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2504.01197	Eleni Adamidi	Eleni Adamidi, Panayiotis Deligiannis, Nikos Foutris, Thanasis Vergoulis	A Virtual Laboratory for Managing Computational Experiments	6 pages, 5 figures	null	null	null	cs.DC	http://creativecommons.org/licenses/by/4.0/	Computational experiments have become essential for scientific discovery, allowing researchers to test hypotheses, analyze complex datasets, and validate findings. However, as computational experiments grow in scale and complexity, ensuring reproducibility and managing detailed metadata becomes increasingly challenging, especially when orchestrating complex sequence of computational tasks. To address these challenges we have developed a virtual laboratory called SCHEMA lab, focusing on capturing rich metadata such as experiment configurations and performance metrics, to support computational reproducibility. SCHEMA lab enables researchers to create experiments by grouping together multiple executions and manage them throughout their life cycle. In this demonstration paper, we present the SCHEMA lab architecture, core functionalities, and implementation, emphasizing its potential to significantly enhance reproducibility and efficiency in computational research.	[ { "version": "v1", "created": "Tue, 1 Apr 2025 21:25:23 GMT" } ]	2025-04-03T00:00:00	[ [ "Adamidi", "Eleni", "" ], [ "Deligiannis", "Panayiotis", "" ], [ "Foutris", "Nikos", "" ], [ "Vergoulis", "Thanasis", "" ] ]	TITLE: A Virtual Laboratory for Managing Computational Experiments ABSTRACT: Computational experiments have become essential for scientific discovery, allowing researchers to test hypotheses, analyze complex datasets, and validate findings. However, as computational experiments grow in scale and complexity, ensuring reproducibility and managing detailed metadata becomes increasingly challenging, especially when orchestrating complex sequence of computational tasks. To address these challenges we have developed a virtual laboratory called SCHEMA lab, focusing on capturing rich metadata such as experiment configurations and performance metrics, to support computational reproducibility. SCHEMA lab enables researchers to create experiments by grouping together multiple executions and manage them throughout their life cycle. In this demonstration paper, we present the SCHEMA lab architecture, core functionalities, and implementation, emphasizing its potential to significantly enhance reproducibility and efficiency in computational research.
2504.01206	Pavel Vesel\'y	Aleksander {\L}ukasiewicz and Jakub T\v{e}tek and Pavel Vesel\'y	SplineSketch: Even More Accurate Quantiles with Error Guarantees	null	null	null	null	cs.DS cs.DB stat.CO	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Space-efficient estimation of quantiles in massive datasets is a fundamental problem with numerous applications in data monitoring and analysis. While theoretical research led to optimal algorithms, such as the Greenwald-Khanna algorithm or the KLL sketch, practitioners often use other sketches that perform significantly better in practice but lack theoretical guarantees. Most notably, the widely used t-digest has unbounded worst-case error. In this paper, we seek to get the best of both worlds. We present a new quantile summary, SplineSketch, for numeric data, offering near-optimal theoretical guarantees and outperforming t-digest by a factor of 2-20 on a range of synthetic and real-world datasets with non-skewed frequency distributions. To achieve such performance, we develop a novel approach that maintains a dynamic subdivision of the input range into buckets while fitting the input distribution using monotone cubic spline interpolation. The core challenge is implementing this method in a space-efficient manner while ensuring strong worst-case guarantees.	[ { "version": "v1", "created": "Tue, 1 Apr 2025 21:39:50 GMT" } ]	2025-04-03T00:00:00	[ [ "Łukasiewicz", "Aleksander", "" ], [ "Tětek", "Jakub", "" ], [ "Veselý", "Pavel", "" ] ]	TITLE: SplineSketch: Even More Accurate Quantiles with Error Guarantees ABSTRACT: Space-efficient estimation of quantiles in massive datasets is a fundamental problem with numerous applications in data monitoring and analysis. While theoretical research led to optimal algorithms, such as the Greenwald-Khanna algorithm or the KLL sketch, practitioners often use other sketches that perform significantly better in practice but lack theoretical guarantees. Most notably, the widely used t-digest has unbounded worst-case error. In this paper, we seek to get the best of both worlds. We present a new quantile summary, SplineSketch, for numeric data, offering near-optimal theoretical guarantees and outperforming t-digest by a factor of 2-20 on a range of synthetic and real-world datasets with non-skewed frequency distributions. To achieve such performance, we develop a novel approach that maintains a dynamic subdivision of the input range into buckets while fitting the input distribution using monotone cubic spline interpolation. The core challenge is implementing this method in a space-efficient manner while ensuring strong worst-case guarantees.
2504.01208	Jes\'us Garc\'ia-Ram\'irez	Ian Mateos Gonzalez, Estefani Jaramilla Nava, Abraham S\'anchez Morales, Jes\'us Garc\'ia-Ram\'irez and Ricardo Ramos-Aguilar	Lightweight Deep Models for Dermatological Disease Detection: A Study on Instance Selection and Channel Optimization	Submitted to Mexican Conference on Pattern Recognition 2025	null	null	null	eess.IV cs.AI cs.CV	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	The identification of dermatological disease is an important problem in Mexico according with different studies. Several works in literature use the datasets of different repositories without applying a study of the data behavior, especially in medical images domain. In this work, we propose a methodology to preprocess dermaMNIST dataset in order to improve its quality for the classification stage, where we use lightweight convolutional neural networks. In our results, we reduce the number of instances for the neural network training obtaining a similar performance of models as ResNet.	[ { "version": "v1", "created": "Tue, 1 Apr 2025 21:47:57 GMT" } ]	2025-04-03T00:00:00	[ [ "Gonzalez", "Ian Mateos", "" ], [ "Nava", "Estefani Jaramilla", "" ], [ "Morales", "Abraham Sánchez", "" ], [ "García-Ramírez", "Jesús", "" ], [ "Ramos-Aguilar", "Ricardo", "" ] ]	TITLE: Lightweight Deep Models for Dermatological Disease Detection: A Study on Instance Selection and Channel Optimization ABSTRACT: The identification of dermatological disease is an important problem in Mexico according with different studies. Several works in literature use the datasets of different repositories without applying a study of the data behavior, especially in medical images domain. In this work, we propose a methodology to preprocess dermaMNIST dataset in order to improve its quality for the classification stage, where we use lightweight convolutional neural networks. In our results, we reduce the number of instances for the neural network training obtaining a similar performance of models as ResNet.
2504.01213	Banafsheh Adami	Banafsheh Adami, Nima Karimian	GRU-AUNet: A Domain Adaptation Framework for Contactless Fingerprint Presentation Attack Detection	null	null	null	null	cs.CV	http://creativecommons.org/licenses/by/4.0/	Although contactless fingerprints offer user comfort, they are more vulnerable to spoofing. The current solution for anti-spoofing in the area of contactless fingerprints relies on domain adaptation learning, limiting their generalization and scalability. To address these limitations, we introduce GRU-AUNet, a domain adaptation approach that integrates a Swin Transformer-based UNet architecture with GRU-enhanced attention mechanisms, a Dynamic Filter Network in the bottleneck, and a combined Focal and Contrastive Loss function. Trained in both genuine and spoof fingerprint images, GRU-AUNet demonstrates robust resilience against presentation attacks, achieving an average BPCER of 0.09\% and APCER of 1.2\% in the CLARKSON, COLFISPOOF, and IIITD datasets, outperforming state-of-the-art domain adaptation methods.	[ { "version": "v1", "created": "Tue, 1 Apr 2025 22:02:41 GMT" } ]	2025-04-03T00:00:00	[ [ "Adami", "Banafsheh", "" ], [ "Karimian", "Nima", "" ] ]	TITLE: GRU-AUNet: A Domain Adaptation Framework for Contactless Fingerprint Presentation Attack Detection ABSTRACT: Although contactless fingerprints offer user comfort, they are more vulnerable to spoofing. The current solution for anti-spoofing in the area of contactless fingerprints relies on domain adaptation learning, limiting their generalization and scalability. To address these limitations, we introduce GRU-AUNet, a domain adaptation approach that integrates a Swin Transformer-based UNet architecture with GRU-enhanced attention mechanisms, a Dynamic Filter Network in the bottleneck, and a combined Focal and Contrastive Loss function. Trained in both genuine and spoof fingerprint images, GRU-AUNet demonstrates robust resilience against presentation attacks, achieving an average BPCER of 0.09\% and APCER of 1.2\% in the CLARKSON, COLFISPOOF, and IIITD datasets, outperforming state-of-the-art domain adaptation methods.
