannucci, Hisham A. Kholidy Amrita Dhakar Ghimire, +Rui Jia, Sherif Abdelwahed, Ioana Banicescu, “A Comparison of +Graph-Based Synthetic Data Generators for Benchmarking Next- +Generation Intrusion Detection Systems”, IEEE Cluster 2017, Sept 5 +2017, Hawaii, USA. +[51] Mustafa, F.M., Kholidy, H.A., Sayed, A.F. et al. Enhanced +dispersion reduction using apodized uniform fiber Bragg grating for +optical MTDM transmission systems. Opt Quant Electron 55, 55 (2023). +https://doi.org/10.1007/s11082-022-04339-7 +[52] Abuzamak, M., & Kholidy, H. (2022). UAV Based 5G Network: A +Practical +Survey +Study. arXiv. https://doi.org/10.48550/arXiv.2212.13329 +[53] Abuzamak, M., & Kholidy, H. (2022). UAV Based 5G Network: A +Practical +Survey +Study. arXiv. https://doi.org/10.48550/arXiv.2212.13329 + + + diff --git a/6NAyT4oBgHgl3EQfQfY1/content/tmp_files/load_file.txt b/6NAyT4oBgHgl3EQfQfY1/content/tmp_files/load_file.txt new file mode 100644 index 0000000000000000000000000000000000000000..4ad94305dda1a39919bbf19d02dcbf66c537bbf7 --- /dev/null +++ b/6NAyT4oBgHgl3EQfQfY1/content/tmp_files/load_file.txt @@ -0,0 +1,825 @@ +filepath=/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf,len=824 +page_content='1 An Analysis of Honeypots and their Impact as a Cyber Deception Tactic Daniel Zielinski, Hisham A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Kholidy State University of New York (SUNY) Polytechnic Institute, College of Engineering, Network and Computer Security Department, Utica, NY USA zielind@sunypoly.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='edu, hisham.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='kholidy@sunypoly.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='edu Abstract— This paper explores deploying a cyber honeypot system to learn how cyber defenders can use a honeypot system as a deception mechanism to gather intelligence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Defenders can gather intelligence about an attacker such as the autonomous system that the attacker’s IP is allocated from, the way the attacker is trying to penetrate the system, what different types of attacks are being used, the commands the attacker is running once they are inside the honeypot, and what malware the attacker is downloading to the deployed system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' We demonstrate an experiment to implement a honeypot system that can lure in attackers and gather all the information mentioned above.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' The data collected is then thoroughly analyzed and explained to understand all this information.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' This experiment can be recreated and makes use of many open-source tools to successfully create a honeypot system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Keywords—honeypots, deception mechanism, autonomous system, deceptive honeypot system, open-source tools I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' INTRODUCTION Cyber honeypots can be very useful tools when trying to lure in attackers and gain a defensive advantage by learning how attackers are operating and what information they are seeking.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Honeypots are very common systems that many enterprise organizations use to improve their security program and gain insight into the common attacks they face.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' When using a honeypot, defenders can gain various amounts of information about the attackers targeting them.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Defenders can then use this information to better protect their “real” environments.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Defenders can gain insight into the most popular types of attacks that are being used to break into their honeypot systems and then make sure that their actual systems are properly protected against these attacks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Defenders can also learn what data the attackers are trying to reach, or what exactly the motive of the attackers is.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' In addition, defenders will be able to learn the different types of malware that attackers are installing onto the honeypot systems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Modern honeypots are not able to look or be configured the same way that they have in the past.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' This is because over time attackers started learning different ways to detect if what they were attacking is a honeypot.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Once an attacker figures out that what they are attacking is a honeypot, they will no longer use their time or resources on the honeypot.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' This will then make it where the defender will not be able to gain the information they are seeking about the attacker, and the attacker will not use up a great number of resources on the honeypot.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Therefore, modern honeypot systems must be deceptive.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' They need to look, run, and operate like the “real” system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' An effective way of doing so is by looking at your actual system and then placing data that looks the same but is fake into your honeypot.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' This will make attackers spend a lot of time trying to exploit your honeypot and make them spend time searching for artifacts such as valid credentials.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' This will also help dry up a lot of the attackers’ resources and make them waste a lot of money.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' By the time the attackers realize that they have been caught in a honeypot trap, they may become extremely frustrated and decide to use their remaining resources on a different target to prevent them from falling into another honeytrap that you or your organization may have set up.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' In the next sections, We will be explaining the details of a honeypot and the architecture of a modern deceptive honeypot system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' We will also go into the different types of information a defender can gain from using a honeypot and how this information can be leveraged.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' We then conduct an experiment in which we use a Virtual Private Server to deploy an open-source honeypot system that utilizes many different honeypot containers to create an effective system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' This system also utilizes many other features such as an IPS/IDS and a data visualization dashboard to make it easier to analyze data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' II.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' BACKGROUND In this section, we will discuss the network architecture of where to place a honeypot, different types of honeypots, types of data honeypots can collect, and techniques to achieve deception within the honeypot itself.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Honeypot Network Architecture When deciding where to place a honeypot on a network you must be very careful.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Placing a honeypot inside an internal network can be a major security design flaw.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' This is because it would allow an attacker to easily gain access to your internal network by exploiting the honeypot.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' An 2 attacker would then be able to move laterally throughout the network to find real systems and servers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' In essence, this would make it much easier for the attacker because you would be “inviting” them into your internal local area network (LAN).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Instead, a honeypot should be placed inside a network’s Demilitarized Zone (DMZ).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Figure 1: Architecture of a network with a honeypot deployed [1].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' A DMZ is an isolated part of the network that is connected to the internet and is typically where public-facing services are placed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' In this example, a web server, DNS server, and FTP server are all placed inside the DMZ along with the honeypot [1].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' The DMZ is separated on both sides with a firewall.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' The firewall between the DMZ and Internet is typically a weaker firewall that allows traffic to outside users to do tasks like visiting a website hosted or interacting with available files.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' This firewall will not allow in all traffic but will have the necessary ports open to allow functionality.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' The firewall between the DMZ and the production network is a much more hardened firewall.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' This firewall will not let outside, or unauthorized users bypass it easily.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' In an enterprise setting, most organizations will have an Intrusion Detection System (IDS), or Intrusion Prevention System (IPS) configured to alert on attacks being made against this firewall [2].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' This is to keep cyber analysts alert of any attacks that might be going on against their network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' The production network may have sensitive data flowing through it and may have systems that store important data, so it is important to keep the honeypot isolated from it.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Honeypot Characteristics There are different types of modern honeypots that one can deploy into their environments.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' However, all the honeypots do have some things in common such as being low-maintenance, low cost, and easy to deploy [3].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' The reason honeypots need to need to be low maintenance is that, from an organizational perspective, engineers cannot be using up large amounts of their time manually making changes and fixes to the honeypot.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' They have many other duties to take care of and should be able to look at and analyze data collected from the honeypot easily.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' This is also connected to why a honeypot is easy to deploy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' You should take your time deploying a honeypot to ensure that everything is working properly, but honeypots are easy to deploy because more time and focus should be spent on securing the real environment.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' If anything goes wrong with your honeypot you should be able to just reboot it and most of the time it will fix itself.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Furthermore, a honeypot is often relatively low cost compared to the number of resources an individual or organization may have.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Now, one can make a massive honeypot that is extremely expensive, but it would not make any sense from a financial perspective or provide a great amount of additional benefit.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Most individuals and organizations deploy honeypots that are not very expensive since they can provide great benefits at a low cost.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' This also then allows for a great number of financial resources to be spent on other things like employees, software, and other systems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Types of Honeypots Based on their Design Many different types of Honeypots can be deployed with various complexities.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Figure 2: Types of Honeypots [Based on Design] [4].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' A pure honeypot is meant to be a full-scale replica of a production environment that contains fake data that is meant to pose as real data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' A high-interactive honeypot is like a pure honeypot because it runs many different real-like services, but it does not hold as much data and is not a replica of a full-scale production environment.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' A mild- interaction honeypot is different than a high-interaction honeypot because it just emulates aspects of the application layer but does not have its own OS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' This can be used to stall or confuse attackers who are trying to attack your systems [5].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Finally, a low-interaction honeypot is very lightweight and can be used to match a small number of services and applications that are in use.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' This type of honeypot can be used to keep track of UDP, TCP, and ICMP ports and TYPESOFHONEYPOT [Based on the design] Low-interaction Honeypots Medium-interaction Honeypots High-interaction Honeypots Pure HoneypotsDMZ Webserver DNSserver Internet Firewall Firewall Production Honeypot network FTPserver3 services.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' In which we make can make use of things like fake databases, data, and files as bait to trap attackers to understand the attacks that happen in real-time [4].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Types of Honeypots Based on their Technologies There are different types of honeypots based on the deception technologies that they utilize.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Figure 3: Types of Honeypots [Based on their deception technology] [4].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' A malware honeypot is a type of honeypot that is meant to identify and trap malware inside a network or system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' A sophisticated malware honeypot will recognize the malware’s signature and alert based on its Common Vulnerabilities and Exposures (CVE) ID.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' It will also keep a count of the CVEs to help recognize the most used malware that is being installed onto a system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' A database honeypot is exactly what it sounds like – a honeypot that appears to be a vulnerable database.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Often it will be something like an SQL database that will allow things like injection attacks to occur to attract attackers looking to gain sensitive information that can be stored in a database such as credit card numbers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' A spider honeypot is installed to trap web crawlers that target web applications with the intent of stealing data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' An email honeypot is a fake email server that utilizes hoax email addresses and emails to attract attackers to interact with it.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Any suspicious email received by attackers can be scanned, and their email addresses can be then blacklisted on the real email servers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' A spam honeypot is like an email honeypot but is meant to attract spammers to exploit vulnerable email elements and give details about their activities.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Finally, a honeynet is a honeypot system that can contain various types of honeypots mentioned.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' It will often aggregate data from all the honeypots into a central location to make alerting and monitoring easier [4].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Data that Honeypots Collect Honeypots can collect a large amount of data that can be very useful in establishing a more secure security program as well as serve to be very useful for research purposes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' It is often useful to have a service configured that aggregates this data into a dashboard that makes it easier to look at and understand.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' One of the most basic artifacts that a honeypot can collect is the attacker’s IP address.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' This is a useful artifact because from the IP address we can see things like the general location of the attacker to help identify if potentially many attacks are coming from a specific area.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Additionally, we can see if this attacker is launching many attacks on our systems, and we can see how long they have been attacking our systems [6].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' We can also block this IP address from accessing our real network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Finally, from the IP address, we can find the attacker’s Autonomous System Number (ASN) to see what Autonomous System the attacker’s IP is allocated from.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Another artifact we can gather about the attacker is what Operating System (OS) they are using on their host.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' We may not be able to figure out the exact version of what OS they are using, but we will be able to figure out a general version of the OS they are using.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' For instance, we can see if the attacker is running Windows 7 or if the attacker is running a Linux version from 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='x-3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='x.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Many attackers utilize automation tools to try to break into a server.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Figure 4: A sample Dictionary Attack trying common passwords on an SSH server [7].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' They often run dictionary attacks, a type of attack that tries to log in to a server using a list of commonly used or default usernames and passwords, to crack into your system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' We can use a honeypot to see what passwords and usernames the attackers are trying to use to log in and even take it a step further by making a count of each username and password attempted to find the most popular credentials attackers are trying to use.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Furthermore, when an attacker gains access to a honeypot system we can see what commands they are running once they break in.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' This can help us figure out what their motives may be and what they are looking for.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' We can also see what they are downloading onto the system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' They ACCoUNTCHECK:[ssh)Host:192.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='168.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='18.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='132(1of1,complete)User:owmedb(1of1,complete Password:123456(1of3546complete ICcoUNTCHECK:[ssh)Host:192.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='168.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='18.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='132(1of1,complete)User:ownedb(1of1,ecomplete Password:12345(2of3546complete) AccoUNTCHECK:[ssh)Host:192.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='168.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='18.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='132(1of1,0complete)User:Ounedb(1of1,0complete Password:password(3of3546complete) ICcoUNTCHECK:[ssh]Host:192.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='168.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='18.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='132(1of1,0complete)User:ownedb(1of1,0complete Password:password1（4of3546complete） ACCouNTCHECK:ssh)Host:192.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='168.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='18.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='132(1of1,0complete)User:ownedb(1of1,0complete Pas5word:123456789（5of3546complete ACCoUNTCHECK:[ssh)Host:192.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='168.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='18.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='132(1of1,complete)User:ownedb(1of1,complete as5word:12345678(6of3546complete CCoUNTCHECK:[ssh)Host:192.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='168.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='18.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='132(1of1,ecomplete)User:ownedb(1of1,ecomplete assword:1234567890(7of3546complete) ACCoUNTCHECK:[ssh)Host:192.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='168.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='18.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='132(1of1,complete)User:ownedb(1of1,ecomplete assword:abc123(8of3546.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='complete ACcoUNTCHECK:[ssh)Host:192.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='168.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='18.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='132(1of1,0complete)User:ownedb(1of1,@complete Password:computer(9of3546complete) ICoUNTCHECK:[ssh)Host:192.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='168.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='18.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='132(1of1,0complete)User:ownedb(1of1,0complete Password:Th3Basics(10of3546complete ACCOUNTFOUND:[ssh]Host:192.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='168.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='18.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='132User:OwnedbPassword:Th3B@sics[SUCCESS] rootebt:~#TypesofHoneypot Based on theirdeception technology MalwareHoneypots Database Honeypots Spam Honeypots Email Honeypots SpiderHoneypots Honeynet Honeypots4 can download worms or viruses onto the system, or maybe even agents that will try to clean up log files to keep them undetected so they can persist more.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' They even might install things like rootkits to help with this persistence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' In essence, any time the attacker attempts to interact with the honeypot system, you can access and track that information.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Honeypot Deception Techniques Many different techniques can be utilized to achieve deception within a honeypot system to trick attackers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' To start, a commonly used technique is to not allow open access to a server.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' If attackers can connect to your server on the first try, it is often a sign that you are inviting them in, and it may cause them to be very suspicious.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' To solve this, make it that after many failed attempts they will finally be able to gain access and login into your system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' This will make them think that their brute-forcing or dictionary attack worked, but it still took a while for it to be a success.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' This will cause them to have less suspicion.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Another more complex technique is to utilize honey credentials.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Figure 5: Honey Credentials being stored in memory [8].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Honey credentials help catch malicious actors by injecting fake credentials into a system’s memory.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' When an attacker gains access to your network and finds the honey credentials, they will attempt to use them.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Since these credentials don’t exist, any attempt to use them can trigger an alert and notify you immediately.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' In a targeted attack, the attacker will be able to dump recovered honey credentials from the system’s memory through privilege escalation or a system flaw.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' The attacker will then attempt to perform lateral movement into the fake objects, resulting in their exposure and making it easy to trace them [9].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Finally, the most important deception technique is to make the honeypot blend in and look realistic.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Earlier we mentioned the different types of honeypots based on their deception technology.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' It is important to be able to look at these honeypots from the attacker’s perspective.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' For instance, an SSH server must look like an actual SSH server that you or your organization would utilize.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' If it looks fake no one will take the bait and they will be able to easily detect what they are attacking is a honeypot.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' You should be configuring and storing data on your honeypot that you would store on your actual systems but just make sure it is fake information [10].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' You can install real services you would normally use onto your honeypot, but just make sure they do not contain sensitive information.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' By having your honeypot blend in, it will keep your attacker occupied and cause them to reveal more information about themselves and what their motives are.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' III.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' T-POT HONEYPOT SYSTEM FRAMEWORK In this section, we detail the honeypot system we will be deploying to gather data and intelligence about attackers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' System Fundamentals T-Pot is an open-source all-in-one honeypot platform that runs on Debian Linux.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' The honeypot daemons as well as other support components are dockered.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' This allows T- Pot to run multiple honeypot daemons and tools on the same network interface while maintaining a small footprint and while constraining each honeypot within its own environment [11].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Documentation to the platform can be found at https://github.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='com/telekom-security/tpotce.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' T-Pot uses docker images for 25 different honeypots to create a massive honeypot system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' All the honeypots each represent different systems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' For instance, cowrie is a medium-to-high interaction SSH and Telnet honeypot designed to log brute force attacks and the shell interaction performed by the attacker [12].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' In addition, another example is the honeytrap honeypot is a network security tool written to observe attacks against TCP or UDP services [13].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' You can read about all 25 different honeypots deployed in the system on the documentation page for T-Pot.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' For the most part, understanding each honeypot will not be very important because we will mainly focus on the aggregated data collected for the whole system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Tools Utilized by T-Pot T-Pot uses a variety of different tools to aggregate, alert, monitor, and analyze data collected.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Figure 6: Description of tools used by T-Pot [11].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' mitrikate20alptax6foeto) msy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' [0000003] Primary Username adninistrator Donain NTLM microsoft 76d202631 SHAI bc059168c2d8d1200c tspkg: KO wdigest Username adninistrator Donain Passuord microsoft.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='con (nuil) kerberos : Csername adninistrator Donain microsoft.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='con Password ssp: superpass credman : AuthenticationIdt Q91755333（00000090：05781345) UOTSSOC NewCredentials fron 0 Jser Name HindouaBMarkB nark Domsin SID S-1-5-21-1469176257-2007698836-3967804884-1001 mSY.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' (00800003] Primary Username root Donain : NTLM 211394dc229g20 200018a69ac04 SHA1 Sa71afbecd987668b917e4425c82ac29a0e39b93 tspkg: KO wdsgest Username root Donain Iinux,org Password : (ltnu) livessp kerberos : Username root Donain linux.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content='org Password notreallythepassword ssp: credman : 2 HACyberchefawebappforencryption,encoding,compressionanddataanalysis ELK stack to beautifulyvisualizeallthe events captured byT-Pot ElasticsearchHeadawebfrontendforbrowsingandinteractingwithanElasticSearchcluster Fattapysharkbasedscriptforextractingnetworkmetadataandfingerprintsfrompcapfilesandlivenetwork traffic.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Spiderfoot a open source intelligenceautomation tool Suricata a Network Security Monitoring engine5 All the tools mentioned in Figure 6 can be further analyzed by examining the information found on the T-Pot documentation page.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' For our experiment, we will focus on the ELK stack and Suricata tools.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' ELK stack has a tool named Kibana, which we will utilize to visualize all the data collected by the different honeypots.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' We can use Kibana to analyze the data for each individual honeypot as well as look at an aggregated view of information collected from all the honeypots deployed in one dashboard [14].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Suricata is a network security monitoring engine that we can use to monitor and alert us about suspicious activity occurring within the system [15].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' The information from Suricata will be viewable and aggregated into the Kibana Dashboard.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' As mentioned previously and displayed in Figure 6 there are many other tools pre-built into T-Pot, but we will not be using them as they are not necessary to understand and analyze the data collected.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Deploying the T-Pot Honeypot System The system requirements for deploying the system are as follows: 8 GB Ram, 128 GB SSD, Network via DHCP, and a non-proxied internet connection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' You can download the Pre-built ISO Image (~50 MB), or you can create your own ISO Image that allows you to customize the system to fit your needs better.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Since we are doing this for research purposes to see what information we can learn about attackers and not using the system in an enterprise environment, xutilized the Pre-built ISO Image.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' To deploy the honeypot system, I used a Virtual Private Server (VPS).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' I uploaded the ISO file to the VPS provider’s website and then began installation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' The reason I used a VPS is to avoid large amounts of unwanted traffic coming towards my personal network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' I also did not want to reveal my actual public IP address to attackers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' Once the ISO image finished installing in my VM the honeypot system was fully functional and ready to lure in attackers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/6NAyT4oBgHgl3EQfQfY1/content/2301.00045v1.pdf'} +page_content=' To reach the T-Pot landing page to gain access to all the tools deployed in the system I had to go into a web browser and enter “https:// 0 for all v ∈ �D, x ∈ M. +• (Regularity): F is smooth, i.e. C∞ on �D. +• (Positive homogeneity): Fx(λv) = λFx(v) for all v ∈ �Dx and λ ∈ R+. +• (Strong convexity condition): The Hessian matrix of F 2 with respect +to the coordinates on the fibre is positive definite. +One can replace the strong convexity condition by the following subad- +ditivity property (in an equivalent terminology, a triangle inequality): +Fx(v + u) ⩽ Fx(v) + Fx(u), for all v, u ∈ �D. +A sub-Finsler manifold is a smooth manifold M endowed with a sub-Finslerian +structure, i.e. the triple (D, σ, F). +Let Dx be the fiber over x ∈ M. The last condition of the sub-Finsler metric +means that the matrix +∂2F 2 +∂vi∂vj (x, v) is positive definite for all v = (v1, . . . , vk) ∈ Dx. +Equivalently, the corresponding indicatrix +Ix = {v | v ∈ Dx, Fx(v) = 1} +is strictly convex. +The following technique describes the association between the sub-Finsler struc- +ture (D, σ, F) and a Finsler metric ˆF on Im(σ) ⊂ T M: +For each u ∈ Im(σ)x ⊂ TxM and x ∈ M, we have +ˆFx(u) = inf +v {Fx(v)| v ∈ Dx, σ(v) = u}. + +HOPF-RINOW THEOREM OF SUB-FINSLERIAN GEOMETRY +3 +From now on we suppose that D ⊂ T M, +σ : D +� T M is the inclusion i : +D +� T M and F is a sub-Finsler metric on D. +As in the sub-Riemannian case, we call D the horizontal distribution. A piecewise +smooth curve γ : [0, T ] → M is called horizontal, or admissible if ˙γ(t) ∈ Dγ(t) for +all t ∈ [0, T ], that is, γ(t) is tangent to D. The length of γ is defined as usual by +ℓ(γ) = +� T +0 +F(˙γ(t))dt. +Equivalently, as in the Finslerian case, we observe that it suffices to minimize +the energy +E(γ) = 1 +2 +� T +0 +F 2(˙γ(t))dt. +instead of length ℓ(γ). +The length induces a sub-Finslerian distance d(x, y) between two points x and +y as in Finsler geometry: +d(x, y) = inf{ℓ(γ) |γ : [0, T ] +� M horizontal, γ(0) = x, γ(T ) = y}, +where we consider the infimum over all horizontal curves joining x and y. The +distance is infinite if there is no such a horizontal curve between x and y. +In +addition, the horizontal curve γ : [0, T ] → M is called a length minimizing (or +simply a minimizing) geodesic, if it realizes the distance between its end points, +that is, ℓ(γ) = d(γ(0), γ(T )). +Chow theorem answers to the following question: given two points x and y in a +sub-Finsler manifold, is there a horizontal curve that joins x and y? +In the case of an involutive distribution D the Frobenius theorem asserts that +the set of the horizontal paths through S form a smooth immersed submanifold, the +leaf through x, of dimension equal to the rank of distribution k. In this case, if D +is involutive and y is not contained in the leaf through y, there is no any horizontal +curve joining x and y. +A positive answer is given by the Chow theorem in the case of bracket generating +distributions, which are the ”contrary” of the involutive distributions. +Definition 2. [9] A distribution D is said to be bracket generating if any local frame +Xi of D, together with all of its iterated Lie brackets spans the whole tangent bundle +T M. +Theorem 3. (Chow’s theorem [9]) If D is a bracket generating distribution on a +connected manifold M then any two points of M can be joined by a horizontal path. +Remark 1. The problem of minimizing the length of a curve joining two given +points x and y is equivalent to a time optimal problem: where the control bundle +is (D, πD, M) and we are searching for such a curve γ(t) and a control curve v(t) ∈ +Dγ(t) minimizing the time T needed to connect x and y. +3. Legendre transformation of sub-Finslerian geometry +Let D∗ be a distribution of rank s on a smooth manifold M that assigns to +each point x ∈ U ⊂ M a linear subspace D∗ +x ⊂ T ∗ +xM of dimension s, see [4]. In +other words, D∗ of rank s is a smooth subbundle of rank s of the cotangent bundle + +4 +LAYTH M. ALABDULSADA AND L´ASZL ´O KOZMA +T ∗M. Such a field of cotangent s-planes is spanned locally by s pointwise linear +independent smooth differential 1-forms, namely, +D∗ +x = span{α1(x), . . . , αs(x)}, +αi(x) ∈ X∗(M). +In addition, we refer to D0 +x as the annihilator of the distribution D (isomorphic to +D), of rank n − k, which is the set of all covectors that annihilates the vectors in +Dx, i.e. +D0 +x = {α ∈ T ∗ +xM : α(v) = 0 ∀ v ∈ Dx}. +(1) +In [2], we introduced the Legendre transformation of sub-Finsler geometry. Let +us briefly recall it: +The sub-Lagrange function L : D +�R, determined by F is given in the following +way: L = 1 +2F 2. The fiber derivative of L defines the map +LL : D +� D∗, +LL(v)(w) = d +dtLx(v + tw), where v, w ∈ Dx, +called the Legendre transformation of (M, D, F). +We denote by (xi) the coordinate in a neighborhood U ⊂ M with (xi, va) in +D|U ⊂ T M, and (xi, pa) in D∗|U ⊂ T ∗M, respectively, where i = 1, . . . , n, a = +1, . . . , k. Then the relation of the distribution D of the tangent bundle and the +distribution D∗ of the cotangent bundle is given by the Legendre transformation in +local coordinates as follows +LL(xi, va) = (xi, ∂L +∂va ). +Then the sub-Hamiltonian is given by +H : D∗ +� R, +H = ιL−1 +L − L ◦ L−1 +L , +where ιv(p) = ⟨v, p⟩ = p(v) for any v = L−1 +L (p) ∈ D and p ∈ D∗. Moreover, locally +given by, +H(xi, pa) = vapa − L(xi, va), where pa = ∂L +∂va . +Secondly, using the fiber derivative of H, we define the Legendre transformation of +the sub-Hamiltonian H in the following way: +LH : D∗ +� D, +For any p, q ∈ D∗ +x, it holds +q(LH(p)) = d +dtH(x, p + tq). +This locally relates the distribution D∗ of the cotangent bundle and the distribution +D of the tangent bundle according to the next expression: +LH(xi, pa) = (xi, ∂H +∂pa +). +Naturally, LL and LH are inverses of each other: +LH ◦ LL = 1D, +LL ◦ LH = 1D∗. + +HOPF-RINOW THEOREM OF SUB-FINSLERIAN GEOMETRY +5 +In other hand, for every p ∈ D∗ +x, one can define the sub-Finsler metric F ∗ ∈ +�D∗ ∼ T ∗M \ D0 with help of the indicatrix Ix as follows: +F ∗ +x(p) := sup +w∈Ix +p(w) = +sup +0̸=v∈Dx +p[ +v +Fx(v)]. +Observed that �D∗ is the subbundle of the cotangent bundle obtained by removing +the zero cotangent vector from each fibre. In fact, F ∗ turns out to meet the same +properties that mentioned in Definition 1, but on D∗ instead of D. Then +F ∗(p) = F(v), where p = LL(v), +and +H := 1 +2(F ∗)2, +see details in [5]. +4. Sub-Finsler bundle +We define in this section a sub-Finsler vector bundle which will play a major role +in the formalization of the sub-Hamiltonian in sub-Finsler geometry. Let us consider +first the covector subbundle (D∗, τ, M) with the projection τ : D∗ +� M, which is +a subbundle of rank k (= dim D∗) in the cotangent bundle of T ∗M. The illustrious +role in our consideration will play by the pullback bundle τ ∗(τ) = (D∗×D∗, pr1, D∗) +of τ by τ as follows: +D∗ ×M D∗ := {(p, q) ∈ D∗ × D∗| τ(p) = τ(q)}, +pr1 : D∗ ×M D∗ +� D∗, (p, q) �→ p. +Throughout, we call the above pullback bundle as the sub-Finsler bundle over +D∗. Now, if p is fixed, then +(pr1)−1(p) = {(p, q) ∈ D∗ × D∗| q ∈ D∗ +τ(q)} += {p} × D∗ +τ(p), +is a fiber of the sub-Finsler bundle over p ∈ D∗. +We can introduce a Riemannian metric g∗ on the sub-Finsler vector bundle +induced by the sub-Hamiltonian H as follows: +⟨q, r⟩p = g∗ +p(q, r) := ∂2H(p + tq + sr) +∂t∂s +|t,s=0 +for all q, r ∈ D∗ +τ(p), +which locally means +g∗ij = +∂2H +∂pi∂pj +. +Now the sub-Finsler bundle τ ∗(τ) allows k covector fields X1, X2, . . . , Xk which +form an orthonormal frame with respect to the induced Riemannian metric g∗. +Notice that Xi(p) is a covector field that depends on the position x ∈ M and the +direction p ∈ D∗. Moreover, one can choose in a way that Xi(p) is a homogeneous of +degree zero in p, i.e. Xi(tp) = t0Xi(p) = Xi(p). According to the above metric g∗ij +on M which is homogeneous of degree zero, we could generate a new formalism of +the sub-Hamiltonian function in the components pi (induces naturally by the inner +product, see [6]) +H(x, p) = 1 +2 +n +� +i,j=1 +g∗ijpipj, +(2) + +6 +LAYTH M. ALABDULSADA AND L´ASZL ´O KOZMA +such that this metric defined in the extended Finsler metric which was shown in +[2]. We can write the sub-Hamiltonian function (2) in a more useful way using the +orthonormality of Xi as follows +H(x, p) = 1 +2 +k +� +i=1 +⟨p, Xi(p)⟩2, +p ∈ D∗ +x. +(3) +One can easily check the homogeneity of degree 2 in p of the sub-Hamiltonian +function H(x, p): +H(x, tp) = 1 +2 +k +� +i=1 +⟨tp, Xi(tp)⟩2 = t2 +2 +k +� +i=1 +⟨p, Xi(p)⟩2 = t2H(x, p). +(4) +The importance of H(x, p) is to define sub-Finslerian geodesics. Our function +H(x, p) produces a system of sub-Hamiltonian differential equations, since it is +a smooth function on D∗. Such differential equations are in terms of canonical +coordinates (xi, pi). +Definition 4. The generated sub-Hamiltonian differential equations +˙xi = ∂H +∂pi +(x, p), +˙pi = −∂H +∂xi (x, p), +i = 1, . . . , n, +are called normal geodesic equations. +Lemma 5. If ξ(t) := (x(t), p(t)) is a solution of the sub-Hamiltonian system for +all t ∈ R, then there exists a constant c ∈ R such that H(x(t), p(t)) = c. +Proof. Taking the derivative of H(x(t), p(t)) w.r.t. t, we get +d +dtH(x(t), p(t)) = ∂H +∂xi (x(t), p(t)) ˙x(t) + ∂H +∂pi +(x(t), p(t)) ˙p(t). +Replacing ˙x(t) and ˙p(t) by the above sub-Hamiltonian differential equations in the +Definition 4, we obtain +d +dtH(x(t), p(t))) = ∂H +∂xi (x(t), p(t))∂H +∂pi +(x(t), p(t)) − ∂H +∂pi +(x(t), p(t))∂H +∂xi (x(t), p(t)) += 0. +Therefore H(x(t), p(t)) is constant. +□ +Remark 2. From Lemma 5, it follows that any solution ξ(t) := (x(t), p(t)) of +the sub-Hamiltonian differential equations on D∗ for a sub-Hamiltonian function +H(p) satisfies H(x(t), p(t)) = c. Let the projection x(t) = τ(ξ(t)) ∈ M, so each +sufficiently short subarc of x(t) is a minimizer sub-Finslerian geodesic, (see [11, +Corollary 2.2]). In addition, this subarc is the unique minimizer joining its end +points. +The projection curve x(t) mentioned above is said to be the normal sub-Finslerian +geodesics or simply the normal geodesics. +Remark 3. In the sub-Finslerian geometry, not all the sub-Finslerian geodesics +are normal (contrary to the Finsler geometry). This is due to the fact that the +sub-Finslerian geodesics which are also a minimizing geodesic might not be solved + +HOPF-RINOW THEOREM OF SUB-FINSLERIAN GEOMETRY +7 +the sub-Hamiltonian system. Those minimizer that are not normal geodesics called +singular or abnormal geodesics (see [9] for more details). +Moreover, we call the extremal pair ξ(t) = (x(t), p(t)) a normal extremal if it +is a solution for the sub-Hamiltonian system, otherwise it is called an abnormal +extremal. +Turning to the relationship between the normal geodesic and the locally length- +minimizing horizontal curves, Calin et al. proved in [6] that any normal geodesic is +a horizontal curve and a locally length-minimizing horizontal curve. After all, by +using (3) one can generate the system of differential equations in terms of canonical +coordinates (x, p) as follows: +˙xi = ∂H +∂pi += +k +� +j=1 +⟨p, Xj(p)⟩ (δi(Xj(p)) + ⟨p, DpiXj(p)⟩), +(5) +˙pi = −∂H +∂xi = − +k +� +j=1 +⟨p, Xj(p)⟩⟨p, DxiXj(p)⟩, +(6) +where δi is the i-th coordinate function. +5. Exponential map in sub-Finsler geometry +Let (M, d) be a general metric space, such that M is an n-dimensional manifold +and the function d : M × M +� R+ ∪ {∞}, is called a metric if have the following +properties: for all x, y, z ∈ M, +(i) d(x, y) = 0, with equality if and only if x = y; +(ii) d(x, y) + d(y, z) ≤ d(x, z). +If the function d is an asymmetric, then we can define the forward metric balls and +forward metric spheres, with center x ∈ M and radius r > 0 as follows: +Bx(r) = { y ∈ M : d(x, y) < r}, +Sx(r) = { y ∈ M : d(x, y) = r}. +The cotangent balls and the cotangent spheres in D∗ are defined as follows: +B∗ +x(r) = { p ∈ D∗ : F ∗ +x(p) < r}, +S∗ +x(r) = { p ∈ D∗ : F ∗ +x(p) = r}, +for any fix x ∈ M and radius r. +A subset U ⊂ M is said to be open if, for each point x ∈ U, there is a forward +metric ball about x contained in U. Then we get the topology on M and all metric +spaces are first countable and T1-spaces. In general, we assume that the metric d +of any metric space (M, d) is continuous with respect to the product topology on +M × M. Thus, every backward metric ball, i.e. B− +x (r) = { y ∈ M : d(y, x) < r}, +is open and the metric space is a Hausdorff (T2) space. Hence the compact sets in +such a space are closed. +As a result of the above, we immediately have the following +Proposition 6. In a metric space (M, d) the following are equivalent: +(i) A sequence {xk} in (M, d) converges to x ∈ M in the sense of topology. +(ii) limk→∞ d(x, xk) = 0. + +8 +LAYTH M. ALABDULSADA AND L´ASZL ´O KOZMA +Proposition 7. Let x be any point in a (reversible) sub-Finslerian manifold M, +and ¯Bx(r) is a compact ball, for some r > 0. Then for any y ∈ Bx(r) there is a +minimizing geodesic from x to y, that is, +d(x, y) = min{ℓ(γ) |γ : [0, T ] +� M horizontal, γ(0) = x, γ(T ) = y}. +Proof. Fix y ∈ Bx(r) and suppose that γk : [0, T ] +� M is a minimizing sequence +of horizontal paths with unit speed from x to y and such that +lim +k→∞ γk(0) = x, +lim +k→∞ γk(T ) = y, +lim +k→∞ ℓ(γk) = d(x, y). +For the reason that d(x, y) < r, we get ℓ(γk) ≤ r for all k ≥ k0 large enough. +Proposition 6 asserts that the metric d is continuous under the topology of the +manifold and the reversibility of F holds on a compact set. +Consequently, any +sequence γk of curves which have uniformly bounded lengths has an uniformly +convergent subsequence (Ascoli—Arzela theorem), we denote this subsequence by +the same symbol, and a Lipschitz curve γ : [0, T ] +� M. +From above one can assume that γk : [0, T ] +� M is a convergent subsequence +of length minimizers parametrized by arc length (i.e. F(˙γ(t)) = 1) on M such that +such that γk +� γ uniformly on [0, T ]. This gives that +ℓ(γk) = d(γk(0), γk(T )), +which is due to the claim that γk is a minimizing geodesic. +The sequence γk +converges uniformly if for every ǫ > 0 there is a natural number N such that for +all n ≥ N and all t ∈ [0, T ] one has d(γk(t), γ(t)) < ǫ. Further, the semicontinuity +of the length implies that if limk→∞ γk = γ then +ℓ(γ) ≤ lim +k→∞ inf ℓ(γk). +Now, by continuity of the distance, we obtain +ℓ(γ) ≤ lim +k→∞ inf ℓ(γk) = lim +k→∞ inf d(γk(0), γk(T )) = d(γ(0), γ(T )). +This yields that γ is minimizing geodesic, i.e. +ℓ(γ) = d(x, y). +The horizontal +property of γ follows in the same way as was done in [1], Theorem 3.41. +□ +Next, we define the exponential map. For the general case, roughly speaking, +if M is a smooth Finsler manifold, x a point in M and u ∈ TxM. +Then the +exponential map is given by +expx : TxM +� M, +such that expx(u) = γu(1) for the unique geodesic γ that starts at x and has initial +speed vector u. +Furthermore, in the dual space the exponential map for every +x ∈ M and p ∈ T ∗ +xM defined by +exp∗ +x : T ∗ +xM +� M, +such that exp∗ +x(p) = γp(1) for the unique geodesic γ that starts at x and has initial +speed vector u = L−1 +L (p), where L here is the Lagrangian of the Finsler manifold. +The exponential map is an essential object in sub-Finslerian geometry, parametriz- +ing normal extremals through their initial covectors. We are going to define the +exponential map in both of the distribution D, D∗ of the tangent and the cotangent +bundles respectively. + +HOPF-RINOW THEOREM OF SUB-FINSLERIAN GEOMETRY +9 +Definition 8. Let Ωx ⊂ Dx be the domain of the exponential map over x ∈ M +such that Ωx given by +Ωx = {v ∈ Dx| ξ is defined on the interval [0, 1]} , +where v = LH(p) by the Legendre transformation of sub-Hamiltonian H, and ξ(t) +is the normal extremal. Then the sub-Finsler exponential map is defined as follows +expx : Ωx ⊂ Dx ⊂ TxM +� M, v �→ πD(LH(ξ(1))). +We can do the same in the distribution D∗ +x. Let Ω∗ +x ⊂ D∗ +x be the domain of the +exponential map over x ∈ M such that Ω∗ +x given by +Ω∗ +x = {p ∈ D∗ +x| ξ is defined on the interval [0, 1]} . +Consequently, the sub-Hamiltonian exponential map is given by +exp∗ +x : Ω∗ +x ⊂ D∗ +x ⊂ T ∗ +xM +� M, p �→ τ(ξ(1)), +where ξ(t) is the same normal extremal as above. The set Ω∗ +x contains the origin and +star-shaped with respect to 0. Moreover, with the help of Legendre transformation +it is fairly easy to see that +expx(v) = exp∗ +x(p), +where +p = LL(v). +(7) +It follows that the normal sub-Finslerian geodesics x(t) = τ(ξ(t)) satisfies +x(t) = exp∗ +x(tp), +for all t ∈ [0, T ]. +Theorem 9. The exponential mapping exp∗ +x is a local diffeomorphism on D∗ +x ⊂ +T ∗ +xM\{0}. +Proof. In 4, we show the homogeneity of the sub-Hamiltonian function H(x, p) with +respect to p. So, for any constant a > 0, the curve ξ(at) : (ǫ/a, ǫ/a) +� M is the +same geodesic satisfying the initial conditions τ(ξp(0)) = x and ξp(0) = ap, i.e., +τ(ξp(at)) = τ(ξap(t)). +Since the sub-Hamiltonian vector field +⃗H(x, p) = gab(x, p)pb +∂ +∂xa − 1 +2 +∂gab +∂xk (x, p)papb +∂ +∂pk +, +that introduced in [2], is smooth except for p = 0 where it is C1. Then exp∗ +x is C∞ +on D∗ +x ⊂ T ∗ +xM\{0}, while it is C1 at p = 0 and d(exp∗ +x)|0 = id. Thus, exp∗ +x is a +local diffeomorphism. +□ +By equation (7), one can get the following +Corollary 10. The sub-Finsler exponential map expx is a C∞ away from the zero +section of D and only C1 at the zero section such that for each x ∈ M +d(expx)|0 : Ωx ⊂ Dx +� Ωx ⊂ Dx, +is the identity map at the origin 0 ∈ Dx. +Remark 4. It is clear that in the case of sub-Finsler exponential map the following +expressions holds: +exp∗ +x[B∗ +x(r)] = Bx(r), +exp∗ +x[S∗ +x(r)] = Sx(r), +which are analogous to the Finslerian context, see Bao et al. [5] for more details. + +10 +LAYTH M. ALABDULSADA AND L´ASZL ´O KOZMA +Remark 5. Turning to sub-Riemannian case, Strichartz in [13] stated that for +bracket generating distributions the exponential map is a local diffeomorphism. +This is due to the fact that the solutions of the sub-Hamiltonian system depend +differentially on the initial data. +But this is a difference from the Riemannian +context, the exponential map is not a diffeomorphism at the origin just like the +Finslerian case. +6. Hopf-Rinow Theorem in sub-Finslerian geometry +In the following, one can see the explanation of the terms that will be used in +Hopf-Rinow Theorem. A sub-Finsler manifold is said to be forward complete if +every forward Cauchy sequence converges, and it is a forward geodesically complete +if every normal geodesic γ(t), t ∈ [0, T ) parametrized to have unit speed, can be +extended to a geodesic for all t ∈ [0, ∞). A subset is said to be forward bounded if +it is contained in some forward metric ball Bx(r). +Theorem 11. Let (M, D, F) be any connected sub-Finsler manifold, where D is +bracket generating distribution. The following conditions are equivalent: +(i) The metric space (M, d) is forward complete. +(ii) The sub-Finsler manifold (M, D, F) is forward geodesically complete. +(iii) Ω∗ +x = D∗ +x, additionally, the exponential map is onto if there are no strictly +abnormal minimizer. +(iv) Every closed and forward bounded subset of (M, d) is compact. +Furthermore, for any x, y ∈ M there exists a minimizing geodesic γ joining x to y, +i.e. the length of this geodesic is equal to the distance between these points. +Proof. (i) =⇒ (ii) Let γ(t) : [0, T ) +� M be a unit speed and maximally forward +extended geodesic, t ∈ [0, T ). If we assume that T ̸= ∞, and choose a sequence +{ti} +� T in [0, T ) then γ(ti) is forward Cauchy, since +d(γ(ti), γ(tj)) ≤ |tj − ti|, for all i ≤ j. +Now, (i) makes it obvious that γ(ti) converges to y ∈ M. On one hand, let us +define γ(T ) to be y. On the other hand, Lemma 4.1 in [13] told us that γ(t) can +be extended beyond t = T . This contradicts our assumption the fact that T ̸= ∞. +Thus, T = ∞ for sure, so we have the forward geodesically completeness. +(ii) =⇒ (iii) It is sufficient (for first part Ω∗ +x = D∗ +x) to prove that any normal +extremal pair ξ(t), starting from the initial conditions, is defined for all t ∈ R. +Suppose that the normal extremal is not extendable to the some interval [0, T +δ) for +all δ > 0 and suppose that it is defined on [0, T ). Let {ti} be any increasing sequence +such that the limit of this sequence is T . Hence, the projection x(t) = τ(ξ(t)) is +a curve with unit speed defined on [0, T ), therefore, the sequence {ti} is a forward +Cauchy sequence on M, since +d(x(ti), x(tj)) ≤ |ti − tj|. +By completeness, it follows that the sequence x(ti) converges to some point +y ∈ M. We suppose there are coordinates around the point y and an orthonormal +frame X1, X2, ..., Xk in small ball B∗ +y(r) in the sub-Finsler bundle. Let us show +that in the coordinates ξ(t) = (x(t), p(t)) the curve x(t) is uniformly bounded. This +grants a contradiction that the normal extremal is not extendable. In fact, for every + +HOPF-RINOW THEOREM OF SUB-FINSLERIAN GEOMETRY +11 +p ∈ D∗, we consider the following non-negative form (3) of the sub-Hamiltonian +function H: +H(x, p) = 1 +2 +k +� +i=1 +⟨p, Xi(p)⟩2. +Then, the sub-Hamiltonian system has the form: +˙xi(t) = ∂H +∂pi +(x(t), p(t)) = +k +� +j=1 +⟨p(t), Xj(p(t))⟩(δi(Xj(p)) + ⟨p, DpiXj(p)⟩), +˙pi(t) = −∂H +∂xi (x(t), p(t)) = − +k +� +j=1 +⟨p(t), Xj(p(t))⟩⟨p(t), DxiXj(p(t))⟩, +for t ∈ [T − δ, T ) with δ > 0 small enough. Since Dγ(t)Xi are given in a compact +small ball ¯B∗ +y(r), they are bounded, so there is a constant C > 0 such that +| ˙p(t)| ≤ C|p(t)| +∀t ∈ [T − δ, T ). +If we apply Gronwall’s Lemma (see [12], p.122), it leads us to that |p(t)| is uniformly +bounded on a bounded interval. This contradicts our assumption that the normal +extremal can not be extended beyond T . +(iii) =⇒ (iv) Assume that ¯A is a closed and forward bounded subset of (M, d). +Applying the bracket generating assumption, for every y ∈ ¯A, Proposition 7 asserts +that there is a minimizing geodesic exp∗ +x(tpy), 0 ≤ t ≤ T, from x to y. The set of all +py is subset A of D∗ +x. Since F ∗ +x (py) = d(x, y), and d(x, y) ≤ r for some r due to the +forward boundedness of ¯A, the subset A is bounded and contained in the compact +set B∗ +x(r) ∪ S∗ +x(r). By Remark 4, exp∗ +x[B∗ +x(r) ∪ S∗ +x(r)] is compact and contained in +the closed set ¯A, then ¯A it must be compact. +(iv) =⇒ (i) Let {xi} be a forward Cauchy sequence in M, and by the subaddi- +tivity it must be forward bounded. Choose A := {xi|i ∈ N}, then its closure ¯A is +still forward bounded under the manifold topology of M. Taking into account the +assumption (iv), ¯A should satisfy the compactness property, therefore, the sequence +{xi} contains a convergent subsequence. +Let {xk} be a convergent subsequence, consider it converges to some y ∈ ¯A ⊂ M. +In other hand, we need to check that {xi} converges to y ∈ ¯A ⊂ M. To do this, +fix ǫ > 0, since {xi} is forward Cauchy, there exist a positive number n0 such that +j > i ≥ n0, then +d(xi, xj) < ǫ +2. +At the same time {xk} converge to y. So there is a positive number n1 such that +if k ≥ n1, then +d(xk, y) < ǫ +2. +One can assume that n is greater than n0 and n1. If needed, by expanding n +further, there is no loss of generality in assuming that n indeed equals some index +of the convergent subsequence. Then d(xn, y) ≤ ǫ +2, so, for i > n, we get +d(xi, y) ≤ d(xi, xn) + d(xn, y)< ǫ +2 + ǫ +2 = ǫ. +So, we have been shown that every forward Cauchy sequence is convergent. Hence +(M, d) is forward complete. + +12 +LAYTH M. ALABDULSADA AND L´ASZL ´O KOZMA +At the end, we can use the same proof of Proposition 7 to verify that for every +x, y ∈ M there exists a length minimizing geodesic joining x and y, and it has +to be normal geodesic by Remark 2. +Finally, the property of compactness and +completeness with help of Proposition 7, proves the second part of (iii). +□ +References +[1] A. Agrachev, D. Barilari, U. Boscain, A Comprehensive Introduction to Sub-Riemannian +geometry. Cambridge Studies in Advanced Mathematics (2019). +[2] L. M. Alabdulsada, L. Kozma, On the connection of sub-Finslerian geometry. Int. J. Geom. +Methods Mod. Phys. 16, No. supp02, 1941006 (2019) +[3] L. M. Alabdulsada, A note on the distributions in quantum mechanical systems. J. Phys.: +Conf. Ser. 1999, (2021), 012112 +[4] L. M. Alabdulsada, Sub-Finsler geometry and nonholonomic mechanics. Submitted +[5] D. Bao, S.