2504.01214	Salim Khazem	Salim Khazem, Jeremy Fix, C\'edric Pradalier	PolygoNet: Leveraging Simplified Polygonal Representation for Effective Image Classification	null	null	null	null	cs.CV cs.AI cs.LG	http://creativecommons.org/licenses/by/4.0/	Deep learning models have achieved significant success in various image related tasks. However, they often encounter challenges related to computational complexity and overfitting. In this paper, we propose an efficient approach that leverages polygonal representations of images using dominant points or contour coordinates. By transforming input images into these compact forms, our method significantly reduces computational requirements, accelerates training, and conserves resources making it suitable for real time and resource constrained applications. These representations inherently capture essential image features while filtering noise, providing a natural regularization effect that mitigates overfitting. The resulting lightweight models achieve performance comparable to state of the art methods using full resolution images while enabling deployment on edge devices. Extensive experiments on benchmark datasets validate the effectiveness of our approach in reducing complexity, improving generalization, and facilitating edge computing applications. This work demonstrates the potential of polygonal representations in advancing efficient and scalable deep learning solutions for real world scenarios. The code for the experiments of the paper is provided in https://github.com/salimkhazem/PolygoNet.	[ { "version": "v1", "created": "Tue, 1 Apr 2025 22:05:00 GMT" } ]	2025-04-03T00:00:00	[ [ "Khazem", "Salim", "" ], [ "Fix", "Jeremy", "" ], [ "Pradalier", "Cédric", "" ] ]	TITLE: PolygoNet: Leveraging Simplified Polygonal Representation for Effective Image Classification ABSTRACT: Deep learning models have achieved significant success in various image related tasks. However, they often encounter challenges related to computational complexity and overfitting. In this paper, we propose an efficient approach that leverages polygonal representations of images using dominant points or contour coordinates. By transforming input images into these compact forms, our method significantly reduces computational requirements, accelerates training, and conserves resources making it suitable for real time and resource constrained applications. These representations inherently capture essential image features while filtering noise, providing a natural regularization effect that mitigates overfitting. The resulting lightweight models achieve performance comparable to state of the art methods using full resolution images while enabling deployment on edge devices. Extensive experiments on benchmark datasets validate the effectiveness of our approach in reducing complexity, improving generalization, and facilitating edge computing applications. This work demonstrates the potential of polygonal representations in advancing efficient and scalable deep learning solutions for real world scenarios. The code for the experiments of the paper is provided in https://github.com/salimkhazem/PolygoNet.
2504.01216	Feng Chen	Feng Chen, Dror Ben-Zeev, Gillian Sparks, Arya Kadakia, Trevor Cohen	Detecting PTSD in Clinical Interviews: A Comparative Analysis of NLP Methods and Large Language Models	10 pages, 4 tables, 1 figure	null	null	null	cs.CL cs.AI cs.LG	http://creativecommons.org/licenses/by/4.0/	Post-Traumatic Stress Disorder (PTSD) remains underdiagnosed in clinical settings, presenting opportunities for automated detection to identify patients. This study evaluates natural language processing approaches for detecting PTSD from clinical interview transcripts. We compared general and mental health-specific transformer models (BERT/RoBERTa), embedding-based methods (SentenceBERT/LLaMA), and large language model prompting strategies (zero-shot/few-shot/chain-of-thought) using the DAIC-WOZ dataset. Domain-specific models significantly outperformed general models (Mental-RoBERTa F1=0.643 vs. RoBERTa-base 0.485). LLaMA embeddings with neural networks achieved the highest performance (F1=0.700). Zero-shot prompting using DSM-5 criteria yielded competitive results without training data (F1=0.657). Performance varied significantly across symptom severity and comorbidity status, with higher accuracy for severe PTSD cases and patients with comorbid depression. Our findings highlight the potential of domain-adapted embeddings and LLMs for scalable screening while underscoring the need for improved detection of nuanced presentations and offering insights for developing clinically viable AI tools for PTSD assessment.	[ { "version": "v1", "created": "Tue, 1 Apr 2025 22:06:28 GMT" } ]	2025-04-03T00:00:00	[ [ "Chen", "Feng", "" ], [ "Ben-Zeev", "Dror", "" ], [ "Sparks", "Gillian", "" ], [ "Kadakia", "Arya", "" ], [ "Cohen", "Trevor", "" ] ]	TITLE: Detecting PTSD in Clinical Interviews: A Comparative Analysis of NLP Methods and Large Language Models ABSTRACT: Post-Traumatic Stress Disorder (PTSD) remains underdiagnosed in clinical settings, presenting opportunities for automated detection to identify patients. This study evaluates natural language processing approaches for detecting PTSD from clinical interview transcripts. We compared general and mental health-specific transformer models (BERT/RoBERTa), embedding-based methods (SentenceBERT/LLaMA), and large language model prompting strategies (zero-shot/few-shot/chain-of-thought) using the DAIC-WOZ dataset. Domain-specific models significantly outperformed general models (Mental-RoBERTa F1=0.643 vs. RoBERTa-base 0.485). LLaMA embeddings with neural networks achieved the highest performance (F1=0.700). Zero-shot prompting using DSM-5 criteria yielded competitive results without training data (F1=0.657). Performance varied significantly across symptom severity and comorbidity status, with higher accuracy for severe PTSD cases and patients with comorbid depression. Our findings highlight the potential of domain-adapted embeddings and LLMs for scalable screening while underscoring the need for improved detection of nuanced presentations and offering insights for developing clinically viable AI tools for PTSD assessment.
2504.01218	Piyush Nagasubramaniam	Piyush Nagasubramaniam (1), Neeraj Karamchandani (1), Chen Wu (2), Sencun Zhu (1) ((1) The Pennsylvania State University, (2) Meta)	Prompting Forgetting: Unlearning in GANs via Textual Guidance	null	null	null	null	cs.LG cs.CV	http://creativecommons.org/licenses/by-nc-sa/4.0/	State-of-the-art generative models exhibit powerful image-generation capabilities, introducing various ethical and legal challenges to service providers hosting these models. Consequently, Content Removal Techniques (CRTs) have emerged as a growing area of research to control outputs without full-scale retraining. Recent work has explored the use of Machine Unlearning in generative models to address content removal. However, the focus of such research has been on diffusion models, and unlearning in Generative Adversarial Networks (GANs) has remained largely unexplored. We address this gap by proposing Text-to-Unlearn, a novel framework that selectively unlearns concepts from pre-trained GANs using only text prompts, enabling feature unlearning, identity unlearning, and fine-grained tasks like expression and multi-attribute removal in models trained on human faces. Leveraging natural language descriptions, our approach guides the unlearning process without requiring additional datasets or supervised fine-tuning, offering a scalable and efficient solution. To evaluate its effectiveness, we introduce an automatic unlearning assessment method adapted from state-of-the-art image-text alignment metrics, providing a comprehensive analysis of the unlearning methodology. To our knowledge, Text-to-Unlearn is the first cross-modal unlearning framework for GANs, representing a flexible and efficient advancement in managing generative model behavior.	[ { "version": "v1", "created": "Tue, 1 Apr 2025 22:18:40 GMT" } ]	2025-04-03T00:00:00	[ [ "Nagasubramaniam", "Piyush", "", "The Pennsylvania State University" ], [ "Karamchandani", "Neeraj", "", "The Pennsylvania State University" ], [ "Wu", "Chen", "", "Meta" ], [ "Zhu", "Sencun", "", "The Pennsylvania State University" ] ]	TITLE: Prompting Forgetting: Unlearning in GANs via Textual Guidance ABSTRACT: State-of-the-art generative models exhibit powerful image-generation capabilities, introducing various ethical and legal challenges to service providers hosting these models. Consequently, Content Removal Techniques (CRTs) have emerged as a growing area of research to control outputs without full-scale retraining. Recent work has explored the use of Machine Unlearning in generative models to address content removal. However, the focus of such research has been on diffusion models, and unlearning in Generative Adversarial Networks (GANs) has remained largely unexplored. We address this gap by proposing Text-to-Unlearn, a novel framework that selectively unlearns concepts from pre-trained GANs using only text prompts, enabling feature unlearning, identity unlearning, and fine-grained tasks like expression and multi-attribute removal in models trained on human faces. Leveraging natural language descriptions, our approach guides the unlearning process without requiring additional datasets or supervised fine-tuning, offering a scalable and efficient solution. To evaluate its effectiveness, we introduce an automatic unlearning assessment method adapted from state-of-the-art image-text alignment metrics, providing a comprehensive analysis of the unlearning methodology. To our knowledge, Text-to-Unlearn is the first cross-modal unlearning framework for GANs, representing a flexible and efficient advancement in managing generative model behavior.
2504.01223	Alexey Miroshnikov	Ryan Franks, Alexey Miroshnikov	Explainable post-training bias mitigation with distribution-based fairness metrics	37 pages, 6 figures	null	null	null	cs.LG math.PR	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	We develop a novel optimization framework with distribution-based fairness constraints for efficiently producing demographically blind, explainable models across a wide range of fairness levels. This is accomplished through post-processing, avoiding the need for retraining. Our framework, which is based on stochastic gradient descent, can be applied to a wide range of model types, with a particular emphasis on the post-processing of gradient-boosted decision trees. Additionally, we design a broad class of interpretable global bias metrics compatible with our method by building on previous work. We empirically test our methodology on a variety of datasets and compare it to other methods.	[ { "version": "v1", "created": "Tue, 1 Apr 2025 22:22:25 GMT" } ]	2025-04-03T00:00:00	[ [ "Franks", "Ryan", "" ], [ "Miroshnikov", "Alexey", "" ] ]	TITLE: Explainable post-training bias mitigation with distribution-based fairness metrics ABSTRACT: We develop a novel optimization framework with distribution-based fairness constraints for efficiently producing demographically blind, explainable models across a wide range of fairness levels. This is accomplished through post-processing, avoiding the need for retraining. Our framework, which is based on stochastic gradient descent, can be applied to a wide range of model types, with a particular emphasis on the post-processing of gradient-boosted decision trees. Additionally, we design a broad class of interpretable global bias metrics compatible with our method by building on previous work. We empirically test our methodology on a variety of datasets and compare it to other methods.
2504.01228	Mansoor Rezghi	Kimia haghjooei, Mansoor Rezghi	TenAd: A Tensor-based Low-rank Black Box Adversarial Attack for Video Classification	null	null	null	null	cs.CV cs.AI	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Deep learning models have achieved remarkable success in computer vision but remain vulnerable to adversarial attacks, particularly in black-box settings where model details are unknown. Existing adversarial attack methods(even those works with key frames) often treat video data as simple vectors, ignoring their inherent multi-dimensional structure, and require a large number of queries, making them inefficient and detectable. In this paper, we propose \textbf{TenAd}, a novel tensor-based low-rank adversarial attack that leverages the multi-dimensional properties of video data by representing videos as fourth-order tensors. By exploiting low-rank attack, our method significantly reduces the search space and the number of queries needed to generate adversarial examples in black-box settings. Experimental results on standard video classification datasets demonstrate that \textbf{TenAd} effectively generates imperceptible adversarial perturbations while achieving higher attack success rates and query efficiency compared to state-of-the-art methods. Our approach outperforms existing black-box adversarial attacks in terms of success rate, query efficiency, and perturbation imperceptibility, highlighting the potential of tensor-based methods for adversarial attacks on video models.	[ { "version": "v1", "created": "Tue, 1 Apr 2025 22:35:28 GMT" } ]	2025-04-03T00:00:00	[ [ "haghjooei", "Kimia", "" ], [ "Rezghi", "Mansoor", "" ] ]	TITLE: TenAd: A Tensor-based Low-rank Black Box Adversarial Attack for Video Classification ABSTRACT: Deep learning models have achieved remarkable success in computer vision but remain vulnerable to adversarial attacks, particularly in black-box settings where model details are unknown. Existing adversarial attack methods(even those works with key frames) often treat video data as simple vectors, ignoring their inherent multi-dimensional structure, and require a large number of queries, making them inefficient and detectable. In this paper, we propose \textbf{TenAd}, a novel tensor-based low-rank adversarial attack that leverages the multi-dimensional properties of video data by representing videos as fourth-order tensors. By exploiting low-rank attack, our method significantly reduces the search space and the number of queries needed to generate adversarial examples in black-box settings. Experimental results on standard video classification datasets demonstrate that \textbf{TenAd} effectively generates imperceptible adversarial perturbations while achieving higher attack success rates and query efficiency compared to state-of-the-art methods. Our approach outperforms existing black-box adversarial attacks in terms of success rate, query efficiency, and perturbation imperceptibility, highlighting the potential of tensor-based methods for adversarial attacks on video models.
2504.01243	Jaskaran Singh Walia	Jaskaran Singh Walia, Shravan Venkatraman, Pavithra LK	FUSION: Frequency-guided Underwater Spatial Image recOnstructioN	null	null	null	null	cs.CV cs.AI cs.LG cs.RO eess.IV	http://creativecommons.org/licenses/by/4.0/	Underwater images suffer from severe degradations, including color distortions, reduced visibility, and loss of structural details due to wavelength-dependent attenuation and scattering. Existing enhancement methods primarily focus on spatial-domain processing, neglecting the frequency domain's potential to capture global color distributions and long-range dependencies. To address these limitations, we propose FUSION, a dual-domain deep learning framework that jointly leverages spatial and frequency domain information. FUSION independently processes each RGB channel through multi-scale convolutional kernels and adaptive attention mechanisms in the spatial domain, while simultaneously extracting global structural information via FFT-based frequency attention. A Frequency Guided Fusion module integrates complementary features from both domains, followed by inter-channel fusion and adaptive channel recalibration to ensure balanced color distributions. Extensive experiments on benchmark datasets (UIEB, EUVP, SUIM-E) demonstrate that FUSION achieves state-of-the-art performance, consistently outperforming existing methods in reconstruction fidelity (highest PSNR of 23.717 dB and SSIM of 0.883 on UIEB), perceptual quality (lowest LPIPS of 0.112 on UIEB), and visual enhancement metrics (best UIQM of 3.414 on UIEB), while requiring significantly fewer parameters (0.28M) and lower computational complexity, demonstrating its suitability for real-time underwater imaging applications.	[ { "version": "v1", "created": "Tue, 1 Apr 2025 23:16:19 GMT" } ]	2025-04-03T00:00:00	[ [ "Walia", "Jaskaran Singh", "" ], [ "Venkatraman", "Shravan", "" ], [ "LK", "Pavithra", "" ] ]	TITLE: FUSION: Frequency-guided Underwater Spatial Image recOnstructioN ABSTRACT: Underwater images suffer from severe degradations, including color distortions, reduced visibility, and loss of structural details due to wavelength-dependent attenuation and scattering. Existing enhancement methods primarily focus on spatial-domain processing, neglecting the frequency domain's potential to capture global color distributions and long-range dependencies. To address these limitations, we propose FUSION, a dual-domain deep learning framework that jointly leverages spatial and frequency domain information. FUSION independently processes each RGB channel through multi-scale convolutional kernels and adaptive attention mechanisms in the spatial domain, while simultaneously extracting global structural information via FFT-based frequency attention. A Frequency Guided Fusion module integrates complementary features from both domains, followed by inter-channel fusion and adaptive channel recalibration to ensure balanced color distributions. Extensive experiments on benchmark datasets (UIEB, EUVP, SUIM-E) demonstrate that FUSION achieves state-of-the-art performance, consistently outperforming existing methods in reconstruction fidelity (highest PSNR of 23.717 dB and SSIM of 0.883 on UIEB), perceptual quality (lowest LPIPS of 0.112 on UIEB), and visual enhancement metrics (best UIQM of 3.414 on UIEB), while requiring significantly fewer parameters (0.28M) and lower computational complexity, demonstrating its suitability for real-time underwater imaging applications.