-S. Chern, Z. Shen, An Introduction to Riemann-Finsler geometry, Graduate Texts +in Mathematics 200. Springer-Verlag, New York, (2000). +[6] O. Calin, D. Chang, Subriemannian geometry, a variational approach. J. Differential Geom. +80 (2008), no. 1, 23–43. +[7] P. do Carmo, Riemannian geometry. Mathematics: +Theory & Applications. Birkh¨auser +Boston, Inc., Boston, MA, (1992). +[8] W.-L. Chow, ¨Uber Systeme von linearen partiellen Differentialgleichungen erster Ordnung. +Math. Ann. 117 (1939) 98-105. +[9] R. Montgomery, A Tour of Subriemannian Geometries, their Geodesics and Applications, +Mathematical Surveys and Monographs 91. Amer. Math. Soc., Providence, RI, (2002). +[10] B. O’Neill, Semi-Riemannian Geometry. With applications to Relativity. Pure and Applied +Mathematics, 103. Academic Press, Inc. New York, (1983). +[11] C. B. Rayner, The Exponential Map for the Lagrange Problem on Differentiable Manifolds. +Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Phys- +ical Sciences Vol. 262, No. 1127 (Oct. 6, 1967), pp. 299-344 (46 pages) Published By: Royal +Society +[12] L. Rifford, Sub-Riemannian geometry and optimal transport. Springer, (2014). +[13] R. Strichartz, Sub-Riemannian geometry. J. Differ. Geom. 24 (1986), 221-263; correction +ibid. 30 (1989), 595-596. +Layth M. Alabdulsada, Institute of Mathematics, University of Debrecen, H-4002 +Debrecen, P.O. Box 400, Hungary +Email address: layth.muhsin@science.unideb.hu +L´aszl´o Kozma, Institute of Mathematics, University of Debrecen, H-4002 Debrecen, +P.O. Box 400, Hungary +Email address: kozma@unideb.hu + diff --git a/99FQT4oBgHgl3EQf6TZx/content/tmp_files/load_file.txt b/99FQT4oBgHgl3EQf6TZx/content/tmp_files/load_file.txt new file mode 100644 index 0000000000000000000000000000000000000000..21a47007c21d19f119096b8893ddb79919637b41 --- /dev/null +++ b/99FQT4oBgHgl3EQf6TZx/content/tmp_files/load_file.txt @@ -0,0 +1,388 @@ +filepath=/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf,len=387 +page_content='arXiv:2301.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='13438v1 [math.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='DG] 31 Jan 2023 HOPF-RINOW THEOREM OF SUB-FINSLERIAN GEOMETRY LAYTH M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' ALABDULSADA AND L´ASZL ´O KOZMA Abstract.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' The sub-Finslerian geometry means that the metric F is defined only on a given subbundle of the tangent bundle, called a horizontal bundle.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' In the paper, a version of the Hopf-Rinow theorem is proved in the case of sub- Finslerian manifolds, which relates the properties of completeness, geodesically completeness, and compactness.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' The sub-Finsler bundle, the exponential map and the Legendre transformation are deeply involved in this investigation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Introduction In the Riemannian and Finslerian geometry, there are two concepts of complete- ness.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' The first is the completeness in the sense of metric spaces, using the Riemann- ian metric.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Secondly, a Riemannian or Finsler manifold M is called geodesically complete if any geodesic γ(t) starting from x ∈ M is defined for all values of t ∈ R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' On the other hand, the completeness in the Finsler geometry is divided into forward and backward geodesically completenesses, according to forward and backward distance metrics, resp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Hopf-Rinow theorem is a basic theorem of complete Riemannian manifolds, which connects the completeness properties with compactness, and the exponen- tial map.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Its consequence says that any two points of a complete manifold can be connected by a length minimizing geodesic.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' In 1931, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Hopf and W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Rinow showed their theorem only for surfaces, but the proof in higher dimensions is not significantly different.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Hopf-Rinow theorem has been studied in detail in both Rie- mannian and Finslerian geometries in the literature, the best general references here are [5, 7], [10].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' In the Finsler case forward geodesic completeness is involved, only.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' After a development of the sub-Riemannian geometry as well as its generaliza- tion, namely sub-Finslerian geometry, the generalization of core theorems of Rie- mannian geometry has been started.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Relating to our issue, Strichartz [13], Rifford [12] and Agrachev et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' [1] gave an extension for a sub-Riemannian case, while Bao et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' [5] showed the Finslerian version of Hopf-Rinow theorem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' It turned out that in sub-Riemannian geometry, for general complete sub-Riemannian structures, the exponential mapping is not surjective.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' This is due to the fact that we may have abnormal minimizing curves and this is the case in the sub-Finslerian context, too.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' To prove the statements of Hopf-Rinow theorem in the sub-Finsler setting, we need the following concepts and explanations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' First, in Section 2 we review some of the standard facts of sub-Finsler geometry.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' In the third Section, we extend our discussion about the Legendre transformation (see [2]) to define the sub-Finsler 2000 Mathematics Subject Classification.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' 53C60, 53C17, 53C22.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Key words and phrases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' sub-Finslerian geometry;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' sub-Hamiltonian geometry;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Legendre trans- formation;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' sub-Finsler bundle;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' normal geodesics;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Exponential map;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Hopf-Rinow theorem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' 1 2 LAYTH M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' ALABDULSADA AND L´ASZL ´O KOZMA manifold on the distribution D∗ of the cotangent space, where we look more closely at a sub-Hamiltonian H defined on D∗, induced by the sub-Finslerian metric F ∗.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Afterwards, we construct a sub-Finsler bundle, which plays a major role in the for- malization of the sub-Hamiltonian in sub-Finsler geometry, in Section 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Moreover, the sub-Finsler bundle allows an orthonormal frame for the sub-Finsler structure.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' In Section 5, we introduce the notion of an exponential map in sub-Finsler geometry.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' In the last section our main theorem is stated and proved.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Definitions and some properties of sub-Finsler manifolds In this section we review some of the standard facts on the sub-Finsler metrics and set up the notations and the terminology which will play an essential role in this paper, for more details we refer the reader to [2, 3, 4].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Definition 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Let M be an n–dimensional connected manifold.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' A sub-Finslerian structure on M is a triple (D, σ, F) where: (1) (D, πD) is a vector bundle on M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' (2) σ : D � T M is a morphism of vector bundles.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' In particular, the following diagram is commutative M π � D T M σ � M πD �❄ ❄ ❄ ❄ ❄ ❄ ❄ ❄ ❄ such that πD : D � M and π : T M � M are the canonical projections.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' (3) A function F : �D → R, where �D = D \\ {0}, called a sub-Finsler metric, which satisfies the following properties: (Positive definiteness): Fx(v) > 0 for all v ∈ �D, x ∈ M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' (Regularity): F is smooth, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' C∞ on �D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' (Positive homogeneity): Fx(λv) = λFx(v) for all v ∈ �Dx and λ ∈ R+.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' (Strong convexity condition): The Hessian matrix of F 2 with respect to the coordinates on the fibre is positive definite.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' One can replace the strong convexity condition by the following subad- ditivity property (in an equivalent terminology, a triangle inequality): Fx(v + u) ⩽ Fx(v) + Fx(u), for all v, u ∈ �D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' A sub-Finsler manifold is a smooth manifold M endowed with a sub-Finslerian structure, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' the triple (D, σ, F).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Let Dx be the fiber over x ∈ M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' The last condition of the sub-Finsler metric means that the matrix ∂2F 2 ∂vi∂vj (x, v) is positive definite for all v = (v1, .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' , vk) ∈ Dx.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Equivalently, the corresponding indicatrix Ix = {v | v ∈ Dx, Fx(v) = 1} is strictly convex.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' The following technique describes the association between the sub-Finsler struc- ture (D, σ, F) and a Finsler metric ˆF on Im(σ) ⊂ T M: For each u ∈ Im(σ)x ⊂ TxM and x ∈ M, we have ˆFx(u) = inf v {Fx(v)| v ∈ Dx, σ(v) = u}.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' HOPF-RINOW THEOREM OF SUB-FINSLERIAN GEOMETRY 3 From now on we suppose that D ⊂ T M, σ : D � T M is the inclusion i : D � T M and F is a sub-Finsler metric on D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' As in the sub-Riemannian case, we call D the horizontal distribution.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' A piecewise smooth curve γ : [0, T ] → M is called horizontal, or admissible if ˙γ(t) ∈ Dγ(t) for all t ∈ [0, T ], that is, γ(t) is tangent to D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' The length of γ is defined as usual by ℓ(γ) = � T 0 F(˙γ(t))dt.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Equivalently, as in the Finslerian case, we observe that it suffices to minimize the energy E(γ) = 1 2 � T 0 F 2(˙γ(t))dt.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' instead of length ℓ(γ).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' The length induces a sub-Finslerian distance d(x, y) between two points x and y as in Finsler geometry: d(x, y) = inf{ℓ(γ) |γ : [0, T ] � M horizontal, γ(0) = x, γ(T ) = y}, where we consider the infimum over all horizontal curves joining x and y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' The distance is infinite if there is no such a horizontal curve between x and y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' In addition, the horizontal curve γ : [0, T ] → M is called a length minimizing (or simply a minimizing) geodesic, if it realizes the distance between its end points, that is, ℓ(γ) = d(γ(0), γ(T )).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Chow theorem answers to the following question: given two points x and y in a sub-Finsler manifold, is there a horizontal curve that joins x and y?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' In the case of an involutive distribution D the Frobenius theorem asserts that the set of the horizontal paths through S form a smooth immersed submanifold, the leaf through x, of dimension equal to the rank of distribution k.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' In this case, if D is involutive and y is not contained in the leaf through y, there is no any horizontal curve joining x and y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' A positive answer is given by the Chow theorem in the case of bracket generating distributions, which are the ”contrary” of the involutive distributions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Definition 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' [9] A distribution D is said to be bracket generating if any local frame Xi of D, together with all of its iterated Lie brackets spans the whole tangent bundle T M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Theorem 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' (Chow’s theorem [9]) If D is a bracket generating distribution on a connected manifold M then any two points of M can be joined by a horizontal path.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Remark 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' The problem of minimizing the length of a curve joining two given points x and y is equivalent to a time optimal problem: where the control bundle is (D, πD, M) and we are searching for such a curve γ(t) and a control curve v(t) ∈ Dγ(t) minimizing the time T needed to connect x and y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Legendre transformation of sub-Finslerian geometry Let D∗ be a distribution of rank s on a smooth manifold M that assigns to each point x ∈ U ⊂ M a linear subspace D∗ x ⊂ T ∗ xM of dimension s, see [4].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' In other words, D∗ of rank s is a smooth subbundle of rank s of the cotangent bundle 4 LAYTH M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' ALABDULSADA AND L´ASZL ´O KOZMA T ∗M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Such a field of cotangent s-planes is spanned locally by s pointwise linear independent smooth differential 1-forms, namely, D∗ x = span{α1(x), .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' , αs(x)}, αi(x) ∈ X∗(M).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' In addition, we refer to D0 x as the annihilator of the distribution D (isomorphic to D), of rank n − k, which is the set of all covectors that annihilates the vectors in Dx, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' D0 x = {α ∈ T ∗ xM : α(v) = 0 ∀ v ∈ Dx}.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' (1) In [2], we introduced the Legendre transformation of sub-Finsler geometry.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Let us briefly recall it: The sub-Lagrange function L : D �R, determined by F is given in the following way: L = 1 2F 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' The fiber derivative of L defines the map LL : D � D∗, LL(v)(w) = d dtLx(v + tw), where v, w ∈ Dx, called the Legendre transformation of (M, D, F).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' We denote by (xi) the coordinate in a neighborhood U ⊂ M with (xi, va) in D|U ⊂ T M, and (xi, pa) in D∗|U ⊂ T ∗M, respectively, where i = 1, .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' , n, a = 1, .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' , k.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Then the relation of the distribution D of the tangent bundle and the distribution D∗ of the cotangent bundle is given by the Legendre transformation in local coordinates as follows LL(xi, va) = (xi, ∂L ∂va ).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Then the sub-Hamiltonian is given by H : D∗ � R, H = ιL−1 L − L ◦ L−1 L , where ιv(p) = ⟨v, p⟩ = p(v) for any v = L−1 L (p) ∈ D and p ∈ D∗.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Moreover, locally given by, H(xi, pa) = vapa − L(xi, va), where pa = ∂L ∂va .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Secondly, using the fiber derivative of H, we define the Legendre transformation of the sub-Hamiltonian H in the following way: LH : D∗ � D, For any p, q ∈ D∗ x, it holds q(LH(p)) = d dtH(x, p + tq).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' This locally relates the distribution D∗ of the cotangent bundle and the distribution D of the tangent bundle according to the next expression: LH(xi, pa) = (xi, ∂H ∂pa ).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Naturally, LL and LH are inverses of each other: LH ◦ LL = 1D, LL ◦ LH = 1D∗.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' HOPF-RINOW THEOREM OF SUB-FINSLERIAN GEOMETRY 5 In other hand, for every p ∈ D∗ x, one can define the sub-Finsler metric F ∗ ∈ �D∗ ∼ T ∗M \\ D0 with help of the indicatrix Ix as follows: F ∗ x(p) := sup w∈Ix p(w) = sup 0̸=v∈Dx p[ v Fx(v)].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Observed that �D∗ is the subbundle of the cotangent bundle obtained by removing the zero cotangent vector from each fibre.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' In fact, F ∗ turns out to meet the same properties that mentioned in Definition 1, but on D∗ instead of D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Then F ∗(p) = F(v), where p = LL(v), and H := 1 2(F ∗)2, see details in [5].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Sub-Finsler bundle We define in this section a sub-Finsler vector bundle which will play a major role in the formalization of the sub-Hamiltonian in sub-Finsler geometry.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Let us consider first the covector subbundle (D∗, τ, M) with the projection τ : D∗ � M, which is a subbundle of rank k (= dim D∗) in the cotangent bundle of T ∗M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' The illustrious role in our consideration will play by the pullback bundle τ ∗(τ) = (D∗×D∗, pr1, D∗) of τ by τ as follows: D∗ ×M D∗ := {(p, q) ∈ D∗ × D∗| τ(p) = τ(q)}, pr1 : D∗ ×M D∗ � D∗, (p, q) �→ p.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Throughout, we call the above pullback bundle as the sub-Finsler bundle over D∗.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Now, if p is fixed, then (pr1)−1(p) = {(p, q) ∈ D∗ × D∗| q ∈ D∗ τ(q)} = {p} × D∗ τ(p), is a fiber of the sub-Finsler bundle over p ∈ D∗.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' We can introduce a Riemannian metric g∗ on the sub-Finsler vector bundle induced by the sub-Hamiltonian H as follows: ⟨q, r⟩p = g∗ p(q, r) := ∂2H(p + tq + sr) ∂t∂s |t,s=0 for all q, r ∈ D∗ τ(p), which locally means g∗ij = ∂2H ∂pi∂pj .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Now the sub-Finsler bundle τ ∗(τ) allows k covector fields X1, X2, .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' , Xk which form an orthonormal frame with respect to the induced Riemannian metric g∗.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Notice that Xi(p) is a covector field that depends on the position x ∈ M and the direction p ∈ D∗.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Moreover, one can choose in a way that Xi(p) is a homogeneous of degree zero in p, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Xi(tp) = t0Xi(p) = Xi(p).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' According to the above metric g∗ij on M which is homogeneous of degree zero, we could generate a new formalism of the sub-Hamiltonian function in the components pi (induces naturally by the inner product, see [6]) H(x, p) = 1 2 n � i,j=1 g∗ijpipj, (2) 6 LAYTH M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' ALABDULSADA AND L´ASZL ´O KOZMA such that this metric defined in the extended Finsler metric which was shown in [2].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' We can write the sub-Hamiltonian function (2) in a more useful way using the orthonormality of Xi as follows H(x, p) = 1 2 k � i=1 ⟨p, Xi(p)⟩2, p ∈ D∗ x.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' (3) One can easily check the homogeneity of degree 2 in p of the sub-Hamiltonian function H(x, p): H(x, tp) = 1 2 k � i=1 ⟨tp, Xi(tp)⟩2 = t2 2 k � i=1 ⟨p, Xi(p)⟩2 = t2H(x, p).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' (4) The importance of H(x, p) is to define sub-Finslerian geodesics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Our function H(x, p) produces a system of sub-Hamiltonian differential equations, since it is a smooth function on D∗.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Such differential equations are in terms of canonical coordinates (xi, pi).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Definition 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' The generated sub-Hamiltonian differential equations ˙xi = ∂H ∂pi (x, p), ˙pi = −∂H ∂xi (x, p), i = 1, .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' , n, are called normal geodesic equations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Lemma 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' If ξ(t) := (x(t), p(t)) is a solution of the sub-Hamiltonian system for all t ∈ R, then there exists a constant c ∈ R such that H(x(t), p(t)) = c.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Proof.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Taking the derivative of H(x(t), p(t)) w.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='r.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='t.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' t, we get d dtH(x(t), p(t)) = ∂H ∂xi (x(t), p(t)) ˙x(t) + ∂H ∂pi (x(t), p(t)) ˙p(t).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Replacing ˙x(t) and ˙p(t) by the above sub-Hamiltonian differential equations in the Definition 4, we obtain d dtH(x(t), p(t))) = ∂H ∂xi (x(t), p(t))∂H ∂pi (x(t), p(t)) − ∂H ∂pi (x(t), p(t))∂H ∂xi (x(t), p(t)) = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Therefore H(x(t), p(t)) is constant.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' □ Remark 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' From Lemma 5, it follows that any solution ξ(t) := (x(t), p(t)) of the sub-Hamiltonian differential equations on D∗ for a sub-Hamiltonian function H(p) satisfies H(x(t), p(t)) = c.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Let the projection x(t) = τ(ξ(t)) ∈ M, so each sufficiently short subarc of x(t) is a minimizer sub-Finslerian geodesic, (see [11, Corollary 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='2]).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' In addition, this subarc is the unique minimizer joining its end points.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' The projection curve x(t) mentioned above is said to be the normal sub-Finslerian geodesics or simply the normal geodesics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Remark 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' In the sub-Finslerian geometry, not all the sub-Finslerian geodesics are normal (contrary to the Finsler geometry).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' This is due to the fact that the sub-Finslerian geodesics which are also a minimizing geodesic might not be solved HOPF-RINOW THEOREM OF SUB-FINSLERIAN GEOMETRY 7 the sub-Hamiltonian system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Those minimizer that are not normal geodesics called singular or abnormal geodesics (see [9] for more details).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Moreover, we call the extremal pair ξ(t) = (x(t), p(t)) a normal extremal if it is a solution for the sub-Hamiltonian system, otherwise it is called an abnormal extremal.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Turning to the relationship between the normal geodesic and the locally length- minimizing horizontal curves, Calin et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' proved in [6] that any normal geodesic is a horizontal curve and a locally length-minimizing horizontal curve.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' After all, by using (3) one can generate the system of differential equations in terms of canonical coordinates (x, p) as follows: ˙xi = ∂H ∂pi = k � j=1 ⟨p, Xj(p)⟩ (δi(Xj(p)) + ⟨p, DpiXj(p)⟩), (5) ˙pi = −∂H ∂xi = − k � j=1 ⟨p, Xj(p)⟩⟨p, DxiXj(p)⟩, (6) where δi is the i-th coordinate function.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Exponential map in sub-Finsler geometry Let (M, d) be a general metric space, such that M is an n-dimensional manifold and the function d : M × M � R+ ∪ {∞}, is called a metric if have the following properties: for all x, y, z ∈ M, (i) d(x, y) = 0, with equality if and only if x = y;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' (ii) d(x, y) + d(y, z) ≤ d(x, z).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' If the function d is an asymmetric, then we can define the forward metric balls and forward metric spheres, with center x ∈ M and radius r > 0 as follows: Bx(r) = { y ∈ M : d(x, y) < r}, Sx(r) = { y ∈ M : d(x, y) = r}.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' The cotangent balls and the cotangent spheres in D∗ are defined as follows: B∗ x(r) = { p ∈ D∗ : F ∗ x(p) < r}, S∗ x(r) = { p ∈ D∗ : F ∗ x(p) = r}, for any fix x ∈ M and radius r.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' A subset U ⊂ M is said to be open if, for each point x ∈ U, there is a forward metric ball about x contained in U.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Then we get the topology on M and all metric spaces are first countable and T1-spaces.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' In general, we assume that the metric d of any metric space (M, d) is continuous with respect to the product topology on M × M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Thus, every backward metric ball, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' B− x (r) = { y ∈ M : d(y, x) < r}, is open and the metric space is a Hausdorff (T2) space.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Hence the compact sets in such a space are closed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' As a result of the above, we immediately have the following Proposition 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' In a metric space (M, d) the following are equivalent: (i) A sequence {xk} in (M, d) converges to x ∈ M in the sense of topology.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' (ii) limk→∞ d(x, xk) = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' 8 LAYTH M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' ALABDULSADA AND L´ASZL ´O KOZMA Proposition 7.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Let x be any point in a (reversible) sub-Finslerian manifold M, and ¯Bx(r) is a compact ball, for some r > 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Then for any y ∈ Bx(r) there is a minimizing geodesic from x to y, that is, d(x, y) = min{ℓ(γ) |γ : [0, T ] � M horizontal, γ(0) = x, γ(T ) = y}.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Proof.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Fix y ∈ Bx(r) and suppose that γk : [0, T ] � M is a minimizing sequence of horizontal paths with unit speed from x to y and such that lim k→∞ γk(0) = x, lim k→∞ γk(T ) = y, lim k→∞ ℓ(γk) = d(x, y).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' For the reason that d(x, y) < r, we get ℓ(γk) ≤ r for all k ≥ k0 large enough.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Proposition 6 asserts that the metric d is continuous under the topology of the manifold and the reversibility of F holds on a compact set.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Consequently, any sequence γk of curves which have uniformly bounded lengths has an uniformly convergent subsequence (Ascoli—Arzela theorem), we denote this subsequence by the same symbol, and a Lipschitz curve γ : [0, T ] � M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' From above one can assume that γk : [0, T ] � M is a convergent subsequence of length minimizers parametrized by arc length (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' F(˙γ(t)) = 1) on M such that such that γk � γ uniformly on [0, T ].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' This gives that ℓ(γk) = d(γk(0), γk(T )), which is due to the claim that γk is a minimizing geodesic.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' The sequence γk converges uniformly if for every ǫ > 0 there is a natural number N such that for all n ≥ N and all t ∈ [0, T ] one has d(γk(t), γ(t)) < ǫ.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Further, the semicontinuity of the length implies that if limk→∞ γk = γ then ℓ(γ) ≤ lim k→∞ inf ℓ(γk).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Now, by continuity of the distance, we obtain ℓ(γ) ≤ lim k→∞ inf ℓ(γk) = lim k→∞ inf d(γk(0), γk(T )) = d(γ(0), γ(T )).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' This yields that γ is minimizing geodesic, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' ℓ(γ) = d(x, y).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' The horizontal property of γ follows in the same way as was done in [1], Theorem 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='41.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' □ Next, we define the exponential map.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' For the general case, roughly speaking, if M is a smooth Finsler manifold, x a point in M and u ∈ TxM.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Then the exponential map is given by expx : TxM � M, such that expx(u) = γu(1) for the unique geodesic γ that starts at x and has initial speed vector u.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Furthermore, in the dual space the exponential map for every x ∈ M and p ∈ T ∗ xM defined by exp∗ x : T ∗ xM � M, such that exp∗ x(p) = γp(1) for the unique geodesic γ that starts at x and has initial speed vector u = L−1 L (p), where L here is the Lagrangian of the Finsler manifold.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' The exponential map is an essential object in sub-Finslerian geometry, parametriz- ing normal extremals through their initial covectors.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' We are going to define the exponential map in both of the distribution D, D∗ of the tangent and the cotangent bundles respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' HOPF-RINOW THEOREM OF SUB-FINSLERIAN GEOMETRY 9 Definition 8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Let Ωx ⊂ Dx be the domain of the exponential map over x ∈ M such that Ωx given by Ωx = {v ∈ Dx| ξ is defined on the interval [0, 1]} , where v = LH(p) by the Legendre transformation of sub-Hamiltonian H, and ξ(t) is the normal extremal.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Then the sub-Finsler exponential map is defined as follows expx : Ωx ⊂ Dx ⊂ TxM � M, v �→ πD(LH(ξ(1))).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' We can do the same in the distribution D∗ x.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Let Ω∗ x ⊂ D∗ x be the domain of the exponential map over x ∈ M such that Ω∗ x given by Ω∗ x = {p ∈ D∗ x| ξ is defined on the interval [0, 1]} .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Consequently, the sub-Hamiltonian exponential map is given by exp∗ x : Ω∗ x ⊂ D∗ x ⊂ T ∗ xM � M, p �→ τ(ξ(1)), where ξ(t) is the same normal extremal as above.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' The set Ω∗ x contains the origin and star-shaped with respect to 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Moreover, with the help of Legendre transformation it is fairly easy to see that expx(v) = exp∗ x(p), where p = LL(v).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' (7) It follows that the normal sub-Finslerian geodesics x(t) = τ(ξ(t)) satisfies x(t) = exp∗ x(tp), for all t ∈ [0, T ].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Theorem 9.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' The exponential mapping exp∗ x is a local diffeomorphism on D∗ x ⊂ T ∗ xM\\{0}.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Proof.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' In 4, we show the homogeneity of the sub-Hamiltonian function H(x, p) with respect to p.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' So, for any constant a > 0, the curve ξ(at) : (ǫ/a, ǫ/a) � M is the same geodesic satisfying the initial conditions τ(ξp(0)) = x and ξp(0) = ap, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=', τ(ξp(at)) = τ(ξap(t)).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Since the sub-Hamiltonian vector field ⃗H(x, p) = gab(x, p)pb ∂ ∂xa − 1 2 ∂gab ∂xk (x, p)papb ∂ ∂pk , that introduced in [2], is smooth except for p = 0 where it is C1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Then exp∗ x is C∞ on D∗ x ⊂ T ∗ xM\\{0}, while it is C1 at p = 0 and d(exp∗ x)|0 = id.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Thus, exp∗ x is a local diffeomorphism.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' □ By equation (7), one can get the following Corollary 10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' The sub-Finsler exponential map expx is a C∞ away from the zero section of D and only C1 at the zero section such that for each x ∈ M d(expx)|0 : Ωx ⊂ Dx � Ωx ⊂ Dx, is the identity map at the origin 0 ∈ Dx.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Remark 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' It is clear that in the case of sub-Finsler exponential map the following expressions holds: exp∗ x[B∗ x(r)] = Bx(r), exp∗ x[S∗ x(r)] = Sx(r), which are analogous to the Finslerian context, see Bao et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' [5] for more details.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' 10 LAYTH M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' ALABDULSADA AND L´ASZL ´O KOZMA Remark 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Turning to sub-Riemannian case, Strichartz in [13] stated that for bracket generating distributions the exponential map is a local diffeomorphism.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' This is due to the fact that the solutions of the sub-Hamiltonian system depend differentially on the initial data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' But this is a difference from the Riemannian context, the exponential map is not a diffeomorphism at the origin just like the Finslerian case.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Hopf-Rinow Theorem in sub-Finslerian geometry In the following, one can see the explanation of the terms that will be used in Hopf-Rinow Theorem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' A sub-Finsler manifold is said to be forward complete if every forward Cauchy sequence converges, and it is a forward geodesically complete if every normal geodesic γ(t), t ∈ [0, T ) parametrized to have unit speed, can be extended to a geodesic for all t ∈ [0, ∞).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' A subset is said to be forward bounded if it is contained in some forward metric ball Bx(r).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Theorem 11.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Let (M, D, F) be any connected sub-Finsler manifold, where D is bracket generating distribution.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' The following conditions are equivalent: (i) The metric space (M, d) is forward complete.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' (ii) The sub-Finsler manifold (M, D, F) is forward geodesically complete.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' (iii) Ω∗ x = D∗ x, additionally, the exponential map is onto if there are no strictly abnormal minimizer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' (iv) Every closed and forward bounded subset of (M, d) is compact.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Furthermore, for any x, y ∈ M there exists a minimizing geodesic γ joining x to y, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' the length of this geodesic is equal to the distance between these points.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Proof.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' (i) =⇒ (ii) Let γ(t) : [0, T ) � M be a unit speed and maximally forward extended geodesic, t ∈ [0, T ).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' If we assume that T ̸= ∞, and choose a sequence {ti} � T in [0, T ) then γ(ti) is forward Cauchy, since d(γ(ti), γ(tj)) ≤ |tj − ti|, for all i ≤ j.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Now, (i) makes it obvious that γ(ti) converges to y ∈ M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' On one hand, let us define γ(T ) to be y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' On the other hand, Lemma 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='1 in [13] told us that γ(t) can be extended beyond t = T .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' This contradicts our assumption the fact that T ̸= ∞.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Thus, T = ∞ for sure, so we have the forward geodesically completeness.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' (ii) =⇒ (iii) It is sufficient (for first part Ω∗ x = D∗ x) to prove that any normal extremal pair ξ(t), starting from the initial conditions, is defined for all t ∈ R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Suppose that the normal extremal is not extendable to the some interval [0, T +δ) for all δ > 0 and suppose that it is defined on [0, T ).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Let {ti} be any increasing sequence such that the limit of this sequence is T .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Hence, the projection x(t) = τ(ξ(t)) is a curve with unit speed defined on [0, T ), therefore, the sequence {ti} is a forward Cauchy sequence on M, since d(x(ti), x(tj)) ≤ |ti − tj|.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' By completeness, it follows that the sequence x(ti) converges to some point y ∈ M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' We suppose there are coordinates around the point y and an orthonormal frame X1, X2, .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='..' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=', Xk in small ball B∗ y(r) in the sub-Finsler bundle.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Let us show that in the coordinates ξ(t) = (x(t), p(t)) the curve x(t) is uniformly bounded.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' This grants a contradiction that the normal extremal is not extendable.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' In fact, for every HOPF-RINOW THEOREM OF SUB-FINSLERIAN GEOMETRY 11 p ∈ D∗, we consider the following non-negative form (3) of the sub-Hamiltonian function H: H(x, p) = 1 2 k � i=1 ⟨p, Xi(p)⟩2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Then, the sub-Hamiltonian system has the form: ˙xi(t) = ∂H ∂pi (x(t), p(t)) = k � j=1 ⟨p(t), Xj(p(t))⟩(δi(Xj(p)) + ⟨p, DpiXj(p)⟩), ˙pi(t) = −∂H ∂xi (x(t), p(t)) = − k � j=1 ⟨p(t), Xj(p(t))⟩⟨p(t), DxiXj(p(t))⟩, for t ∈ [T − δ, T ) with δ > 0 small enough.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Since Dγ(t)Xi are given in a compact small ball ¯B∗ y(r), they are bounded, so there is a constant C > 0 such that | ˙p(t)| ≤ C|p(t)| ∀t ∈ [T − δ, T ).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' If we apply Gronwall’s Lemma (see [12], p.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='122), it leads us to that |p(t)| is uniformly bounded on a bounded interval.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' This contradicts our assumption that the normal extremal can not be extended beyond T .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' (iii) =⇒ (iv) Assume that ¯A is a closed and forward bounded subset of (M, d).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Applying the bracket generating assumption, for every y ∈ ¯A, Proposition 7 asserts that there is a minimizing geodesic exp∗ x(tpy), 0 ≤ t ≤ T, from x to y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' The set of all py is subset A of D∗ x.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Since F ∗ x (py) = d(x, y), and d(x, y) ≤ r for some r due to the forward boundedness of ¯A, the subset A is bounded and contained in the compact set B∗ x(r) ∪ S∗ x(r).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' By Remark 4, exp∗ x[B∗ x(r) ∪ S∗ x(r)] is compact and contained in the closed set ¯A, then ¯A it must be compact.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' (iv) =⇒ (i) Let {xi} be a forward Cauchy sequence in M, and by the subaddi- tivity it must be forward bounded.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Choose A := {xi|i ∈ N}, then its closure ¯A is still forward bounded under the manifold topology of M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Taking into account the assumption (iv), ¯A should satisfy the compactness property, therefore, the sequence {xi} contains a convergent subsequence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Let {xk} be a convergent subsequence, consider it converges to some y ∈ ¯A ⊂ M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' In other hand, we need to check that {xi} converges to y ∈ ¯A ⊂ M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' To do this, fix ǫ > 0, since {xi} is forward Cauchy, there exist a positive number n0 such that j > i ≥ n0, then d(xi, xj) < ǫ 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' At the same time {xk} converge to y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' So there is a positive number n1 such that if k ≥ n1, then d(xk, y) < ǫ 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' One can assume that n is greater than n0 and n1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' If needed, by expanding n further, there is no loss of generality in assuming that n indeed equals some index of the convergent subsequence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Then d(xn, y) ≤ ǫ 2, so, for i > n, we get d(xi, y) ≤ d(xi, xn) + d(xn, y)< ǫ 2 + ǫ 2 = ǫ.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' So, we have been shown that every forward Cauchy sequence is convergent.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Hence (M, d) is forward complete.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' 12 LAYTH M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' ALABDULSADA AND L´ASZL ´O KOZMA At the end, we can use the same proof of Proposition 7 to verify that for every x, y ∈ M there exists a length minimizing geodesic joining x and y, and it has to be normal geodesic by Remark 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Finally, the property of compactness and completeness with help of Proposition 7, proves the second part of (iii).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' □ References [1] A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Agrachev, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Barilari, U.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Boscain, A Comprehensive Introduction to Sub-Riemannian geometry.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Cambridge Studies in Advanced Mathematics (2019).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' [2] L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Alabdulsada, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Kozma, On the connection of sub-Finslerian geometry.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Int.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Geom.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Methods Mod.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Phys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' 16, No.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' supp02, 1941006 (2019) [3] L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Alabdulsada, A note on the distributions in quantum mechanical systems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Phys.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' : Conf.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Ser.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' 1999, (2021), 012112 [4] L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Alabdulsada, Sub-Finsler geometry and nonholonomic mechanics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Submitted [5] D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Bao, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='-S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Chern, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Shen, An Introduction to Riemann-Finsler geometry, Graduate Texts in Mathematics 200.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Springer-Verlag, New York, (2000).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' [6] O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Calin, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Chang, Subriemannian geometry, a variational approach.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Differential Geom.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' 80 (2008), no.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' 1, 23–43.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' [7] P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' do Carmo, Riemannian geometry.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Mathematics: Theory & Applications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Birkh¨auser Boston, Inc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=', Boston, MA, (1992).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' [8] W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='-L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Chow, ¨Uber Systeme von linearen partiellen Differentialgleichungen erster Ordnung.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Math.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Ann.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' 117 (1939) 98-105.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' [9] R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Montgomery, A Tour of Subriemannian Geometries, their Geodesics and Applications, Mathematical Surveys and Monographs 91.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Amer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Math.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Soc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=', Providence, RI, (2002).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' [10] B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' O’Neill, Semi-Riemannian Geometry.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' With applications to Relativity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Pure and Applied Mathematics, 103.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Academic Press, Inc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' New York, (1983).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' [11] C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Rayner, The Exponential Map for the Lagrange Problem on Differentiable Manifolds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Philosophical Transactions of the Royal Society of London.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Series A, Mathematical and Phys- ical Sciences Vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' 262, No.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' 1127 (Oct.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' 6, 1967), pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' 299-344 (46 pages) Published By: Royal Society [12] L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Rifford, Sub-Riemannian geometry and optimal transport.