2504.01246	Biswadeep Chakraborty	Biswadeep Chakraborty, Hemant Kumawat, Beomseok Kang, Saibal Mukhopadhyay	Dynamic Graph Structure Estimation for Learning Multivariate Point Process using Spiking Neural Networks	18 pages, 3 figures	null	null	null	cs.LG cs.AI	http://creativecommons.org/licenses/by/4.0/	Modeling and predicting temporal point processes (TPPs) is critical in domains such as neuroscience, epidemiology, finance, and social sciences. We introduce the Spiking Dynamic Graph Network (SDGN), a novel framework that leverages the temporal processing capabilities of spiking neural networks (SNNs) and spike-timing-dependent plasticity (STDP) to dynamically estimate underlying spatio-temporal functional graphs. Unlike existing methods that rely on predefined or static graph structures, SDGN adapts to any dataset by learning dynamic spatio-temporal dependencies directly from the event data, enhancing generalizability and robustness. While SDGN offers significant improvements over prior methods, we acknowledge its limitations in handling dense graphs and certain non-Gaussian dependencies, providing opportunities for future refinement. Our evaluations, conducted on both synthetic and real-world datasets including NYC Taxi, 911, Reddit, and Stack Overflow, demonstrate that SDGN achieves superior predictive accuracy while maintaining computational efficiency. Furthermore, we include ablation studies to highlight the contributions of its core components.	[ { "version": "v1", "created": "Tue, 1 Apr 2025 23:23:10 GMT" } ]	2025-04-03T00:00:00	[ [ "Chakraborty", "Biswadeep", "" ], [ "Kumawat", "Hemant", "" ], [ "Kang", "Beomseok", "" ], [ "Mukhopadhyay", "Saibal", "" ] ]	TITLE: Dynamic Graph Structure Estimation for Learning Multivariate Point Process using Spiking Neural Networks ABSTRACT: Modeling and predicting temporal point processes (TPPs) is critical in domains such as neuroscience, epidemiology, finance, and social sciences. We introduce the Spiking Dynamic Graph Network (SDGN), a novel framework that leverages the temporal processing capabilities of spiking neural networks (SNNs) and spike-timing-dependent plasticity (STDP) to dynamically estimate underlying spatio-temporal functional graphs. Unlike existing methods that rely on predefined or static graph structures, SDGN adapts to any dataset by learning dynamic spatio-temporal dependencies directly from the event data, enhancing generalizability and robustness. While SDGN offers significant improvements over prior methods, we acknowledge its limitations in handling dense graphs and certain non-Gaussian dependencies, providing opportunities for future refinement. Our evaluations, conducted on both synthetic and real-world datasets including NYC Taxi, 911, Reddit, and Stack Overflow, demonstrate that SDGN achieves superior predictive accuracy while maintaining computational efficiency. Furthermore, we include ablation studies to highlight the contributions of its core components.
2504.01248	Lev Sorokin	Rafael Giebisch and Ken E. Friedl and Lev Sorokin and Andrea Stocco	Automated Factual Benchmarking for In-Car Conversational Systems using Large Language Models	Accepted in IEEE Intelligent Vehicles Symposium Conference (IV 2025)	null	null	null	cs.CL cs.AI cs.LG cs.SE	http://creativecommons.org/licenses/by/4.0/	In-car conversational systems bring the promise to improve the in-vehicle user experience. Modern conversational systems are based on Large Language Models (LLMs), which makes them prone to errors such as hallucinations, i.e., inaccurate, fictitious, and therefore factually incorrect information. In this paper, we present an LLM-based methodology for the automatic factual benchmarking of in-car conversational systems. We instantiate our methodology with five LLM-based methods, leveraging ensembling techniques and diverse personae to enhance agreement and minimize hallucinations. We use our methodology to evaluate CarExpert, an in-car retrieval-augmented conversational question answering system, with respect to the factual correctness to a vehicle's manual. We produced a novel dataset specifically created for the in-car domain, and tested our methodology against an expert evaluation. Our results show that the combination of GPT-4 with the Input Output Prompting achieves over 90 per cent factual correctness agreement rate with expert evaluations, other than being the most efficient approach yielding an average response time of 4.5s. Our findings suggest that LLM-based testing constitutes a viable approach for the validation of conversational systems regarding their factual correctness.	[ { "version": "v1", "created": "Tue, 1 Apr 2025 23:25:30 GMT" } ]	2025-04-03T00:00:00	[ [ "Giebisch", "Rafael", "" ], [ "Friedl", "Ken E.", "" ], [ "Sorokin", "Lev", "" ], [ "Stocco", "Andrea", "" ] ]	TITLE: Automated Factual Benchmarking for In-Car Conversational Systems using Large Language Models ABSTRACT: In-car conversational systems bring the promise to improve the in-vehicle user experience. Modern conversational systems are based on Large Language Models (LLMs), which makes them prone to errors such as hallucinations, i.e., inaccurate, fictitious, and therefore factually incorrect information. In this paper, we present an LLM-based methodology for the automatic factual benchmarking of in-car conversational systems. We instantiate our methodology with five LLM-based methods, leveraging ensembling techniques and diverse personae to enhance agreement and minimize hallucinations. We use our methodology to evaluate CarExpert, an in-car retrieval-augmented conversational question answering system, with respect to the factual correctness to a vehicle's manual. We produced a novel dataset specifically created for the in-car domain, and tested our methodology against an expert evaluation. Our results show that the combination of GPT-4 with the Input Output Prompting achieves over 90 per cent factual correctness agreement rate with expert evaluations, other than being the most efficient approach yielding an average response time of 4.5s. Our findings suggest that LLM-based testing constitutes a viable approach for the validation of conversational systems regarding their factual correctness.
2504.01253	Niharika Dadu	Niharika Dadu, Harsh Vardhan Singh, Romi Banerjee (Indian Institute of Technology Jodhpur)	Grade Guard: A Smart System for Short Answer Automated Grading	11 pages, 18 figures	null	10.36227/techrxiv.174114489.93670234/v1	null	cs.CL	http://creativecommons.org/licenses/by/4.0/	The advent of large language models (LLMs) in the education sector has provided impetus to automate grading short answer questions. LLMs make evaluating short answers very efficient, thus addressing issues like staff shortage. However, in the task of Automated Short Answer Grading (ASAG), LLM responses are influenced by diverse perspectives in their training dataset, leading to inaccuracies in evaluating nuanced or partially correct answers. To address this challenge, we propose a novel framework, Grade Guard. 1. To enhance the task-based specialization of the LLMs, the temperature parameter has been fine-tuned using Root Mean Square Error (RMSE). 2. Unlike traditional approaches, LLMs in Grade Guard compute an Indecisiveness Score (IS) along with the grade to reflect uncertainty in predicted grades. 3. Introduced Confidence-Aware Loss (CAL) to generate an optimized Indecisiveness Score (IS). 4. To improve reliability, self-reflection based on the optimized IS has been introduced into the framework, enabling human re-evaluation to minimize incorrect grade assignments. Our experimentation shows that the best setting of Grade Guard outperforms traditional methods by 19.16% RMSE in Upstage Solar Pro, 23.64% RMSE in Upstage Solar Mini, 4.00% RMSE in Gemini 1.5 Flash, and 10.20% RMSE in GPT 4-o Mini. Future work includes improving interpretability by generating rationales for grades to enhance accuracy. Expanding benchmark datasets and annotating them with domain-specific nuances will enhance grading accuracy. Finally, analyzing feedback to enhance confidence in predicted grades, reduce biases, optimize grading criteria, and personalize learning while supporting multilingual grading systems will make the solution more accurate, adaptable, fair, and inclusive.	[ { "version": "v1", "created": "Tue, 1 Apr 2025 23:45:44 GMT" } ]	2025-04-03T00:00:00	[ [ "Dadu", "Niharika", "", "Indian Institute of\n Technology Jodhpur" ], [ "Singh", "Harsh Vardhan", "", "Indian Institute of\n Technology Jodhpur" ], [ "Banerjee", "Romi", "", "Indian Institute of\n Technology Jodhpur" ] ]	TITLE: Grade Guard: A Smart System for Short Answer Automated Grading ABSTRACT: The advent of large language models (LLMs) in the education sector has provided impetus to automate grading short answer questions. LLMs make evaluating short answers very efficient, thus addressing issues like staff shortage. However, in the task of Automated Short Answer Grading (ASAG), LLM responses are influenced by diverse perspectives in their training dataset, leading to inaccuracies in evaluating nuanced or partially correct answers. To address this challenge, we propose a novel framework, Grade Guard. 1. To enhance the task-based specialization of the LLMs, the temperature parameter has been fine-tuned using Root Mean Square Error (RMSE). 2. Unlike traditional approaches, LLMs in Grade Guard compute an Indecisiveness Score (IS) along with the grade to reflect uncertainty in predicted grades. 3. Introduced Confidence-Aware Loss (CAL) to generate an optimized Indecisiveness Score (IS). 4. To improve reliability, self-reflection based on the optimized IS has been introduced into the framework, enabling human re-evaluation to minimize incorrect grade assignments. Our experimentation shows that the best setting of Grade Guard outperforms traditional methods by 19.16% RMSE in Upstage Solar Pro, 23.64% RMSE in Upstage Solar Mini, 4.00% RMSE in Gemini 1.5 Flash, and 10.20% RMSE in GPT 4-o Mini. Future work includes improving interpretability by generating rationales for grades to enhance accuracy. Expanding benchmark datasets and annotating them with domain-specific nuances will enhance grading accuracy. Finally, analyzing feedback to enhance confidence in predicted grades, reduce biases, optimize grading criteria, and personalize learning while supporting multilingual grading systems will make the solution more accurate, adaptable, fair, and inclusive.
2504.01257	Biswadeep Chakraborty	Biswadeep Chakraborty, Saibal Mukhopadhyay	FLAMES: A Hybrid Spiking-State Space Model for Adaptive Memory Retention in Event-Based Learning	9 pages, 6 figures	null	null	null	cs.LG	http://creativecommons.org/licenses/by/4.0/	We propose \textbf{FLAMES (Fast Long-range Adaptive Memory for Event-based Systems)}, a novel hybrid framework integrating structured state-space dynamics with event-driven computation. At its core, the \textit{Spike-Aware HiPPO (SA-HiPPO) mechanism} dynamically adjusts memory retention based on inter-spike intervals, preserving both short- and long-range dependencies. To maintain computational efficiency, we introduce a normal-plus-low-rank (NPLR) decomposition, reducing complexity from $\mathcal{O}(N^2)$ to $\mathcal{O}(Nr)$. FLAMES achieves state-of-the-art results on the Long Range Arena benchmark and event datasets like HAR-DVS and Celex-HAR. By bridging neuromorphic computing and structured sequence modeling, FLAMES enables scalable long-range reasoning in event-driven systems.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 00:08:19 GMT" } ]	2025-04-03T00:00:00	[ [ "Chakraborty", "Biswadeep", "" ], [ "Mukhopadhyay", "Saibal", "" ] ]	TITLE: FLAMES: A Hybrid Spiking-State Space Model for Adaptive Memory Retention in Event-Based Learning ABSTRACT: We propose \textbf{FLAMES (Fast Long-range Adaptive Memory for Event-based Systems)}, a novel hybrid framework integrating structured state-space dynamics with event-driven computation. At its core, the \textit{Spike-Aware HiPPO (SA-HiPPO) mechanism} dynamically adjusts memory retention based on inter-spike intervals, preserving both short- and long-range dependencies. To maintain computational efficiency, we introduce a normal-plus-low-rank (NPLR) decomposition, reducing complexity from $\mathcal{O}(N^2)$ to $\mathcal{O}(Nr)$. FLAMES achieves state-of-the-art results on the Long Range Arena benchmark and event datasets like HAR-DVS and Celex-HAR. By bridging neuromorphic computing and structured sequence modeling, FLAMES enables scalable long-range reasoning in event-driven systems.
2504.01261	Bahadir Kocer	Thomas Pritchard and Saifullah Ijaz and Ronald Clark and Basaran Bahadir Kocer	ForestVO: Enhancing Visual Odometry in Forest Environments through ForestGlue	Accepted to the IEEE Robotics and Automation Letters	null	null	null	cs.RO cs.CV	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Recent advancements in visual odometry systems have improved autonomous navigation; however, challenges persist in complex environments like forests, where dense foliage, variable lighting, and repetitive textures compromise feature correspondence accuracy. To address these challenges, we introduce ForestGlue, enhancing the SuperPoint feature detector through four configurations - grayscale, RGB, RGB-D, and stereo-vision - optimised for various sensing modalities. For feature matching, we employ LightGlue or SuperGlue, retrained with synthetic forest data. ForestGlue achieves comparable pose estimation accuracy to baseline models but requires only 512 keypoints - just 25% of the baseline's 2048 - to reach an LO-RANSAC AUC score of 0.745 at a 10{\deg} threshold. With only a quarter of keypoints needed, ForestGlue significantly reduces computational overhead, demonstrating effectiveness in dynamic forest environments, and making it suitable for real-time deployment on resource-constrained platforms. By combining ForestGlue with a transformer-based pose estimation model, we propose ForestVO, which estimates relative camera poses using matched 2D pixel coordinates between frames. On challenging TartanAir forest sequences, ForestVO achieves an average relative pose error (RPE) of 1.09 m and a kitti_score of 2.33%, outperforming direct-based methods like DSO by 40% in dynamic scenes. Despite using only 10% of the dataset for training, ForestVO maintains competitive performance with TartanVO while being a significantly lighter model. This work establishes an end-to-end deep learning pipeline specifically tailored for visual odometry in forested environments, leveraging forest-specific training data to optimise feature correspondence and pose estimation, thereby enhancing the accuracy and robustness of autonomous navigation systems.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 00:20:05 GMT" } ]	2025-04-03T00:00:00	[ [ "Pritchard", "Thomas", "" ], [ "Ijaz", "Saifullah", "" ], [ "Clark", "Ronald", "" ], [ "Kocer", "Basaran Bahadir", "" ] ]	TITLE: ForestVO: Enhancing Visual Odometry in Forest Environments through ForestGlue ABSTRACT: Recent advancements in visual odometry systems have improved autonomous navigation; however, challenges persist in complex environments like forests, where dense foliage, variable lighting, and repetitive textures compromise feature correspondence accuracy. To address these challenges, we introduce ForestGlue, enhancing the SuperPoint feature detector through four configurations - grayscale, RGB, RGB-D, and stereo-vision - optimised for various sensing modalities. For feature matching, we employ LightGlue or SuperGlue, retrained with synthetic forest data. ForestGlue achieves comparable pose estimation accuracy to baseline models but requires only 512 keypoints - just 25% of the baseline's 2048 - to reach an LO-RANSAC AUC score of 0.745 at a 10{\deg} threshold. With only a quarter of keypoints needed, ForestGlue significantly reduces computational overhead, demonstrating effectiveness in dynamic forest environments, and making it suitable for real-time deployment on resource-constrained platforms. By combining ForestGlue with a transformer-based pose estimation model, we propose ForestVO, which estimates relative camera poses using matched 2D pixel coordinates between frames. On challenging TartanAir forest sequences, ForestVO achieves an average relative pose error (RPE) of 1.09 m and a kitti_score of 2.33%, outperforming direct-based methods like DSO by 40% in dynamic scenes. Despite using only 10% of the dataset for training, ForestVO maintains competitive performance with TartanVO while being a significantly lighter model. This work establishes an end-to-end deep learning pipeline specifically tailored for visual odometry in forested environments, leveraging forest-specific training data to optimise feature correspondence and pose estimation, thereby enhancing the accuracy and robustness of autonomous navigation systems.