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Springer, (2014).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' [13] R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Strichartz, Sub-Riemannian geometry.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Differ.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Geom.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' 24 (1986), 221-263;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' correction ibid.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' 30 (1989), 595-596.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Layth M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Alabdulsada, Institute of Mathematics, University of Debrecen, H-4002 Debrecen, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Box 400, Hungary Email address: layth.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='muhsin@science.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='unideb.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='hu L´aszl´o Kozma, Institute of Mathematics, University of Debrecen, H-4002 Debrecen, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content=' Box 400, Hungary Email address: kozma@unideb.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} +page_content='hu' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/99FQT4oBgHgl3EQf6TZx/content/2301.13438v1.pdf'} diff --git a/AtE1T4oBgHgl3EQfpAWX/content/2301.03327v1.pdf b/AtE1T4oBgHgl3EQfpAWX/content/2301.03327v1.pdf new file mode 100644 index 0000000000000000000000000000000000000000..10c9b67cb75f4b9fb35f10687c1b77a37a585c24 --- /dev/null +++ b/AtE1T4oBgHgl3EQfpAWX/content/2301.03327v1.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:feae64199d627c41a69ce1429eb78662e9a653f59c38ca361b91a9a80d4f2124 +size 734544 diff --git a/AtE1T4oBgHgl3EQfpAWX/vector_store/index.faiss b/AtE1T4oBgHgl3EQfpAWX/vector_store/index.faiss new file mode 100644 index 0000000000000000000000000000000000000000..38a96b98850287c17f7318a838cb48bcaf6c9742 --- /dev/null +++ b/AtE1T4oBgHgl3EQfpAWX/vector_store/index.faiss @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0976edfcf02abf4fd229fa998b0c144dd9328c035bf51a19845a2ab1042f3b7e +size 2162733 diff --git a/B9AzT4oBgHgl3EQfTfz1/content/2301.01252v1.pdf b/B9AzT4oBgHgl3EQfTfz1/content/2301.01252v1.pdf new file mode 100644 index 0000000000000000000000000000000000000000..75c4cf2e9f032a19cda312f1a6ca2ad7ef854cd5 --- /dev/null +++ b/B9AzT4oBgHgl3EQfTfz1/content/2301.01252v1.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eeee073aaed5777c2f87602dfaef128ddc2e9578d1184b120432c8d892cc0fd2 +size 2193389 diff --git a/B9AzT4oBgHgl3EQfTfz1/vector_store/index.pkl b/B9AzT4oBgHgl3EQfTfz1/vector_store/index.pkl new file mode 100644 index 0000000000000000000000000000000000000000..dfc5ccc220a60a004089c9f086ecd9510e7ff939 --- /dev/null +++ b/B9AzT4oBgHgl3EQfTfz1/vector_store/index.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1c5c80cd9908a66ab55ca2227c58ba3764c6ede86784b811d65c808bd3458215 +size 227296 diff --git a/CNAzT4oBgHgl3EQfh_3R/content/tmp_files/2301.01494v1.pdf.txt b/CNAzT4oBgHgl3EQfh_3R/content/tmp_files/2301.01494v1.pdf.txt new file mode 100644 index 0000000000000000000000000000000000000000..faa543fd568642ce5d6d9a353b23178af204a409 --- /dev/null +++ b/CNAzT4oBgHgl3EQfh_3R/content/tmp_files/2301.01494v1.pdf.txt @@ -0,0 +1,735 @@ +Analyzing I/O Performance of a Hierarchical +HPC Storage System for Distributed Deep +Learning +Takaaki Fukai1[0000−0003−4216−4807], Kento Sato2, and Takahiro +Hirofuchi1[0000−0002−1253−6625] +1 National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, +Japan +{takaaki.fukai, t.hirofuchi}@aist.go.jp +2 RIKEN Center for Computational Science, Kobe, Japan +kento.sato@riken.jp +Abstract. Today, deep learning is an essential technology for our life. +To solve more complex problems with deep learning, both sizes of train- +ing datasets and neural networks are increasing. To train a model with +large datasets and networks, distributed deep neural network (DDNN) +training technique is necessary. For large-scale DDNN training, HPC +clusters are a promising computation environment. In large-scale DDNN +on HPC clusters, I/O performance is critical because it is becoming a +bottleneck. Most flagship-class HPC clusters have hierarchical storage +systems. For designing future HPC storage systems, it is necessary to +quantify the performance improvement effect of the hierarchical storage +system on the workloads. This paper demonstrates the quantitative per- +formance analysis of the hierarchical storage system for DDNN workload +in a flagship-class supercomputer. Our analysis shows how much perfor- +mance improvement and volume increment of the storage will be required +to meet the performance goal. +Keywords: Deep neural network · Distributed deep neural network +training · I/O performance · Hierarchical storage system · High per- +formance computing +1 +Introduction +Today, the demand for large-scale deep learning has significantly increased. The +sizes of training models and datasets for the training are expanding to meet +the demand. For example, training models for image classification, such as Ef- +ficientNet [19] with large datasets, such as OpenImages dataset [10] are used. +Some computational science applications also use deep learning methods such +as CosmoFlow [12] and DeepCam [9]. A single machine does not have enough +computational and memory capacity for these large workloads. Therefore, dis- +tributed deep neural network (DDNN) training technique, which allows training +arXiv:2301.01494v1 [cs.DC] 4 Jan 2023 + +2 +T. Fukai et al. +models on multiple machines connected by a network, is necessary. HPC, opti- +mized for huge and distributed workloads, are promising environments for large +training workloads. +I/O is becoming a bottleneck in the training workloads for the following +reasons [14,16]. The first reason is dataset growth. The size of datasets is in- +creasing for training higher quality models [11,5]. Large datasets that do not fit +in memory cause training applications to issue many I/O requests. The second +reason is expanding performance gap between computation and I/O. Although +computation time is becoming shorter by distributed execution techniques, I/O +performance is not improved. This expands the performance gap. Therefore the +I/O performance of future HPC clusters is crucial. +For designing a storage system for future HPC, it is crucial but difficult to +find out what improvement of storage systems would achieve a performance goal +of a target I/O intensive DDNN workload. The first reason why it is difficult is +that most storage systems in flagship-class HPC clusters are hierarchical, which +combines fast but small storage, and a slow but large storage system [17,20]. +Therefore, it is unclear which we should pay our cost for the throughput of the +global or local filesystems or the volume for the local file system. The second +reason is that tuning a DNN application for distributed execution requires several +weeks or months for a new cluster or processor architecture. Therefore, we need a +much cost and time to find out the I/O bottleneck in a DDNN training workload. +This paper shows a case study on the performance analysis of a hierarchical +storage system for DDNN workload and the estimation of necessary improve- +ment of the storage systems to meet a performance goal. To reveal the effect +of faster storage, we first measure the I/O operations time of a synthetic I/O +intensive training workload with various proportions of fast and slow storage +sizes. Then we estimate the impact of storage system improvement on the train- +ing performance based on a result of the I/O performance analysis. The method +can estimate the contribution of various improvement of a hierarchical storage +system, to overall the training performance. +The contributions of this work are: (1) A methodology to study the I/O +bottleneck of DDNN training workloads in the hierarchical storage system; (2) +A methodology to explore options for improvement of a storage system to achieve +a performance goal of DDNN training workloads. +The remainder of this paper is organized as follows. Section 2 explains the +background. Section 3 reviews the related work. Section 4 explains our method. +Section 5 demonstrates our methods on a flagship-class supercomputer. Section 6 +discusses the potential of the method. Conclusions are presented in Section 7. +2 +Background +2.1 +File access in distributed neural network workloads +The file access pattern by DNN training applications is different from that in +scientific computational applications. In training, stochastic gradient descent + +Title Suppressed Due to Excessive Length +3 +(SGD) is a common technique to improve training speed and accuracy[3,13,22]. +In SGD, a program splits a training dataset into mini-batch and inputs a mini- +batch to the neural network. To avoid the degradation of training accuracy due +to a fixed input order, it shuffles the order of the files of the dataset every +time when inputting all samples to the neural network. Therefore the program +accesses each file once an epoch in a random order. It is hard to apply general +cache policies because of less time and spatial locality. If the dataset is larger +than the memory volume for page caches, the cache miss on reading file often +occurs. It is a reason why I/O is easy to be the bottleneck. +In distributed training, multiple compute nodes read the dataset simultane- +ously. In data-parallel, which is one of common parallelizing techniques, each +compute node has a part of the dataset and calculates it. There are two data +shuffling manners called local shuffling and global shuffling. Local shuffle means +that each process only shuffles and reads a part of the dataset. On the other +hand, global shuffle means that the application shuffles the whole dataset and +splits it for each computer every epoch. Local shuffling is easy to use local stor- +age for each computer because the computer needs to access only the initial +allocated part of the dataset. However, it reduces the training accuracy because +it reduces the randomness of the input dataset. In some cases, the local shuffle +approach is not suitable due to the accuracy degradation. On the other hand, +the global shuffling does not affect the training accuracy, but replacing the part +of the dataset for each epoch is a heavy I/O workload, especially, training with +a large dataset and a large number of computers. Therefore, we focus on the +global shuffling in this paper. +2.2 +Storage system in HPC +Recent flagship class HPC clusters provide a hierarchical storage system. Hi- +erarchical storage systems typically consist of a small but fast storage system +and a large but slow storage system. HPC clusters often provide the former as +a local file system (LFS) and the latter as a global file system (GFS). Summit +[20] provides node-local burst buffers (node-local NVMe SSD) and a parallel file +system (IBM’s SpectrumScale GPFS™). Fugaku[17] also provides a hierarchical +storage system that consists of the 1st level storage (an SSD for every 16 nodes) +and the 2nd level storage (a Global storage system). We assume that DDNN +applications in a global shuffle manner use the local storage in the hierarchical +storage as the cache of global storage. An important question to answer to de- +sign future storage systems in HPC for machine learning workload is the best +balance of fast and slow storage from the viewpoint of size and performance. +3 +Related work +There are several works to analyze and model the DNN performance. Wang et +al. proposed a modeling method for the DNN training workload based on the + +4 +T. Fukai et al. +Roofline model [21]. They focus on the performance of the computation and +memory accesses however the I/O performance is not considered. +There are several works for analyzing and optimizing I/O performance for +DDNN workloads. Several works [16,14] have analyzed the I/O performance +for the DDNN and proposed optimization methods. Devarajan et al. proposed a +benchmark to measure the I/O performance for DDNN and find the opportunity +for tuning I/O parameters [7,6]. These works assume a non-hierarchical storage +system. We focus on I/O performance of hierarchical storage systems. +Several works [23,18,24,8] assume hierarchical storage systems in their I/O +optimization method for DDNN workloads. They focus on application-level op- +timization to solve the I/O bottleneck. On the other hand, our work is toward +performance improvement of storage systems. +Paul et al. analyzed the I/O log generated by all the jobs on Supercomputer +Summit during a year [15]. They revealed the tendency of ML jobs and the usage +of the storage system by them, especially, the usage of the burst buffer. In this +work, the 23,389 ML jobs of 845,036 jobs in 2020 on Summit were analyzed. The +analysis results suggested a rapid increment in the use of ML technologies in +HPC, and some of the ML jobs used the burst buffer in addition to the GPFS. +This work analyzes the comprehensive analysis of the real ML workload from the +viewpoint of usage of the hierarchical storage system in the HPC environment. +Our work analyzes the I/O performance of hierarchical storage systems in detail. +4 +Methodology +4.1 +Overview +Our analysis method is composed of three steps, (1) measuring I/O performance, +(2) analyzing measurement results, and (3) estimating the impact of the speed- +up of global and local storage on training performance. +In the measurement, we execute a DDNN benchmark with profiling the I/O +on a hierarchical storage system. The benchmark reads a dataset from the hier- +archical storage system and uses LFS as the cache of GFS. To reveal how LFS +contributes to overall the I/O performance, we measure the I/O performance +with various proportions of the size of the cached data on LFS. We expect that +the performance characteristics depend on a performance balance of GFS and +LFS as well as the sizes of files in a dataset. Therefore, we measure the I/O +performance with multiple performance balances and the file sizes combination. +In the analyzing step, we analyze the profiling data separately by file system +and by type of I/O operations. To do this, we break down the I/O time into +the following four I/O classes based on the I/O profiling data obtained in the +benchmark execution. GFS-READ is a class for read operations on a GFS, GFS- +META is a class for metadata operations (open(), close()) on a GFS, LFS- +READ is a class for read operations on an LFS, and LFS-META is a class for +metadata operations on an LFS. Note that file operations on the dataset in a +DNN training are only open(), close(), and read() because applications do + +Title Suppressed Due to Excessive Length +5 +not make any modifications and new samples. We target the I/O time of the +slowest process among all parallel processes, because it is the most dominant for +the overall training time. +In the estimating step, we extrapolate from the above results, expected train- +ing time enabled by the speed-up of global and local storage. We first calculate +the expected overall I/O operation time on the assumption that the speed of an +I/O class is improved by a given ratio. We also calculate the expected impact +on training time by the performance improvement of multiple I/O classes and +which combination of the improvement will satisfy the performance goal. +4.2 +Measuring I/O performance by benchmark +To measure the I/O performance, we use DLIO [7] benchmark, a benchmark for +I/O performance on distributed deep neural network workloads. DLIO bench- +mark supports distributed execution and generating the synthetic dataset for +the benchmark. However, it does not support hierarchical storage. Therefore, we +add the three functions to DLIO for our measurement of hierarchical storage sys- +tems. The functions are (1) Reading the dataset from both GFS and LFS with a +specified proportion, (2) Global shuffling, and (3) Generating the synthetic files +on the local filesystem by each compute node. +In the benchmark execution, the cached files are not evicted, in other words, +the cache policy is pinning. As described in Section 2, the training application +accesses all samples the same number of times. Therefore, the cache hit rate with +the pinning policy is the same as the percentage of the cached file [14]. +We prepare two datasets, a small file dataset and a large file emulating Ima- +geNet dataset and CosmoFlow dataset respectively. The small file dataset con- +sists of 128 KiB files and the large file dataset consists of 12 MiB files. The +numbers of files in the small and large datasets are 589824 and 6144 respec- +tively. The total size of both datasets is 72 GiB so that whole of the dataset can +be on the LFS and all of the processes read the same number of files. Because +the entire dataset cannot be put on the memory of each compute node (32 GiB), +the benchmark application reads the files from the filesystem. The file format in +both datasets is tfrecord, and the number of samples in each file is one. +To measure the I/O performance with multiple performance balances of the +GFS and LFS, we measure the I/O performance with different numbers of the +object storage targets (OSTs) of the lustre-based GFS. We can limit the number +of OSTs to 1 by lfs command. Therefore, we measure the I/O performance with +all provided OSTs (faster GFS) and 1 OST (slower GFS). +To measure the I/O performance in the benchmark execution, we use Dar- +shan [2], which is a profiling tool for I/O. Darshan can capture and record each +file operations such as open(), close(), and read(). +4.3 +Analyzing the I/O performance +To reveal the bottleneck in detail, we break down the I/O time of the slowest +process into the following four I/O classes and recognize which I/O class is a + +6 +T. Fukai et al. +bottleneck. We calculate the I/O time for each process and find the slowest one, +which dominates the training performance. We analyze the I/O performance +from the log generated by darshan using darshan-parser command [1]. We cal- +culate for each I/O time based on POSIX_F_READ_TIME and POSIX_F_META_TIME. +4.4 +Estimate performance by storage improvement +To estimate impact of N% throughput improvement of a I/O class, we calcu- +late × +100 +100+N of the measured time of the I/O class. The improvement may not +directly affect the total I/O time because the improvement may change the bot- +tleneck to another I/O class. Therefore, we calculate total I/O time for each +process with the improvement of a class, then pick the slowest process. +5 +Experiment results +5.1 +Setup for experiment +We perform the experiments on Supercomputer Fugaku[17]. Compute nodes of +Fugaku has 48 computing cores of A64FX and 32 GiB HBM2 memory. Fugaku +has a hierarchical filesystem comprising the 1st- and 2nd-level filesystems named +LLIO and FEFS, respectively. In our measurement, we regard the LLIO as a local +filesystem (LFS) and the FEFS as a global filesystem (GFS). FEFS is a lustre- +based parallel filesystem and it has 60 OSTs in Fugaku. One per 192 compute +nodes connected to the FEFS by InfiniBand EDR. The other compute nodes +connected by TofuD access to FEFS via the network and the compute node. +For LLIO, one per 16 compute nodes has an NVMe SSD and the other compute +nodes connected access to the SSD via the network and the compute node. LLIO +provides three areas, node temporary area, shared temporary area, and 2nd-layer +cache area[4]. In our measurement, we only use node temporary areas, which is +a dedicated area for a compute node, because the transparent cache does not +allow us to control caching files of the dataset on LLIO. +In our measurement, we run the DLIO benchmark on the 768 compute nodes +of Supercomputer Fugaku as batch jobs. The four processes execute on every +compute nodes, so that total number of processes is 3072. The node layout is +8×6×16 in the TofuD torus network. We also pass an option to the job scheduler +to strict the position of the node connected to the GFS. +Before executing the benchmark job, we generate the datasets on the GFS. +Because the system removes data on LFS after finishing the job, every benchmark +job generates the same dataset on LFS as GFS. Note that the job generates the +dataset instead of copying the dataset from GFS to reduce the setup time. +We execute the benchmark with every 5% from 0% to 100% cache rate. +We set the calculation time in the DLIO to zero. Therefore, the DLIO reports +only the I/O and data processing time. The number of epochs is three to avoid +making the darshan log files huge. The prefetch of the dataloader is enabled so +that it is not synchronized for each iteration even if the computation threads are +synchronized for all-reduce communication. The batch size is 12 for the small +file dataset and 2 for the large file dataset. + +Title Suppressed Due to Excessive Length +7 +0 +10 +20 +30 +40 +50 +60 +70 +80 +90 +100 +Percentage of the cache(%) +0 +10 +20 +30 +40 +Time in an epoch (seconds) +Small file dataset with faster GFS +0 +10 +20 +30 +40 +50 +60 +70 +80 +90 +100 +Percentage of the cache(%) +0 +20 +40 +60 +80 +100 +Small file dataset with slower GFS +0 +10 +20 +30 +40 +50 +60 +70 +80 +90 +100 +Percentage of the cache(%) +0 +2 +4 +6 +Large file dataset with faster GFS +Epoch +#0 (DLIO) +#1 (DLIO) +#2 (DLIO) +#0 (Darshan) +#1 (Darshan) +#2 (Darshan) +Fig. 1: Execution time of DLIO benchmark and I/O time reported by Darshan +5.2 +Measuring execution time for epochs +In our experiments, we first measure the execution time of the DLIO benchmark +and I/O time in the benchmark execution with various settings as mentioned +in 4.2. We reveal the difference in the performance depending on file sizes in +the datasets and speeds of GFS. Additionally, by comparing the execution time +reported by the benchmark and the total I/O time reported by the I/O profiler, +we verify that the benchmark is I/O intensive. +Figure 1 shows the execution time and I/O time for each epoch in a job. The +x-axes of the graphs show the percentage of the files on the LFS. The y-axes +show the execution time of an epoch. The graph shows the results of 3 epochs +in a benchmark execution. The lines with round markers are the execution time +reported by the DLIO benchmark, and those with triangular markers are the +I/O time reported by Darshan. Because the I/O time of the slowest I/O process +is dominant, we calculate and plot them as I/O time on the graph. +The results show that the impact of the LFS on the training performance +depends on the file sizes and the performance balance of GFS and LFS. The +effect of the LFS with the 1 OST of GFS is larger than that with the 60 OSTs. +The reason is the performance difference between LFS and GFS on the 1 OST +is larger than that on 60 OSTs. In 12 MiB files workload with 60 OST of GFS, +the LFS does not contribute to the performance improvement, and using only +the GFS with the 60 OST is the best. +About the I/O time, the graph indicates that the execution time is constantly +longer about 2 sec, but it is strongly related to the I/O time. This result indicate +that the I/O time of the slowest process during the synchronizations among the +processes strongly interrelates to the training performance. +5.3 +Analyzing I/O performance +Next, we classify the Darshan records into the four I/O classes and calculate +the I/O time for each class. Figure 2 shows the result of the breakdown of the +#2 epoch in the previous graphs. Each line shows the total I/O time same as + +8 +T. Fukai et al. +0 +10 +20 +30 +40 +50 +60 +70 +80 +90 +100 +Percentage of the cache(%) +0 +10 +20 +30 +Time in an epoch (seconds) +Small file dataset with faster GFS +0 +10 +20 +30 +40 +50 +60 +70 +80 +90 +100 +Percentage of the cache(%) +0 +25 +50 +75 +100 +Small file dataset with slower GFS +0 +10 +20 +30 +40 +50 +60 +70 +80 +90 +100 +Percentage of the cache(%) +0 +1 +2 +Large file dataset with faster GFS +Total I/O time +GFS-META +GFS-READ +LFS-META +LFS-READ +Fig. 2: Break down the I/O time of the slowest process for each epoch (Note: +Range of y-axis are different) +Figure 1. According to the result, the bottleneck is different depending on the +setup. +In 128 KiB files workload with the faster GFS, the bottleneck is GFS-META +when less than 60% of data is on the LFS. On the other hand, the bottleneck is +changed to LFS-READ with more than 60% cached data on LFS. We think +when more than 60% of data is put on LFS, LFS throughput is saturated. +Therefore, the read time from LFS is linearly increased with the percentage of +the cached data. So putting more than 60% of data on the cache in the workload +does not contribute to the training performance. For example, in training with +ImageNet dataset whose size is almost 150 GB, almost the 90 GiB LFS for each +compute node is enough to achieve the best I/O performance by the hierarchical +storage system. With the 60% cached data, both GFS-META and LFS-READ +are included in the I/O time of the slowest process. +As compared with the faster GFS, the I/O bottleneck in the workload with +the slower GFS is much different. The graph in the middle of Figure 2 shows +that the bottleneck is GFS-READ instead of GFS-META with small percentages +of the LFS (less than 80%). We think that the reason why GFS-META time +becomes shorter is reducing the load on the metadata server of FEFS due to the +lower throughput of the GFS. +As compared with the small files workload, the I/O bottleneck in the large +files workload with the faster GFS is also much different. The right side graph +in Figure 2 shows that the bottleneck is the LFS-READ in most of the cases. +Because the number of metadata operations is much smaller than that in the +small file workload, the GFS fully provides its bandwidth without the bottleneck +by the metadata operation. As a result, the total bandwidth of the GFS is higher +than LFS. It means that the number of compute nodes is not enough to take +advantage of the scalability of the LFS. Note that the 768 nodes are not so large +scale as a workload in Fugaku, however from viewpoint of the machine learning +workload, the number of nodes is large enough to lead to a large batch problem. +From viewpoint of exploration of storage design for a performance goal, the +result on small file dataset and faster GFS (the left side graph in Figure 2) + +Title Suppressed Due to Excessive Length +9 +0 +5 +10 +15 +20 +25 +30 +35 +40 +45 +50 +55 +60 +65 +70 +75 +80 +85 +90 +95 +100 +Percentage of the cache(%) +0 +5 +10 +15 +20 +25 +30 +Time in an epoch (seconds) +Epoch #2, min: 7.226sec (60 %) +Orig: min: 8.119sec (65 %) +Total I/O time (Orig.) +Total I/O time (Expected) +GFS-META +GFS-READ +LFS-META +LFS-READ +(a) Improving GFS-META +0 +5 +10 +15 +20 +25 +30 +35 +40 +45 +50 +55 +60 +65 +70 +75 +80 +85 +90 +95 +100 +Percentage of the cache(%) +0 +5 +10 +15 +20 +25 +30 +Time in an epoch (seconds) +Epoch #2, min: 6.027sec (65 %) +Orig: min: 8.119sec (65 %) +Total I/O time (Orig.) +Total I/O time (Expected) +GFS-META +GFS-READ +LFS-META +LFS-READ +(b) Improving LFS-READ +Fig. 3: Expectation the I/O time with 50% improvement with small file dataset +and 60 OSTs of the GFS +is challenging situation because multiple I/O class is included in the I/O time +in the fastest result (cache rate = 65%). This means that improving only one +I/O class processing will not be enough to improve entire I/O performance. +Therefore, we pick up the result to demonstrate our estimation method of the +I/O improvement effect. +5.4 +Estimating the impact of the storage improvement +As mentioned in 4.4, we estimate the performance improvement by simple cal- +culation based on the analysis result. Figure 3 shows the result of the estimation +of the impact of a 50% improvement of GFS-META (Figure 3a) and LFS-READ +(Figure 3b). The axes in the graph are the same as in Figure 2. +Figure 3a shows the estimation result of improving the GFS-META by 50%. +The best combination of the GFS and LFS is changed from 65% to 60% LFS, +and the slowest I/O time is reduced by almost 12.8% in the best case. Figure +3a shows the estimation result of improving the LFS-READ by 50%. The best +cache rate is not changed, and the slowest I/O time in the best case is reduced +by 24%. +Next, we show the estimation of the impact of improvement of two oper- +ations classes simultaneously. There are many parameters and values such as +improvement rate for each operation, cache rate, and the I/O time. All of them +are too many to put on a single graph. Again, the architect of the system needs +knowledge of the given performance goal. Therefore, we show the estimation by +indicating which improvement combination would meet the performance goal. +Figure 4 shows the sufficient combinations of the performance improvement +on two classes, GFS-META and LFS-READ, on the small files dataset and the +faster GFS workload. The result in the graph is based on the measurement of + +10 +T. Fukai et al. +0 +25 +50 +75 +100 +125 +150 +175 +200 +Improvement rate of GFS META(%) +0 +25 +50 +75 +100 +125 +150 +175 +200 +Improvement rate of LFS READ(%) +0.55 +0.60 +0.65 +0.70 +0.75 +Cache rate (%) +Fig. 4: The estimation of performance improvement of GFS-META and LFS- +META for meeting performance goal of 4 sec / epoch (128 KiB, 60 OST GFS) +I/O time in the #2 epoch. The x and y axes show each improvement rate. On the +graph, the dot is plotted if the improvement combination will meet the given +performance goal. The graph indicates a result for the performance goal of 4 +seconds I/O time in an epoch. Additionally, the colors of the dots indicate the +minimum cache rate to meet the goal. For example, to achieve 4 seconds I/O time +in an epoch with a 65% cache rate, at least 120% improvement of LFS-READ is +required. In that case, a 140% improvement of the GFS-META is required. The +architect can explore the option of the improvement choice by the plot. +6 +Discussion +In our evaluation, we assume the global shuffling manner to exploit GFS. How- +ever, training applications with the local shuffle also can combine the LFS and +GFS to put larger chunks of the dataset than that with only the LFS. In this +case, the size of the LFS and the randomness of the shuffling are a trade-off. To +consider how large chunks are preferred, the application user also can use our +method to find the contributions to the performance of the LFS. +In our evaluation, we assume a pinning cache policy on the LFS. However, +our analysis can apply to the other cache policy if the cache hit rate can be +calculated. You can replace the "cache rate" with "cache hit rate" in the analysis +result because both are the same in DNN workloads with the pinning policy. +Then you can find the required size of the LFS from the relation between the +cache hit rate and the size of the cache in your better cache policy. +In our evaluation, we estimate the improvement by a simple calculation. +However, the performance characteristic may not be simple. For example, the +estimation from the measurement results with 1 OST of the GFS with the sim- +ple calculation does not fit that with the 60 OSTs of GFS. For more accurate + +Title Suppressed Due to Excessive Length +11 +estimation, improving the calculation method is necessary by modeling the char- +acteristic. The considerable approach is based on machine learning or queueing +theory. Even if the calculation method will be improved, our plot method shown +in Figure 4 is useful for the storage system architect. +7 +Conclusion +This paper presented a case study on the performance analysis of a hierarchical +storage system for a DDNN workload in a flagship-class HPC cluster, discussing +potential performance improvement enabled by the speed-up of the storage sys- +tem. We also estimated the improvement of training performance by various +improvement of the hierarchical filesystem. The analysis result showed that the +I/O bottleneck in the training workload depends on performance balance be- +tween global and local storage as well as file sizes in a dataset. +Our estimation showed that the performance improvement of a global filesys- +tem will contribute to reducing the necessary volume size of a local filesystem, +and the performance improvement of the local file system will contribute to re- +ducing fastest I/O time. Our estimation method can help architects of HPC +filesystems to find the necessary performance and the volume size of the local +and global filesystems to meet a given performance goal. +Because our proposed method needs the measurement of I/O performance +at least once, one of our future works is exploring a simpler or no measurement- +required method. The other future work is to build the performance modeling +of the storage system for more accurate estimation. +References +1. Darshan-util +installation +and +usage, +https://www.mcs.anl.gov/research/ +projects/darshan/docs/darshan-util.html +2. Darshan – HPC I/O Characterization Tool, https://www.mcs.anl.gov/research/ +projects/darshan/ +3. Akiba, T., et al.: Extremely large minibatch sgd: Training resnet-50 on imagenet +in 15 minutes (2017), https://arxiv.org/abs/1711.04325 +4. Akimoto, H., et al.: File system and power management enhanced for supercom- +puter fugaku. Fujitsu Technical Review (3), 2020–03 (2020) +5. Beal, J., et al.: Billion-scale pretraining with vision transformers for multi-task +visual representations. In: Proceedings of the IEEE/CVF Winter Conference on +Applications of Computer Vision. pp. 564–573 (2022) +6. Devarajan, H.: DLIO Benchmark, https://github.com/hariharan-devarajan/ +dlio_benchmark +7. Devarajan, H., et al.: DLIO: A Data-Centric Benchmark for Scientific Deep Learn- +ing Applications. In: 2021 IEEE/ACM 21st International Symposium on Cluster, +Cloud and Internet Computing (CCGrid). pp. 81–91 (2021) +8. Dryden, N., et al.: Clairvoyant prefetching for distributed machine learning i/o. +In: Proceedings of the International Conference for High Performance Computing, +Networking, Storage and Analysis. SC ’21, Association for Computing Machinery, +New York, NY, USA (2021) + +12 +T. Fukai et al. +9. Kurth, T., et al.: Exascale deep learning for climate analytics. In: SC18: Inter- +national Conference for High Performance Computing, Networking, Storage and +Analysis. pp. 649–660. IEEE (2018) +10. Kuznetsova, A., et al.: The open images dataset v4. International Journal of Com- +puter Vision 128(7), 1956–1981 (2020) +11. Mahajan, D., et al.: Exploring the limits of weakly supervised pretraining. In: +Proceedings of the European conference on computer vision (ECCV). pp. 181–196 +(2018) +12. Mathuriya, A., et al.: Cosmoflow: Using deep learning to learn the universe at scale. +In: Proceedings of the International Conference for High Performance Computing, +Networking, Storage, and Analysis. SC ’18, IEEE Press (2018) +13. Mikami, H., et al.: Massively distributed sgd: Imagenet/resnet-50 training in a flash +(2018), https://arxiv.org/abs/1811.05233 +14. Mohan, J., et al.: Analyzing and mitigating data stalls in dnn training. Proc. VLDB +Endow. 14(5), 771–784 (Jan 2021) +15. Paul, A.K., et al.: Characterizing machine learning i/o workloads on leadership +scale hpc systems. In: 2021 29th International Symposium on Modeling, Analysis, +and Simulation of Computer and Telecommunication Systems (MASCOTS). pp. 1– +8 (2021) +16. Pumma, S., et al.: Scalable Deep Learning via I/O Analysis and Optimization. +ACM Trans. Parallel Comput. 6(2) (Jul 2019) +17. Sato, M., et al.: Co-design for a64fx manycore processor and ”fugaku”. In: SC20: +International Conference for High Performance Computing, Networking, Storage +and Analysis. pp. 1–15 (2020) +18. Serizawa, K., Tatebe, O.: Accelerating machine learning i/o by overlapping data +staging and mini-batch generations. In: Proceedings of the 6th IEEE/ACM Inter- +national Conference on Big Data Computing, Applications and Technologies. p. +31–34. BDCAT ’19, Association for Computing Machinery, New York, NY, USA +(2019) +19. Tan, M., Le, Q.: EfficientNet: Rethinking model scaling for convolutional neural +networks. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th +International Conference on Machine Learning. Proceedings of Machine Learning +Research, vol. 97, pp. 6105–6114. PMLR (09–15 Jun 2019), https://proceedings. +mlr.press/v97/tan19a.html +20. Vazhkudai, S.S., et al.: The design, deployment, and evaluation of the coral pre- +exascale systems. In: SC18: International Conference for High Performance Com- +puting, Networking, Storage and Analysis. pp. 661–672 (2018) +21. Wang, Y., et al.: Time-Based Roofline for Deep Learning Performance Analysis. In: +2020 IEEE/ACM Fourth Workshop on Deep Learning on Supercomputers (DLS). +pp. 10–19 (2020) +22. Yamazaki, M., et al.: Yet another accelerated sgd: Resnet-50 training on imagenet +in 74.7 seconds (2019), https://arxiv.org/abs/1903.12650 +23. Zhu, Y., et al.: Entropy-aware i/o pipelining for large-scale deep learning on hpc +systems. In: 2018 IEEE 26th International Symposium on Modeling, Analysis, and +Simulation of Computer and Telecommunication Systems (MASCOTS). pp. 145– +156 (2018) +24. Zhu, Y., et al.: Efficient user-level storage disaggregation for deep learning. In: +2019 IEEE International Conference on Cluster Computing (CLUSTER). pp. 1– +12 (2019) + diff --git a/CNAzT4oBgHgl3EQfh_3R/content/tmp_files/load_file.txt b/CNAzT4oBgHgl3EQfh_3R/content/tmp_files/load_file.txt new file mode 100644 index 0000000000000000000000000000000000000000..35be21be12bec20a31b0e63024643500c1782410 --- /dev/null +++ b/CNAzT4oBgHgl3EQfh_3R/content/tmp_files/load_file.txt @@ -0,0 +1,488 @@ +filepath=/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf,len=487 +page_content='Analyzing I/O Performance of a Hierarchical HPC Storage System for Distributed Deep Learning Takaaki Fukai1[0000−0003−4216−4807], Kento Sato2, and Takahiro Hirofuchi1[0000−0002−1253−6625] 1 National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan {takaaki.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='fukai, t.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='hirofuchi}@aist.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='go.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='jp 2 RIKEN Center for Computational Science, Kobe, Japan kento.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='sato@riken.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='jp Abstract.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Today, deep learning is an essential technology for our life.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' To solve more complex problems with deep learning, both sizes of train- ing datasets and neural networks are increasing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' To train a model with large datasets and networks, distributed deep neural network (DDNN) training technique is necessary.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' For large-scale DDNN training, HPC clusters are a promising computation environment.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In large-scale DDNN on HPC clusters, I/O performance is critical because it is becoming a bottleneck.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Most flagship-class HPC clusters have hierarchical storage systems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' For designing future HPC storage systems, it is necessary to quantify the performance improvement effect of the hierarchical storage system on the workloads.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' This paper demonstrates the quantitative per- formance analysis of the hierarchical storage system for DDNN workload in a flagship-class supercomputer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Our analysis shows how much perfor- mance improvement and volume increment of the storage will be required to meet the performance goal.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Keywords: Deep neural network · Distributed deep neural network training · I/O performance · Hierarchical storage system · High per- formance computing 1 Introduction Today, the demand for large-scale deep learning has significantly increased.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The sizes of training models and datasets for the training are expanding to meet the demand.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' For example, training models for image classification, such as Ef- ficientNet [19] with large datasets, such as OpenImages dataset [10] are used.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Some computational science applications also use deep learning methods such as CosmoFlow [12] and DeepCam [9].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' A single machine does not have enough computational and memory capacity for these large workloads.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Therefore, dis- tributed deep neural network (DDNN) training technique, which allows training arXiv:2301.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='01494v1 [cs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='DC] 4 Jan 2023 2 T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Fukai et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' models on multiple machines connected by a network, is necessary.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' HPC, opti- mized for huge and distributed workloads, are promising environments for large training workloads.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' I/O is becoming a bottleneck in the training workloads for the following reasons [14,16].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The first reason is dataset growth.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The size of datasets is in- creasing for training higher quality models [11,5].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Large datasets that do not fit in memory cause training applications to issue many I/O requests.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The second reason is expanding performance gap between computation and I/O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Although computation time is becoming shorter by distributed execution techniques, I/O performance is not improved.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' This expands the performance gap.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Therefore the I/O performance of future HPC clusters is crucial.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' For designing a storage system for future HPC, it is crucial but difficult to find out what improvement of storage systems would achieve a performance goal of a target I/O intensive DDNN workload.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The first reason why it is difficult is that most storage systems in flagship-class HPC clusters are hierarchical, which combines fast but small storage, and a slow but large storage system [17,20].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Therefore, it is unclear which we should pay our cost for the throughput of the global or local filesystems or the volume for the local file system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The second reason is that tuning a DNN application for distributed execution requires several weeks or months for a new cluster or processor architecture.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Therefore, we need a much cost and time to find out the I/O bottleneck in a DDNN training workload.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' This paper shows a case study on the performance analysis of a hierarchical storage system for DDNN workload and the estimation of necessary improve- ment of the storage systems to meet a performance goal.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' To reveal the effect of faster storage, we first measure the I/O operations time of a synthetic I/O intensive training workload with various proportions of fast and slow storage sizes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Then we estimate the impact of storage system improvement on the train- ing performance based on a result of the I/O performance analysis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The method can estimate the contribution of various improvement of a hierarchical storage system, to overall the training performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The contributions of this work are: (1) A methodology to study the I/O bottleneck of DDNN training workloads in the hierarchical storage system;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' (2) A methodology to explore options for improvement of a storage system to achieve a performance goal of DDNN training workloads.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The remainder of this paper is organized as follows.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Section 2 explains the background.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Section 3 reviews the related work.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Section 4 explains our method.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Section 5 demonstrates our methods on a flagship-class supercomputer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Section 6 discusses the potential of the method.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Conclusions are presented in Section 7.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 2 Background 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='1 File access in distributed neural network workloads The file access pattern by DNN training applications is different from that in scientific computational applications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In training, stochastic gradient descent Title Suppressed Due to Excessive Length 3 (SGD) is a common technique to improve training speed and accuracy[3,13,22].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In SGD, a program splits a training dataset into mini-batch and inputs a mini- batch to the neural network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' To avoid the degradation of training accuracy due to a fixed input order, it shuffles the order of the files of the dataset every time when inputting all samples to the neural network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Therefore the program accesses each file once an epoch in a random order.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' It is hard to apply general cache policies because of less time and spatial locality.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' If the dataset is larger than the memory volume for page caches, the cache miss on reading file often occurs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' It is a reason why I/O is easy to be the bottleneck.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In distributed training, multiple compute nodes read the dataset simultane- ously.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In data-parallel, which is one of common parallelizing techniques, each compute node has a part of the dataset and calculates it.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' There are two data shuffling manners called local shuffling and global shuffling.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Local shuffle means that each process only shuffles and reads a part of the dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' On the other hand, global shuffle means that the application shuffles the whole dataset and splits it for each computer every epoch.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Local shuffling is easy to use local stor- age for each computer because the computer needs to access only the initial allocated part of the dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' However, it reduces the training accuracy because it reduces the randomness of the input dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In some cases, the local shuffle approach is not suitable due to the accuracy degradation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' On the other hand, the global shuffling does not affect the training accuracy, but replacing the part of the dataset for each epoch is a heavy I/O workload, especially, training with a large dataset and a large number of computers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Therefore, we focus on the global shuffling in this paper.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='2 Storage system in HPC Recent flagship class HPC clusters provide a hierarchical storage system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Hi- erarchical storage systems typically consist of a small but fast storage system and a large but slow storage system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' HPC clusters often provide the former as a local file system (LFS) and the latter as a global file system (GFS).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Summit [20] provides node-local burst buffers (node-local NVMe SSD) and a parallel file system (IBM’s SpectrumScale GPFS™).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Fugaku[17] also provides a hierarchical storage system that consists of the 1st level storage (an SSD for every 16 nodes) and the 2nd level storage (a Global storage system).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' We assume that DDNN applications in a global shuffle manner use the local storage in the hierarchical storage as the cache of global storage.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' An important question to answer to de- sign future storage systems in HPC for machine learning workload is the best balance of fast and slow storage from the viewpoint of size and performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 3 Related work There are several works to analyze and model the DNN performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Wang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' proposed a modeling method for the DNN training workload based on the 4 T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Fukai et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Roofline model [21].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' They focus on the performance of the computation and memory accesses however the I/O performance is not considered.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' There are several works for analyzing and optimizing I/O performance for DDNN workloads.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Several works [16,14] have analyzed the I/O performance for the DDNN and proposed optimization methods.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Devarajan et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' proposed a benchmark to measure the I/O performance for DDNN and find the opportunity for tuning I/O parameters [7,6].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' These works assume a non-hierarchical storage system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' We focus on I/O performance of hierarchical storage systems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Several works [23,18,24,8] assume hierarchical storage systems in their I/O optimization method for DDNN workloads.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' They focus on application-level op- timization to solve the I/O bottleneck.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' On the other hand, our work is toward performance improvement of storage systems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Paul et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' analyzed the I/O log generated by all the jobs on Supercomputer Summit during a year [15].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' They revealed the tendency of ML jobs and the usage of the storage system by them, especially, the usage of the burst buffer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In this work, the 23,389 ML jobs of 845,036 jobs in 2020 on Summit were analyzed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The analysis results suggested a rapid increment in the use of ML technologies in HPC, and some of the ML jobs used the burst buffer in addition to the GPFS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' This work analyzes the comprehensive analysis of the real ML workload from the viewpoint of usage of the hierarchical storage system in the HPC environment.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Our work analyzes the I/O performance of hierarchical storage systems in detail.