2504.01292	Yongyi Liu	Yongyi Liu, Ahmed Mahmood, Amr Magdy, Minyao Zhu	SOLAR: Scalable Distributed Spatial Joins through Learning-based Optimization	13 pages, current in submission to VLDB	null	null	null	cs.DB	http://creativecommons.org/licenses/by/4.0/	The proliferation of location-based services has led to massive spatial data generation. Spatial join is a crucial database operation that identifies pairs of objects from two spatial datasets based on spatial relationships. Due to the intensive computational demands, spatial joins are often executed in a distributed manner across clusters. However, current systems fail to recognize similarities in the partitioning of spatial data, leading to redundant computations and increased overhead. Recently, incorporating machine learning optimizations into database operations has enhanced efficiency in traditional joins by predicting optimal strategies. However, applying these optimizations to spatial joins poses challenges due to the complex nature of spatial relationships and the variability of spatial data. This paper introduces SOLAR, scalable distributed spatial joins through learning-based optimization. SOLAR operates through offline and online phases. In the offline phase, it learns balanced spatial partitioning based on the similarities between datasets in query workloads seen so far. In the online phase, when a new join query is received, SOLAR evaluates the similarity between the datasets in the new query and the already-seen workloads using the trained learning model. Then, it decides to either reuse an existing partitioner, avoiding unnecessary computational overhead, or partition from scratch. Our extensive experimental evaluation on real-world datasets demonstrates that SOLAR achieves up to 3.6X speedup in overall join runtime and 2.71X speedup in partitioning time compared to state-of-the-art systems.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 01:52:52 GMT" } ]	2025-04-03T00:00:00	[ [ "Liu", "Yongyi", "" ], [ "Mahmood", "Ahmed", "" ], [ "Magdy", "Amr", "" ], [ "Zhu", "Minyao", "" ] ]	TITLE: SOLAR: Scalable Distributed Spatial Joins through Learning-based Optimization ABSTRACT: The proliferation of location-based services has led to massive spatial data generation. Spatial join is a crucial database operation that identifies pairs of objects from two spatial datasets based on spatial relationships. Due to the intensive computational demands, spatial joins are often executed in a distributed manner across clusters. However, current systems fail to recognize similarities in the partitioning of spatial data, leading to redundant computations and increased overhead. Recently, incorporating machine learning optimizations into database operations has enhanced efficiency in traditional joins by predicting optimal strategies. However, applying these optimizations to spatial joins poses challenges due to the complex nature of spatial relationships and the variability of spatial data. This paper introduces SOLAR, scalable distributed spatial joins through learning-based optimization. SOLAR operates through offline and online phases. In the offline phase, it learns balanced spatial partitioning based on the similarities between datasets in query workloads seen so far. In the online phase, when a new join query is received, SOLAR evaluates the similarity between the datasets in the new query and the already-seen workloads using the trained learning model. Then, it decides to either reuse an existing partitioner, avoiding unnecessary computational overhead, or partition from scratch. Our extensive experimental evaluation on real-world datasets demonstrates that SOLAR achieves up to 3.6X speedup in overall join runtime and 2.71X speedup in partitioning time compared to state-of-the-art systems.
2504.01294	Hui Li	Hui Li, Zhen Dong, Siao Wang, Hui Zhang, Liwei Shen, Xin Peng, Dongdong She	Extracting Formal Specifications from Documents Using LLMs for Automated Testing	null	null	null	null	cs.SE	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Automated testing plays a crucial role in ensuring software security. It heavily relies on formal specifications to validate the correctness of the system behavior. However, the main approach to defining these formal specifications is through manual analysis of software documents, which requires a significant amount of engineering effort from experienced researchers and engineers. Meanwhile, system update further increases the human labor cost to maintain a corresponding formal specification, making the manual analysis approach a time-consuming and error-prone task. Recent advances in Large Language Models (LLMs) have demonstrated promising capabilities in natural language understanding. Yet, the feasibility of using LLMs to automate the extraction of formal specifications from software documents remains unexplored. We conduct an empirical study by constructing a comprehensive dataset comprising 603 specifications from 37 documents across three representative open-source software. We then evaluate the most recent LLMs' capabilities in extracting formal specifications from documents in an end-to-end fashion, including GPT-4o, Claude, and Llama. Our study demonstrates the application of LLMs in formal specification extraction tasks while identifying two major limitations: specification oversimplification and specification fabrication. We attribute these deficiencies to the LLMs' inherent limitations in processing and expressive capabilities, as well as their tendency to fabricate fictional information. Inspired by human cognitive processes, we propose a two-stage method, annotation-then-conversion, to address these challenges. Our method demonstrates significant improvements over the end-to-end method, with a 29.2% increase in the number of correctly extracted specifications and a 14.0% improvement in average accuracy. In particular, our best-performing LLM achieves an accuracy of 71.6%.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 01:58:11 GMT" } ]	2025-04-03T00:00:00	[ [ "Li", "Hui", "" ], [ "Dong", "Zhen", "" ], [ "Wang", "Siao", "" ], [ "Zhang", "Hui", "" ], [ "Shen", "Liwei", "" ], [ "Peng", "Xin", "" ], [ "She", "Dongdong", "" ] ]	TITLE: Extracting Formal Specifications from Documents Using LLMs for Automated Testing ABSTRACT: Automated testing plays a crucial role in ensuring software security. It heavily relies on formal specifications to validate the correctness of the system behavior. However, the main approach to defining these formal specifications is through manual analysis of software documents, which requires a significant amount of engineering effort from experienced researchers and engineers. Meanwhile, system update further increases the human labor cost to maintain a corresponding formal specification, making the manual analysis approach a time-consuming and error-prone task. Recent advances in Large Language Models (LLMs) have demonstrated promising capabilities in natural language understanding. Yet, the feasibility of using LLMs to automate the extraction of formal specifications from software documents remains unexplored. We conduct an empirical study by constructing a comprehensive dataset comprising 603 specifications from 37 documents across three representative open-source software. We then evaluate the most recent LLMs' capabilities in extracting formal specifications from documents in an end-to-end fashion, including GPT-4o, Claude, and Llama. Our study demonstrates the application of LLMs in formal specification extraction tasks while identifying two major limitations: specification oversimplification and specification fabrication. We attribute these deficiencies to the LLMs' inherent limitations in processing and expressive capabilities, as well as their tendency to fabricate fictional information. Inspired by human cognitive processes, we propose a two-stage method, annotation-then-conversion, to address these challenges. Our method demonstrates significant improvements over the end-to-end method, with a 29.2% increase in the number of correctly extracted specifications and a 14.0% improvement in average accuracy. In particular, our best-performing LLM achieves an accuracy of 71.6%.
2504.01296	Bairu Hou	Bairu Hou, Yang Zhang, Jiabao Ji, Yujian Liu, Kaizhi Qian, Jacob Andreas, Shiyu Chang	ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning	15 pages, 7 figures	null	null	null	cs.CL	http://creativecommons.org/licenses/by/4.0/	We present ThinkPrune, a simple yet effective method for pruning the thinking length for long-thinking LLMs, which has been found to often produce inefficient and redundant thinking processes. Existing preliminary explorations of reducing thinking length primarily focus on forcing the thinking process to early exit, rather than adapting the LLM to optimize and consolidate the thinking process, and therefore the length-performance tradeoff observed so far is sub-optimal. To fill this gap, ThinkPrune offers a simple solution that continuously trains the long-thinking LLMs via reinforcement learning (RL) with an added token limit, beyond which any unfinished thoughts and answers will be discarded, resulting in a zero reward. To further preserve model performance, we introduce an iterative length pruning approach, where multiple rounds of RL are conducted, each with an increasingly more stringent token limit. We observed that ThinkPrune results in a remarkable performance-length tradeoff -- on the AIME24 dataset, the reasoning length of DeepSeek-R1-Distill-Qwen-1.5B can be reduced by half with only 2% drop in performance. We also observed that after pruning, the LLMs can bypass unnecessary steps while keeping the core reasoning process complete. Code is available at https://github.com/UCSB-NLP-Chang/ThinkPrune.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 01:59:26 GMT" } ]	2025-04-03T00:00:00	[ [ "Hou", "Bairu", "" ], [ "Zhang", "Yang", "" ], [ "Ji", "Jiabao", "" ], [ "Liu", "Yujian", "" ], [ "Qian", "Kaizhi", "" ], [ "Andreas", "Jacob", "" ], [ "Chang", "Shiyu", "" ] ]	TITLE: ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning ABSTRACT: We present ThinkPrune, a simple yet effective method for pruning the thinking length for long-thinking LLMs, which has been found to often produce inefficient and redundant thinking processes. Existing preliminary explorations of reducing thinking length primarily focus on forcing the thinking process to early exit, rather than adapting the LLM to optimize and consolidate the thinking process, and therefore the length-performance tradeoff observed so far is sub-optimal. To fill this gap, ThinkPrune offers a simple solution that continuously trains the long-thinking LLMs via reinforcement learning (RL) with an added token limit, beyond which any unfinished thoughts and answers will be discarded, resulting in a zero reward. To further preserve model performance, we introduce an iterative length pruning approach, where multiple rounds of RL are conducted, each with an increasingly more stringent token limit. We observed that ThinkPrune results in a remarkable performance-length tradeoff -- on the AIME24 dataset, the reasoning length of DeepSeek-R1-Distill-Qwen-1.5B can be reduced by half with only 2% drop in performance. We also observed that after pruning, the LLMs can bypass unnecessary steps while keeping the core reasoning process complete. Code is available at https://github.com/UCSB-NLP-Chang/ThinkPrune.