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 4 Methodology 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='1 Overview Our analysis method is composed of three steps, (1) measuring I/O performance, (2) analyzing measurement results, and (3) estimating the impact of the speed- up of global and local storage on training performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In the measurement, we execute a DDNN benchmark with profiling the I/O on a hierarchical storage system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The benchmark reads a dataset from the hier- archical storage system and uses LFS as the cache of GFS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' To reveal how LFS contributes to overall the I/O performance, we measure the I/O performance with various proportions of the size of the cached data on LFS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' We expect that the performance characteristics depend on a performance balance of GFS and LFS as well as the sizes of files in a dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Therefore, we measure the I/O performance with multiple performance balances and the file sizes combination.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In the analyzing step, we analyze the profiling data separately by file system and by type of I/O operations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' To do this, we break down the I/O time into the following four I/O classes based on the I/O profiling data obtained in the benchmark execution.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' GFS-READ is a class for read operations on a GFS, GFS- META is a class for metadata operations (open(), close()) on a GFS, LFS- READ is a class for read operations on an LFS, and LFS-META is a class for metadata operations on an LFS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Note that file operations on the dataset in a DNN training are only open(), close(), and read() because applications do Title Suppressed Due to Excessive Length 5 not make any modifications and new samples.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' We target the I/O time of the slowest process among all parallel processes, because it is the most dominant for the overall training time.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In the estimating step, we extrapolate from the above results, expected train- ing time enabled by the speed-up of global and local storage.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' We first calculate the expected overall I/O operation time on the assumption that the speed of an I/O class is improved by a given ratio.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' We also calculate the expected impact on training time by the performance improvement of multiple I/O classes and which combination of the improvement will satisfy the performance goal.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='2 Measuring I/O performance by benchmark To measure the I/O performance, we use DLIO [7] benchmark, a benchmark for I/O performance on distributed deep neural network workloads.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' DLIO bench- mark supports distributed execution and generating the synthetic dataset for the benchmark.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' However, it does not support hierarchical storage.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Therefore, we add the three functions to DLIO for our measurement of hierarchical storage sys- tems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The functions are (1) Reading the dataset from both GFS and LFS with a specified proportion, (2) Global shuffling, and (3) Generating the synthetic files on the local filesystem by each compute node.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In the benchmark execution, the cached files are not evicted, in other words, the cache policy is pinning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' As described in Section 2, the training application accesses all samples the same number of times.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Therefore, the cache hit rate with the pinning policy is the same as the percentage of the cached file [14].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' We prepare two datasets, a small file dataset and a large file emulating Ima- geNet dataset and CosmoFlow dataset respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The small file dataset con- sists of 128 KiB files and the large file dataset consists of 12 MiB files.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The numbers of files in the small and large datasets are 589824 and 6144 respec- tively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The total size of both datasets is 72 GiB so that whole of the dataset can be on the LFS and all of the processes read the same number of files.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Because the entire dataset cannot be put on the memory of each compute node (32 GiB), the benchmark application reads the files from the filesystem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The file format in both datasets is tfrecord, and the number of samples in each file is one.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' To measure the I/O performance with multiple performance balances of the GFS and LFS, we measure the I/O performance with different numbers of the object storage targets (OSTs) of the lustre-based GFS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' We can limit the number of OSTs to 1 by lfs command.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Therefore, we measure the I/O performance with all provided OSTs (faster GFS) and 1 OST (slower GFS).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' To measure the I/O performance in the benchmark execution, we use Dar- shan [2], which is a profiling tool for I/O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Darshan can capture and record each file operations such as open(), close(), and read().' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='3 Analyzing the I/O performance To reveal the bottleneck in detail, we break down the I/O time of the slowest process into the following four I/O classes and recognize which I/O class is a 6 T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Fukai et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' bottleneck.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' We calculate the I/O time for each process and find the slowest one, which dominates the training performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' We analyze the I/O performance from the log generated by darshan using darshan-parser command [1].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' We cal- culate for each I/O time based on POSIX_F_READ_TIME and POSIX_F_META_TIME.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='4 Estimate performance by storage improvement To estimate impact of N% throughput improvement of a I/O class, we calcu- late × 100 100+N of the measured time of the I/O class.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The improvement may not directly affect the total I/O time because the improvement may change the bot- tleneck to another I/O class.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Therefore, we calculate total I/O time for each process with the improvement of a class, then pick the slowest process.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 5 Experiment results 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='1 Setup for experiment We perform the experiments on Supercomputer Fugaku[17].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Compute nodes of Fugaku has 48 computing cores of A64FX and 32 GiB HBM2 memory.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Fugaku has a hierarchical filesystem comprising the 1st- and 2nd-level filesystems named LLIO and FEFS, respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In our measurement, we regard the LLIO as a local filesystem (LFS) and the FEFS as a global filesystem (GFS).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' FEFS is a lustre- based parallel filesystem and it has 60 OSTs in Fugaku.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' One per 192 compute nodes connected to the FEFS by InfiniBand EDR.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The other compute nodes connected by TofuD access to FEFS via the network and the compute node.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' For LLIO, one per 16 compute nodes has an NVMe SSD and the other compute nodes connected access to the SSD via the network and the compute node.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' LLIO provides three areas, node temporary area, shared temporary area, and 2nd-layer cache area[4].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In our measurement, we only use node temporary areas, which is a dedicated area for a compute node, because the transparent cache does not allow us to control caching files of the dataset on LLIO.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In our measurement, we run the DLIO benchmark on the 768 compute nodes of Supercomputer Fugaku as batch jobs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The four processes execute on every compute nodes, so that total number of processes is 3072.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The node layout is 8×6×16 in the TofuD torus network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' We also pass an option to the job scheduler to strict the position of the node connected to the GFS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Before executing the benchmark job, we generate the datasets on the GFS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Because the system removes data on LFS after finishing the job, every benchmark job generates the same dataset on LFS as GFS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Note that the job generates the dataset instead of copying the dataset from GFS to reduce the setup time.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' We execute the benchmark with every 5% from 0% to 100% cache rate.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' We set the calculation time in the DLIO to zero.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Therefore, the DLIO reports only the I/O and data processing time.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The number of epochs is three to avoid making the darshan log files huge.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The prefetch of the dataloader is enabled so that it is not synchronized for each iteration even if the computation threads are synchronized for all-reduce communication.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The batch size is 12 for the small file dataset and 2 for the large file dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='Title Suppressed Due to Excessive Length ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='7 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='0 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='10 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='20 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='30 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='40 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='50 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='60 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='70 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='80 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='90 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='100 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='Percentage of the cache(%) ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='0 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='10 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='20 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='30 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='40 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='Time in an epoch (seconds) ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='Small file dataset with faster GFS ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='0 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='10 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='20 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='30 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='40 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='50 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='60 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='70 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='80 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='90 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='100 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='Percentage of the cache(%) ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='0 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='20 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='40 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='60 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='80 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='100 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='Small file dataset with slower GFS ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='0 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='10 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='20 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='30 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='40 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='50 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='60 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='70 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='80 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='90 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='100 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='Percentage of the cache(%) ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='0 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='2 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='4 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='6 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='Large file dataset with faster GFS ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='Epoch ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='#0 (DLIO) ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='#1 (DLIO) ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='#2 (DLIO) ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='#0 (Darshan) ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='#1 (Darshan) ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='#2 (Darshan) ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 1: Execution time of DLIO benchmark and I/O time reported by Darshan 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='2 Measuring execution time for epochs In our experiments, we first measure the execution time of the DLIO benchmark and I/O time in the benchmark execution with various settings as mentioned in 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' We reveal the difference in the performance depending on file sizes in the datasets and speeds of GFS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Additionally, by comparing the execution time reported by the benchmark and the total I/O time reported by the I/O profiler, we verify that the benchmark is I/O intensive.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Figure 1 shows the execution time and I/O time for each epoch in a job.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The x-axes of the graphs show the percentage of the files on the LFS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The y-axes show the execution time of an epoch.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The graph shows the results of 3 epochs in a benchmark execution.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The lines with round markers are the execution time reported by the DLIO benchmark, and those with triangular markers are the I/O time reported by Darshan.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Because the I/O time of the slowest I/O process is dominant, we calculate and plot them as I/O time on the graph.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The results show that the impact of the LFS on the training performance depends on the file sizes and the performance balance of GFS and LFS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The effect of the LFS with the 1 OST of GFS is larger than that with the 60 OSTs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The reason is the performance difference between LFS and GFS on the 1 OST is larger than that on 60 OSTs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In 12 MiB files workload with 60 OST of GFS, the LFS does not contribute to the performance improvement, and using only the GFS with the 60 OST is the best.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' About the I/O time, the graph indicates that the execution time is constantly longer about 2 sec, but it is strongly related to the I/O time.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' This result indicate that the I/O time of the slowest process during the synchronizations among the processes strongly interrelates to the training performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='3 Analyzing I/O performance Next, we classify the Darshan records into the four I/O classes and calculate the I/O time for each class.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Figure 2 shows the result of the breakdown of the #2 epoch in the previous graphs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Each line shows the total I/O time same as 8 T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Fukai et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 0 10 20 30 40 50 60 70 80 90 100 Percentage of the cache(%) 0 10 20 30 Time in an epoch (seconds) Small file dataset with faster GFS 0 10 20 30 40 50 60 70 80 90 100 Percentage of the cache(%) 0 25 50 75 100 Small file dataset with slower GFS 0 10 20 30 40 50 60 70 80 90 100 Percentage of the cache(%) 0 1 2 Large file dataset with faster GFS Total I/O time GFS-META GFS-READ LFS-META LFS-READ Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 2: Break down the I/O time of the slowest process for each epoch (Note: Range of y-axis are different) Figure 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' According to the result, the bottleneck is different depending on the setup.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In 128 KiB files workload with the faster GFS, the bottleneck is GFS-META when less than 60% of data is on the LFS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' On the other hand, the bottleneck is changed to LFS-READ with more than 60% cached data on LFS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' We think when more than 60% of data is put on LFS, LFS throughput is saturated.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Therefore, the read time from LFS is linearly increased with the percentage of the cached data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' So putting more than 60% of data on the cache in the workload does not contribute to the training performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' For example, in training with ImageNet dataset whose size is almost 150 GB, almost the 90 GiB LFS for each compute node is enough to achieve the best I/O performance by the hierarchical storage system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' With the 60% cached data, both GFS-META and LFS-READ are included in the I/O time of the slowest process.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' As compared with the faster GFS, the I/O bottleneck in the workload with the slower GFS is much different.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The graph in the middle of Figure 2 shows that the bottleneck is GFS-READ instead of GFS-META with small percentages of the LFS (less than 80%).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' We think that the reason why GFS-META time becomes shorter is reducing the load on the metadata server of FEFS due to the lower throughput of the GFS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' As compared with the small files workload, the I/O bottleneck in the large files workload with the faster GFS is also much different.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The right side graph in Figure 2 shows that the bottleneck is the LFS-READ in most of the cases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Because the number of metadata operations is much smaller than that in the small file workload, the GFS fully provides its bandwidth without the bottleneck by the metadata operation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' As a result, the total bandwidth of the GFS is higher than LFS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' It means that the number of compute nodes is not enough to take advantage of the scalability of the LFS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Note that the 768 nodes are not so large scale as a workload in Fugaku, however from viewpoint of the machine learning workload, the number of nodes is large enough to lead to a large batch problem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' From viewpoint of exploration of storage design for a performance goal, the result on small file dataset and faster GFS (the left side graph in Figure 2) Title Suppressed Due to Excessive Length 9 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 Percentage of the cache(%) 0 5 10 15 20 25 30 Time in an epoch (seconds) Epoch #2, min: 7.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='226sec (60 %) Orig: min: 8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='119sec (65 %) Total I/O time (Orig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=') Total I/O time (Expected) GFS-META GFS-READ LFS-META LFS-READ (a) Improving GFS-META 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 Percentage of the cache(%) 0 5 10 15 20 25 30 Time in an epoch (seconds) Epoch #2, min: 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='027sec (65 %) Orig: min: 8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='119sec (65 %) Total I/O time (Orig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=') Total I/O time (Expected) GFS-META GFS-READ LFS-META LFS-READ (b) Improving LFS-READ Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 3: Expectation the I/O time with 50% improvement with small file dataset and 60 OSTs of the GFS is challenging situation because multiple I/O class is included in the I/O time in the fastest result (cache rate = 65%).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' This means that improving only one I/O class processing will not be enough to improve entire I/O performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Therefore, we pick up the result to demonstrate our estimation method of the I/O improvement effect.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='4 Estimating the impact of the storage improvement As mentioned in 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='4, we estimate the performance improvement by simple cal- culation based on the analysis result.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Figure 3 shows the result of the estimation of the impact of a 50% improvement of GFS-META (Figure 3a) and LFS-READ (Figure 3b).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The axes in the graph are the same as in Figure 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Figure 3a shows the estimation result of improving the GFS-META by 50%.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The best combination of the GFS and LFS is changed from 65% to 60% LFS, and the slowest I/O time is reduced by almost 12.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='8% in the best case.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Figure 3a shows the estimation result of improving the LFS-READ by 50%.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The best cache rate is not changed, and the slowest I/O time in the best case is reduced by 24%.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Next, we show the estimation of the impact of improvement of two oper- ations classes simultaneously.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' There are many parameters and values such as improvement rate for each operation, cache rate, and the I/O time.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' All of them are too many to put on a single graph.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Again, the architect of the system needs knowledge of the given performance goal.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Therefore, we show the estimation by indicating which improvement combination would meet the performance goal.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Figure 4 shows the sufficient combinations of the performance improvement on two classes, GFS-META and LFS-READ, on the small files dataset and the faster GFS workload.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The result in the graph is based on the measurement of 10 T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Fukai et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 0 25 50 75 100 125 150 175 200 Improvement rate of GFS META(%) 0 25 50 75 100 125 150 175 200 Improvement rate of LFS READ(%) 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='55 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='60 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='65 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='70 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='75 Cache rate (%) Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 4: The estimation of performance improvement of GFS-META and LFS- META for meeting performance goal of 4 sec / epoch (128 KiB, 60 OST GFS) I/O time in the #2 epoch.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The x and y axes show each improvement rate.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' On the graph, the dot is plotted if the improvement combination will meet the given performance goal.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The graph indicates a result for the performance goal of 4 seconds I/O time in an epoch.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Additionally, the colors of the dots indicate the minimum cache rate to meet the goal.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' For example, to achieve 4 seconds I/O time in an epoch with a 65% cache rate, at least 120% improvement of LFS-READ is required.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In that case, a 140% improvement of the GFS-META is required.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The architect can explore the option of the improvement choice by the plot.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 6 Discussion In our evaluation, we assume the global shuffling manner to exploit GFS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' How- ever, training applications with the local shuffle also can combine the LFS and GFS to put larger chunks of the dataset than that with only the LFS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In this case, the size of the LFS and the randomness of the shuffling are a trade-off.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' To consider how large chunks are preferred, the application user also can use our method to find the contributions to the performance of the LFS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In our evaluation, we assume a pinning cache policy on the LFS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' However, our analysis can apply to the other cache policy if the cache hit rate can be calculated.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' You can replace the "cache rate" with "cache hit rate" in the analysis result because both are the same in DNN workloads with the pinning policy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Then you can find the required size of the LFS from the relation between the cache hit rate and the size of the cache in your better cache policy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In our evaluation, we estimate the improvement by a simple calculation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' However, the performance characteristic may not be simple.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' For example, the estimation from the measurement results with 1 OST of the GFS with the sim- ple calculation does not fit that with the 60 OSTs of GFS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' For more accurate Title Suppressed Due to Excessive Length 11 estimation, improving the calculation method is necessary by modeling the char- acteristic.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The considerable approach is based on machine learning or queueing theory.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Even if the calculation method will be improved, our plot method shown in Figure 4 is useful for the storage system architect.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 7 Conclusion This paper presented a case study on the performance analysis of a hierarchical storage system for a DDNN workload in a flagship-class HPC cluster, discussing potential performance improvement enabled by the speed-up of the storage sys- tem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' We also estimated the improvement of training performance by various improvement of the hierarchical filesystem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The analysis result showed that the I/O bottleneck in the training workload depends on performance balance be- tween global and local storage as well as file sizes in a dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Our estimation showed that the performance improvement of a global filesys- tem will contribute to reducing the necessary volume size of a local filesystem, and the performance improvement of the local file system will contribute to re- ducing fastest I/O time.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Our estimation method can help architects of HPC filesystems to find the necessary performance and the volume size of the local and global filesystems to meet a given performance goal.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Because our proposed method needs the measurement of I/O performance at least once, one of our future works is exploring a simpler or no measurement- required method.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' The other future work is to build the performance modeling of the storage system for more accurate estimation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' References 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Darshan-util installation and usage, https://www.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='mcs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='anl.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='gov/research/ projects/darshan/docs/darshan-util.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='html 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Darshan – HPC I/O Characterization Tool, https://www.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='mcs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='anl.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='gov/research/ projects/darshan/ 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Akiba, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' : Extremely large minibatch sgd: Training resnet-50 on imagenet in 15 minutes (2017), https://arxiv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='org/abs/1711.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='04325 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Akimoto, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' : File system and power management enhanced for supercom- puter fugaku.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Fujitsu Technical Review (3), 2020–03 (2020) 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Beal, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' : Billion-scale pretraining with vision transformers for multi-task visual representations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 564–573 (2022) 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Devarajan, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=': DLIO Benchmark, https://github.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='com/hariharan-devarajan/ dlio_benchmark 7.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Devarajan, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' : DLIO: A Data-Centric Benchmark for Scientific Deep Learn- ing Applications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In: 2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 81–91 (2021) 8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Dryden, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' : Clairvoyant prefetching for distributed machine learning i/o.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' SC ’21, Association for Computing Machinery, New York, NY, USA (2021) 12 T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Fukai et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 9.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Kurth, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' : Exascale deep learning for climate analytics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In: SC18: Inter- national Conference for High Performance Computing, Networking, Storage and Analysis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 649–660.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' IEEE (2018) 10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Kuznetsova, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' : The open images dataset v4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' International Journal of Com- puter Vision 128(7), 1956–1981 (2020) 11.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Mahajan, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' : Exploring the limits of weakly supervised pretraining.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In: Proceedings of the European conference on computer vision (ECCV).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 181–196 (2018) 12.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Mathuriya, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' : Cosmoflow: Using deep learning to learn the universe at scale.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In: Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' SC ’18, IEEE Press (2018) 13.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Mikami, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' : Massively distributed sgd: Imagenet/resnet-50 training in a flash (2018), https://arxiv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='org/abs/1811.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='05233 14.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Mohan, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' : Analyzing and mitigating data stalls in dnn training.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Proc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' VLDB Endow.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 14(5), 771–784 (Jan 2021) 15.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Paul, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' : Characterizing machine learning i/o workloads on leadership scale hpc systems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In: 2021 29th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 1– 8 (2021) 16.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Pumma, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' : Scalable Deep Learning via I/O Analysis and Optimization.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' ACM Trans.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Parallel Comput.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 6(2) (Jul 2019) 17.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Sato, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' : Co-design for a64fx manycore processor and ”fugaku”.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In: SC20: International Conference for High Performance Computing, Networking, Storage and Analysis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 1–15 (2020) 18.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Serizawa, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=', Tatebe, O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=': Accelerating machine learning i/o by overlapping data staging and mini-batch generations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In: Proceedings of the 6th IEEE/ACM Inter- national Conference on Big Data Computing, Applications and Technologies.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' p.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 31–34.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' BDCAT ’19, Association for Computing Machinery, New York, NY, USA (2019) 19.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Tan, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=', Le, Q.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=': EfficientNet: Rethinking model scaling for convolutional neural networks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In: Chaudhuri, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=', Salakhutdinov, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' (eds.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=') Proceedings of the 36th International Conference on Machine Learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Proceedings of Machine Learning Research, vol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 97, pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 6105–6114.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' PMLR (09–15 Jun 2019), https://proceedings.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' mlr.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='press/v97/tan19a.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='html 20.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Vazhkudai, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' : The design, deployment, and evaluation of the coral pre- exascale systems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In: SC18: International Conference for High Performance Com- puting, Networking, Storage and Analysis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 661–672 (2018) 21.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Wang, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' : Time-Based Roofline for Deep Learning Performance Analysis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In: 2020 IEEE/ACM Fourth Workshop on Deep Learning on Supercomputers (DLS).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 10–19 (2020) 22.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Yamazaki, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' : Yet another accelerated sgd: Resnet-50 training on imagenet in 74.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='7 seconds (2019), https://arxiv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='org/abs/1903.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content='12650 23.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Zhu, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' : Entropy-aware i/o pipelining for large-scale deep learning on hpc systems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In: 2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 145– 156 (2018) 24.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' Zhu, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=', et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' : Efficient user-level storage disaggregation for deep learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' In: 2019 IEEE International Conference on Cluster Computing (CLUSTER).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} +page_content=' 1– 12 (2019)' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNAzT4oBgHgl3EQfh_3R/content/2301.01494v1.pdf'} diff --git a/CNE1T4oBgHgl3EQfVwTa/content/2301.03107v1.pdf b/CNE1T4oBgHgl3EQfVwTa/content/2301.03107v1.pdf new file mode 100644 index 0000000000000000000000000000000000000000..cb67079f586e21811697b3b1d8bc38e545fc7a4e --- /dev/null +++ b/CNE1T4oBgHgl3EQfVwTa/content/2301.03107v1.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:266ba611da28d6061c01802c0deb594685f469bda23e2639a1998307e145bd30 +size 1551420 diff --git a/CNE1T4oBgHgl3EQfVwTa/vector_store/index.pkl b/CNE1T4oBgHgl3EQfVwTa/vector_store/index.pkl new file mode 100644 index 0000000000000000000000000000000000000000..494f09146a7ec5dba0b5030bff06e6795b5ccaba --- /dev/null +++ b/CNE1T4oBgHgl3EQfVwTa/vector_store/index.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:be68562fc63ae6b2eaa72c139acee4f603f2aeb64b837cb5357dcd4edf1895ae +size 222853 diff --git a/CNE1T4oBgHgl3EQfpgW7/content/tmp_files/2301.03333v1.pdf.txt b/CNE1T4oBgHgl3EQfpgW7/content/tmp_files/2301.03333v1.pdf.txt new file mode 100644 index 0000000000000000000000000000000000000000..13e364ecd74f266846315a35b3686b93a1a03290 --- /dev/null +++ b/CNE1T4oBgHgl3EQfpgW7/content/tmp_files/2301.03333v1.pdf.txt @@ -0,0 +1,916 @@ +A tutorial on the conservation of momentum in +photonic time-varying media +Angel Ortega-Gomez,1 Micha¨el Lobet,2, 3 J. Enrique V´azquez-Lozano,1 and I˜nigo Liberal1, ∗ +1Department of Electrical, Electronic and Communications +Engineering, Institute of Smart Cities (ISC), +Public University of Navarre (UPNA), 31006 Pamplona, Spain +2John A. Paulson School of Engineering and Applied Sciences, +Harvard University, 9 Oxford Street, Cambridge, MA 02138, USA +3Department of Physics and Namur Institute of Structured Materials, +University of Namur, Rue de Bruxelles 61, 5000 Namur, Belgium +1 +arXiv:2301.03333v1 [physics.optics] 9 Jan 2023 + +Abstract +Time-varying media break temporal symmetries while preserving spatial symmetries intact. +Thus, it represents an excellent conceptual framework to investigate the fundamental implications +of Noether’s theorem for the electromagnetic field. At the same time, addressing momentum con- +servation in time-varying media sheds light on the Abraham-Minkowski debate, where two opposing +forms of the electromagnetic field momentum are defended. Here, we present a tutorial review on +the conservation of momentum in time-varying media. We demonstrate that the Minkowski mo- +mentum is a conserved quantity with three independent approaches of increasing complexity: (i) +via the application of the boundary conditions for Maxwell equations at a temporal boundary, (ii) +testing for constants of motion and deriving conservation laws, and (iii) applying temporal and +spatial translations within the framework of the Lagrangian theory of the electromagnetic field. +Each approach provides a different and complementary insight into the problem. +I. +INTRODUCTION +Time-varying media are revolutionizing the fields of optics and nanophotonics by har- +nessing time as an additional resource for controlling light-matter interactions [1–4]. Dy- +namically modulating matter offers new possibilities for the manipulation of electromagnetic +fields including compact and low-energy nonreciprocal devices [5], inverse prism and tempo- +ral aiming effects [6, 7], overcoming bandwidth bounds in impedance matching [8], energy +accumulation without a theoretical limit [9], quantum state frequency shifting [10], and +ultra-fast switching without thermal noise amplification [10], to name a few. Time-varying +media also empower new amplification [11] and photon generation mechanisms, such as +directional vacuum amplification effects [12], amplified light emission from quantum emit- +ters [13] and free electrons [14], as well as incandescent sources not constrained within the +black-body spectrum [15]. +Because a homogeneous time-varying medium is invariant under spatial translations (see +Fig.1), it is usually argued that time-varying media preserves the momentum of the elec- +tromagnetic field [1–4, 16–18]. This intuition stems from Noether’s theorem [19–22], which +more generally states that symmetries of the action of a physical system have an associated +∗ Corresponding author: inigo.liberal@unavarra.es +2 + +conserved quantity. However, a direct connection between invariance under spatial transla- +tions and momentum conservation in time-varying media is not specified. In addition, the +notion of the momentum of the electromagnetic field is quite subtle. In fact, according to the +Abraham-Minkowski debate [23–27], there is more than one definition for the momentum of +the electromagnetic field. On the one hand, one can define the Abraham momentum, +PA (t) = +� +d3r pA (r, t) = µ0ε0 +� +d3r E (r, t) × H (r, t) +(1) +where we have also defined the Abraham momentum density, which is proportional to the +Poynting vector field, pA = µ0ε0S. On the other hand, the Minkowski momentum reads as +PM (t) = +� +d3r pM (r, t) = +� +d3r D (r, t) × B (r, t) +(2) +A common simplification of those definitions for a plane wave in non-dispersive media +is pA = ℏω/nc and pM = nℏω/c, which highlights the role of the refractive index n. In- +terestingly, as pointed out by Leonhardt [28] one should call for the Minkowski momentum +whenever the wave aspects dominate, for example, in experiments involving momentum re- +coil [26, 29], while the Abraham momentum appears when the particle aspects are probed +[23]. +A resolution of the debate was offered among others by Barnett [30, 31]. +It is sug- +gested that the Abraham momentum is the kinetic momentum of the electromagnetic field, +associated with energy transport. The Minkowski momentum is, however, the canonical +momentum of the electromagnetic field, being the generator of spatial translations. Never- +theless, certain aspects of the momentum of the electromagnetic field are still under question +[32]. Moreover, the avenue of near-zero-index (NZI) media exacerbates the differences be- +tween the forms of the momentum [33–35] giving rise to zero Minkowski momentum but +nonzero Abraham momentum inside epsilon-and-mu-near-zero (EMNZ) media where both +permittivity and permeability approach zero. +Since time-varying media preserve spatial symmetries while breaking temporal symme- +tries, it represents an excellent conceptual playground to illuminate the Abraham-Minkowski +debate. Following the interpretation offered by Barnett [30], it should be expected that the +Minkowski momentum - related to spatial translations - is a conserved quantity, while the +Abraham momentum - related to energy transport - is not. This work aims to provide a +3 + +FIG. 1. Schematic depiction of time-varying media, in which both permittivity ε (t) and perme- +ability µ (t) change with time. Thus, the systems is invariant with respect to spatial translations, +but is not invariant with respect to temporal translations. +tutorial review of different aspects on the conservation of the momentum of the electromag- +netic field in time-varying media. We address three independent derivations showing that +only the Minkowski momentum is a conserved quantity in time-varying media based on: (i) +boundary conditions on Maxwell equations, (ii) directly evaluating constants of motion and +deriving conservation laws, and (iii) inducing spatial translations to the Lagrangian of the +electromagnetic field. Each approach provides a different physical insight into the problem. +II. +MOMENTUM CONSERVATION FROM INSPECTING MAXWELL EQUA- +TIONS AT A TEMPORAL BOUNDARY +Our starting point is Maxwell curl equations in time-varying media, which, in the absence +of charges and currents, can be written as follows +∇ × E (r, t) = −∂tB (r, t) +(3) +∇ × H (r, t) = ∂tD (r, t) +(4) +For the sake of simplicity, we assume homogeneous and instantaneous time-varying media, +with constitutive relations +D (r, t) = ε (t) E (r, t) +(5) +4 + +(tn),μ(tn) +... +(t2),μ(t2) +(ti),μ(ti) +(to), μ(to) +toB (r, t) = µ (t) H (r, t) +(6) +A more complete description of time-varying media would include the impact of dispersion +and loss [17, 36]. However, a system with dissipation does not necessarily conserve quantities +even in the presence of symmetries. In addition, the assumption of instantaneous media is +widespread in the field of temporal metamaterials [2]. +Integrating Maxwell equations (3)-(4) accross a temporal boundary taking place at t0, +where material parameters suddenly change from ε(t− +0 ), µ(t− +0 ) to ε(t+ +0 ), µ(t+ +0 ), gives +� t+ +0 +t− +0 +dt ∇ × H (r, t) = +� t+ +0 +t− +0 +dt ∂tD (r, t) = D +� +r, t+ +0 +� +− D +� +r, t− +0 +� +(7) +− +� t+ +0 +t− +0 +dt ∇ × E (r, t) = +� t+ +0 +t− +0 +dt ∂tB (r, t) = B +� +r, t+ +0 +� +− B +� +r, t− +0 +� +(8) +Therefore, we find that D (r, t) and B (r, t) must be continuous accross changes of the +constitutive parameters, for finite E (r, t) and H (r, t) fields. This property is well-known +since early works on time-varying media [16]. As a consequence, this reasoning confirms that +the Minkowski momentum – uniquely defined as a function of D and B fields via Eq. (2) - is +a continuous quantity across a temporal boundary, suggesting that is should be a conserved +quantity in time-varying media. However, this approach fails at providing any insight on +the associated conservation law and/or how it can be related to invariance under spatial +translations. Moreover, it does not clarify the (non) conservation of Abraham momentum. +III. +CONSTANTS OF MOTION AND CONSERVATION LAWS +In this section, we address the conservation of momentum in time-varying media by +direcly testing if a given quantity is a constant of motion. To this end, one can take the time +derivative of the quantity under question and check if it is zero, in which case it shall be a +constant of motion/conserved quantity. Before addressing the momentum, it is instructive to +analyze the energy of the electromagnetic field, which in time-varying media can be written +as +U (t) = +� +d3r u (r, t) +(9) +5 + +with energy density +u (r, t) = 1 +2 +� +ε (t) E2 (r, t) + µ (t) H2 (r, t) +� +(10) +Taking the time derivative of the energy and, substituting Maxwell equations (3)-(4), +leads to the following expression +dU +dt = − 1 +µ0ε0 +� +dS · pA − 1 +2 +� +d3r +�dε (t) +dt E2 (r, t) + dµ (t) +dt +H2 (r, t) +� +(11) +On the one hand, the first term in the r.h.s. of (11) is a surface term proportional to the +E and H fields. This term physically means that the change of energy over time is partly +due to energy either leaking out or coming into the system. It can be seen as a flux of either +outgoing or incoming Poynting vector field, hence setting down a link with PA (Eq. (1)). +It confirms the role of the Abraham momentum as the kinetic momentum, associated with +energy transport. If the volume is large enough to capture the entirety of the E and H +fields within the time interval of interest, its contribution vanishes. On the other hand, the +second term in the r.h.s. of (11) is a volume integral directly linked to the time modulation +of the permittivity and permeability, which results in a change of the energy of the system. +It represents the energy that must be pumped into or retracted from the system in order to +realize the time modulation of the material parameters. In other words, the time variation +of the material parameters act as sources or sinks of electromagnetic energy. By contrast, +Eq. (11) shows that for a medium with static material properties dU/dt = 0 and energy +would be a conserved quantity. +Eq. (11) can also be casted as a local conservation law as a function of the energy and +momentum densities +du (r, t) +dt ++ +1 +µ0ε0 +∇ · pA (r, t) = −1 +2 +�dε (t) +dt E2 (r, t) + dµ (t) +dt +H2 (r, t) +� +(12) +where we clearly identify the source/sink at the r.h.s.. +Let us now tackle the conservation of Minkowski momentum and examine the time varia- +tion of Abraham momentum. By introducing Maxwell equations and applying a few vector +calculus identities, it can be found that the time derivative of the Minkowski momentum is +given by +dPM (t) +dt += ε (t) +� � +p=x,y,z +up +� +dS · (EpE) − 1 +2 +� +dS (E · E) +� +6 + ++ µ (t) +� � +p=x,y,z +up +� +dS · (HpH) − 1 +2 +� +dS (H · H) +� +(13) +By doing so, we find that the time derivative of the Minkowski momentum reduces to +surface terms. Once again, if the volume of integration is taken large enough so that all the +E and H fields are confined within its interior, all surface terms vanish. In other words, +dPM (t) /dt = 0, proving that the Minkowski momentum is a constant of motion as expected. +It is also instructive to note that the above equation can be written in a differential form as +a conservation law for the momentum density: +dpM (t) +dt += ∇ · TM (r, t) +(14) +where we define the Minkowski stress tensor for time-varying media as +TM (r, t) = ε (t) +� +E ⊗ E − 1 +2I (E · E) +� ++ µ (t) +� +H ⊗ H − 1 +2I (H · H) +� +(15) +with I being the identity dyadic. Conservation laws in the form of (14) can be found scattered +in the literature, for example, in the appendix of [18]. +Proceeding similarly with the Abraham momentum reveals that in general it is not a +conserved quantity: +dPA +dt += − +� 1 +ε (t) +dε (t) +dt ++ +1 +µ (t) +dµ (t) +dt +� +PA (t) +−ε0µ0 +µ (t) +� +1 +2 +� +dS (E · E) − +� +p=x,y,z +up +� +dS · (EpE) +� +− ε0µ0 +ε (t) +� +1 +2 +� +dS (H · H) − +� +p=x,y,z +up +� +dS · (HpH) +� +(16) +Here again, the second and third terms are surface terms that would vanish for a suffi- +ciently large volume. However, the first term illustrates that the Abraham momentum does +change in time, following the change in the permittivity and permeability of the medium. +Equation (16) can also be compactly written as a local conservation law for the momentum +density +dpA +dt = ∇ · TA − +� 1 +ε (t) +dε (t) +dt ++ +1 +µ (t) +dµ (t) +dt +� +pA (r, t) +(17) +where we define the Abraham stress tensor in time-varying media, related to the Minkowski +stress tensor as follows +TA = +ε0µ0 +µ (t) ε (t) TM (r, t) +(18) +7 + +In conclusion, testing for constants of motions provides an independent confirmation +that the Minkowski momentum is indeed a conserved quantity in time-varying media. In +addition, it provides insight in the form of the conservation law that supports its invariance. +Furthermore, it shows that the Abraham momentum is not a constant of motion in close +connection to energy considerations, and re-emphasizes its role as the kinetic momentum of +the electromagnetic field. Nevertheless, writing the conservation law does not clarify the role +of the invariance of the system under spatial translations in the conservation of momentum. +IV. +MOMENTUM CONSERVATION AS A CONSEQUENCE OF INVARIANCE +UNDER SPATIAL TRANSLATIONS: A LAGRANGIAN APPROACH +In this section we address the conservation of momentum in time-varying media from the +perspective of the Lagrangian formalism for electromagnetic fields. Using the Lagrangian +formalism adds an extra layer of complexity, but allows to unequivocally identify momen- +tum conservation as a fundamental consequence of the invariance of time-varying media +under spatial translations. We note that most works identifying the Minkowski momentum +as the generator of spatial translations do it from a quantum description of the electro- +magnetic field, where the Minkowski momentum appears as an operator [30]. However, it +is important to understand that momentum conservation as a consequence of invariance +under spatial translations is also a classical effect. Therefore, we keep here a classical La- +grangian description of the electromagnetic fields, without introducing the quantization of +the electromagnetic field. +In the following, we first review the Lagrangian description of electromagnetic fields +extended to time-varying media. Then, we derive a form of Noether’s theorem in our for- +malism and we finally show the quantities associated with temporal and spatial translations +for time-varying media. +A. +Lagrangian description of the electromagnetic field +An in-depth review of the Lagrangian theory of the electromagnetic field can be found in +Cohen-Tannoudji’s book [21]. Here we review it and extend it to time-varying media. From +the perspective of Lagrangian theory, Maxwell equations are equations of motion that can +8 + +be derived from the principle of least (or stationary) action. This principle states that true +path of motion corresponds to a stationary point of the action. By motion we refer to the +values that the dynamical variables have in a given interval of time, which, when position +is a dynamical variable, aligns with the common notion of motion. The action is defined as +the integral of the Lagrangian between two instants of time t1 and t2: +S (t1, t2) = +� t2 +t1 +dt L (t) +(19) +with the Lagrangian +L (t) = +� +d3r L (r, t) +(20) +and the Lagrangian density +L (r, t) = 1 +2 +� +d3r [ε (t) E (r, t) · E (r, t) − µ (t) H (r, t) · H (r, t)] +(21) +The choice of this Lagrangian density is a direct extension from the case with no time +modulation. It is justified because Lagrange’s equation correctly recovers the equations of +motion for the electromagnetic field, as shown below. For the Lagrangian description of +the electromagnetic field, it is convenient to work with scalar V (r, t) and vector A (r, t) +potentials instead of fields. For the sake of simplicity, we work in the Coulomb gauge, for +which ∇ · A (r, t) = 0. By doing so, the scalar potential is zero in the absence of charges +V (r, t) = 0, all the fields are transversal, and they can be simply written as a function of +the vector potential +D (r, t) = −ε (t) ∂tA (r, t) +(22) +B (r, t) = ∇ × A (r, t) +(23) +Then, Maxwell equations lead to the following wave equation for the components of the +vector potential (p = x, y, z): +∇2Ap (r, t) − µ (t) ∂t {ε (t) ∂tAp (r, t)} = 0 +(24) +Due to field transversality, the Minkowski momentum can be compactly written as +PM = −ε (t) +� +p +� +d3r ∂tAp (r, t) ∇Ap (r, t) +(25) +Similarly, the Lagrangian density reduces to +L = 1 +2 +� +p +� +ε (t) ˙A2 +p (r, t) − +1 +µ (t) (∇ × A (r, t))2 +p +� +(26) +9 + +where we have used ˙Ap as a shorter way to write the time derivative. From this description, +it lies that the components of the vector potential, Ap, and its time derivatives, ˙Ap, are the +dynamical variables of the system. +Imposing that a true path of motion is a stationary point of the action, for which δS = 0, +leads to Lagrange’s equations +∂L +∂Ap +− +� +q +∂q +� +∂L +∂ (∂qAp) +� +− d +dt +∂L +∂ ˙Ap += 0 +(27) +which reduces to the wave equation for Ap in (24), justifying the direct extension of the +Lagrangian to time-varying media. +With equation (26), we find that the conjugate momentum of each vector potential com- +ponent, Ap, is the negative of the electric displacement field components +Πp (r, t) = ∂L +∂ ˙Ap += ε (t) ˙Ap (r, t) = −Dp (r, t) +(28) +This point allow us to clarify another ambiguity related to the momentum of the elec- +tromagnetic field. For a freely moving particle of mass m with Lagrangian, L = � +p +1 +2 m ˙r2 +p, +the dynamical variables are the position coordinates rp, p = x, y, z. Thus, their associated +conjugate momenta pp = ∂L/∂ ˙rp = m ˙rp correspond to the components of the linear mo- +mentum. The latter is also the momentum associated with the spatial translations of the +system. However, for the electromagnetic field, position is not a dynamical variable of the +system while the vector potential is. For this reason, one has to differentiate between the +conjugate momentum and the momentum associated with spatial translations, as clarified +below. +Finally, the Hamiltonian is defined as a function of the conjugate momentum as follows +H = +� +p +� +d3r Πp (r, t) ˙Ap (r, t) − L +(29) +which can be found to be fully equivalent to the form of the electromagnetic energy in +time-varing media employed in the previous section, and given by Eqs. (9)-(10). +B. +Noether’s theorem in the Coulomb gauge +In this section, we cast a form of Noether’s theorem which allows us to discern the +conserved quantities associated with the continuous symmetries of time-varying media. To +10 + +FIG. 2. (a) Schematic depiction of the motion of a dynamical variable Ap (r, t) between times t1 +and t2, and an infinitesimally close motion, described by A′ +p (r, t) between times t′ +1 and t′ +2. The +difference between both motions at time t is given by dA (r, t) = A′ (r, t) − A (r, t). The difference +between the initial and final temporal points is given by dt1 = t′ +1−t1 and dt2 = t′ +2−t2, respectively. +(b) Schematic depiction of trajectories for systems with (left) temporal translation symmetry, and +(right) spatial translation symmetry. +this end, we note that any continuous symmetry can be described as an infinitesimal variation +of the action. Therefore, as schematically depicted in Fig. 2(a), we consider a motion between +times t1 and t2, defined by the dynamical variables Ap (r, t), and an infinitesimally close +motion between times t′ +1 and t′ +2, described by A′ +p (r, t). +The variation of the dynamical +variables at a given point of time is dAp (r, t) = A′ +p (r, t) − Ap (r, t), and the variation of the +action can be written as +dS = S′ − S = +� t′ +2 +t′ +1 +dt L +� +A′ +p +� +− +� t2 +t1 +dt L (Ap) += +� t2 +t1 +dt +� +L +� +A′ +p +� +− L (Ap) +� ++ +� t′ +2 +t2 +dt L +� +A′ +p +� +− +� t′ +1 +t1 +dt L +� +A′ +p +� +(30) +11 + +(a) +p (r,t) +Ap(r,t2) +Ap(r,t2) +Ap(r,t1) +Ap(r,t') +dt2 +dti +ti +t2 +34 +(b) +Temporal translation symmetry +Spatial translation symmetry +Ap(r -n,t2) +Ap (r,ti) +A"(r,t)) +Ap (r,ti) +dAp (r,t) As(r;t2) +Ap(r,t2) +Ap (r,t2) +t1 +t2 +dt +dt +t +ti +ti +t2 +t2 +ti +t2To first order, last two terms can be approximated by +� t′ +2 +t2 +dt L +� +A′ +p +� += L (Ap)|t2 dt2 +(31) +and the equivalent expression for t1. +Similarly, for two infinitesimally closed motions, the first term is given by +� t2 +t1 +dt +� +L +� +A′ +p +� +− L (Ap) +� += += +� t2 +t1 +dt +� +p +� +d3r +� +∂L +∂Ap (r, t) dAp (r, t) + +∂L +∂ ˙Ap (r, t) +d ˙Ap (r, t) ++ +� +q +� +∂L +∂ (∂qAp (r, t)) +� +d∂qAp (r, t) +� +(32) +Similarly to the derivation of Lagrange’s equation, we integrate by parts the second term +with respect to time and the third term with respect to r, so the variation of the action +reduces to +� t2 +t1 +dt +� +L +� +A′ +p +� +− L (Ap) +� += += +� t2 +t1 +dt +� +p +� +d3r +� +∂L +∂Ap (r, t) − d +dt +∂L +∂ ˙Ap (r, t) +− +� +q +∂q +� +∂L +∂ (∂qAp (r, t)) +�� +dAp (r, t) ++ +� +p +� +d3r +∂L +∂ ˙Ap (r, t) +dAp (r, t) +����� +t2 +t1 +(33) +Note that, in deriving the above equation we have assumed that the fields vanish at +infinity, so that there are no surface contributions. By contrast, the fields do not need to +vanish at the initial and final temporal boundaries, leading the contribution from the last +term. In addition, the integrand of the first term is a solution to Lagrange’s equation (27), +which reduces to zero. Thus, by substituting (31)-(33) into (30) we find that the variation +of the action is given by: +dS = +� +p +� +d3r +� +∂L +∂ ˙Ap (r, t) +dAp (r, t) +����� +t2 ++ L (Ap)|t2 dt2 +− +∂L +∂ ˙Ap (r, t) +dAp (r, t) +����� +t1 +− L (Ap)|t1 dt1 +� +(34) +If a system has a continuous symmetry, then the corresponding action remains invariant +with respect to infinitesimal displacements, i.e., dS = 0. In addition, since dS = 0 must +12 + +hold for any pair of times t1 and t2 we find that the term within brackets must be a constant +of motion. These relations correspond to Noether’s theorem applied to our formulation of +the electromagnetic field in time-varying media in the Coulomb Gauge. Given a continuous +symmetry, specified by the variation dAp (r, t) and the boundary condition on the Lagrangian +L (Ap) dt, one can identify an associated conserved quantity. +C. +Temporal and spatial translations +First, let us assume that the variation is produced by an infinitesimal temporal displace- +ment dt, such that dt2 = dt1 = dt (see Fig. 2(b)). If the system is invariant with respect to +temporal translations we can write A′ +p (r, t) = Ap (r, t − dt) ≃ Ap (r, t) − ˙Ap (r, t) dt. Then, +we have dAp (r, t) = − ˙Ap (r, t) dt. Substituting this result in (34) and factoring out dt we +find that the conserved quantity is +� +p +� +d3r +� +− +∂L +∂ ˙Ap (r, t) +˙Ap (r, t) + L (Ap) +� += += − +� +p +� +d3r 1 +2 +� +p +� +ε (t) ˙A2 +p (r, t) + +1 +µ (t) (∇ × A (r, t))2 +p +� += −H +(35) +Therefore, it is found that invariance with respect to temporal translations implies that +the Hamiltonian must be a conserved quantity. In time-varying media, the system is not +invariant under temporal translations, and, consequently, the Hamiltonian manifestly de- +pends on time. As shown in the previous section, taking its time derivative explicitly shows +that it is not a constant of motion. +Second, we assume that the variation is produced by an infinitesimal spatial displacement +η (see Fig. 2(b)). Then, if the system is invariant under spatial translations we must have +A′ +p (r, t) = Ap (r − η, t) ≃ Ap (r, t)−η·∇Ap (r, t), so that dAp (r, t) = −η·∇Ap (r, t) . Again, +substituting this result into (34) and factoring out η we find that the conserved quantity +must be +P = +� +p +� +d3r +∂L +∂ ˙Ap (r, t) +∇Ap (r, t) = − +� +p +� +d3r ε (t) ˙Ap (r, t) ∇Ap (r, t) +(36) +which equals the Minkowski momentum in (25). Therefore, we finally found that the fact +that time-varying media are invariant under spatial translations directly enforces that the +Minkowski momentum is a conserved quantity. +13 + +V. +CONCLUDING REMARKS +Symmetries play a fundamental role in physics. They reduce the complexity of difficult +problems, as well as the computational cost needed to solve them. Symmetries also en- +able the identification of conserved quantities and the formal link between both symmetries +and conserved quantities is Noether’s theorem. One of the reasons why time-varying media +and/or temporal metamaterials provide a fresh view on electromagnetic theory is because +they break temporal symmetries, which are conserved in most traditional photonics systems, +while they maintain spatial symmetries. However, the connection between spatial and tem- +poral symmetries and the properties of time-varying media is not always explicitely stated, +or analyzed through the point of view of Lagrangian mechanics. The present tutorial aims +at filling this gap. Furthermore, we hope that this tutorial may clarify the subtleties of the +conservation of the electromagnetic momentum in time-varying media, the nuances of defin- +ing the momentum of the electromagnetic fields within the Abraham-Minkowski debate, and +that it will foster further research on the role and significance of symmetries in temporal +metamaterials. +[1] C. Caloz and Z.-L. Deck-Leger, Spacetime metamaterials—part II: theory and applications, +IEEE Transactions on Antennas and Propagation 68, 1583 (2019). +[2] E. Galiffi, R. Tirole, S. Yin, H. Li, S. Vezzoli, P. A. Huidobro, M. G. Silveirinha, R. Sapienza, +A. Al`u, and J. Pendry, Photonics of time-varying media, Advanced Photonics 4, 014002 (2022). +[3] S. Yin, E. Galiffi, and A. Al`u, Floquet metamaterials, eLight 2, 1 (2022). +[4] N. Engheta, Metamaterials with high degrees of freedom: space, time, and more, Nanopho- +tonics 10, 639 (2021). +[5] D. L. Sounas and A. Alu, Non-reciprocal photonics based on time modulation, Nature Pho- +tonics 11, 774 (2017). +[6] A. Akbarzadeh, N. Chamanara, and C. Caloz, Inverse prism based on temporal discontinuity +and spatial dispersion, Optics Letters 43, 3297 (2018). +[7] V. Pacheco-Pe˜na and N. Engheta, Temporal aiming, Light: Science & Applications 9, 1 (2020). +14 + +[8] A. Shlivinski and Y. Hadad, Beyond the bode-fano bound: Wideband impedance matching +for short pulses using temporal switching of transmission-line parameters, Physical Review +Letters 121, 204301 (2018). +[9] M. S. Mirmoosa, G. Ptitcyn, V. S. Asadchy, and S. A. Tretyakov, Time-varying reactive +elements for extreme accumulation of electromagnetic energy, Physical Review Applied 11, +014024 (2019). +[10] I. Liberal, J. E. V´azquez-Lozano, and V. Pachecho-Pe˜na, Quantum antireflection temporal +coatings: quantum state frequency shifting and inhibited thermal noise amplification, arXiv +preprint arXiv:2208.10089 (2022). +[11] J. Pendry, E. Galiffi, and P. Huidobro, Gain in time-dependent media—a new mechanism, +Journal of the Optical Society of America B 38, 3360 (2021). +[12] J. E. V´azquez-Lozano and I. Liberal, Shaping the quantum vacuum with anisotropic temporal +boundaries, Nanophotonics (2022). +[13] M. Lyubarov, Y. Lumer, A. Dikopoltsev, E. Lustig, Y. Sharabi, and M. Segev, Amplified +emission and lasing in photonic time crystals, Science 377, 425 (2022). +[14] A. Dikopoltsev, Y. Sharabi, M. Lyubarov, Y. Lumer, S. Tsesses, E. Lustig, I. Kaminer, and +M. Segev, Light emission by free electrons in photonic time-crystals, Proceedings of the Na- +tional Academy of Sciences 119, e2119705119 (2022). +[15] J. E. V´azquez-Lozano and I. Liberal, Incandescent temporal metamaterials, arXiv preprint +arXiv:2210.05565 (2022). +[16] F. R. Morgenthaler, Velocity modulation of electromagnetic waves, IRE Transactions on Mi- +crowave Theory and Techniques 6, 167 (1958). +[17] D. M. Sol´ıs, R. Kastner, and N. Engheta, Time-varying materials in the presence of disper- +sion: plane-wave propagation in a lorentzian medium with temporal discontinuity, Photonics +Research 9, 1842 (2021). +[18] T. T. Koutserimpas and R. Fleury, Electromagnetic fields in a time-varying medium: excep- +tional points and operator symmetries, IEEE Transactions on Antennas and Propagation 68, +6717 (2020). +[19] M. Banados and I. Reyes, A short review on noether’s theorems, gauge symmetries and bound- +ary terms, International Journal of Modern Physics D 25, 1630021 (2016). +15 + +[20] Y. Kosmann-Schwarzbach, The noether theorems, in The Noether Theorems (Springer, 2011) +pp. 55–64. +[21] C. Cohen-Tannoudji, J. Dupont-Roc, and G. Grynberg, Photons and atoms-Introduction to +quantum electrodynamics (1997). +[22] J. Sakurai and J. Napolitano, Modern quantum mechanics. 2-nd edition (Person, 2014) p. 39. +[23] I. Brevik, Experiments in phenomenological electrodynamics and the electromagnetic energy- +momentum tensor, Physics Reports 52, 133 (1979). +[24] R. N. Pfeifer, T. A. Nieminen, N. R. Heckenberg, and H. Rubinsztein-Dunlop, Colloquium: +Momentum of an electromagnetic wave in dielectric media, Reviews of Modern Physics 79, +1197 (2007). +[25] B. Kemp, Resolution of the abraham-minkowski debate: Implications for the electromagnetic +wave theory of light in matter, Journal of Applied Physics 109, 7 (2011). +[26] P. Milonni and R. Boyd, Recoil and photon momentum in a dielectric, Laser Physics 15, 1432 +(2005). +[27] M. Mansuripur, Radiation pressure and the linear momentum of the electromagnetic field, +Optics Express 12, 5375 (2004). +[28] U. Leonhardt, Momentum in an uncertain light, Nature 444, 823 (2006). +[29] G. K. Campbell, A. E. Leanhardt, J. Mun, M. Boyd, E. W. Streed, W. Ketterle, and D. E. +Pritchard, Photon recoil momentum in dispersive media, Physical Review Letters 94, 170403 +(2005). +[30] S. M. Barnett, Resolution of the abraham-minkowski dilemma, Physical Review Letters 104, +070401 (2010). +[31] S. M. Barnett and R. Loudon, The enigma of optical momentum in a medium, Philosophical +Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368, +927 (2010). +[32] M. G. Silveirinha, Reexamination of the abraham-minkowski dilemma, Physical Review A 96, +033831 (2017). +[33] M. Lobet, I. Liberal, L. Vertchenko, A. V. Lavrinenko, N. Engheta, and E. Mazur, Momentum +considerations inside near-zero index materials, Light: Science & Applications 11, 1 (2022). +[34] I. Liberal and N. Engheta, Near-zero refractive index photonics, Nature Photonics 11, 149 +(2017). +16 + +[35] N. Kinsey, Developing momentum in vanishing index photonics, Light Science & Applications +11, 1 (2022). +[36] J. Gratus, R. Seviour, P. Kinsler, and D. A. Jaroszynski, Temporal boundaries in electromag- +netic materials, New Journal of Physics 23, 083032 (2021). +17 + diff --git a/CNE1T4oBgHgl3EQfpgW7/content/tmp_files/load_file.txt b/CNE1T4oBgHgl3EQfpgW7/content/tmp_files/load_file.txt new file mode 100644 index 0000000000000000000000000000000000000000..518f5fec02904e7b1e10b4abb7fd0de2f8254d0d --- /dev/null +++ b/CNE1T4oBgHgl3EQfpgW7/content/tmp_files/load_file.txt @@ -0,0 +1,412 @@ +filepath=/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf,len=411 +page_content='A tutorial on the conservation of momentum in photonic time-varying media Angel Ortega-Gomez,1 Micha¨el Lobet,2, 3 J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Enrique V´azquez-Lozano,1 and I˜nigo Liberal1, ∗ 1Department of Electrical, Electronic and Communications Engineering, Institute of Smart Cities (ISC), Public University of Navarre (UPNA), 31006 Pamplona, Spain 2John A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Paulson School of Engineering and Applied Sciences, Harvard University, 9 Oxford Street, Cambridge, MA 02138, USA 3Department of Physics and Namur Institute of Structured Materials, University of Namur, Rue de Bruxelles 61, 5000 Namur, Belgium 1 arXiv:2301.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='03333v1 [physics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='optics] 9 Jan 2023 Abstract Time-varying media break temporal symmetries while preserving spatial symmetries intact.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Thus, it represents an excellent conceptual framework to investigate the fundamental implications of Noether’s theorem for the electromagnetic field.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' At the same time, addressing momentum con- servation in time-varying media sheds light on the Abraham-Minkowski debate, where two opposing forms of the electromagnetic field momentum are defended.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Here, we present a tutorial review on the conservation of momentum in time-varying media.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' We demonstrate that the Minkowski mo- mentum is a conserved quantity with three independent approaches of increasing complexity: (i) via the application of the boundary conditions for Maxwell equations at a temporal boundary, (ii) testing for constants of motion and deriving conservation laws, and (iii) applying temporal and spatial translations within the framework of the Lagrangian theory of the electromagnetic field.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Each approach provides a different and complementary insight into the problem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' INTRODUCTION Time-varying media are revolutionizing the fields of optics and nanophotonics by har- nessing time as an additional resource for controlling light-matter interactions [1–4].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Dy- namically modulating matter offers new possibilities for the manipulation of electromagnetic fields including compact and low-energy nonreciprocal devices [5], inverse prism and tempo- ral aiming effects [6, 7], overcoming bandwidth bounds in impedance matching [8], energy accumulation without a theoretical limit [9], quantum state frequency shifting [10], and ultra-fast switching without thermal noise amplification [10], to name a few.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Time-varying media also empower new amplification [11] and photon generation mechanisms, such as directional vacuum amplification effects [12], amplified light emission from quantum emit- ters [13] and free electrons [14], as well as incandescent sources not constrained within the black-body spectrum [15].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Because a homogeneous time-varying medium is invariant under spatial translations (see Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='1), it is usually argued that time-varying media preserves the momentum of the elec- tromagnetic field [1–4, 16–18].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' This intuition stems from Noether’s theorem [19–22], which more generally states that symmetries of the action of a physical system have an associated ∗ Corresponding author: inigo.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='liberal@unavarra.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='es 2 conserved quantity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' However, a direct connection between invariance under spatial transla- tions and momentum conservation in time-varying media is not specified.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' In addition, the notion of the momentum of the electromagnetic field is quite subtle.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' In fact, according to the Abraham-Minkowski debate [23–27], there is more than one definition for the momentum of the electromagnetic field.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' On the one hand, one can define the Abraham momentum, PA (t) = � d3r pA (r, t) = µ0ε0 � d3r E (r, t) × H (r, t) (1) where we have also defined the Abraham momentum density, which is proportional to the Poynting vector field, pA = µ0ε0S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' On the other hand, the Minkowski momentum reads as PM (t) = � d3r pM (r, t) = � d3r D (r, t) × B (r, t) (2) A common simplification of those definitions for a plane wave in non-dispersive media is pA = ℏω/nc and pM = nℏω/c, which highlights the role of the refractive index n.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' In- terestingly, as pointed out by Leonhardt [28] one should call for the Minkowski momentum whenever the wave aspects dominate, for example, in experiments involving momentum re- coil [26, 29], while the Abraham momentum appears when the particle aspects are probed [23].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' A resolution of the debate was offered among others by Barnett [30, 31].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' It is sug- gested that the Abraham momentum is the kinetic momentum of the electromagnetic field, associated with energy transport.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' The Minkowski momentum is, however, the canonical momentum of the electromagnetic field, being the generator of spatial translations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Never- theless, certain aspects of the momentum of the electromagnetic field are still under question [32].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Moreover, the avenue of near-zero-index (NZI) media exacerbates the differences be- tween the forms of the momentum [33–35] giving rise to zero Minkowski momentum but nonzero Abraham momentum inside epsilon-and-mu-near-zero (EMNZ) media where both permittivity and permeability approach zero.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Since time-varying media preserve spatial symmetries while breaking temporal symme- tries, it represents an excellent conceptual playground to illuminate the Abraham-Minkowski debate.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Following the interpretation offered by Barnett [30], it should be expected that the Minkowski momentum - related to spatial translations - is a conserved quantity, while the Abraham momentum - related to energy transport - is not.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' This work aims to provide a 3 FIG.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Schematic depiction of time-varying media, in which both permittivity ε (t) and perme- ability µ (t) change with time.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Thus, the systems is invariant with respect to spatial translations, but is not invariant with respect to temporal translations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' tutorial review of different aspects on the conservation of the momentum of the electromag- netic field in time-varying media.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' We address three independent derivations showing that only the Minkowski momentum is a conserved quantity in time-varying media based on: (i) boundary conditions on Maxwell equations, (ii) directly evaluating constants of motion and deriving conservation laws, and (iii) inducing spatial translations to the Lagrangian of the electromagnetic field.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Each approach provides a different physical insight into the problem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' II.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' MOMENTUM CONSERVATION FROM INSPECTING MAXWELL EQUA- TIONS AT A TEMPORAL BOUNDARY Our starting point is Maxwell curl equations in time-varying media, which, in the absence of charges and currents, can be written as follows ∇ × E (r, t) = −∂tB (r, t) (3) ∇ × H (r, t) = ∂tD (r, t) (4) For the sake of simplicity, we assume homogeneous and instantaneous time-varying media, with constitutive relations D (r, t) = ε (t) E (r, t) (5) 4 (tn),μ(tn) .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='..' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' (t2),μ(t2) (ti),μ(ti) (to), μ(to) toB (r, t) = µ (t) H (r, t) (6) A more complete description of time-varying media would include the impact of dispersion and loss [17, 36].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' However, a system with dissipation does not necessarily conserve quantities even in the presence of symmetries.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' In addition, the assumption of instantaneous media is widespread in the field of temporal metamaterials [2].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Integrating Maxwell equations (3)-(4) accross a temporal boundary taking place at t0,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' where material parameters suddenly change from ε(t− 0 ),' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' µ(t− 0 ) to ε(t+ 0 ),' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' µ(t+ 0 ),' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' gives � t+ 0 t− 0 dt ∇ × H (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) = � t+ 0 t− 0 dt ∂tD (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) = D � r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t+ 0 � − D � r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t− 0 � (7) − � t+ 0 t− 0 dt ∇ × E (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) = � t+ 0 t− 0 dt ∂tB (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) = B � r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t+ 0 � − B � r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t− 0 � (8) Therefore,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' we find that D (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) and B (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) must be continuous accross changes of the constitutive parameters,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' for finite E (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) and H (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) fields.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' This property is well-known since early works on time-varying media [16].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' As a consequence, this reasoning confirms that the Minkowski momentum – uniquely defined as a function of D and B fields via Eq.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' (2) - is a continuous quantity across a temporal boundary, suggesting that is should be a conserved quantity in time-varying media.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' However, this approach fails at providing any insight on the associated conservation law and/or how it can be related to invariance under spatial translations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Moreover, it does not clarify the (non) conservation of Abraham momentum.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' III.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' CONSTANTS OF MOTION AND CONSERVATION LAWS In this section, we address the conservation of momentum in time-varying media by direcly testing if a given quantity is a constant of motion.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' To this end, one can take the time derivative of the quantity under question and check if it is zero, in which case it shall be a constant of motion/conserved quantity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Before addressing the momentum,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' it is instructive to analyze the energy of the electromagnetic field,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' which in time-varying media can be written as U (t) = � d3r u (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) (9) 5 with energy density u (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) = 1 2 � ε (t) E2 (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) + µ (t) H2 (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) � (10) Taking the time derivative of the energy and,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' substituting Maxwell equations (3)-(4),' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' leads to the following expression dU dt = − 1 µ0ε0 � dS · pA − 1 2 � d3r �dε (t) dt E2 (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) + dµ (t) dt H2 (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) � (11) On the one hand,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' the first term in the r.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='h.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='s.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' of (11) is a surface term proportional to the E and H fields.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' This term physically means that the change of energy over time is partly due to energy either leaking out or coming into the system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' It can be seen as a flux of either outgoing or incoming Poynting vector field, hence setting down a link with PA (Eq.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' (1)).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' It confirms the role of the Abraham momentum as the kinetic momentum, associated with energy transport.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' If the volume is large enough to capture the entirety of the E and H fields within the time interval of interest, its contribution vanishes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' On the other hand, the second term in the r.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='h.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='s.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' of (11) is a volume integral directly linked to the time modulation of the permittivity and permeability, which results in a change of the energy of the system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' It represents the energy that must be pumped into or retracted from the system in order to realize the time modulation of the material parameters.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' In other words, the time variation of the material parameters act as sources or sinks of electromagnetic energy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' By contrast, Eq.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' (11) shows that for a medium with static material properties dU/dt = 0 and energy would be a conserved quantity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Eq.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' (11) can also be casted as a local conservation law as a function of the energy and momentum densities du (r, t) dt + 1 µ0ε0 ∇ · pA (r, t) = −1 2 �dε (t) dt E2 (r, t) + dµ (t) dt H2 (r, t) � (12) where we clearly identify the source/sink at the r.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='h.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='s.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='. Let us now tackle the conservation of Minkowski momentum and examine the time varia- tion of Abraham momentum.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' By introducing Maxwell equations and applying a few vector calculus identities, it can be found that the time derivative of the Minkowski momentum is given by dPM (t) dt = ε (t) � � p=x,y,z up � dS · (EpE) − 1 2 � dS (E · E) � 6 + µ (t) � � p=x,y,z up � dS · (HpH) − 1 2 � dS (H · H) � (13) By doing so, we find that the time derivative of the Minkowski momentum reduces to surface terms.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Once again, if the volume of integration is taken large enough so that all the E and H fields are confined within its interior, all surface terms vanish.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' In other words, dPM (t) /dt = 0, proving that the Minkowski momentum is a constant of motion as expected.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' It is also instructive to note that the above equation can be written in a differential form as a conservation law for the momentum density: dpM (t) dt = ∇ · TM (r, t) (14) where we define the Minkowski stress tensor for time-varying media as TM (r, t) = ε (t) � E ⊗ E − 1 2I (E · E) � + µ (t) � H ⊗ H − 1 2I (H · H) � (15) with I being the identity dyadic.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Conservation laws in the form of (14) can be found scattered in the literature, for example, in the appendix of [18].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Proceeding similarly with the Abraham momentum reveals that in general it is not a conserved quantity: dPA dt = − � 1 ε (t) dε (t) dt + 1 µ (t) dµ (t) dt � PA (t) −ε0µ0 µ (t) � 1 2 � dS (E · E) − � p=x,y,z up � dS · (EpE) � − ε0µ0 ε (t) � 1 2 � dS (H · H) − � p=x,y,z up � dS · (HpH) � (16) Here again, the second and third terms are surface terms that would vanish for a suffi- ciently large volume.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' However, the first term illustrates that the Abraham momentum does change in time, following the change in the permittivity and permeability of the medium.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Equation (16) can also be compactly written as a local conservation law for the momentum density dpA dt = ∇ · TA − � 1 ε (t) dε (t) dt + 1 µ (t) dµ (t) dt � pA (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) (17) where we define the Abraham stress tensor in time-varying media,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' related to the Minkowski stress tensor as follows TA = ε0µ0 µ (t) ε (t) TM (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) (18) 7 In conclusion,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' testing for constants of motions provides an independent confirmation that the Minkowski momentum is indeed a conserved quantity in time-varying media.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' In addition, it provides insight in the form of the conservation law that supports its invariance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Furthermore, it shows that the Abraham momentum is not a constant of motion in close connection to energy considerations, and re-emphasizes its role as the kinetic momentum of the electromagnetic field.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Nevertheless, writing the conservation law does not clarify the role of the invariance of the system under spatial translations in the conservation of momentum.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' IV.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' MOMENTUM CONSERVATION AS A CONSEQUENCE OF INVARIANCE UNDER SPATIAL TRANSLATIONS: A LAGRANGIAN APPROACH In this section we address the conservation of momentum in time-varying media from the perspective of the Lagrangian formalism for electromagnetic fields.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Using the Lagrangian formalism adds an extra layer of complexity, but allows to unequivocally identify momen- tum conservation as a fundamental consequence of the invariance of time-varying media under spatial translations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' We note that most works identifying the Minkowski momentum as the generator of spatial translations do it from a quantum description of the electro- magnetic field, where the Minkowski momentum appears as an operator [30].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' However, it is important to understand that momentum conservation as a consequence of invariance under spatial translations is also a classical effect.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Therefore, we keep here a classical La- grangian description of the electromagnetic fields, without introducing the quantization of the electromagnetic field.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' In the following, we first review the Lagrangian description of electromagnetic fields extended to time-varying media.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Then, we derive a form of Noether’s theorem in our for- malism and we finally show the quantities associated with temporal and spatial translations for time-varying media.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Lagrangian description of the electromagnetic field An in-depth review of the Lagrangian theory of the electromagnetic field can be found in Cohen-Tannoudji’s book [21].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Here we review it and extend it to time-varying media.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' From the perspective of Lagrangian theory, Maxwell equations are equations of motion that can 8 be derived from the principle of least (or stationary) action.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' This principle states that true path of motion corresponds to a stationary point of the action.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' By motion we refer to the values that the dynamical variables have in a given interval of time, which, when position is a dynamical variable, aligns with the common notion of motion.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' The action is defined as the integral of the Lagrangian between two instants of time t1 and t2: S (t1, t2) = � t2 t1 dt L (t) (19) with the Lagrangian L (t) = � d3r L (r, t) (20) and the Lagrangian density L (r, t) = 1 2 � d3r [ε (t) E (r, t) · E (r, t) − µ (t) H (r, t) · H (r, t)] (21) The choice of this Lagrangian density is a direct extension from the case with no time modulation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' It is justified because Lagrange’s equation correctly recovers the equations of motion for the electromagnetic field, as shown below.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' For the Lagrangian description of the electromagnetic field, it is convenient to work with scalar V (r, t) and vector A (r, t) potentials instead of fields.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' For the sake of simplicity, we work in the Coulomb gauge, for which ∇ · A (r, t) = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' By doing so,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' the scalar potential is zero in the absence of charges V (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) = 0,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' all the fields are transversal,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' and they can be simply written as a function of the vector potential D (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) = −ε (t) ∂tA (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) (22) B (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) = ∇ × A (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) (23) Then,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Maxwell equations lead to the following wave equation for the components of the vector potential (p = x,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' y,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' z): ∇2Ap (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) − µ (t) ∂t {ε (t) ∂tAp (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t)} = 0 (24) Due to field transversality,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' the Minkowski momentum can be compactly written as PM = −ε (t) � p � d3r ∂tAp (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) ∇Ap (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) (25) Similarly,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' the Lagrangian density reduces to L = 1 2 � p � ε (t) ˙A2 p (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) − 1 µ (t) (∇ × A (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t))2 p � (26) 9 where we have used ˙Ap as a shorter way to write the time derivative.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' From this description, it lies that the components of the vector potential, Ap, and its time derivatives, ˙Ap, are the dynamical variables of the system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Imposing that a true path of motion is a stationary point of the action, for which δS = 0, leads to Lagrange’s equations ∂L ∂Ap − � q ∂q � ∂L ∂ (∂qAp) � − d dt ∂L ∂ ˙Ap = 0 (27) which reduces to the wave equation for Ap in (24), justifying the direct extension of the Lagrangian to time-varying media.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' With equation (26), we find that the conjugate momentum of each vector potential com- ponent, Ap, is the negative of the electric displacement field components Πp (r, t) = ∂L ∂ ˙Ap = ε (t) ˙Ap (r, t) = −Dp (r, t) (28) This point allow us to clarify another ambiguity related to the momentum of the elec- tromagnetic field.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' For a freely moving particle of mass m with Lagrangian, L = � p 1 2 m ˙r2 p, the dynamical variables are the position coordinates rp, p = x, y, z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Thus, their associated conjugate momenta pp = ∂L/∂ ˙rp = m ˙rp correspond to the components of the linear mo- mentum.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' The latter is also the momentum associated with the spatial translations of the system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' However, for the electromagnetic field, position is not a dynamical variable of the system while the vector potential is.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' For this reason, one has to differentiate between the conjugate momentum and the momentum associated with spatial translations, as clarified below.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Finally, the Hamiltonian is defined as a function of the conjugate momentum as follows H = � p � d3r Πp (r, t) ˙Ap (r, t) − L (29) which can be found to be fully equivalent to the form of the electromagnetic energy in time-varing media employed in the previous section, and given by Eqs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' (9)-(10).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Noether’s theorem in the Coulomb gauge In this section, we cast a form of Noether’s theorem which allows us to discern the conserved quantities associated with the continuous symmetries of time-varying media.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' To 10 FIG.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' (a) Schematic depiction of the motion of a dynamical variable Ap (r, t) between times t1 and t2, and an infinitesimally close motion, described by A′ p (r, t) between times t′ 1 and t′ 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' The difference between both motions at time t is given by dA (r, t) = A′ (r, t) − A (r, t).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' The difference between the initial and final temporal points is given by dt1 = t′ 1−t1 and dt2 = t′ 2−t2, respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' (b) Schematic depiction of trajectories for systems with (left) temporal translation symmetry, and (right) spatial translation symmetry.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' this end, we note that any continuous symmetry can be described as an infinitesimal variation of the action.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Therefore, as schematically depicted in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' 2(a), we consider a motion between times t1 and t2, defined by the dynamical variables Ap (r, t), and an infinitesimally close motion between times t′ 1 and t′ 2, described by A′ p (r, t).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' The variation of the dynamical variables at a given point of time is dAp (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) = A′ p (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) − Ap (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t),' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' and the variation of the action can be written as dS = S′ − S = � t′ 2 t′ 1 dt L � A′ p � − � t2 t1 dt L (Ap) = � t2 t1 dt � L � A′ p � − L (Ap) � + � t′ 2 t2 dt L � A′ p � − � t′ 1 t1 dt L � A′ p � (30) 11 (a) p (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='t) Ap(r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='t2) Ap(r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='t2) Ap(r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='t1) Ap(r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content="t') dt2 dti ti t2 34 (b) Temporal translation symmetry Spatial translation symmetry Ap(r -n," metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='t2) Ap (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='ti) A"(r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='t)) Ap (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='ti) dAp (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='t) As(r;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='t2) Ap(r,t2) Ap (r,t2) t1 t2 dt dt t ti ti t2 t2 ti t2To first order, last two terms can be approximated by � t′ 2 t2 dt L � A′ p � = L (Ap)|t2 dt2 (31) and the equivalent expression for t1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Similarly,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' for two infinitesimally closed motions,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' the first term is given by � t2 t1 dt � L � A′ p � − L (Ap) � = = � t2 t1 dt � p � d3r � ∂L ∂Ap (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) dAp (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) + ∂L ∂ ˙Ap (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) d ˙Ap (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) + � q � ∂L ∂ (∂qAp (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t)) � d∂qAp (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) � (32) Similarly to the derivation of Lagrange’s equation,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' we integrate by parts the second term with respect to time and the third term with respect to r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' so the variation of the action reduces to � t2 t1 dt � L � A′ p � − L (Ap) � = = � t2 t1 dt � p � d3r � ∂L ∂Ap (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) − d dt ∂L ∂ ˙Ap (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) − � q ∂q � ∂L ∂ (∂qAp (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t)) �� dAp (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) + � p � d3r ∂L ∂ ˙Ap (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) dAp (r,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' t) ����� t2 t1 (33) Note that,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' in deriving the above equation we have assumed that the fields vanish at infinity,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' so that there are no surface contributions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' By contrast, the fields do not need to vanish at the initial and final temporal boundaries, leading the contribution from the last term.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' In addition, the integrand of the first term is a solution to Lagrange’s equation (27), which reduces to zero.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Thus, by substituting (31)-(33) into (30) we find that the variation of the action is given by: dS = � p � d3r � ∂L ∂ ˙Ap (r, t) dAp (r, t) ����� t2 + L (Ap)|t2 dt2 − ∂L ∂ ˙Ap (r, t) dAp (r, t) ����� t1 − L (Ap)|t1 dt1 � (34) If a system has a continuous symmetry, then the corresponding action remains invariant with respect to infinitesimal displacements, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=', dS = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' In addition, since dS = 0 must 12 hold for any pair of times t1 and t2 we find that the term within brackets must be a constant of motion.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' These relations correspond to Noether’s theorem applied to our formulation of the electromagnetic field in time-varying media in the Coulomb Gauge.