2504.01321	Chunhui Zhang	Chunhui Zhang, Li Liu, Jialin Gao, Xin Sun, Hao Wen, Xi Zhou, Shiming Ge, Yanfeng Wang	COST: Contrastive One-Stage Transformer for Vision-Language Small Object Tracking	Preprint submitted to Elsevier. https://github.com/983632847/Awesome-Multimodal-Object-Tracking	null	null	null	cs.CV cs.AI	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Transformer has recently demonstrated great potential in improving vision-language (VL) tracking algorithms. However, most of the existing VL trackers rely on carefully designed mechanisms to perform the multi-stage multi-modal fusion. Additionally, direct multi-modal fusion without alignment ignores distribution discrepancy between modalities in feature space, potentially leading to suboptimal representations. In this work, we propose COST, a contrastive one-stage transformer fusion framework for VL tracking, aiming to learn semantically consistent and unified VL representations. Specifically, we introduce a contrastive alignment strategy that maximizes mutual information (MI) between a video and its corresponding language description. This enables effective cross-modal alignment, yielding semantically consistent features in the representation space. By leveraging a visual-linguistic transformer, we establish an efficient multi-modal fusion and reasoning mechanism, empirically demonstrating that a simple stack of transformer encoders effectively enables unified VL representations. Moreover, we contribute a newly collected VL tracking benchmark dataset for small object tracking, named VL-SOT500, with bounding boxes and language descriptions. Our dataset comprises two challenging subsets, VL-SOT230 and VL-SOT270, dedicated to evaluating generic and high-speed small object tracking, respectively. Small object tracking is notoriously challenging due to weak appearance and limited features, and this dataset is, to the best of our knowledge, the first to explore the usage of language cues to enhance visual representation for small object tracking. Extensive experiments demonstrate that COST achieves state-of-the-art performance on five existing VL tracking datasets, as well as on our proposed VL-SOT500 dataset. Source codes and dataset will be made publicly available.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 03:12:38 GMT" } ]	2025-04-03T00:00:00	[ [ "Zhang", "Chunhui", "" ], [ "Liu", "Li", "" ], [ "Gao", "Jialin", "" ], [ "Sun", "Xin", "" ], [ "Wen", "Hao", "" ], [ "Zhou", "Xi", "" ], [ "Ge", "Shiming", "" ], [ "Wang", "Yanfeng", "" ] ]	TITLE: COST: Contrastive One-Stage Transformer for Vision-Language Small Object Tracking ABSTRACT: Transformer has recently demonstrated great potential in improving vision-language (VL) tracking algorithms. However, most of the existing VL trackers rely on carefully designed mechanisms to perform the multi-stage multi-modal fusion. Additionally, direct multi-modal fusion without alignment ignores distribution discrepancy between modalities in feature space, potentially leading to suboptimal representations. In this work, we propose COST, a contrastive one-stage transformer fusion framework for VL tracking, aiming to learn semantically consistent and unified VL representations. Specifically, we introduce a contrastive alignment strategy that maximizes mutual information (MI) between a video and its corresponding language description. This enables effective cross-modal alignment, yielding semantically consistent features in the representation space. By leveraging a visual-linguistic transformer, we establish an efficient multi-modal fusion and reasoning mechanism, empirically demonstrating that a simple stack of transformer encoders effectively enables unified VL representations. Moreover, we contribute a newly collected VL tracking benchmark dataset for small object tracking, named VL-SOT500, with bounding boxes and language descriptions. Our dataset comprises two challenging subsets, VL-SOT230 and VL-SOT270, dedicated to evaluating generic and high-speed small object tracking, respectively. Small object tracking is notoriously challenging due to weak appearance and limited features, and this dataset is, to the best of our knowledge, the first to explore the usage of language cues to enhance visual representation for small object tracking. Extensive experiments demonstrate that COST achieves state-of-the-art performance on five existing VL tracking datasets, as well as on our proposed VL-SOT500 dataset. Source codes and dataset will be made publicly available.
2504.01348	Yuji Nozawa	Yuji Nozawa, Yu-Chieh Lin, Kazumoto Nakamura, Youyang Ng	Prompt-Guided Attention Head Selection for Focus-Oriented Image Retrieval	Accepted to CVPR 2025 PixFoundation Workshop	null	null	null	cs.CV cs.IR	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	The goal of this paper is to enhance pretrained Vision Transformer (ViT) models for focus-oriented image retrieval with visual prompting. In real-world image retrieval scenarios, both query and database images often exhibit complexity, with multiple objects and intricate backgrounds. Users often want to retrieve images with specific object, which we define as the Focus-Oriented Image Retrieval (FOIR) task. While a standard image encoder can be employed to extract image features for similarity matching, it may not perform optimally in the multi-object-based FOIR task. This is because each image is represented by a single global feature vector. To overcome this, a prompt-based image retrieval solution is required. We propose an approach called Prompt-guided attention Head Selection (PHS) to leverage the head-wise potential of the multi-head attention mechanism in ViT in a promptable manner. PHS selects specific attention heads by matching their attention maps with user's visual prompts, such as a point, box, or segmentation. This empowers the model to focus on specific object of interest while preserving the surrounding visual context. Notably, PHS does not necessitate model re-training and avoids any image alteration. Experimental results show that PHS substantially improves performance on multiple datasets, offering a practical and training-free solution to enhance model performance in the FOIR task.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 04:33:27 GMT" } ]	2025-04-03T00:00:00	[ [ "Nozawa", "Yuji", "" ], [ "Lin", "Yu-Chieh", "" ], [ "Nakamura", "Kazumoto", "" ], [ "Ng", "Youyang", "" ] ]	TITLE: Prompt-Guided Attention Head Selection for Focus-Oriented Image Retrieval ABSTRACT: The goal of this paper is to enhance pretrained Vision Transformer (ViT) models for focus-oriented image retrieval with visual prompting. In real-world image retrieval scenarios, both query and database images often exhibit complexity, with multiple objects and intricate backgrounds. Users often want to retrieve images with specific object, which we define as the Focus-Oriented Image Retrieval (FOIR) task. While a standard image encoder can be employed to extract image features for similarity matching, it may not perform optimally in the multi-object-based FOIR task. This is because each image is represented by a single global feature vector. To overcome this, a prompt-based image retrieval solution is required. We propose an approach called Prompt-guided attention Head Selection (PHS) to leverage the head-wise potential of the multi-head attention mechanism in ViT in a promptable manner. PHS selects specific attention heads by matching their attention maps with user's visual prompts, such as a point, box, or segmentation. This empowers the model to focus on specific object of interest while preserving the surrounding visual context. Notably, PHS does not necessitate model re-training and avoids any image alteration. Experimental results show that PHS substantially improves performance on multiple datasets, offering a practical and training-free solution to enhance model performance in the FOIR task.
2504.01357	Ruihao Du	Ruihao Du, Zeshen Li, Howard H. Yang	Age-Aware Partial Gradient Update Strategy for Federated Learning Over the Air	null	null	null	null	cs.DC	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	We propose an age-aware strategy to update gradients in an over-the-air federated learning system. The system comprises an edge server and multiple clients, collaborating to minimize a global loss function. In each communication round, clients perform local training, modulate their gradient updates onto a set of shared orthogonal waveforms, and simultaneously transmit the analog signals to the edge server. The edge server then extracts a noisy aggregated gradient from the received radio signal, updates the global model, and broadcasts it to the clients for the next round of local computing. Despite enabling all clients to upload information in every communication round, the system is constrained by the limited number of available waveform carriers, allowing only a subset of gradient parameters to be transmitted. To address this issue, our method maintains an age vector on the edge server, tracking the time elapsed since each coordinate of the global model was last updated. The server leverages this information to prioritize gradient entries for transmission, ensuring that outdated yet significant parameters are updated more frequently. We derive the convergence rate of the proposed algorithm to quantify its effectiveness. Furthermore, experimental evaluations on the MNIST and CIFAR-10 datasets demonstrate that our approach achieves higher accuracy and more stable convergence performance compared to baseline methods, highlighting its potential for improving communication efficiency in over-the-air federated learning systems.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 05:01:53 GMT" } ]	2025-04-03T00:00:00	[ [ "Du", "Ruihao", "" ], [ "Li", "Zeshen", "" ], [ "Yang", "Howard H.", "" ] ]	TITLE: Age-Aware Partial Gradient Update Strategy for Federated Learning Over the Air ABSTRACT: We propose an age-aware strategy to update gradients in an over-the-air federated learning system. The system comprises an edge server and multiple clients, collaborating to minimize a global loss function. In each communication round, clients perform local training, modulate their gradient updates onto a set of shared orthogonal waveforms, and simultaneously transmit the analog signals to the edge server. The edge server then extracts a noisy aggregated gradient from the received radio signal, updates the global model, and broadcasts it to the clients for the next round of local computing. Despite enabling all clients to upload information in every communication round, the system is constrained by the limited number of available waveform carriers, allowing only a subset of gradient parameters to be transmitted. To address this issue, our method maintains an age vector on the edge server, tracking the time elapsed since each coordinate of the global model was last updated. The server leverages this information to prioritize gradient entries for transmission, ensuring that outdated yet significant parameters are updated more frequently. We derive the convergence rate of the proposed algorithm to quantify its effectiveness. Furthermore, experimental evaluations on the MNIST and CIFAR-10 datasets demonstrate that our approach achieves higher accuracy and more stable convergence performance compared to baseline methods, highlighting its potential for improving communication efficiency in over-the-air federated learning systems.