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Given a continuous symmetry, specified by the variation dAp (r, t) and the boundary condition on the Lagrangian L (Ap) dt, one can identify an associated conserved quantity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Temporal and spatial translations First, let us assume that the variation is produced by an infinitesimal temporal displace- ment dt, such that dt2 = dt1 = dt (see Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' 2(b)).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' If the system is invariant with respect to temporal translations we can write A′ p (r, t) = Ap (r, t − dt) ≃ Ap (r, t) − ˙Ap (r, t) dt.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Then, we have dAp (r, t) = − ˙Ap (r, t) dt.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Substituting this result in (34) and factoring out dt we find that the conserved quantity is � p � d3r � − ∂L ∂ ˙Ap (r, t) ˙Ap (r, t) + L (Ap) � = = − � p � d3r 1 2 � p � ε (t) ˙A2 p (r, t) + 1 µ (t) (∇ × A (r, t))2 p � = −H (35) Therefore, it is found that invariance with respect to temporal translations implies that the Hamiltonian must be a conserved quantity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' In time-varying media, the system is not invariant under temporal translations, and, consequently, the Hamiltonian manifestly de- pends on time.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' As shown in the previous section, taking its time derivative explicitly shows that it is not a constant of motion.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Second, we assume that the variation is produced by an infinitesimal spatial displacement η (see Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' 2(b)).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Then, if the system is invariant under spatial translations we must have A′ p (r, t) = Ap (r − η, t) ≃ Ap (r, t)−η·∇Ap (r, t), so that dAp (r, t) = −η·∇Ap (r, t) .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Again, substituting this result into (34) and factoring out η we find that the conserved quantity must be P = � p � d3r ∂L ∂ ˙Ap (r, t) ∇Ap (r, t) = − � p � d3r ε (t) ˙Ap (r, t) ∇Ap (r, t) (36) which equals the Minkowski momentum in (25).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Therefore, we finally found that the fact that time-varying media are invariant under spatial translations directly enforces that the Minkowski momentum is a conserved quantity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' 13 V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' CONCLUDING REMARKS Symmetries play a fundamental role in physics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' They reduce the complexity of difficult problems, as well as the computational cost needed to solve them.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Symmetries also en- able the identification of conserved quantities and the formal link between both symmetries and conserved quantities is Noether’s theorem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' One of the reasons why time-varying media and/or temporal metamaterials provide a fresh view on electromagnetic theory is because they break temporal symmetries, which are conserved in most traditional photonics systems, while they maintain spatial symmetries.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' However, the connection between spatial and tem- poral symmetries and the properties of time-varying media is not always explicitely stated, or analyzed through the point of view of Lagrangian mechanics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' The present tutorial aims at filling this gap.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Furthermore, we hope that this tutorial may clarify the subtleties of the conservation of the electromagnetic momentum in time-varying media, the nuances of defin- ing the momentum of the electromagnetic fields within the Abraham-Minkowski debate, and that it will foster further research on the role and significance of symmetries in temporal metamaterials.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [1] C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Caloz and Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='-L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Deck-Leger, Spacetime metamaterials—part II: theory and applications, IEEE Transactions on Antennas and Propagation 68, 1583 (2019).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [2] E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Galiffi, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Tirole, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Yin, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Li, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Vezzoli, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Huidobro, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Silveirinha, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Sapienza, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Al`u, and J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Pendry, Photonics of time-varying media, Advanced Photonics 4, 014002 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [3] S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Yin, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Galiffi, and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Al`u, Floquet metamaterials, eLight 2, 1 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [4] N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Engheta, Metamaterials with high degrees of freedom: space, time, and more, Nanopho- tonics 10, 639 (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [5] D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Sounas and A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Alu, Non-reciprocal photonics based on time modulation, Nature Pho- tonics 11, 774 (2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [6] A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Akbarzadeh, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Chamanara, and C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Caloz, Inverse prism based on temporal discontinuity and spatial dispersion, Optics Letters 43, 3297 (2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [7] V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Pacheco-Pe˜na and N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Engheta, Temporal aiming, Light: Science & Applications 9, 1 (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' 14 [8] A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Shlivinski and Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Hadad, Beyond the bode-fano bound: Wideband impedance matching for short pulses using temporal switching of transmission-line parameters, Physical Review Letters 121, 204301 (2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [9] M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Mirmoosa, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Ptitcyn, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Asadchy, and S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Tretyakov, Time-varying reactive elements for extreme accumulation of electromagnetic energy, Physical Review Applied 11, 014024 (2019).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [10] I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Liberal, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' V´azquez-Lozano, and V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Pachecho-Pe˜na, Quantum antireflection temporal coatings: quantum state frequency shifting and inhibited thermal noise amplification, arXiv preprint arXiv:2208.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='10089 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [11] J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Pendry, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Galiffi, and P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Huidobro, Gain in time-dependent media—a new mechanism, Journal of the Optical Society of America B 38, 3360 (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [12] J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' V´azquez-Lozano and I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Liberal, Shaping the quantum vacuum with anisotropic temporal boundaries, Nanophotonics (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [13] M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Lyubarov, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Lumer, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Dikopoltsev, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Lustig, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Sharabi, and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Segev, Amplified emission and lasing in photonic time crystals, Science 377, 425 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [14] A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Dikopoltsev, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Sharabi, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Lyubarov, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Lumer, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Tsesses, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Lustig, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Kaminer, and M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Segev, Light emission by free electrons in photonic time-crystals, Proceedings of the Na- tional Academy of Sciences 119, e2119705119 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [15] J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' V´azquez-Lozano and I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Liberal, Incandescent temporal metamaterials, arXiv preprint arXiv:2210.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content='05565 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [16] F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Morgenthaler, Velocity modulation of electromagnetic waves, IRE Transactions on Mi- crowave Theory and Techniques 6, 167 (1958).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [17] D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Sol´ıs, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Kastner, and N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Engheta, Time-varying materials in the presence of disper- sion: plane-wave propagation in a lorentzian medium with temporal discontinuity, Photonics Research 9, 1842 (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [18] T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Koutserimpas and R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Fleury, Electromagnetic fields in a time-varying medium: excep- tional points and operator symmetries, IEEE Transactions on Antennas and Propagation 68, 6717 (2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [19] M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Banados and I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Reyes, A short review on noether’s theorems, gauge symmetries and bound- ary terms, International Journal of Modern Physics D 25, 1630021 (2016).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' 15 [20] Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Kosmann-Schwarzbach, The noether theorems, in The Noether Theorems (Springer, 2011) pp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' 55–64.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [21] C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Cohen-Tannoudji, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Dupont-Roc, and G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Grynberg, Photons and atoms-Introduction to quantum electrodynamics (1997).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [22] J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Sakurai and J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Napolitano, Modern quantum mechanics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' 2-nd edition (Person, 2014) p.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' 39.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [23] I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Brevik, Experiments in phenomenological electrodynamics and the electromagnetic energy- momentum tensor, Physics Reports 52, 133 (1979).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [24] R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Pfeifer, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Nieminen, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Heckenberg, and H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Rubinsztein-Dunlop, Colloquium: Momentum of an electromagnetic wave in dielectric media, Reviews of Modern Physics 79, 1197 (2007).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [25] B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Kemp, Resolution of the abraham-minkowski debate: Implications for the electromagnetic wave theory of light in matter, Journal of Applied Physics 109, 7 (2011).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [26] P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Milonni and R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Boyd, Recoil and photon momentum in a dielectric, Laser Physics 15, 1432 (2005).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [27] M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Mansuripur, Radiation pressure and the linear momentum of the electromagnetic field, Optics Express 12, 5375 (2004).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [28] U.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Leonhardt, Momentum in an uncertain light, Nature 444, 823 (2006).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [29] G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Campbell, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Leanhardt, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Mun, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Boyd, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Streed, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Ketterle, and D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Pritchard, Photon recoil momentum in dispersive media, Physical Review Letters 94, 170403 (2005).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [30] S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Barnett, Resolution of the abraham-minkowski dilemma, Physical Review Letters 104, 070401 (2010).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [31] S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Barnett and R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Loudon, The enigma of optical momentum in a medium, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368, 927 (2010).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [32] M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Silveirinha, Reexamination of the abraham-minkowski dilemma, Physical Review A 96, 033831 (2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [33] M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Lobet, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Liberal, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Vertchenko, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Lavrinenko, N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Engheta, and E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Mazur, Momentum considerations inside near-zero index materials, Light: Science & Applications 11, 1 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [34] I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Liberal and N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Engheta, Near-zero refractive index photonics, Nature Photonics 11, 149 (2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' 16 [35] N.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Kinsey, Developing momentum in vanishing index photonics, Light Science & Applications 11, 1 (2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' [36] J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Gratus, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Seviour, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Kinsler, and D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' Jaroszynski, Temporal boundaries in electromag- netic materials, New Journal of Physics 23, 083032 (2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} +page_content=' 17' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/CNE1T4oBgHgl3EQfpgW7/content/2301.03333v1.pdf'} diff --git a/EtAyT4oBgHgl3EQf4_op/content/tmp_files/2301.00795v1.pdf.txt b/EtAyT4oBgHgl3EQf4_op/content/tmp_files/2301.00795v1.pdf.txt new file mode 100644 index 0000000000000000000000000000000000000000..567d024b71319f9573c66e22d37dcdd860f172df --- /dev/null +++ b/EtAyT4oBgHgl3EQf4_op/content/tmp_files/2301.00795v1.pdf.txt @@ -0,0 +1,2193 @@ +Draft version January 3, 2023 +Typeset using LATEX default style in AASTeX631 +Data-Constrained Solar Modeling with GX Simulator +Gelu M. Nita +,1 Gregory D. Fleishman +,1 Alexey A. Kuznetsov +,2 Sergey A. Anfinogentov +,2 +Alexey G. Stupishin +,3 Eduard P. Kontar +,4 Samuel J. Schonfeld +,5 James A. Klimchuk +,6 and +Dale E. Gary +1 +1New Jersey Institute of Technology, Newark 07102-1982, NJ, USA +2Institute of Solar-Terrestrial Physics, Irkutsk 664033, Russia +3Saint Petersburg State University, 7/9 Universitetskaya nab., St. Petersburg, 199034 Russia +4University of Glasgow, Glasgow, G12 8QQ, UK +5Institute for Scientific Research, Boston College, Newton, MA 02459, USA +6NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA +ABSTRACT +To facilitate the study of solar active regions and flaring loops, we have created a modeling framework, +the freely distributed GX Simulator IDL package, that combines 3D magnetic and plasma structures +with thermal and non-thermal models of the chromosphere, transition region, and corona. The package +has integrated tools to visualize the model data cubes, compute multi-wavelength emission maps from +them, and quantitatively compare the resulting maps with observations. Its object-based modular +architecture, which runs on Windows, Mac, and Unix/Linux platforms, offers capabilities that include +the ability to either import 3D density and temperature distribution models, or to assign numerically +defined coronal or chromospheric temperatures and densities, or their distributions to each individual +voxel. GX Simulator can apply parametric heating models involving average properties of the magnetic +field lines crossing a given voxel, as well as compute and investigate the spatial and spectral properties +of radio, (sub-)millimeter, EUV, and X-ray emissions calculated from the model. The application in- +tegrates FORTRAN and C++ libraries for fast calculation of radio emission (free-free, gyroresonance, +and gyrosynchrotron emission) along with soft and hard X-ray and EUV codes developed in IDL. +To facilitate the creation of models, we have developed a fully automatic model production pipeline +that, based on minimal users input, downloads the required SDO/HMI vector magnetic field data and +(optionally) the contextual SDO/AIA images, performs potential or nonlinear force free field extrapo- +lations, populates the magnetic field skeleton with parameterized heated plasma coronal models that +assume either steady-state or impulsive plasma heating, and generates non-LTE density and tempera- +ture distribution models of the chromosphere that are constrained by photospheric measurements. The +standardized models produced by this pipeline may be further customized through a set of interactive +tools provided by the graphical user interface. Here we describe the GX Simulator framework and its +applications. +Keywords: active regions—solar flares—microwave—imaging spectroscopy—nonthermal electrons— +numerical modeling—X-ray—corona +1. INTRODUCTION +The fundamental problems of modern solar physics require analysis of multiple vast data sets obtained with a +multitude of ground- and space-based instruments. The sheer level of complexity in newly available datasets calls for +adequate theoretical modeling in order to derive the target physical parameters of a given measurement. Examples +include extrapolating the photospheric magnetic field data from optical observations, or deducing the distribution of +Corresponding author: Gelu M. Nita +gelu.m.nita@njit.edu +arXiv:2301.00795v1 [astro-ph.SR] 2 Jan 2023 + +ID2 +thermal coronal plasma from extreme ultraviolet (EUV) observations. Larger caliber theoretical and modeling efforts +are needed to meaningfully combine and cross-validate multiple data sets. These data come from space missions, +e.g Helioseismic and Magnetic Imager (HMI; Scherrer et al. 2012) onboard the Solar Dynamic Observatory (SDO; +Pesnell et al. 2011), and ground-based high-resolution optical and infrared (IR) instruments such as Goode Solar +Telescope (GST; Cao et al. 2010; Goode and Cao 2012) and Daniel K. Inouye Solar Telescope (DKIST; Rimmele et al. +2010; Tritschler et al. 2016). Fundamental enhancements of theory and modeling are demanded to fully exploit new +observational windows such as microwave and millimeter-wave imaging spectropolarimetry data from the Expanded +Owens Valley Solar Array (EOVSA; Nita et al. 2016; Gary et al. 2018), the Siberian Radio Heliograph (SRH; Lesovoi +et al. 2017; Altyntsev et al. 2020) and the Atacama Large Millimeter/submillimeter Array (ALMA), in addition to +more traditional X-ray and EUV data, e.g the Reuven Ramaty High-Energy Solar Spectroscopic Imager (RHESSI; Lin +et al. 2003), the Atmospheric Imaging Assembly(AIA; Lemen et al. 2012) onboard SDO, or the Spectrometer/Telescope +for Imaging X-rays (STIX; Krucker et al. 2020). Dynamic solar phenomena that constitute solar activity either occur +in or are sensitive to the physical conditions in the solar corona. The dominant form of energy in the solar corona +is magnetic energy; thus, the knowledge of the coronal magnetic field is central for understanding coronal physics. +However, there is no observational technique that provides the 3D magnetic vector field over a significant coronal +volume. This is why data-constrained modeling of the coronal magnetic field is extremely important. +The GX Simulator modeling framework that we present here is based on a magnetic model (magnetic skeleton), +which, once created, can be populated by thermal plasma and nonthermal particles, and then various emissions can +be computed from the volume and compared with observations. When all synthesized observables match all available +data, the model is proved to be valid. +The challenges that our modeling framework solves are: (i) automated creation of the magnetic model; (ii) addition of +an objectively defined thermal structure of the corona and chromosphere; (iii) rigorous calculation of radio, EUV, and +X-ray continuum emission from the model; and (iv) provision for model-to-data comparison. To facilitate the creation +and manipulation of the models, the tool offers numerous options. Earlier versions of the tool were described by Nita +et al. (2015) for the flare science and Nita et al. (2018) for the active region (AR) science. This paper summarizes the +functionality of those initial versions and describes numerous updates and enhancements of the tool. +2. THE GX SIMULATOR AUTOMATIC MODEL PRODUCTION PIPELINE +2.1. General Description of the Pipeline Functionality +To facilitate the use of the GX Simulator modeling package (Nita et al. 2015, 2018), we have developed a fully +automatic model production pipeline (AMPP) that, based on minimal user’s input, downloads the required vector +magnetic field data produced by the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory +(SDO, Scherrer et al. 2012) and (optionally) the contextual Atmospheric Imaging Assembly maps (AIA, Lemen et al. +2012), performs potential and/or nonlinear force free field (NLFFF) extrapolations, populates the magnetic field +skeleton with parameterized heated plasma coronal models that assume either steady-state or impulsive plasma heating, +and generates non-LTE density and temperature distribution models of the chromosphere that are constrained by +photospheric measurements. The standardized models produced by this pipeline may be further customized through +a set of interactive tools provided by the GX Simulator graphical user interface (GUI). +The AMPP sub-module is exposed to the users through a single top-level IDL routine, namely gx fov2box.pro, +which provides a series of options that may be used to customize its functionality, as detailed in Appendix A, where +we provide an AMPP script to generate a magneto-thermal model for an instance of AR11520 observed on 12-Jul-2012 +04:58:26 UT, which we use as an illustrative example in the subsequent sections. +The GX Simulator package also provides a standalone GUI application, gx ampp.pro, which may be used +to conveniently generate and run AMPP scripts, as illustrated in Figure 1, which displays a set of default set- +tings and corresponding run-time messages that match the demo script create box 20160220.pro included in the +/gx simulator/demo/ sub-folder of the GX Simulator distribution. +Any interactive change of the input fields of the gx ampp GUI updates the functional gx fov2box script, which +may be launched from the interface, or copied and run directly from the IDL command line. The gx ampp GUI also +provides the option of uploading an already existing GX Simulator compatible box structure (such as a potential or +NLFFF extrapolation box), which may be used as a starting point for adding properties to the model, such as optional +magnetic field tracing parameters and/or chromosphere models, as described in the subsequent sections. + +3 +Figure 1. +Snapshot of the gx ampp GUI application displaying a set of default settings and corresponding run-time execution +messages that match the demo script create box 20160220.pro included in the /gx simulator/demo/ sub-folder of the +GX Simulator distribution. +Figure 2 illustrates the main building blocks and the workflow of the GX Simulator AMPP module and Figure +3 displays a series of snapshots of the 3D magnetic model produced by the AMPP script provided in Appendix A. +The initialization of an AMPP run, illustrated by the first two blocks of the workflow diagram shown in Figure 2, +requires only the time, field of view (FOV), height, and desired spatial resolution of the model. The time and location +input parameters are used by the AMPP to identify and download the available SDO HMI/AIA maps closest to the +requested time, after checking the specified local repository in case they were already downloaded during a previous +AMPP run. +These input SDO HMI/AIA data products are used to prepare a data structure and boundary conditions needed +to perform the subsequent AMPP tasks (blocks 3 and 4 of Figure 2). To do so, the AMPP creates an initial empty +volume structure on top of the photospheric vector magnetogram boundary conditions that are prepared by performing +Carrington-Heliographic or Helioprojective-Cartesian projection (Thompson 2006), as illustrated in panels (a) and (b) + + GX Automatic Production Pipeline Interface +口 +X +SDo Data Repository +c:\jsoc_cache +GX Model Repository +c: Igx_mode1s +External Box path +Jump-to Action +O none +Opotential +On1ff +Olines +O chromo +Model Time +2016-02-20 17:00:00.000 +Model Coordinates +Xc : +-15" +Yc: +185" +O Heliocentric +Ocarrington +X: +64 +64 +64 +Resolution 1400km +Model Gridpoints +Y: +Z: +Geometrical Projection +O CEA +O TOP +pi-disambiguation +O HMI +O SFQ +Buffer Zone size +10% + (default, 1o% of the box dimensions recommended) +Download AIA/uv contextual maps +stop after the potential box is generated + Download AIA/EUV contextual maps +skip NLFFF extrapolation +save Empty Box +stop after the NLFFF box is generated +save Potential Box +center voxel magnetic field 1ine tracing + save Bounds Box +Do not add Fontenla chromosphere model +gx_fov2box,'20-Feb-16 17:00:00' +center_arcsec=[-15,185], size_pix=[64,64,64],dx_km=1400,/cea, +/uv， /euV， tmp_dir= 'c:\jsoc_cache' +， out_dir= 'c:\gx_models +X日O装 +% Downloading data. +% Data aiready found' in the local repository or downloaded in 136.038 seconds +% creating the box structure +% Box structure created in 22.162 seconds +% Performing initial potential extrapolation +% Potential extrapolation performed in 0.246 seconds +% Performing NLFFF extrapolation +% NLFFF extrapolation performedin 18.093_ seconds +% NLFFF box structure saved to C:/gx_models\2016-02- +20\hmi.M_720s.20160220_165811.E34N4cR.CEA.NAS.SaV +% Computing field 1ines for each voxel in the model. +% B0x structure saved to_ c:\gx_mode1s\2016-02-20\hmi.M_720s.20160220_165811.E34N4CR.CEA.NAS.GEN.sav +% Generating chromo model. +% chromo model generated in 1.038 seconds +% Box structure saved to C:\gX_models\2016-02-20\hmi.M_720s.20160220_165811.E34N4cR.CEA.NAs.CHR.saV +% This AMPp script has been executed in 185.713 seconds and generated the following files: +% +% C:\gX_mode1s\2016-02-20\hmi.M_720s.20160220_165811.E34N4CR.CEA.NAS.saV +C:\gx_mode1s\2016-02-20\hmi.M_720s.20160220_165811.E34N4CR.CEA.NAS.GEN.SaV +% C:\gx_mode1s\2016-02-20\hmi.M_720s.20160220_165811.E34N4CR.CEA.NAS.CHR.saV +% You may use "Import Model Data" file menu option to import any of these models in GX_simulator4 +Figure 2. +GX Simulator Automatic Model Production Pipeline workflow. +of Figure 3. Unlike the standard, general-purpose Spaceweather HMI Active Region Patch (SHARP, Bobra et al. +2014) data products routinely used as boundary conditions by other magnetic field reconstruction packages, the +AMPP boundary condition maps are exactly centered on the user-requested FOV, which minimizes to the maximum +extent possible the unavoidable projection effects. +In the next stage (blocks 5 and 6 of Figure 2), the AMPP applies the method described in §2.2.2 to produce an +initial potential field extrapolation 3D structure, which is used in the next stage as an initial condition for generating a +NLFFF model using the optimization code described in §2.2.3. If not explicitly disabled by the user, the next AMPP +block computes the averaged magnetic field ⟨B⟩ and length L of the potential or NLFFF magnetic field lines crossing +each volume voxel. This enables GX Simulator to interactively dress the magnetic skeleton with a parameterized +thermal structure, as detailed in §2.5. Panels (c) and (d) in Figure 3 illustrate a series of magnetic field lines and +their associated magneto-thermal structure corresponding to the NLFFF magneto-thermal model generated by the +AMPP script presented in Appendix A. Finally, if not explicitly disabled by the user, the last block of the AMPP +workflow diagram replaces the bottom layers of the potential or NLFFF model with a non-LTE chromosphere model, +as described in §2.4. +As illustrated in Figure 1, there is a set of optional keyword switches to skip some optional execution blocks and/or to +save, in addition to the final model, any intermediary models generated by the workflow. Thus, to help distinguish these +AMPP products without the need to inspect the output files, we have adopted a file naming convention that combines a +series of distinctive tags that uniquely identify each type of model, as listed in Table 1. For example, an AMPP output +file tagged as “.NAS.GEN.CHR.” would indicate a NLFFF model augmented by adding ⟨B⟩-L properties and a +non-LTE chromosphere, while “.POT.GEN.CHR.” would denote that the same additional properties were added +to a potential magnetic field model, if the user chooses to skip the NLFFF optimization block. Any IDL structure +produced by the AMPP contains a string tag named “EXECUTE,” which provides an exact copy of the execution +script used to generate it. +2.2. AMPP NLFFF Magnetic Field Models +2.2.1. Preparation of the boundary and initial conditions for the NLFFF extrapolation + +Selection of time, position and spatial resolution of the +Initial potential field extrapolation +model +NLFFF optimization +Automatic download of SDO HMI/AIA data +closest to the time requested +Computation of the length and averaged magnetic +field for all voxels crossed by closed magnetic field +lines, to be used by GX Simulator to assign +parametrized differential emission measure and +WCS coordinate transformation of HMI data to create +density and temperature distribution models of the +the base of the subsequent extrapolations +corona +Creation of an empty-box structure containing a WCS. +Adding non-LTE density and temperature distribution +compatible index, LOS Bz, Ic, and the requested AIA +UV/EUV reference maps +models of the chromosphere5 +(a) +(b) +(c) +(d) +Figure 3. +Snapshots of the AR11520 model generated by the script presented in Appendix A illustrating different stages of +the AMPP process. (a) The photospheric LOS magnetic field map, located at the bottom of a rectangular box co-aligned with +the observer’s LOS (blue lines). The inscribed rectangular box (red lines), which is aligned with the direction normal to the +solar surface, defines the 3D volume in which the magnetic field extrapolation is performed. (b) The elements in (a) plus Bz, +obtained by projecting the photospheric vector magnetic field HMI map onto the bottom boundary. Two projection options are +provided by AMPP: the default cylindrical area projection (CEA), or a simple parallel projection (TOP), selected by using the +”Geometrical Projection” radio button shown in Figure 1. (c) A sub-set of model field lines that do, or do not, close within +the 3D model boundaries (green and yellow lines, respectively), illustrating the magnetic connectivity in the model. (d) The +coronal temperature distribution along the closed field lines, which corresponds to the parameterized magneto-thermal model +defined by the AMPP script (refer to §2.5 for a detailed description). A user-specified hydrostatic model is used for the volume +outside these closed field lines. +The first stage of the AMPP is the production of initial and boundary conditions for the subsequent NLFFF ex- +trapolation. The user provides AR coordinates, observation time, and the size and spatial resolution for the resulting +3D data cube. Then the pipeline will download required data and produce a data cube with the photospheric mea- +surements of the magnetic field vector in its bottom layer. The rest of the volume will be filled with the extrapolated + +6 +Table 1. GX Simulator filename extension naming convention +Tag +Model Type +.NONE. +An empty-box IDL structure that contains all geometrical information and context SDO/AIA maps +requested, as well as properly sized zeroed arrays ready to store the Cartesian components of the +magnetic field model not yet generated, as described in §2.2.1 +.POT. +An IDL structure containing a true potential solution based on only the Bz base map component, as +described in §2.2.2. +.BND. +An IDL structure containing potential solution except for the bottom layer, which is replaced by the +observed By and Bz components – to be used as initial conditions for the following NLFFF optimization +step. +.NAS. +An IDL box structure filled with a non linear force free field magnetic field model (Stupishin 2020), as +described in §2.2.3 +.GEN. +An IDL box structure containing the length L and averaged magnetic field ⟨B⟩ along the field lines +crossing a given volume voxel. These additional parameters are ready to be used for the purpose of +adding a parameterized heated plasma coronal models that assume either steady-state or impulsive +plasma heating, as described in §2.5 +.CHR. +An IDL box structure having the bottom layers of uniform-height replaced by a non-uniform height, +non-LTE density and temperature distribution model of the chromosphere that is constrained by +photospheric measurements (Fontenla et al. 2009), as described in §2.4. +potential field. These operations are fully automated and do not require any additional actions from the user. The +flowchart of production of the initial and boundary condition is shown in Figure 4. The individual steps of this AMPP +stage are described below. +Firstly, the pipeline script automatically downloads, from the JSOC data processing center, SDO/HMI vector mag- +netograms (data series:hmi.B 720s) taken at the time closest to the time requested by a user. For further processing, +the limited field of view (FOV) maps are cut out from the full Sun magnetograms. The precomputed π-disambiguation +provided by the JSOC data processing center is applied using the HMI DISAMBIG routine from the Solar Soft library. +In the case of disambiguation artifacts, there is an option to perform π-disambiguation with the Super Fast and Qual- +ity azimuth disambiguation library (SFQ, Rudenko and Anfinogentov 2014), also known as the new disambiguation +method (NDA), which is supplied as a part of the AMPP. Although both methods,which may be interchanged by using +the HMI/SFQ switch, work comparably well (Fleishman et al. 2017), in some cases, especially for near limb observa- +tions, the SFQ library may provide better results than the standard HMI disambiguation (Rudenko and Anfinogentov +2014). +After the disambiguation, the vector magnetic field map is deprojected from the LOS coordinate system to the +spherical components Bφ, Bθ, and Br using the +HMI B2PTR procedure from the SDO/HMI package in solar soft. +These components will become Bx, −By, and Bz components in the cartesian coordinate system of the computational +box. +At the next step, the deprojected magnetic field components are remapped to the local coordinate system of a +computational box. +Since the current version of AMPP uses cartesian coordinates, the magnetic field maps are +projected from the spherical surface of the Sun to the flat bottom of the box. The AMPP supports two projections: +top view which is a simple parallel projection and cylindrical equal area (CEA) projection. The latter is the default +option and preferable for extrapolation purposes since it preserves the area of magnetic elements and, hence, the +magnetic flux is not changed by the projection effects. While performing coordinate transformation AMPP relies on +a WCS general purpose library that is supplied by the Solar SoftWare (SSW) repository. To improve the quality of +remapping, we use cubic interpolation when the requested resolution of the computational box is higher or comparable +with the pixel size of the available magnetograms. In the opposite case, when the spatial resolution of the computational +box is lower than the resolution of a magnetogram, we use an over-sampling antialising technique by dividing every +computational pixel into 8 subpixels. The resulting magnetic field components are then converted to the computational +resolution by direct summation of the values interpolated to subpixels. + +7 +After remapping, the map of a deprojected magnetic field is placed in the computational box as a bottom layer, the +rest of the computational box consisting of zeroed arrays, ready to store the Cartesian components of the magnetic field +model not yet generated. This geometrical structure, which may be optionally saved to disk as a file with “.NONE.” +tag, is forwarded to the next stage of the AMPP. +Figure 4. Production of initial and boundary conditions flowchart. Gray boxes represent individual steps of the pipeline, while +intermediate data products are shown as arrows with labels. +2.2.2. Potential Field Initialization of the AMPP Model +During this stage of the AMPP process, the empty box volume is filled with a potential field solution obtained from +the normal component of the magnetic field at the lower boundary using the Fast Fourier Transform (FFT) method +described in Alissandrakis (1981). The FFT solution for the potential field problem implies periodic, flux-balanced +boundary conditions at lateral boundaries, which is not realistic. To simulate a more appropriate “open” boundaries, +we expand the computational domain (Lx,y) by Lx,y/2 in each direction. Then, the normal component of the field +at the lower boundary is padded with a constant, generally non-zero value. This value is computed such as the total +signed magnetic flux from the added areas perfectly compensates the unbalanced flux at the original lower boundary. +The final potential field solution is then obtained by cutting out from the expanded domain and is then used as initial +condition for the NLFFF extrapolation. +2.2.3. NLFFF Reconstruction and Magnetic Field Line Tracing Dynamic Link Library +The NLFFF reconstruction code employed by AMPP was developed in C++ using multithreaded functionality. Its +source, the compilation scripts designed for Windows and Linux platforms, a set of compiled libraries for both platforms, +and their calling IDL wrappers are included in the GX Simulator SSW distribution package, and are automatically + + Observation time +Downloading SDO/HMI magnetograms +Vector magnetogram +Small FOV maps cutout + position and FOV +and T-disambiguation +Disambiguated magnetogram +Deprojecting vector magnetograms +Deprojected magnetic field map +Remapping magnetogram to the bottom + Spatial resolution +boundary of the computational box +Empty box with the bottom BC +Extrapolation of the magnetic field using + Box with potential field +approximation of the potential field.8 +updated from their independently maintained GitHub development repository1. +In addition, the package may be +directly downloaded from a Zenodo© digital repository (Stupishin 2020)2. Since the general platform compatibility +of the pre-compiled Linux library is not guaranteed, the AMPP automatically invokes the distributed source code to +compile and save a local copy3 of the shared library on its first call on a Linux platform, which is used in all subsequent +calls. +This NLFFF reconstruction code follows the development proposed by Wheatland et al. (2000) and Wiegelmann +(2004). +The basic idea is to reduce the Lorentz force in the coronal volume (i.e., to eliminate transverse electric +currents) and reduce the field divergence as much as possible by minimization of the functional +L = +� +V +� +B−2 [[∇ × B] × B]2 + |∇ · B|2� +w(x, y, z)dV, +(1) +where the first term, (B−2 [[∇ × B] × B]2), represents the Lorentz force, the second one, (|∇ · B|2), evaluates the +field divergence, while w(x, y, z) is a “weight” function. The weight function is intended to diminish the influence of +uncertainties of the field at the side and top boundaries, and can be adjusted by the user. By default, the weights are +constant (=1) in the entire volume except for 10 % of the length of each dimension on each side, where the weights +decrease to zero on the boundaries following a cosine function (the bottom boundary is not weighted, because it is set +to the observed photospheric field). +The initial state of the magnetic field may be inferred from several reasonable approaches. By default, the algorithm +uses the preliminary potential field reconstruction described in §2.2.2. Alternatively, the NLFFF reconstruction may +be started from an AMPP-compatible geometrical box pre-filled with an initial magnetic field configuration obtained +by any means, from which the boundary conditions are also inferred with or without buffer zones, as indicated by the +user. +If one considers the magnetic field as a function of the conditional evolution parameter t, B(x, y, z, t), the evolution +of the functional may be estimated by computing ∂B/∂t at ith step (Li) and modifying B for the next (i + 1)th step +as Bi+1 = Bi + (∂B/∂t)∆t (where ∆t is a small evolution step), to get the next functional value Li+1. The step size +is varied such as to increase the iteration speed, being chosen automatically depending on the speed of convergence: +it is increased by 10 % at a successful step and decreased by 10 % at an unsuccessful one. +Due to the numerical errors affecting the computation of the functional, the value of the functional may slightly +increase at the next iteration even for a small step ∆t, which is allowed by the algorithm up to a 10−4 relative factor. +If the functional increases above this limit, the step is reduced by 10 %. The iterations stop when the step becomes +too small, i.e. less than 1 % of the initial value. In addition, the iterations are terminated if (i) the relative variation +of the function for the last 10 iterations is small (less than 5 · 10−4), or (ii) if the maximum value of |Li/Li+1 − 1| does +not exceed 10−4 during the previous 100 iterations. In such cases, no further significant decrease of the functional is +expected, and it is assumed that the current solution is reasonably close to the optimal one. +Another approach used to decrease the computation time is the technique of “multigrids” (Metcalf et al. 2008). +Instead of performing the computations using directly the desired volumetric grid resolution (e.g. 257x257x129), the +initial potential field is firstly computed over a smaller resolution grid, let us say, 65x65x33, a first stage NLFFF +functional minimization is performed, and then the procedure is repeated twice, increasing the grid resolution at each +step (to 129x129x65 and, finally, to 257x257x129), while interpolating the solution obtained at each step to the next +grid resolution and using it as the initial condition for the next step. +The same code may also be used to compute the magnetic field lines passing through each voxel of the volume, or +through a set of predefined “seed-voxels” (box coordinates). The lines are computed using the Runge-Kutta-Feldberg +algorithm of 4th(−5th) orders (the code was ported from original FORTRAN implementation, see Forsythe et al. +(1977), and adapted to C++ using multi-thread functionality). When computation of a set of seeded lines is requested +by the user, the code returns each line as a collection of fractional box indices indicating at least one intersection point +for each volume element that is intersected. Such lines may be used for the purpose of visualizing the magnetic field +connectivity, as well as for constructing flux-tubes that may be used in flare studies, as described in §4. +1 Magnetic-Field Library +2 Magnetic Field Library: NLFFF and magnetic lines +3 The user may invoke the IDL command line “print, gx libpath(’nlfff’)” to retrieve the location of the shared library , or “print, +gx libpath(’nlffff’,/update)”, to also request a new compilation of the library, provided that a g++ compiler is installed on the system. + +9 +2.3. AMPP Default Coronal Models +The computation of the magnetic field lines passing through each voxel of the volume is performed for dressing the +magnetic field structure with a thermal plasma model (Nita et al. 2018, see §2.5 for more details). In this case, the +code returns the following parameters associated with each voxel of the volume: +• length of the field line intersecting the voxel, +• average magnetic field along the line, +• connectivity with the two boundary voxels associated with the line +• a flag indicating whether the voxel is intersected by a closed field line (both footpoints at the chromospheric +layer are located inside the box) or by an open line (only one footpoint is located inside the box). +For a quantitative assessment of the computational speed, the reader may refer to the console messages generated +when running the AMPP script presented in Appendix A,which was used to generate a 240 × 168 × 200 magnetic +field cube for an instance of AR11520 on a Windows 10 system equipped with an Intel Xeon E-2286M CPU 2.4 +GHz, 8 cores, 64 GB RAM. In this particular case, the NLFFF reconstruction was performed in ∼ 250 seconds and +the computation of the lines intersecting all volume voxels was performed in ∼ 105 seconds. For a given size of the +computational box the computational time of an NLFFF reconstruction may vary as much as one order of magnitude +depending on the complexity of the magnetic field configuration (e.g. isolated sunspot versus a complex AR), while +the speed of the full-volume line computation scales roughy linearly with the number of volume elements. +More +detailed benchmark tests performed for the purpose of assessing the code accuracy when compared with a ground +truth magnetic field model may be found in Fleishman et al. (2017), where the code is referred to as the AS NLFFF +reconstruction code. +2.4. AMPP Default Chromosphere Models +The general approach employed by the AMPP to populate the chromospheric volume, described in detail in Nita +et al. (2018), uses observationally established thresholds to distinguish 7 quiet-Sun (QS) and AR features based on +photospheric data, and selects one of a set of 7 corresponding 1D solar atmospheric models proposed by Fontenla +et al. (2009) to fill the chromospheric volume above a particular chromospheric pixel. The 7 feature types comprise 3 +QS components, namely internetwork (IN), network lane (NW), and enhanced network (ENW), and 4 AR features: +sunspot umbra (UBR), penumbra (PEN), plage (PL), and facula (FA). +The AMPP applies the above selection thresholds automatically, and thus produces the corresponding chromo- +spheric model based on a given HMI limb-darkening-removed white-light map and LOS magnetogram pair, which are +downloaded from the SDO/HMI data repository. Thus, using the model mask computed by these means, the pipeline +generates a chromospheric volume consisting of a collection of variable height (number of grid-steps) vertical columns, +each corresponding to one of the seven chromospheric models associated with the photospheric mask pixel onto which +such a column is projected. The chromospheric volume thus generated is then used to replace the bottom layers of +the uniformly spaced magnetic skeleton with a composite slab having the minimum thickness needed to contain the +variable height chromosphere, and any height-dependent properties of the original volume are interpolated and trans- +ferred to the non-uniform chromospheric voxels. The GX Simulator model structures that include such chromosphere +models are by default stored on the disk with a filename that includes the “.CHR.” tag (although GX Simulator does +not rely on this naming convention to recognize the type of models produced by the pipeline). +However, one may choose to skip this step of the model production pipeline, and assign instead a chromosphere +represented by a uniform slab of adjustable height, and constant temperature Tchr and density nchr, interactively +chosen through the GX Simulator GUI, this option being available for any of the POT, NAS, NAS.GEN, or +POT.GEN models produced by the pipeline. This simpler option is often appropriate for modeling of flaring loops. +2.5. AMPP User-Adjustable Coronal Models +The default background corona is populated with an analytically defined horizontally uniform hydrostatic equilibrium +model (Nita et al. 2018). Alternatively, a field-aligned hydrodynamic model is available to replace the background + +10 +thermal plasma in voxels on closed loops. This model assumes a heating along the individual magnetic flux tubes +defined by the extrapolated field line structure, The hydrodynamic simulation code called the Enthalpy-Based Thermal +Evolution of Loops (EBTEL) (Klimchuk et al. 2008; Cargill et al. 2012a,b; Barnes et al. 2016; Bradshaw and Viall +2016; Ugarte-Urra et al. 2017), which assumes an impulsive heating (including a nonoflare mechanism), is used in this +case. EBTEL includes the important link between the corona and lower atmosphere in order to realistically model the +plasma response to coronal heating. +As illustrated in Figure 2 (see Sec. 2.3), the AMPP automatically generates models ready to be populated with +EBTEL solutions by computing the average magnetic field ⟨B⟩ and length L of the magnetic field lines crossing each +volume voxel.These parameters are used to compute the time-averaged volumetric heating rate, ⟨Q⟩, obtained from +Q(t) = Q0 +�⟨B⟩ +B0 +�a �L0 +L +�b +f(t), +(2) +and assign it to each voxel crossed by a closed field line. Here the heating profile f(t) incorporates the duration ∆t of +the nanoflares, the time interval between successive events τ, and it may also include a dependence on mass density +ρ. We have adopted the normalization convention ⟨f(t)⟩ ≡ 1, which, for any heating model, ensures that the averaged +heating rate, ⟨Q⟩, stays the same for a fixed choice of the Q0, a, and b parameters. +There are five independent parameters: Q0, a, b, τ, and ∆t. Q0 is a typical heating rate, which can depend on +the driver velocity v and the electric current density along the flux tube, or, equivalently, on the force-free parameter +α, and so it can be different for different flux tubes. The actual numerical value of Q0 (measured in erg cm−3 s−1) +depends on the normalization constants, which are chosen as B0 = 100 G and L0 = 2×109 cm. The power-law indices, +a and b, have certain values for a given heating model; for example a = 2 and b = 1 within the critical shear angle +model (Mandrini et al. 2000). +The time constants, τ and ∆t, are additional free parameters of the model, which are informed by analysis of the +EUV AR lightcurves (Viall and Klimchuk 2012) and EBTEL modeling of these line-of-sight-integrated light curves +(Viall and Klimchuk 2013). EBTEL is capable of accurately simulating the entire range of τ, from effectively “steady,” +to fully “impulsive” (see Nita et al. (2018) for more details). +As detailed in §3.3, for a given set of input free parameters, the most recent version of the hydrodynamic simulation +code, dubbed EBTEL++, outputs a pair of distributions over a relevant temperature range, which are the commonly +used Differential Emission Measure (DEM): +ξ(T) = n2 +e(T)dV +V dT +, +[cm−6 K−1] +(3) +and the Differential Density Metrics (DDM, Fleishman et al. 2021) +ν(T) = ne(T)dV +V dT +, +[cm−3 K−1]. +(4) +When only the DEM distributions are available, as was the case of the output provided by the original EBTEL code, +or if explicitly requested by the user, GX Simulator uses them to assign effective density and temperature pairs to any +model voxel crossed by a closed magnetic field line characterized by a {⟨B⟩, L} pair: +nξ ≡ +� +⟨n2e⟩ = +�� +ξ(T) dT +�1/2 +, +(5) +Tξ = +� +T · ξ(T) dT +nξ +2 +. +However, if the DDM distributions defined by Equation 4 are also available, as is the case of the output provided by +the upgraded EBTEL++ code, GX Simulator computes, by default, the effective density–temperature pairs defined +as +nν = +� +ν(T) dT, +(6) +Tν = +� +T · ν(T) dT +nν +. + +11 +Given the fact that a typical GX Simulator model contains a large number of coronal voxels that need to be +populated at the run-time with EBTEL solutions, the practical approach that has been adopted is to run off-line the +EBTEL code, to pre-compute several thousand combinations of the flux tube lengths, L, and nanoflare magnitudes +(heating-model-specific time averaged volumetric heating rates), ⟨Q⟩, to create lookup tables that contain the coronal +and transition region DEM and DDM distributions for each pair of flux tube length and nanoflare magnitude. Thus, +using the ⟨B⟩ and L properties computed by the pipeline for each coronal or transition region voxel, the adjustable +Equation 2 is used at the run-time to select the corresponding nanoflare magnitudes and assign the DEM and DDM +distributions from pre-computed lookup tables to a given voxel, an approach that has been tested and validated by +Nita et al. (2018). By default, GX Simulator assigns the DEM-DDM pair corresponding the closest {⟨Q⟩, L} neighbor +grid node found in the lookup tables, but the GUI provides a series of alternative irregular grid interpolation methods +from which to choose, including 4-closest-neighbor weighted interpolation. +The DEM distributions are used by the GX Simulator EUV radiation transfer codes to compute synthetic emission +maps corresponding to the SDO/AIA channels. The effective thermal plasma density and temperature pairs inferred +from the DDM or DEM distributions are used by the GX Simulator GUI for volume visualisation purposes, and by +the legacy Fast Codes microwave rendering routines to compute synthetic multi-frequency radio emission. However, +following the recent upgrade of the microwave Fast Codes (Fleishman et al. 2021), GX Simulator offers the option to +select a rendering routine that employs these codes to compute synthesized microwave emission maps directly from +the DEM/DDM distributions, as detailed in §3.4. +3. CUSTOMIZATION OF ACTIVE REGION PIPELINE MODELS +3.1. Command line customization scripts +The current release of the GX Simulator package includes a series of macro commands that allow batch mode +customization of the pipeline models and generation of the multi wavelength synthetic maps, as well as a series of +benchmark tools that may be used to perform quantitative model to data spectral and image comparison for the +purpose of model validation, as described in this section. To illustrate how some of the macro commands included in +the GX Simulator package may be used to customize an AR model generated by AMPP and synthesize microwave +emission maps corresponding to a user’s selected field of view (FOV), we list in Appendix B an IDL script that performs +the following actions: +• imports a magneto-thermal structure prepared by the AMPP script provided in Appendix A. +• defines the desired FOV and spatial resolution for producing the synthesized maps. +• defines a set of volumetric heating rate parameters +• defines a heating rate formula following Equation 2, which takes into account the user defined input parameters +and the AMPP pre-computed ⟨B⟩ and L voxel properties. +• selects a specific EBTEL table +• defines a set of frequencies for which the synthetic microwave maps will be computed +• calls a microwave rendering module that performs the geometrical rendering of the 3D models and solves the +radiation transfer equation along each image LOS to produce the set of requested microwave maps +• saves on disk the output data produced by this script in two alternative forms: an SSW-compatible IDL map +object and a GX-specific IDL structure that contains both image data, as well as metadata documenting the +entire process involved in generating the script output. +The Stokes I and V brightness temperature maps generated by this script are illustrated in Figure 5. When combined +with the data-to-model comparison tools described in §3.2, the script presented in Appendix B may be employed to +perform a fully automatic systematic search in a multidimensional parameter space for the combination of such +magneto-thermal model parameters that produce synthesized maps that are simultaneously in best agreement with all +multi-wavelength observational data available for a given instance of an AR, a methodology successfully employed by +Fleishman et al. (2021) for finding the best scaling parameters within the low frequency heating assumption for the +same instance of AR 11520 illustrated here. + +12 +Figure 5. +Brightness temperature Stokes I (top row) and V (bottom row) at 5.7 GHz (left column) and 17 GHz (right +column) produced by the IDL script listed in Appendix B. +3.2. Model to Data Comparison Tools +The current release of GX Simulator provides a series of data-to-model comparison routines, located in the metrics +sub-module, that are integrated in the GUI interface, but may be also called programmatically by customized analysis +IDL scripts. The top level routine included in this submodule, gx metrics map.pro, takes, as the input arguments, a +model map structure and a reference map structure to be compared with, and returns a structure containing spatially +resolved and FOV-averaged metrics such as absolute and normalized residuals, as well as χ2 metrics, if a map of +standard deviations representing the observational or model uncertainties is provided as optional input. + +13 +By default, before computing the data to model metrics, this top level routine also performs a map alignment by +computing a cross correlation of the input map images, to which an optional mask may be applied. However, the user +may choose not to align the input maps, or to apply a user-supplied spatial shift. +The FOV-averaged absolute and normalized residuals metrics returned by the top level routine are defined as follows: +⟨R⟩ = 1 +N +� +ROI +(mij ⊗ dP SF − dij) , +(7) +⟨ρ⟩ = 1 +N +� +ROI +�mij ⊗ dP SF +dij +− 1 +� +, +⟨χ⟩ = 1 +N +� +ROI +�mij ⊗ dP SF − dij +σij +� +, +where dij is the observed brightness in the image pixel ij and mij ⊗ dP F S is the corresponding model map brightness +convolved with the instrumental point spread function (PSF), the model or observational uncertainties are denoted +by σij, and N represents the total number of pixels in the region of interest (ROI) over which the comparison is made +(which may be all or only a subset of the FOV pixels selected by applying an optional, user defined byte mask). +The corresponding squared metrics are defined as follows: +⟨R2⟩ = 1 +N +� +ROI +(mij ⊗ dP SF − dij)2 . +(8) +⟨ρ2⟩ = 1 +N +� +ROI +�mij ⊗ dP SF +dij +− 1 +�2 +, +and the metrics of success for the data to model comparison are defined as +σ2 +ρ = 1 +N +� +ROI +�mij ⊗ dP SF +dij +− 1 − ⟨ρ⟩ +�2 += ⟨ρ2⟩ − ⟨ρ⟩2 +(9) +σ2 +R = 1 +N +� +ROI +(mij ⊗ dP SF − dij − ⟨R⟩)2 = ⟨R2⟩ − ⟨R⟩2, +⟨χ2⟩ = 1 +N +� +ROI +�mij ⊗ dP SF − dij +σi +�2 +− ⟨χ⟩2, +where, as motivated by Fleishman et al. (2021), the last term of each of the metrics of success defined above is +subtracted to account for any imperfections in fine tuning the model, which may result in minimized but not exactly +null averaged residuals that, as defined by Equation 7, are expected to vanish in the case of a perfect data to model +match. As an example of data to model comparison performed using the GX Simulator metrics routine, we reproduce +in Figure 6 the results obtained by Fleishman et al. (2021) using observational maps obtained with data from the +Siberian Solar Radio Telescope (SSRT, Grechnev et al. 2003) and the Nobeyama Radio Heliograph (NoRH, Nakajima +et al. 1994) and the 5.7 GHz and 17 GHz synthetic images shown in Figure 5(a, b), which were convolved with the +corresponding instrumental beams before their coalignment with the observational maps. +3.2.1. Coronal Heating Modeling Pipeline (CHMP) +To identify the combination of {a, b} and Q0 parameters that provides the best possible model to data agreement, +quantitatively measured by the metrics described in §3.2, we have included in the most recent release of the GX Simu- +lator package a macro routine, namely gx search4bestq.pro, which, starting from a pair of initial guess heating rates, +{Q01, Q02}, performs a self-adaptive search for the optimal EBTEL heating rate Q0 corresponding to a predefined +{a, b} pair chosen by the user. +The gx search4bestq.pro macro routine provides the core functionality for a top-level command line application, +namely chmp.pro, which allows the user to interactively setup and start a multi-threaded search for the best possible +EBTEL model over {a, b} parameter grid, which takes advantage of the fact that such search is an embarrassingly +parallel process. + +14 +Figure 6. +Model to data comparison for the AR11520 model (reproduced from Fleishman et al. (2021)). Top row-left panel: +The [12, 30, 80]% contours of the 5.7 GHz SSRT observational map (yellow) and the synthetic map (black) are shown on top +of synthetic 5.7 GHz map background image. The alignment shifts, ∆x and ∆y, and the heating parameters, Q0, a, and b are +indicated in the figure inset. Top row-right panel: corresponding model to data residual map normalized by the observed SSRT +brightness temperature. Pearson cross-correlation coefficient, r, the averaged normalized residual, ρ, and the squared residual +metrics, ρ2, are indicated in the figure inset. Bottom row: the same data to model comparison as in the top row, between the +17 GHz synthetic image and the NoRH 17 GHz image. +The options provided by the CHMP command line application may be explored by calling the routine with no +keyword arguments, which generates the following console messages: +\input{chmp_cmd.bat} +The left panel of Figure 7 displays a flowchart that illustrates the iterative search process of the CHMP application. As +an illustrative example, the right panel of Figure 7 displays the distribution of the best ⟨χ2⟩ metrics over the searched +parameter space that has been obtained for the same AR11520 GX Simulator model that was previously tuned by +Fleishman et al. (2021) using a direct trial and error approach, which sought to minimize the σ2 +ρ metrics using reference +observational microwave data at 17 GHz provided by NoRH. +The optimal parameter combination found by the automated CHMP application in this illustrative example, (a = +0.4, b = 1.4, ⟨χ⟩2 = 6.70), is marked as red squared on the metrics image shown in the right panel of Figure 7. It is + +0 +1 ×106 +2×106 +3×106 +-1.0 +-0.5 +0.0 +0.5 +1.0 +GX 5.7 GHz, Stokes 1 [K] +Residual +△x 二 +2.7 +AY +0.994 +-250 +-250 +a = 1.00, b = 0.75 += +0.050 +Q。= +0.4150×10-3 +0.037 +-300 +[arcsec] +-300 +[arcsec] +y +-350 +y +-350 +Extrapolation:AS +-400 +-400 +-150 +-100 +-50 +0 +-150 +-100 +-50 +0 +x [arcsec] +x [arcsec]0 +1×1052×1053×105 +-1.0 +-0.5 +0.0 +0.5 +1.0 +GX 17 GHz, Stokes 1 [K] +Residual +Ax = -3.2', △y = -1.2 += 0.998 +-250 +-250 +a = 1.00, b = 0.75 +-0.020 +[arcsec] +-300 +Q。= +0.4150×10-3 +[arcsec] +-300 +0.007 +y +-350 +y +-350 +-400 +Extrapolation:AS +-400 +-150 +-100 +-50 +0 +-150 +-100 +-50 +0 +x [arcsec] +x [arcsec]15 +different from the brute-force solution found by Fleishman et al. (2021), (a = 1.0, b = 0.75, ⟨χ⟩2 = 13.98), marked +as a cyan rectangle on the grid, although the data-to-model comparison metrics are of the same order of magnitude. +As revealed by the ⟨χ2⟩ metrics distribution image, both solutions belong to the same region of comparatively good +metrics, which stand out as a diagonal path against the surrounding parameter space. We interpret this preliminary +result as an indication of a certain degree of degeneracy of the EBTEL solution, which we speculate might be possible +to remove by combining the results of a CHMP search independently performed at more than one observational +frequency, an avenue that we consider worth being pursued, but out of the scope of this paper. +Figure 7. +The automated CHMP architecture and illustrative example. Left panel: The CHMP iterative flowchart. The outer +loop steps through a user-defined grid of {a, b} parameter pairs for which the inner loop employs the gx search4bestq.pro core +macro to search for the optimal heating rate Q0 by attempting to bring the absolute value of ⟨χ2⟩ metrics below a predefined +maximum threshold ϵ. Right panel: The output of the automated CHMP search using NoRH 17 GHz observational data for +the same AR11520 GX Simulator model as previously tuned by Fleishman et al. (2021). The image displays, in logarithmic +scale, the ⟨χ2⟩ corresponding to each investigated grid point. +As indicated by the right side color bar, the ⟨χ2⟩ metrics +range in this case between 6.70 and 215.3. The red rectangle marks the grid point corresponding to the best found metrics +(a = 0.4, b = 1.4; ⟨χ⟩2 = 6.70), which may be compared with the ⟨χ2⟩ = 13.98 metrics corresponding to the best parameter +combination, (a = 1.0, b = 0.75), found by Fleishman et al. (2021) by minimizing the σ2 metrics. +3.2.2. CHMP graphical user interface (GX CHMP) +In addition to the CHMP command line application, the core gx search4bestq.pro routine includes many more +built in options that provide more user flexibility, including the option of performing a search over an arbitrarily +shaped, or sparse, grid space. The full flexibility of the gx search4bestq.pro routine is exposed to the user by the +gx chmp.pro application. Figure 8 shows this GUI application, which allows one to take advantage of all available +options provided by the core macro routine. It also interactively visualizes, at run-time, the data to model comparison +metrics maps corresponding to each grid point for which a solution has been computed. +3.3. DEM and DDM EBTEL/EBTEL++ Tables +GX Simulator includes EBTEL results from a number of different coronal heating scenarios. Each is provided in the +form of an IDL sav file containing the following floating point array variables: +• LOGTDEM[NT ]: logarithm of temperature bins over which the DEM/DDM distributions are computed +• QRUN[NQ × NL]: the average heating rates, ⟨Q⟩ corresponding to each grid point + +(a, b} +(x2) +215.3 +Model Image +2.0 +(Qo} +>mij +1.8 +173.6 +(mijdpFs) - dij +1.6 += +131.9 - +Oij +N +ROI +1.4 +b +1.2 +90.1 +Imij 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/EtAyT4oBgHgl3EQf4_op/content/2301.00795v1.pdf'} +page_content='8 173.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/EtAyT4oBgHgl3EQf4_op/content/2301.00795v1.pdf'} +page_content='6 (mijdpFs) - dij 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/EtAyT4oBgHgl3EQf4_op/content/2301.00795v1.pdf'} +page_content='6 = 131.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/EtAyT4oBgHgl3EQf4_op/content/2301.00795v1.pdf'} +page_content='9 - Oij N ROI 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/EtAyT4oBgHgl3EQf4_op/content/2301.00795v1.pdf'} +page_content='4 b 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/EtAyT4oBgHgl3EQf4_op/content/2301.00795v1.pdf'} +page_content='2 90.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/EtAyT4oBgHgl3EQf4_op/content/2301.00795v1.pdf'} +page_content='1 I 0 for particle q last propagated by r, decrement αq and assign q +again. If αq = 0 continue with (2); +2: Select a different particle q′ with αq′ > 0; +15 + +3: Compute split factor sq′. If Rq′ < sq′ assign q′, increment Rq′, and decre- +ment αq′. If Rq′ = sq′ continue with (2). +Notice that when the server recognizes the loss of one runner, it needs to +update the bookkeeping to reintegrate the particle that this runner was prop- +agating. +In conditions of even propagation time and a static number of runners, +this algorithm leads to the same distribution as for the static schedule and +respects the upper bound of Equation 15. +3.5.3. Cache Aware Scheduling +We now remove the last assumption to propose a scheduling strategy that +takes into consideration the particle cache. This is a heuristic build upon the +previous strategy and validated though several experiments. The particle +selection strategy is: +1. Select a parent particle pi already loaded in the runner cache (cache +hit); +2. Select a parent particle pi that is in no runner cache (cache miss); +3. Select a particle pi fulfilling the split factor criterion (cache miss); +4. Select a parent particle pi with maximal split factor si (even if voids +the split factor) (cache miss). +The three first items comply with the scheduling proposed in Section 3.5.2. +The first item gives priority to particles already in the cache, before they may +be evicted to provide space for a particle load. The next two items pursue +with the strategy of Section 3.5.2, favoring particles with no previous propa- +gation. The rational is to start as soon as possible with new parent particles +and, once in a cache, propagate them has often as required, and intend to +reduce the need for splitting. The last item departs from the strategy of +Section 3.5.2, but its addition proved efficient by our experiments. This case +occurs when reaching the end of a cycle. It proved to be an efficient strat- +egy to keep runners busy, even at the cost of extra loads, to improve load +balancing and so completion time. +16 + +3.6. Job Submission and Monitoring +The workflow is controlled by the launcher. The launcher is the user +entry point to configure and start the application. The launcher starts first +and is responsible to start and monitor the runner and server instances, +that all run in separate executables/jobs. The launcher is also in charge of +killing and restarting the runners or server as requested by the fault tolerance +protocol (Section 3.7), or for elasticity purpose. +The launcher tightly interacts with the job scheduler (Slurm or OAR +for instance) of the machine. +The launcher can be configured to submit +one job per runner and server to the batch scheduler. This strategy offers +the maximum flexibility for the batch scheduler to optimize the machine +ressource allocation, but the execution progress becomes very dependent on +the machine availability. The user may need more control on the number of +concurrently running runners. In that case the launcher can be set to request +to the batch scheduler one or several large resource allocations and fit several +runner instances in each one. To support this feature the launcher relies on +a combination of Slurm salloc/srun [15], or OAR containers[16]. For even +more flexible schemes, we plan to support workflow pilot-based schedulers +like Radical-Pilot [17] or QCG-PilotJob [18]. +3.7. Fault Tolerance +The fault tolerance relies on the fast identification of failures from run- +ner and server instances. Runner failures are detected in two different ways. +Runner crashes are recognized by the launcher, which is monitoring their +execution using the cluster scheduler. Unresponsive runners are identified +by the server relying on timeouts for the particle propagations. If propaga- +tions exceed the timeout, the server requests the launcher to terminate the +respective runner. In both cases, the launcher eventually starts a new runner +instance. The new runner connects to the server and requests work. If a +runner fails, the server cancels the on-going propagation, and the time spent +in the propagation plus the time to recognize the runner failure is lost. +Server failures are detected similarly, either directly if the server crashes, +or if the server exceeds a timeout. The timeout is mediated by a periodical +exchange of signals between launcher and server (heartbeats). If the server +fails, the launcher terminates all runner instances and restarts the framework +as a whole. The server frequently stores the status of the propagations in +checkpoints, and in case of failures, the framework can recover to the point +of the last successful propagations. +17 + +Finally, a launcher failure is detected by the server monitoring the heart- +beat connection between launcher and server. In case of a missing heartbeat, +the server checkpoints the current particle state and shuts down. In parallel, +the runners detect the server crash and shut down, again using timeouts. +While runner or server failures lead to an automatic restart, the framework +needs to be restarted manually if the launcher fails. +3.8. Implementation Details +The launcher and server are developed in Python. +The runner relies +on the simulation code instrumented with our framework API, supporting +C/C++, Fortran and Python. +The implementation reuses some software +components, like the launcher, from the framework developed for EnKF +DA [19]. The distributed cache implementation relies on the Fault Tolerance +Interface (FTI) [20]. FTI is a multilevel checkpoint-restart library supporting +asynchronous checkpointing to global storage. One of the main modification +performed to FTI is related to its event loop. Events are triggered in form of +MPI communication between the application and FTI processes. The events +are identified by tags. To extend this mechanism, we enabled to register a +callback function. This callback function is called inside the event loop and +can trigger user defined events using unique tags. With this, it becomes pos- +sible to use the application checkpointing into all available levels of reliability +FTI provides, and to implement the cache mangement on the dedicated FTI +processes. +The communication between helper and model processes relies on asyn- +chronous MPI messages. Communications with the server are implemented +in two steps for efficiency purpose. Only rank 0 (master) of the application +(i.e., model) communicator and the rank 0 (master) of the helper process +communicator communicate with the server. As a dynamic connection is +needed, each master connects to the server using a socket through the ZMQ +library. Information that needs to be propagated between helper or model +processes relies on MPI collective communications in the associated commu- +nicator (Figure 3). +The framework code is available at https://gitlab.inria.fr/melissa/ +melissa-da. +18 + +4. Experiments +4.1. WRF Use Case +Experiments rely on an established Numerical Weather Prediction (NWP) +system; the Weather Research and Forecasting Model (WRF) (V3.7.1)[8]. +The core of WRF is based on solving fully compressible non-hydrostatic equa- +tions with complete Coriolis and curvature terms, and a large set of physics +options. The simulation domain covers most of Europe (See Figure 6) and is +composed of 220 by 220 grid cells with a horizontal resolution of 15 km and +49 vertical levels with uneven thickness. We perform one day-ahead weather +forecasting (24 hours of initial time plus 48 hours of production time) of an +arbitrary date (2018-10-12) with 24-seconds or 100-seconds time steps. The +model employs the WSM6 microphysics, MYNN2 boundary layer physics, +Grell-3 cumulus parameterization, Eta Monin-Obukhov similarity surface +layer processes, and RUC land surface model. +Also non-hydrostatics are +activated to provide more details in simulated clouds and precipitation. The +input, initial, and boundary conditions are based on the reanalyzed ERA5 +dataset from the European Center for Medium-Range Weather Forecasts +(ECMWF), which is updated every 3 hours. Data assimilation is performed +using the cloud fraction (CFRACT). The particle weights are determined by +comparison against the observed cloud mask obtained from the EUMETSAT +Level-2 satellite data of the cloud mask. The simulated cloud fraction is con- +verted into cloud mask and the observed cloud mask data is upscaling to the +size of the model gridcells for the further applications. The data is hourly +available, thus, one assimilation cycle comprises 150 (36) model time steps +(150 × 24 s �= 1 h or 36 × 100 s �= 1 h). +The experiments presented in this article leverage our proposed Particle +Filtering (PF) implementation with a sample size of up to 2,555 particles +on the European domain. In that case, we utilize 20,442 compute cores on +512 Nodes of the Jean-Zay supercomputer. The compute nodes are equipped +with 2 Intel Cascade Lake 6,248 processors, summing up to 40 cores with +2.5 GHz and 192 GiB RAM per node. Intel Omni-Path (100 GB/s) connects +the compute nodes, and the global file system is an IBM Spectrum Scale +(ex-GPFS) parallel file system with SSD disks (GridScaler GS18K SSD). For +all experiments the node-local caches were stored on RAM disk. In Table 1 +we list the parameters of our main experiments. +The meteorological state of the European domain associated to one par- +ticle comprises 2.5 GiB of data. Hence, the data from 2,555 particles for the +19 + +Figure 6: The topography of the target domain of Europe for the simulation. +full simulation period of 48h (48 time steps) correspond to an aggregated +size of about 300 TiB. The experiments performed for this article, including +small beta-stage experiments, account for about 900,000 CPU hours split +between the JUWELS, Jean-Zay and Marenostrum supercomputers. +4.2. Runner Activity +The benefit of the local cache in combination with the cache-aware schedul- +ing leads to a drastic reduction in transfers from global to local file system +layers. The cache hit ratio, i.e., the ratio of particles found inside the cache +to the total number of particle loads, depends on the cache size and the ratio +of particles per runner. Figure 7 shows how the cache misses develop for +different cache sizes. Our experiments demonstrate a cache hit ratio of 88 % +for 128 particles, 32 runners, and a cache size of 9 particles. This translates +to a saving of 88 % in transfers from global to local storage. The pattern of +cache hits and misses is visualized in Figure 8. The initial phase is dominated +by starting up the runners, and all the particles are fetched from the global +storage (cache warm up). But beginning with the next assimilation cycle, +the low transfer ratio from global to local storage starts to establish. +Runners are designed to separate I/O operations to the PFS from other +tasks: model processes only perform local I/O operations. We observe in +our experiments that this leads to a high computational efficiency. The local +I/O accesses are negligible compared to the computational tasks (< 0.1 s +20 + +20°W +10°W +00 +10°E +20°E +30°E +40°E +3000 +2500 +60°N +20°W +2000 +Latitude +Elevation +1500 +50°N +1000 +500 +40°N +10°W +10°E +20°E +Longitude3 +4 +5 +6 +7 +8 +9 +10 +11 +12 +cache size +0.2 +0.3 +0.4 +0.5 +cache miss ratio +Figure 7: Cache miss ratio for different cache sizes on each runner. In total 128 particles +run on 32 runners. First and last assimilation cycles were disregarded to remove warm up +effects and not fully recorded cycles. +21 + +Experimental Setup +Particles +315 +635 +1,275 +2,555 +Number of runners +63 +127 +255 +511 +Number of nodes +64 +128 +256 +512 +Model processes +2,457 +4,953 +9,945 +19,929 +Particles per runner (avg.) +5 +5 +5 +5 +Particle state size (GiB) +2.5 +2.5 +2.5 +2.5 +Performance Data +Scaling efficiency +92% +91% +92% +87% +Resampling (ms) +2.21 +4.06 +8.16 +16.37 +Assimilation cycle (s) +136 +138 +139 +146 +Propagation (s) +25.1 +25.2 +25.1 +25.0 +Load particle state +from PFS to cache (s) +2.1 +2.1 +2.4 +4.1 +Write particle state +from cache to PFS (s) +1.4 +1.6 +1.8 +2.3 +Writes to PFS per cycle (TiB) +0.77 +1.55 +3.11 +6.24 +Reads from PFS per cycle (TiB) +0.30-0.40 +0.64-0.79 +1.27-1.79 +2.54-3.82 +Table 1: Experimental setting and performance overview at 4 different scales. The times +are given as average in all cases. Model time steps were set to 100 seconds. +compared to up to 6 s). Some general idle periods can be observed between +assimilation cycles when runners are waiting for the last propagations of one +cycle to finish so that the server can normalize weights, resample and start to +distribute work again. This is illustrated in Figure 9 where we show a trace +recorded from the execution of an arbitrary runner. The trace illustrates the +efficiency of the runners in performing the actual tasks of the simulation, +particle propagation and weight calculation, while the I/O tasks are moved +to the background. +A global parallelization of the computational tasks is achieved by dynami- +cally distributing the particle propagations to the available runners. The fully +parallelized case corresponds to R = P, i.e., the number of runners matches +the number of particles. The sequential case corresponds to R = 1, i.e., all +propagations are performed by only one runner. However, The best parallel +efficiency is achieved at values between those limits. Because WRF propa- +gates particles with very low time variability (maximum variation of 10%), +we observe an even distribution of propagations to runners when R divides P +(Figure 8). A single-particle propagation takes between 24 and 26.5 seconds, +22 + +0 +1000 +2000 +3000 +4000 +5000 +Time (s) +0 +10 +20 +30 +Runner ID +state load +Cache hit +Cache miss +Assimilation cycle +0 +1 +2 +3 +4 +5 +6 +7 +8 +9 +10 +11 +12 +13 +14 +15 +cachesize: 9, cache hit ratio (cycles 2-14): 0.88 +Figure 8: +Gantt chart of particle propagations executed by the 32 runners over 15 +assimilation cycles. Tasks are green if the associated parent particle state was already +present in the runner cache and did not require a load from the PFS (red otherwise). +globally making from 87% to 92% of an average assimilation cycle. Calcu- +lating weights takes 1% of the time and communicating with the server from +7%to 12% including the idle time at the end of each cycle (Table 1 – Perfor- +mance Data). The extra resources for helper processes, one core per runner +node, and the server, 1 node, comprise only 2.7% for our largest experiment +at 512 nodes. On the other hand, leveraging the runner’s particle cache, and +the cache aware dynamic scheduling on the server, move > 97% of the state +loads completely into the background. Loading and writing particle states +synchronously would otherwise add about 6.4 seconds to each single-particle +propagation corresponding to 14% of the average propagation time (Table 1 +– 2,555 particles). +Note that in contrast to the traditional offline approach, we start-up the +numerical simulation code only once for several particle propagations. The +setup involves the request and allocation of the runner job and initializing +the simulation code. +On the other hand, the traditional offline approach +associates each particle propagation with a different job, and the start-up +has to be performed anew for each particle. Starting up the WRF model on +23 + +Figure 9: Trace detailing the activity of a runner over the course of an assimilation cycle. +Helper processes enable to keep model processes busy with particle propagation, except at +the end of assimilation cycles when they wait for the server to finish particle resampling +(dark blue). Some activities are so thin that they are not visible here (state copies from +cache to model). +the European domain on one node until the first model propagation begins +takes 3-4 s, excluding the provisioning of the job allocation via the cluster +scheduler. +4.3. Server Activity +We further measured the server responsivity to runner requests. +The +response time is always in the order of a few hundred microseconds, except +for some job requests that take up to a few seconds (Figure 10). However, +these are outliers at the end of the assimilation cycle, resulting from idle times +due to the load balancing and the particle filter update. During our largest +experiments with 511 runners, the server processes 676 requests per second +at maximum load. This shows that the server is fast enough to support this +scale, even though it is a sequential python code. Simple optimizations are +at reach if the server needs to be accelerated (e.g., adding parallelization). +24 + +Assimilation cycle +Weight calculation +processes +Model +Request job from server- +Propagation - +Load state from cache +Load state from PFS into cache- +processes +Helper +Write state from cache into PFS - +300 +400 +500 +600 +Time (s)Delete request +Job request +Prefetch request +Push weight to server +1 +1e2 +1e4 +Duration (ms) +Figure 10: Server response times on runner requests. +4.4. State Transfers To/From PFS +Next, we take a closer look at the particle loads from the PFS (Figure 11). +With a sample size of 1024 particles, leveraging 256 runners, and a local cache +size of 9 particles, between 121 and 248 particles are loaded to the cache +during each cycle. The number of distinct parent particles Q propagated per +cycle lies between 813 and 889. Each one is propagated at most 5 times to +sum to a total of 1024 particles. The cache enables to achieve significantly +less loads than the Q + R − 1 upper bound expected with static scheduling +and no cache (Equation 15). +The access times to the Parallel File System (PFS) (load/store) vary sig- +nificantly and increase with the number of runners (Figure 12), showing that +25 + +Q +Q+R-1 +Figure 11: Number of parent particles Q, particles loads from the PFS to the cache, +and Q + R − 1 upper bound from Equation 15 for different ensemble sizes, a cache size +of 9 particles with 4 particles per runner. +26 + +our application alone can stress the PFS 1. Each particle is associated with +2.5 GiB of data. During each assimilation cycle, all the propagated parti- +cles are written to the PFS for supporting fault-tolerance and dynamic load +balancing. For our experiments at 512 nodes with 2,555 particles, this accu- +mulates to about 6.2 TiB of data each cycle (compare Table 1). However, our +experiments on the Jean-Zay and JUWELS supercomputer demonstrate that +our framework performs most of those transfers asynchronously (Section 4.2). +In less than 2% of the cases, the model processes wait more than 0.1 seconds +for a particle to be loaded corresponding to cases where helper processes do +not (entirely) finish prefetching. Time to perform the local loads and stores +from the cache shows a constant average independently on the number of +runners (Figure 13). +63 +127 +255 +511 +Number of runners +1000 +2000 +3000 +4000 +5000 +6000 +Duration (ms) +Load state from +PFS into cache +Write state from +cache into PFS +Figure 12: Mean time to load or store particle states of 2.5 GiB from / to the PFS with +different numbers of runners. +4.5. Fault Tolerance, Elasticity and Load Balancing +Fault tolerance relies on 1) persisting the particle to the PFS 2) the +framework elasticity enabling to adjust dynamically the number of runners. +1these numbers may also be impacted by other jobs on the cluster +27 + +128 +256 +512 +1024 +members +1e-4 +1e-2 +1 +duration [s] +Load from local cache +Store to local cache +Figure 13: Box plot of the time spent for loads and stores from/to the local cache with +different numbers of particles. +If a runner fails, the launcher requests the execution of a new one, so as to +maintain a constant number of runners. Once this new runner connects to +the server, it asks for a particle to propagate to the server, assigned according +to the load balancing algorithm. +We tested the fault tolerance and elasticity on an experiment with 63 +runners provoking the crash of 2 runners (Figure 14). First, notice that the +fault tolerance algorithm reacts appropriately as it restarts a new runner after +each crash. The first crash (runner #53) occurs in the worst-case situation: +just when propagating the last particle of the current cycle, leading to a +significant idle period. The idle period is caused first, because the server +needs to wait for the timeout (set to 60 s) to acknowledge that runner #53 +is unresponsive and second, there is no work left except the particle that +runner #53 was propagating, which is re-assigned to runner #44. Meanwhile +all other runners stay idle until the beginning of the next cycle. If the crash +happens earlier during a cycle, smaller idle periods appear. +This can be +observed during the second crash (runner #48), as the other runners are +kept busy with propagation work. +We generally observe an efficient load +balancing, as the work load is kept well distributed amongst runners, even +when their number varies. +28 + +0 +250 +500 +750 +1000 +1250 +Time (s) +0 +20 +40 +60 +Runner ID +Initial propagation +Assimilation cycle +1 +2 +3 +4 +5 +6 +7 +8 +Figure 14: +Gantt chart of particle propagations executed by the 63 runners over 8 +assimilation cycles. +After runners #48 and #53 crashed (black cross), two new ones +restarted (top 2 runners #63 and #64). +4.6. Scaling +We evaluated the performance of the particle filter in a strong scaling +scenario, constant number of runners while increasing the number of particles, +and a weak scaling scenario, constant particle-to-runner ratio while increasing +the number of runners. In the strong scaling scenario we observe that the +runner utilization shows an upwards trend when increasing the number of +particles per runner, with a plateau at about 96% (Figure 15). As global +I/O operations are almost completely shadowed, thanks to the asynchronous +prefetching, increasing the number of particles per runner mainly enables to +better amortize the cost of the synchronization associated with resampling. +We observe an almost constant time for the assimilation cycle, demonstrating +a desirable weak scaling behavior. The time for the cycles increase only by +8% from 63 to 511 runners, indicating an efficient scaling behavior of the +29 + +framework up to production scale (Figure 16). Particle filtering with WRF +on a European domain for short-range weather prediction at this scale is +an important advancement of the previous work done by Berndt et. al. [21]. +Moreover, besides assimilating at a higher frequency, our proposal offers fault +tolerance, automatic load balancing and elasticity while minimizing the I/O +cost and time to calculate weights. +63 particles +(1 per runner) +315 particles +(5 per runner) +630 particles +(10 per runner) +945 particles +(15 per runner) +0 +0.1 +0.2 +0.3 +0.4 +0.5 +0.6 +0.7 +0.8 +0.9 +1 +Scaling efficiency +Figure 15: Scaling efficiency using different numbers of particles with 63 runners. One +runner sets the reference case. +4.7. Comparison to a File-based Approach +Melissa +ESIAS-met +ESIAS-met/Melissa +part. +cores +time (s) +core.s/part. +cores +time(s) +core.s/part. +resource usage ratio +128 +384 +1062 +3186 +1536 +267 +3204 +1.01 +256 +768 +1062 +3186 +3072 +317 +3804 +1.19 +512 +1536 +1068 +3204 +6144 +422 +5064 +1.58 +1024 +3072 +1071 +3213 +12288 +761 +9132 +2.84 +Table 2: Comparing the resource usage (core.second/particle) per cycle for Melissa and +ESIAS-met (file-based) runs. +We compare Melissa with the file-based approach ESIAS-met [22] using +the same simulation code WRF (V3.7.1) and the same data set. For the +same number of particle, both approaches use a very different amount of +30 + +63 +(315) +127 +(635) +255 +(1275) +511 +(2555) +Runners +(particles) +0 +50 +100 +150 +Assimilation cycle +duration (s) +5 particles per runner +2522 +5082 +10202 +20442 +Cores +Figure 16: Weak scaling performance test: assimilation cycle duration for different num- +bers of runners, but always 5 particles per runner. +cores (Table 2). With ESIAS-met each particle propagation requires to start +a dedicated instance of WRF. Each time it includes the cost from loading +and storing the particle state from/to a file. At 1024 particles ESIAS-met +uses 12288 cores while Melissa just needs 3072 cores as runners propagate +several particles each. ESIAS-met execution time is thus shorter as highly +parallelized, but the resource usage (core.second/particle/cycle) is 2.84 times +improved for Melissa due to the combined strategies developed to improve +efficiency. The gain increases with the number of particles, showing that the +Melissa approach is particularly beneficial when targeting the large ensemble +size. +5. Discussion +in Section 4.1 we derived that the total amount of data resulting from +48 time-steps of particle filtering on the european domain with 2,555 parti- +cles accumulates to about 300 TiB. Post processing this amount of data is +challenging. Our framework could be extended using in situ data processing +techniques as presented in Terraz et. al. [23]. +We only considered the case where the propagation time is longer than +the time for loading states from the PFS; while applications where propaga- +tions are shorter than loading the states would limit our proposal efficiency, +as we cannot further hide the I/O cost in that case. On the other hand, +we already have short propagation times in the WRF context as we per- +31 + +form hourly resampling. +We chose this frequency primarily to stress our +proposal. Production runs usually do not require such a high frequency, and +rather have even longer propagation times as in our experiments. However, +to minimize transfer times further, we are evaluating approaches leveraging +node-local persistent storage as globally shared storage layer. Solutions for +this are readily available in form of distributed ad hoc file-systems [24] such +as BeeOND, GekkoFS, and BurstFS. We have also experimented with con- +necting the runners, establishing a peer to peer network, where runners can +exchange directly the required states between each other. +The particle propagation time in our experiments with WRF is relatively +even, showing at most a 10% variability. Situations with more variability are +possible using different physics in WRF, with other simulation codes, or, if +runners execute on heterogeneous resources, some runners propagating faster +than others by leveraging GPUs for instance. Also use cases from other con- +texts such as Simulation Based Inference (SIB) and ensemble classification, +which can be performed using our framework, might lead to vastly different +propagation times. Therefore, testing our framework under such conditions +is an important future work. +Our proposal currently relies on filters that do not compute internal mem- +ber state corrections. +Extending our approach to such particle filters [1] +would possibly require aggregating more than just the particle weights to the +server. Exploring the requirements to align our framework to such cases is +the goal of a future implementation of the particle filter that we propose. We +validated our proposal with the SIR particle filter, but many variations exist +and are active research topics [25, 26]. One challenge is the exponentially +growing required particle number with the dimension of the problem [27, 28]. +This is particularly acute for geoscience use cases that, as in this paper, work +in high dimensional spaces. The survey [1] gives an extensive overlook of DA +by particle filters for geoscience and ways to cope with dimensionality issues. +Particle filters, as used here, require a synchronization point at the end +of each assimilation cycle. For our framework, this is the major remaining +source of inefficiency. Loosening this requirement needs revisiting the particle +filtering algorithm, which constitutes an active topic of research [29, 30, 31]. +6. Related Work +The DA domain encompasses a large variety of techniques and algorithms, +like nudging [32], kriging [33], ensemble Kalman Filter [34], ensemble max- +32 + +imum likelihood filter [35], or particle filter [36]. For an overview, we refer +to [37, 38]. We focus here on statistical DA relying on an ensemble run of +the model to compute a statistical estimator (co-variance matrix for EnKF, +PDF for particle filters). +To aggregate the data produced by all members (i.e., particles) two main +groups of approaches are used. Either the data is stored to files and then +processed in a second step (off-line mode), or the data is processed on-line +usually within a large MPI code in charge of running the members and data +processing. Frameworks relying on the off-line mode include EnTK [39], with +the largest published DA use cases reaching 4,096 members for a molecular +dynamics application with an EnKF filter [40]. OpenDA also follows this +model, using NetCDF for data exchange with the NEMO code [41]. DART +supports both [42], with reports of large scale DA in off-line mode in [43] +(about 1,000 members with an oceanic code), or [44, 45] (1,024 member, +LETKF filter, 6 M Fugaku cores). File based approaches have the benefit of +their simplicity, providing fault tolerance and elasticity. But these solutions +do not support member virtualization, state caching and prefetching. +So +starting or restarting a member requires to request a new resource allocation +launching a new instance of the model code with all the associated start-up +costs. Node-local persistent storage capabilities, for instance with SSDs, can +store intermediate files, avoiding the PFS to loosen the I/O bottleneck. They +are used for member state storage in [44], but without specific fault tolerance +mechanism. So if a node fails and the node-local storage becomes unavailable, +the lost member states need to be recomputed. Besides leveraging the node +storage for the distributed cache, using node-storage rather than the parallel +file system as a globally shared file system layer is one of our future goals. +The on-line mode avoids the I/O bottleneck. PDAF [46], which supports +both modes, has for instance been used on-line for the assimilation of ob- +servations into the regional earth system model TerrSysMP. DA was based +on EnKF with up to 256 members [47]. ESIAS uses on-line DA via particle +filters with up to 4,096 particles on a wind power simulation on Europe [21]. +Notice that we work with the same WRF component of ESIAS in this paper, +using a configuration on a similar domain but at higher spatial resolution and +with more advanced and more time consuming physics. We also find ad hoc +MPI codes for on-line DA as in [48] (atmospheric model, 10,240 members, +Local ENKF filter, 4,608 compute nodes). But all these MPI approaches +lead to monolithic code without support for fault tolerance, elasticity or load +balancing. In [49], the authors analyze various particle propagation schedul- +33 + +ing but at limited scale (6 compute nodes and 300 particles). We performed +experiments on a similar architecture as our proposed one, but for EnKF in- +stead of PF [19]. We demonstrated fault-tolerance, elasticity and scalability +for experiments using up to 16 k members, 16 k cores for DA with EnKF for +the hydrology code Parflow. In contrast to our novel proposal, EnKF re- +quires a centralized filter update, gathering the full ensemble of states at the +central instance for the assimilation of observations. In our novel proposal +for PF, we exploit certain properties of particle filters to suppress the server +bottleneck and significantly reduce data movements. +7. Conclusion +In this article we proposed an architecture for handling very large en- +sembles for particle filters. +The architecture was designed to address the +challenge of exascale computing that will allow massive ensemble runs [50]. +The architecture is based on a server/runner model where runners support a +distributed cache and virtualization of particle propagation, while the server +aggregates the weights computed by the runners and ensures the dynamic +balancing of the work load. Particle propagation is virtualized so the re- +quired number of runners is decoupled from the particle number. With the +addition of a distributed checkpointing mechanism, the architecture supports +dynamic changes in the number of runners during execution for fault toler- +ance and elasticity. Experiments with the WRF weather simulation code +show that our framework can run at least 2,555 particles on 20,442 cores +with a 87% scaling efficiency. Dynamic particle-propagation scheduling and +caching enable to avoid 88% of the global I/O operations. Compared to the +ESIAS file based approach, Melissa improves resource usage 2.83 times at +1024 particles. +Future work includes experimenting with adaptive or localized particle +filters as well as combining particle and Kalman filter. +We also plan to +extend the distributed cache and fault tolerance algorithm to fully avoid the +centralized file system and only rely on node-local SSDs for particle storage. +Acknowledgement +This project has received funding from the European Union’s Horizon +2020 research and innovation program under grant agreement No 824158 +(EoCoE-2). This work was granted access to the HPC resources of IDRIS +34 + +under the allocation 2020-A8 A0080610366 attributed by GENCI (Grand +Equipement National de Calcul Intensif). The authors gratefully acknowl- +edge the Gauss Centre for Supercomputing e.V. (www.gauss-centre.eu) for +funding this project by providing computing time through the John von Neu- +mann Institute for Computing (NIC) on the GCS Supercomputer JUWELS +at J¨ulich Supercomputing Centre (JSC). We acknowledge the access to the +meteorological input data from the Meteocloud of SDL Climate Science, JSC. +We also acknowledge PRACE for awarding us access to JUWELS at J¨ulich +Supercomputing Centre (JSC), Germany. +References +[1] P. J. Van Leeuwen, H. R. K¨unsch, L. Nerger, R. Potthast, S. Reich, +Particle filters for high-dimensional geoscience applications: A review, +Quarterly Journal of the Royal Meteorological Society 145 (723) (2019) +2335–2365. +[2] D. Lund´en, J. Borgstr¨om, D. Broman, Correctness of sequential monte +carlo inference for probabilistic programming languages., in: ESOP, +2021, pp. 404–431. +[3] F. Ronquist, J. Kudlicka, V. Senderov, J. Borgstr¨om, N. Lartillot, +D. Lund´en, L. Murray, T. B. Sch¨on, D. Broman, Universal probabilis- +tic programming offers a powerful approach to statistical phylogenetics, +Communications biology 4 (1) (2021) 1–10. +[4] J.-W. van de Meent, B. Paige, H. Yang, F. Wood, An Introduction to +Probabilistic Programming, arXiv, 2018. doi:10.48550/ARXIV.1809. +10756. +URL https://arxiv.org/abs/1809.10756 +[5] J. F. G. de Freitas, M. Niranjan, A. H. Gee, A. Doucet, Sequential Monte +Carlo Methods to Train Neural Network Models, Neural Computation +12 (4) (2000) 955–993. doi:10.1162/089976600300015664. +[6] P. M. Blok, K. van Boheemen, F. K. van Evert, J. IJsselmuiden, G.-H. +Kim, Robot navigation in orchards with localization based on Particle +filter and Kalman filter, Computers and Electronics in Agriculture 157 +(2019) 261–269. doi:10.1016/j.compag.2018.12.046. +35 + +URL +https://www.sciencedirect.com/science/article/pii/ +S0168169918315230 +[7] P. Bauer, P. D. Dueben, T. Hoefler, T. Quintino, T. C. Schulthess, N. P. +Wedi, The digital revolution of earth-system science, Nature Computa- +tional Science 1 (2) (2021) 104–113. +[8] W. C. Skamarock, J. B. Klemp, J. Dudhia, D. O. Gill, D. Barker, M. G. +Duda, J. G. Powers, A description of the advanced research wrf ver- +sion 3, Tech. Rep. No. NCAR/TN-475+STR, University Corporation +for Atmospheric Research (2008). +[9] J. V. Candy, Bootstrap Particle Filtering, IEEE Signal Processing Mag- +azine 24 (4) (2007) 73–85, conference Name: IEEE Signal Processing +Magazine. doi:10.1109/MSP.2007.4286566. +[10] N. Gordon, D. Salmond, A. Smith, Novel approach to nonlinear/non- +Gaussian Bayesian state estimation, IEE Proceedings F Radar and +Signal Processing 140 (2) (1993) 107. doi:10.1049/ip-f-2.1993.0015. +URL +https://digital-library.theiet.org/content/journals/ +10.1049/ip-f-2.1993.0015 +[11] J. S. Liu, R. Chen, T. Logvinenko, A Theoretical Framework for Sequen- +tial Importance Sampling with Resampling, in: A. Doucet, N. de Freitas, +N. Gordon (Eds.), Sequential Monte Carlo Methods in Practice, Statis- +tics for Engineering and Information Science, Springer, New York, NY, +2001, pp. 225–246. doi:10.1007/978-1-4757-3437-9_11. +URL https://doi.org/10.1007/978-1-4757-3437-9_11 +[12] M. Bolic, P. Djuric, Sangjin Hong, New resampling algorithms for parti- +cle filters, in: 2003 IEEE International Conference on Acoustics, Speech, +and Signal Processing, 2003. Proceedings. (ICASSP ’03)., Vol. 2, IEEE, +Hong Kong, China, 2003, pp. II–589–92. doi:10.1109/ICASSP.2003. +1202435. +URL http://ieeexplore.ieee.org/document/1202435/ +[13] R. L. Graham, Bounds for Certain Multiprocessing Anomalies, Bell Sys- +tem Technical Journal 45 (9) (1966) 1563–1581. +[14] D. Shmoys, J. Wein, D. Williamson, Scheduling parallel machines on- +line, in: [1991] Proceedings 32nd Annual Symposium of Foundations of +36 + +Computer Science, IEEE Comput. Soc. Press, San Juan, Puerto Rico, +1991, pp. 131–140. +[15] The slurm workload manager - https://slurm.schedmd.com/. +[16] The oar resource and task manager - http://oar.imag.fr/. +[17] A. Merzky, M. Turilli, M. Titov, A. Al-Saadi, S. Jha, Design and per- +formance characterization of radical-pilot on leadership-class platforms +(2021). doi:10.48550/ARXIV.2103.00091. +URL https://arxiv.org/abs/2103.00091 +[18] Qcg-pilotjob - https://github.com/psnc-qcg/QCG-PilotJob. +[19] S. Friedemann, B. Raffin, An elastic framework for ensemble-based large- +scale data assimilation, The international journal of high performance +computing applications 36 (4) (2022) 543–563. +URL https://hal.inria.fr/hal-03017033 +[20] L. +Bautista-Gomez, +S. +Tsuboi, +D. +Komatitsch, +F. +Cappello, +N. Maruyama, S. Matsuoka, FTI: High performance Fault Tolerance In- +terface for hybrid systems, in: SC ’11: Proceedings of 2011 International +Conference for High Performance Computing, Networking, Storage and +Analysis, 2011, pp. 1–12. +[21] J. Berndt, On the predictability of exceptional error events in wind +power forecasting —an ultra large ensemble approach—, Ph.D. thesis, +Universit¨at zu K¨oln (2018). +[22] Y.-S. Lu, G. Good, H. Elbern, Optimization of weather forecasting +for cloud cover over the european domain using the meteorological +component +of +the +ensemble +for +stochastic +integration +of +atmo- +spheric simulations version 1.0 1–31Publisher: +Copernicus GmbH. +doi:10.5194/gmd-2022-118. +URL +https://gmd.copernicus.org/preprints/gmd-2022-118/ +#discussion +[23] T. Terraz, A. Ribes, Y. Fournier, B. Iooss, B. Raffin, Melissa: Large +scale in transit sensitivity analysis avoiding intermediate files, in: In- +ternational Conference for High Performance Computing, Networking, +Storage and Analysis (SC’17), Denver, 2017. +37 + +[24] A. Brinkmann, K. Mohror, W. Yu, P. Carns, T. Cortes, S. A. Klasky, +A. Miranda, F.-J. Pfreundt, R. B. Ross, M.-A. Vef, Ad Hoc File Sys- +tems for High-Performance Computing, Journal of Computer Science +and Technology 35 (1) (2020) 4–26. doi:10.1007/s11390-020-9801-1. +URL https://doi.org/10.1007/s11390-020-9801-1 +[25] A. Doucet, S. Godsill, C. Andrieu, On sequential monte carlo sampling +methods for bayesian filtering, Statistics and computing 10 (3) (2000) +197–208. +[26] S. C. Surace, A. Kutschireiter, J.-P. Pfister, How to avoid the curse of di- +mensionality: Scalability of particle filters with and without importance +weights, SIAM review 61 (1) (2019) 79–91. +[27] C. Snyder, T. Bengtsson, M. Morzfeld, Performance Bounds for Particle +Filters Using the Optimal Proposal, MONTHLY WEATHER REVIEW +143 (2015) 12. +[28] P. Fearnhead, H. K¨unsch, Particle Filters and Data Assimilation, Annual +Review of Statistics and Its Application 5 (1) (2018) 421–449, arXiv: +1709.04196. +[29] C. Verg´e, C. Dubarry, P. Del Moral, E. Moulines, On parallel imple- +mentation of sequential monte carlo methods: the island particle model, +Statistics and Computing 25 (2) (2015) 243–260. +[30] A. Jasra, A. Lee, C. Yau, X. Zhang, The alive particle filter, arXiv +preprint arXiv:1304.0151. +[31] V. Elvira, J. M´ıguez, P. M. Djuri´c, Adapting the number of particles +in sequential monte carlo methods through an online scheme for conver- +gence assessment, IEEE Transactions on Signal Processing 65 (7) (2016) +1781–1794. +[32] V. R. N. Pauwels, R. Hoeben, N. E. C. Verhoest, F. P. De Troch, The +importance of the spatial patterns of remotely sensed soil moisture in +the improvement of discharge predictions for small-scale basins through +data assimilation, Journal of Hydrology 251 (1) (2001) 88–102. +38 + +[33] J. L. Williams, R. M. Maxwell, Propagating subsurface uncertainty to +the atmosphere using fully coupled stochastic simulations, Journal of +Hydrometeorology 12 (4) (2011) 690–701. +[34] D. Zhang, H. Madsen, M. E. Ridler, J. Kidmose, K. H. Jensen, J. C. +Refsgaard, Multivariate hydrological data assimilation of soil moisture +and groundwater, Hydrol. Earth Syst. Sci. 20 (10) (2016) 4341–4357. +[35] S. Q. Zhang, M. Zupanski, A. Y. Hou, X. Lin, S. H. Cheung, Assimila- +tion of precipitation-affected radiances in a cloud-resolving wrf ensemble +data assimilation system, Monthly Weather Review 141 (2) (2013) 754– +772. +[36] van Leeuwen, J. P., A variance-minimizing filter for large-scale applica- +tions, Mon. Wea. Rev. 131 (9) (2003) 2071–2084. +[37] M. Asch, M. Bocquet, M. Nodet, Data assimilation: methods, algo- +rithms, and applications, Vol. 11, SIAM, 2016. +[38] G. Evensen, Data assimilation: the ensemble Kalman filter, Springer +Science & Business Media, 2009. +[39] V. Balasubramanian, M. Turilli, W. Hu, M. Lefebvre, W. Lei, G. Cer- +vone, J. Tromp, S. Jha, Harnessing the power of many: Extensible +toolkit for scalable ensemble applications, in: IPDPS 2018, 2018. +[40] V. Balasubramanian, T. Jensen, M. Turilli, P. Kasson, M. Shirts, S. Jha, +Adaptive ensemble biomolecular applications at scale, SN Computer +Science 1 (2) (2020) 1–15. +[41] N. van Velzen, M. U. Altaf, M. Verlaan, OpenDA-NEMO framework for +ocean data assimilation, Ocean Dynamics 66 (5) (2016) 691–702. +[42] J. Anderson, T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, A. Avel- +lano, The Data Assimilation Research Testbed: A Community Facility, +Bulletin of the American Meteorological Society 90 (9) (2009) 1283– +1296, publisher: American Meteorological Society. +[43] H. Toye, S. Kortas, P. Zhan, I. Hoteit, A fault-tolerant hpc scheduler +extension for large and operational ensemble data assimilation: Appli- +cation to the red sea, Journal of Computational Science 27 (2018) 46 – +56. +39 + +[44] H. Yashiro, K. Terasaki, Y. Kawai, S. Kudo, T. Miyoshi, T. Imamura, +K. Minami, H. Inoue, T. Nishiki, T. Saji, M. Satoh, H. Tomita, A 1024- +member ensemble data assimilation with 3.5-km mesh global weather +simulations, in: Supercomputing 2020: International Conference for +High Performance Computing, Networking, Storage and Analysis (SC), +IEEE Computer Society, Los Alamitos, CA, USA, 2020, pp. 1–10. +[45] H. Yashiro, K. Terasaki, T. Miyoshi, H. Tomita, Performance evaluation +of a throughput-aware framework for ensemble data assimilation: The +case of nicam-letkf, Geoscientific Model Development 9 (7). +[46] L. Nerger, W. Hiller, Software for ensemble-based data assimilation +systems—implementation strategies and scalability, Computers & Geo- +sciences 55 (2013) 110–118. +[47] W. Kurtz, G. He, S. J. Kollet, R. M. Maxwell, H. Vereecken, H.-J. +Hendricks Franssen, TerrSysMP–PDAF (version 1.0): a modular high- +performance data assimilation framework for an integrated land sur- +face–subsurface model, Geosci. Model Dev. 9 (4) (2016) 1341–1360. +[48] T. Miyoshi, K. Kondo, T. Imamura, The 10,240-member ensemble +Kalman filtering with an intermediate AGCM: 10240-MEMBER ENKF +WITH AN AGCM, Geophysical Research Letters 41 (14) (2014) 5264– +5271. +[49] F. Bai, F. Gu, X. Hu, S. Guo, Particle routing in distributed particle +filters for large-scale spatial temporal systems, IEEE Transactions on +Parallel and Distributed Systems 27 (2) (2015) 481–493. +[50] T. C. Schulthess, P. Bauer, N. Wedi, O. Fuhrer, T. Hoefler, C. Schar, Re- +flecting on the Goal and Baseline for Exascale Computing: A Roadmap +Based on Weather and Climate Simulations, Computing in Science & +Engineering 21 (1) (2019) 30–41. +40 + diff --git a/FdE0T4oBgHgl3EQfzALi/content/tmp_files/load_file.txt b/FdE0T4oBgHgl3EQfzALi/content/tmp_files/load_file.txt new file mode 100644 index 0000000000000000000000000000000000000000..eadd57f67080befe74f69bea7b0660b69310c85e --- /dev/null +++ b/FdE0T4oBgHgl3EQfzALi/content/tmp_files/load_file.txt @@ -0,0 +1,1168 @@ +filepath=/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf,len=1167 +page_content='A Framework for Large Scale Particle Filters Validated with Data Assimilation for Weather Simulation Sebastian Friedemanna, Kai Kellerb, Yen-Sen Luc, Bruno Raffina,∗, Leonardo Bautista-Gomezb aUniv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, 38000, Grenoble, France bBarcelona Supercomputing Center, Barcelona, 08034, Spain cForschungzentrum Juelich, Juelich, 52428, Germany Abstract Particle filters are a group of algorithms to solve inverse problems through statistical Bayesian methods when the model does not comply with the lin- ear and Gaussian hypothesis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Particle filters are used in domains like data assimilation, probabilistic programming, neural network optimization, local- ization and navigation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Particle filters estimate the probability distribution of model states by running a large number of model instances, the so called particles.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The ability to handle a very large number of particles is critical for high dimensional models.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' This paper proposes a novel paradigm to run very large ensembles of parallel model instances on supercomputers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The approach combines an elastic and fault tolerant runner/server model min- imizing data movements while enabling dynamic load balancing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Particle weights are computed locally on each runner and transmitted when available to a server that normalizes them, resamples new particles based on their weight, and redistributes dynamically the work to runners to react to load imbalance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Our approach relies on a an asynchronously managed distributed particle cache permitting particles to move from one runner to another in the background while particle propagation goes on.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' This also enables the num- ber of runners to vary during the execution either in reaction to failures and ∗Corresponding author Email addresses: sebastian.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='friedemann@inria.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='fr (Sebastian Friedemann), kai.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='keller@bsc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='es (Kai Keller), ye.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='lu@fz-juelich.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='de (Yen-Sen Lu), bruno.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='raffin@inria.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='fr (Bruno Raffin), leonardo.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='bautista@bsc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='es (Leonardo Bautista-Gomez) arXiv:2301.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='02668v1 [cs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='DC] 6 Jan 2023 restarts, or to adapt to changing resource availability dictated by external de- cision processes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The approach is experimented with the Weather Research and Forecasting (WRF) model, to assess its performance for probabilistic weather forecasting.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Up to 2,555 particles on 20,442 compute cores are used to assimilate cloud cover observations into short–range weather forecasts over Europe.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Keywords: ls Data Assimilation, Particle Filter, Ensemble Run, Resilience, Elasticity 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' introduction Given an output and a transformation function, finding the input states represents a so called inverse problem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' A wide range of approaches to ad- dress this central problem exist.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Statistical Bayesian methods stand out as they provide uncertainty measures of the proposed input in form of probabil- ity density functions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' In this paper, we consider particle filters, a statistical Bayesian method combining uncertainties of both the dynamical system and observations, to estimate the system state.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Several realizations of the dy- namical system, called particles, with differently perturbed internal states, are propagated up to a time where observation data are available.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' These particles are then weighted corresponding to their distance to the obser- vations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The weights are used to generate a new sample of particles that better matches the observations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' This process repeats while observations are available.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Particle filters are used for several purposes, like Data Assimilation (DA) [1], probabilistic programming [2, 3, 4], neural network optimization [5], local- ization and navigation[6].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Particle filters stands by their ability to work with nonlinear and/or non-Gaussian state space models in opposition to technics like Ensemble Kalman Filtering (EnKF).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' But this ability comes with a need to run larger number of particles.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' If the dynamical system is an advanced parallel high-dimensional numerical model solver, as for geoscience applica- tions, thousands of particles may be necessary to avoid undersampling and degeneracy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' While high-dimensional large-scale solvers are compute intense already, the execution of several thousands of instances adds orders of magni- tude of calculations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Large scale DA with particle filters is for instance used for geoscience applications such as weather forecasting [7].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Supercomputers, reaching today Exascale, have the compute power to support very large scale 2 particle filters.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' But using such resources efficiently, time and energy wise, is challenging.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Applications need to limit the use of the Parallel File System (PFS), a classical supercomputer bottleneck, and favor instead in situ data processing as well as local data storage to reduce data movements, asyn- chronism to overlap tasks whenever possible.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Applications also need to be flexible to adapt to changes during execution, requiring support for resilience, elasticity and dynamics load balancing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Existing large scale approaches can be divided into two types: online and offline approaches.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Offline approaches use temporary files to exchange data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' To propagate one particle, one model instance starts, loads the particle from a file, propagates it up to a given time, stores the resulting particle back to a file and shuts down.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' This approach is flexible, fault tolerance is easy to support, but performance, especially at scale is impaired by the heavy use of the file system and the cost of starting a new model instance for each propagation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Online approaches bypass the file system by running a large MPI application that encompasses the full workflow, where the particles are distributed to the different model instances through the network via MPI communications.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' While saving I/O overheads, this approach loses flexibility.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' For instance, a fault during a single particle propagation stops the entire application.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Thus existing online approaches, as will be detailled in the related work section (Section 6), usually do not support fault tolerance or dynamic load balancing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' In this paper we develop an alternate approach that leads to a high ef- ficient yet flexible framework.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The key to achieve this goal is the virtual- ization of particle propagations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' We turn a numerical model solver instance into a runner capable of propagating several particles one after the other with low overheads and idle times.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Each runner is coupled with a node-local distributed state cache enabling fast loads and stores of particles.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The caches are asynchronously persisted to the file system for checkpointing and load bal- ancing between runners.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Asynchronous prefetching of particles into the cache enables overlapping particle loads with the particle propagation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' A server organizes the work distribution to the runners and performs the centralized tasks of the particle filter update and (re-)sampling.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Runners and server are each executed as independent executables to support elasticity and facilitate fault tolerance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The association of these different features complemented with a fault tolerance protocol, leads to an elastic and resilient framework, minimizing data movements while enabling dynamic load balancing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Parti- cle virtualization enables to decouple resource allocation from the number 3 Figure 1: Initially particles are uniformly sampled.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' They are propagated to T1 where they are weighted taking into account observation data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Resampling leads to discard some particles with low weights (top and bottom), while others with high weights become parent of several ones (3 here).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' of particles.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The number of runners can vary during the execution either in reaction to failures and restarts, or to adapt to changing resource avail- ability dictated by external decision processes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The proposed architecture is designed for running at extreme scale, leveraging deep storage hierarchies and heterogeneous cluster designs of current and future supercomputers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' We strain our proposed particle filter framework with a realistic use-case, interfacing with the Weather Research and Forecasting (WRF, version 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='7.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='1) model [8].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' WRF is a widely used weather model for operational forecasting and research.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Using our particle filter, we are able to run 2,555 particles on 20,442 compute cores for WRF simulations on a European domain with 87 % efficiency.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The rest of the paper is structured as follows: Section 2 reviews the principles of particle filters and the associated workflow.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Section 3 presents the architecture of our proposed approach, while Section 4 is dedicated to experiments and Section 5 to discussion.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The papers ends with related work in Section 6 and a conclusion in Section 7.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Particle Filters In this section, we give a brief introduction on the particle filter formalism, focusing on properties that we exploit in our proposal.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' For a comprehensive 4 weighting resampling weighting initial To T1 Ti T2 Assimilation Cycle Assimilation Cycleintroduction, we refer to [1, 9].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Let M be a numerical model, that propagates a particle p from state xp,t−1 at time t − 1 to state xp,t at time t: xp,t = M(xp,t−1) + βt 1 Where β is a random forcing representing errors in the model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Let H be the projection operator from the state space to the observation space: y = H(x) + ϵt 2 Where ϵ is a random vector, representing the measurement errors.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The bootstrap particle filter formalism can be derived using Bayes’ the- orem: p(xt|yt) = p(yt|xt) p(xt) p(yt) 3 Where p(xt|yt) is the posterior Probability Density Function (PDF), p(xt) is the prior PDF, p(yt|xt) is the likelihood of observing yt if xt would represent the true state, and p(yt) is the evidence available.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The goal of the filtering formalism is to derive the posterior p(xt|yt), which describes the PDF of the state xt taking into account the evidence yt.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' In the bootstrap particle filter, the prior p(xt) is estimated via sampling an ensemble of P particles xp,t representing different model states p(xt) = 1 P P−1 � p=0 δ(xt − xp,t), 4 The likelihood p(yt|xt) is assumed to be known, estimated when calibrating the sensor.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' It is derived from the PDF of ϵ applied to the distance between state and observation yt − H(xt): p(yt|xt) = pϵ(yt − H(xt)) 5 The evidence p(yt) can be computed by: p(yt) = � p(yt|xt)p(xt) dxt 6 = P−1 � p=0 1 P p(yt|xp,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='t) 7 8 5 Putting all together and replacing the expressions in Bayes’ theorem (Equa- tion 3) we arrive to the expression for the posterior [1]: p(xt|yt) ≈ P−1 � p=0 ˆwp,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='t δ(xt − xp,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='t) 9 With ˆwp,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='t being the normalized particle weights: ˆwp,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='t = p(yt|xp,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='t) �P−1 q=0 p(yt|xq,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='t) = wp,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='t �P−1 q=0 wq,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='t 10 and wp,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='t being the unnormalized particle weights: wp,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='t = p(yt|xp,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='t) wp,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='t−1 11 Note that the initial weights are set equal to wp,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='0 = 1/P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Especially for high dimensional models, particle filters tend to suffer from weight degeneration, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=', one normalized weight is close to one and all the others are close to zero.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' A classical approach against ensemble degeneration is Sequential Importance Resampling (SIR) [10, 11].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The procedure of SIR consists in resampling particles from the posterior (Equation 9) at the end of the propagation step;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' P particles are randomly drawn, resampled, from the existing particles, each with a probability wp,t.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Low weighted particles be- come discarded, while high weighted particles can become the starting point of multiple particle propagations (Figure 1).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' More precisely, the resampling leads to the multiset P defined by the ordered pair (Q, α).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Where Q is the set of unique particles q in P, and αq the number of the occurrences of q in P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The particles q are hereinafter called parent particles.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The resampled particles are all assigned the same weight of wp,t = 1/P again.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Particles departing from the same parent may need to become stochas- tically perturbed if the model does not contain a stochastic component itself.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Otherwise, the trajectories of those particles would be identical.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Different flavors of SIR and resampling algorithms, like Residual Resam- pling, exist [12].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Some perform a resampling step after each propagation phase, while others make this dependent on criteria like the variance of the weights.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' In this paper we rely on SIR with resampling after each propagation phase.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' 6 Box 1 (a) The propagation of particle p depends only on the associated state xp,t and can be performed independently of other particles.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' (b) Weights wp,t depend only on the associated particle p and observa- tion vector yt, and can be computed independently of other weights and particles.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' (c) The filter update only depends on the weights wp,t, and not on the particles and associated states.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' (d) The states xp,t associated to the particles p remain unchanged during the filter update.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Box 1 lists the properties of particle filters that are the basis for our implementation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' We exploit property (d): In contrast to other DA techniques, such as EnKF, particle states remain unchanged during the filter update.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Parti- cles that have departed too much from the observations (low weights) are discarded, and the sample set is narrowed around the best particles (high weights).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The associated states, however, are not changed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Property (a) follows directly from Equation 11.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Property (b) results from decoupling the weight calculation from the filter update (decentralization).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The update itself, only consist of the weight normalization and particle resampling.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Fi- nally, property (c) is an intrinsic property of the bootstrap particle filter, since particles are either withdrawn or selected, but not changed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' In the fol- lowing sections, we will show how we can exploit those properties to improve efficiency of and resilience particle filter implementations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Architecture In this section we detail the proposed architecture to run a large number of particles with parallel numerical models.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The algorithm, as presented in Section 2, is a sequence of two main steps: 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' A first compute intensive massively parallel step where particles can be processed concurrently to: (a) propagate each sate: xp,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='t = M(xp,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='t−1),' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='7 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Batch Scheduler ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Server: ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='schedule propagations and cache evictions ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='gather weights and performs resampling ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Shared Particle Store ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='(PFS) ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Launcher: ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='submit and monitor jobs ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='manage recovery on server or ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='runner fault ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Checkpoints ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Observations ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Submit jobs ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Run Jobs ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Job status ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Monitoring ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Weights ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Cache evictions ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='and ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='particle propagations ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Parallel Runner: ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='propagates particles ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='calculates weights ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='sends weights to server ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Parallel Runner: ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='propagates particles ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='calculates weights ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='sends weights to server ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Parallel Runner: ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='propagate particles ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='calculate weights ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='send weights to server ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Local ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Particle ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Cache ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Particle states ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Figure 2: Architecture overview.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' (b) compute each unormalized weight from each state and observation data: wp,t = pϵ(yt − H(xp,t)) 12 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' A second lightweight step that requires to gather all unormalized weights wp,t, usually one double per weight, for normalization and resampling.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' We attribute the first step work to runners and the second step to a server.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' A runner is designed to propagate several particles one after the other with low overheads and idle times (Figure 2).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Each one is coupled with a node- local distributed cache enabling fast loads and stores of particles.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The caches are asynchronously persisted to the global file system for checkpointing and dynamic load balancing (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=', ensure global availability of the particles).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Be- cause resampling can lead to discard some particles, or duplicate others orig- inating from the same parent (with a local perturbation if needed), states need to be dynamically redistributed to runners to keep them evenly busy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The server drives the dynamic distribution of particle propagation tasks to runners.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Runners use the distributed cache to load from the file system the 8 missing states.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' This design ensures low communications between the server and runners, and reduced state movements.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The runners and the server run as independent executables, enabling to have a dynamically changing number of runners.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' This is a key feature used for fault tolerance and elasticity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Elas- ticity (sometimes also called maleability) is the ability to run under changing resource availability, here varying number of runners.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' In the following we detail this design: the runners (Section 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='1), the server (Section 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='2), the distributed cache (Section 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='3), the workflow be- tween these components (Section 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='4), the particle propagation scheduling (Section 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='5), the jobs monitoring (Section 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='6), and the fault tolerance pro- tocol (Section 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='7) before ending with additional implementation details (Sec- tion 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='8).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Runners Runners are built from the simulation code, often an advanced parallel code or even a coupling of several parallel codes, with significant start-up times to load and build the different internal data structures.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' To avoid paying the cost of a restart for each particle propagation, we augment the simulation code with a mechanism to store and load particle states.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' This is the base of particle virtualization: a runner can load a particle, propagate it, store the result, and repeat this as many times as necessary.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Runners are associated with a distributed cache to accelerate state loads and stores as detailled in Section 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Runners also compute the associated weights wp,t.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Hence, each runner also needs to load the observations yt once per cycle.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Notice that the size of the observations is typically much smaller than the size of the states xp,t.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Server The server is entrusted with multiple tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' First, it is responsible for scheduling the particle propagations to the runners (Section 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='5).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Second, it gathers the weights from the runners and performs the resampling at the end of each assimilation cycle.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Third, it controls the content of a distributed particle cache (Section 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='3).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' To collect the weights wp,t, the server is mes- saged from the runners after each propagation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' If there are still particles to propagate in the current cycle, the server responds to the message with an id uniquely defining a particle (hereinafter called particle-id) for the next propagation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' If not, the server performs the resampling and starts the new cycle by scheduling the sampled particles to the runners.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Very little data is 9 Runner 1 Server Node 1 Node n MPI Communicator Model process (master) Model process Helper process Helper process (master) Node local cache MPI communication ZMQ communication File transfer Runner M Node 1 Node n PFS Figure 3: Internal runner architecture and interactions with the server and global storage (PFS).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Communications with the server combine MPI and ZMQ data exchanges.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' exchanged between a runner and server.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The runners send the particle-id (a single int) and the corresponding weight (a single float), and the server responds with the particle-id next to propagate.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Distributed Particle Cache To allow multiple propagations of one particle on different runners, it is necessary to make them globally available.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' A straight forward approach is to store particles on global storage.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' However, on supercomputers global storage is subject to large throughput variability due to the high workload and the limited bandwidth.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Node-local storage, on the other hand, is only used by the processes that run on the nodes, and the bandwidth can be stacked.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Storing the particles locally results in scalable I/O performance, scaling linearly with the number of nodes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' To leverage node-local storage while still providing the particles globally, runners rely on a distributed particle cache.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Each runner executes helper processes (one per node) in addition to the model processes, where both groups of processes are associated with its own MPI communicator (Figure 3).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The model processes propagate the particles and store the associated states locally on the nodes allocated to the runner (RAM disk or other node-local storage when available).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The helper processes then stage the states from local ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='10 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Init ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Propagate x4 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Store ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='State x4 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Calculate ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='weight w4 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Load State x5 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Calculate ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='weight w5 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Load ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='State x6 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Init ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='App process 0 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Helper process ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Init ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Load ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='State x4 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Propagate x4 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Calculate ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='weight w4 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Calculate ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='weight w5 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='App process 1 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Propagate x5 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Propagate x5 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Request new state ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='from server ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Request new state ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='from server ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Send w4 to server ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Request new state ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='from server ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Time ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Init ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Load ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='State x1 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Propagate x1 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Store ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='State x1 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Calculate ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='weight w1 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Load State ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='x2 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Calculate ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='weight w2 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Load ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='State x3 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Init ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='App process 0 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Helper process ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Init ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Propagate x1 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Calculate ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='weight w1 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Calculate ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='weight w2 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='App process 1 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Propagate x2 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Propagate x2 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Request new state ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='from server ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Request new state ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='from server ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Send w1 to server ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Request new state ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='from server ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Parallel File System ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Runner 1 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Runner 2 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='Figure 4: Schematic Gantt diagram showing the activity of two runners (initialization ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='followed by two assimilation cycles).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Focus on how the helper process asynchronous loads and stores enables to shadow the parallel file system accesses.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' For sake of simplicity no cache is used here.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' 11 to global storage asynchronously, enabling to overlap the associated I/O costs (Figure 4).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Also notice that persisting particles to global storage acts as a particle checkpoint used by the fault tolerance protocol (Section 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='7).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' We allow keeping a number of particles in each of the runner caches to exploit property (d) from Box 1: resampling does not change the parti- cle states.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Hence, keeping propagated particles in the cache, increases the probability to find a particle locally for future propagations (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=', during the next cycle).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' If available in its local cache, a runner can propagate a parti- cle without loading it from global storage.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' To further minimize cache loads, runners implement an optimized cache eviction strategy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The eviction strat- egy becomes especially important if the cache capacity is exceeded by the accumulated size of the particles propagated during one cycle.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Because the runners have no knowledge about the status of the particle filtering (propa- gations, resampling), the evictions are controlled by the server and directed to the runners.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' As explained in Section 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='4, each time a particle has been stored in the cache by the model processes upon successful propagation, the helper pro- cesses copy it in the background to global storage.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Hence, all propagated particle states can be selected for eviction, since they are safely stored on global storage.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' When an eviction is required, the server selects a particle from the cache in the following order: 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' A particle from the previous cycle discarded by resampling;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' A parent particle from the current cycle for which all associated prop- agations have been performed, and all weights received;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The particle with the lowest weight propagated during the current cy- cle;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' A randomly selected particle.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The particle states for cases 1 and 2 can safely be removed from cache, since those particle are not needed anymore for future propagations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' In case 3, we select the particle state with the lowest weight, as it has the lowest probability of being selected for future cycles during the resampling.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Runners/Server Workflow Once a runner job has started, it dynamically connects to the server and requests a particle to propagate from it.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The server selects the particle fol- 12 lowing a scheduling policy described in Section 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The model checks the location of the particle.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' If already located inside the local cache, the prop- agation starts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Otherwise, the model processes request the helper processes to load the state into the cache.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The model processes block until the helper processes fetched the particle into the cache, and afterwards start the prop- agation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Once a particle propagation finishes, the model computes the associated weight wp and stores the particle into the cache.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Further, the weight and particle-id are sent to the helper processes and a new particle is requested for propagation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The helper processes, after receiving the weight from the model processes, stage the particle from the cache to global storage and afterwards sends the weight and particle-id to the server.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' This order ensures that the server receives a weight only after the corresponding particle is propagated and successfully stored on global storage.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The helper processes further prefetch particles in parallel to the propa- gations (Figure 4).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The goal is to avoid blocking the model processes while waiting for a particle load from global storage (cache miss).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Each time helper processes send a weight to the server, they also request the next-to-next particle-id to propagate.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' This particle is prefetched into the cache to become locally available for the next to follow propagation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Prefetching is suspended at the end of each propagation cycle, as propagation work for the next cycle becomes only known after the server has performed the resampling of all par- ticles.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Notice that a helper may need to cancel prefetching if the prefetched particle was in the meantime assigned to another runner, making idle the model process while waiting for the next particle to propagate.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' When the server makes such a decision to better balance the work load, it also takes care of ensuring coherency between runners.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Globally prefetching proved to be very efficient for overlapping particle state loads with propagation (Sec- tion 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='2).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Particle Propagation Scheduling In this section, we present the scheduling algorithm implemented on the server to distribute the particle propagations to the runners.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The algorithm aims to ensure an even load balancing between runners and minimizing the global particle loads, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' transfers of particles from global to local storage.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' 13 Compulsory state load Extra state load due to parallelization Runner work lists Figure 5: Two possible schedules of 24 propagation tasks of equal duration on 4 runners.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' All particles propagated from the same parent particle state have the same color (9 parents here).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Top schedule is optimal with 9 compulsory loads (one per parent), and one for the dark blue parent that cannot fit in one runner.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The bottom schedule, with 2 more sate loads, is a possible one that our on-line scheduling algorithm can produce.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' This is not optimal but still below the general Q+R−1 bound as the algorithm ensures that no more than R − 1 ”color cuts” occur and avoids the same runner loads more than once a given parent particle state.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content='1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Static Scheduling Let R be the number of runners.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Let P be the number of particles to propagate.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Resampling may lead to have some parent particles drawn to be propagated several times.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' Let Q the number of parent particles q, and αq the number of times the particle q needs to be propagated.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/FdE0T4oBgHgl3EQfzALi/content/2301.02668v1.pdf'} +page_content=' The total number of particles to propagate is: P = � 0≤q