2504.01373	Emadeldeen Eldele	Emadeldeen Eldele, Mohamed Ragab, Xu Qing, Edward, Zhenghua Chen, Min Wu, Xiaoli Li, Jay Lee	UniFault: A Fault Diagnosis Foundation Model from Bearing Data	null	null	null	null	cs.LG	http://creativecommons.org/licenses/by/4.0/	Machine fault diagnosis (FD) is a critical task for predictive maintenance, enabling early fault detection and preventing unexpected failures. Despite its importance, existing FD models are operation-specific with limited generalization across diverse datasets. Foundation models (FM) have demonstrated remarkable potential in both visual and language domains, achieving impressive generalization capabilities even with minimal data through few-shot or zero-shot learning. However, translating these advances to FD presents unique hurdles. Unlike the large-scale, cohesive datasets available for images and text, FD datasets are typically smaller and more heterogeneous, with significant variations in sampling frequencies and the number of channels across different systems and applications. This heterogeneity complicates the design of a universal architecture capable of effectively processing such diverse data while maintaining robust feature extraction and learning capabilities. In this paper, we introduce UniFault, a foundation model for fault diagnosis that systematically addresses these issues. Specifically, the model incorporates a comprehensive data harmonization pipeline featuring two key innovations. First, a unification scheme transforms multivariate inputs into standardized univariate sequences while retaining local inter-channel relationships. Second, a novel cross-domain temporal fusion strategy mitigates distribution shifts and enriches sample diversity and count, improving the model generalization across varying conditions. UniFault is pretrained on over 9 billion data points spanning diverse FD datasets, enabling superior few-shot performance. Extensive experiments on real-world FD datasets demonstrate that UniFault achieves SoTA performance, setting a new benchmark for fault diagnosis models and paving the way for more scalable and robust predictive maintenance solutions.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 05:34:27 GMT" } ]	2025-04-03T00:00:00	[ [ "Eldele", "Emadeldeen", "" ], [ "Ragab", "Mohamed", "" ], [ "Qing", "Xu", "" ], [ "Edward", "", "" ], [ "Chen", "Zhenghua", "" ], [ "Wu", "Min", "" ], [ "Li", "Xiaoli", "" ], [ "Lee", "Jay", "" ] ]	TITLE: UniFault: A Fault Diagnosis Foundation Model from Bearing Data ABSTRACT: Machine fault diagnosis (FD) is a critical task for predictive maintenance, enabling early fault detection and preventing unexpected failures. Despite its importance, existing FD models are operation-specific with limited generalization across diverse datasets. Foundation models (FM) have demonstrated remarkable potential in both visual and language domains, achieving impressive generalization capabilities even with minimal data through few-shot or zero-shot learning. However, translating these advances to FD presents unique hurdles. Unlike the large-scale, cohesive datasets available for images and text, FD datasets are typically smaller and more heterogeneous, with significant variations in sampling frequencies and the number of channels across different systems and applications. This heterogeneity complicates the design of a universal architecture capable of effectively processing such diverse data while maintaining robust feature extraction and learning capabilities. In this paper, we introduce UniFault, a foundation model for fault diagnosis that systematically addresses these issues. Specifically, the model incorporates a comprehensive data harmonization pipeline featuring two key innovations. First, a unification scheme transforms multivariate inputs into standardized univariate sequences while retaining local inter-channel relationships. Second, a novel cross-domain temporal fusion strategy mitigates distribution shifts and enriches sample diversity and count, improving the model generalization across varying conditions. UniFault is pretrained on over 9 billion data points spanning diverse FD datasets, enabling superior few-shot performance. Extensive experiments on real-world FD datasets demonstrate that UniFault achieves SoTA performance, setting a new benchmark for fault diagnosis models and paving the way for more scalable and robust predictive maintenance solutions.
2504.01383	Chang-Bin Zhang	Chang-Bin Zhang, Jinhong Ni, Yujie Zhong, Kai Han	v-CLR: View-Consistent Learning for Open-World Instance Segmentation	Accepted by CVPR 2025, Project page: https://visual-ai.github.io/vclr, Code: https://github.com/Visual-AI/vCLR	null	null	null	cs.CV	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	In this paper, we address the challenging problem of open-world instance segmentation. Existing works have shown that vanilla visual networks are biased toward learning appearance information, \eg texture, to recognize objects. This implicit bias causes the model to fail in detecting novel objects with unseen textures in the open-world setting. To address this challenge, we propose a learning framework, called view-Consistent LeaRning (v-CLR), which aims to enforce the model to learn appearance-invariant representations for robust instance segmentation. In v-CLR, we first introduce additional views for each image, where the texture undergoes significant alterations while preserving the image's underlying structure. We then encourage the model to learn the appearance-invariant representation by enforcing the consistency between object features across different views, for which we obtain class-agnostic object proposals using off-the-shelf unsupervised models that possess strong object-awareness. These proposals enable cross-view object feature matching, greatly reducing the appearance dependency while enhancing the object-awareness. We thoroughly evaluate our method on public benchmarks under both cross-class and cross-dataset settings, achieving state-of-the-art performance. Project page: https://visual-ai.github.io/vclr	[ { "version": "v1", "created": "Wed, 2 Apr 2025 05:52:30 GMT" } ]	2025-04-03T00:00:00	[ [ "Zhang", "Chang-Bin", "" ], [ "Ni", "Jinhong", "" ], [ "Zhong", "Yujie", "" ], [ "Han", "Kai", "" ] ]	TITLE: v-CLR: View-Consistent Learning for Open-World Instance Segmentation ABSTRACT: In this paper, we address the challenging problem of open-world instance segmentation. Existing works have shown that vanilla visual networks are biased toward learning appearance information, \eg texture, to recognize objects. This implicit bias causes the model to fail in detecting novel objects with unseen textures in the open-world setting. To address this challenge, we propose a learning framework, called view-Consistent LeaRning (v-CLR), which aims to enforce the model to learn appearance-invariant representations for robust instance segmentation. In v-CLR, we first introduce additional views for each image, where the texture undergoes significant alterations while preserving the image's underlying structure. We then encourage the model to learn the appearance-invariant representation by enforcing the consistency between object features across different views, for which we obtain class-agnostic object proposals using off-the-shelf unsupervised models that possess strong object-awareness. These proposals enable cross-view object feature matching, greatly reducing the appearance dependency while enhancing the object-awareness. We thoroughly evaluate our method on public benchmarks under both cross-class and cross-dataset settings, achieving state-of-the-art performance. Project page: https://visual-ai.github.io/vclr
2504.01386	Junjie Wu	Junjie Wu, Jiangtao Xie, Zhaolin Zhang, Qilong Wang, Qinghua Hu, Peihua Li, Sen Xu	DALIP: Distribution Alignment-based Language-Image Pre-Training for Domain-Specific Data	14 pages	null	null	null	cs.CV	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Recently, Contrastive Language-Image Pre-training (CLIP) has shown promising performance in domain-specific data (e.g., biology), and has attracted increasing research attention. Existing works generally focus on collecting extensive domain-specific data and directly tuning the original CLIP models. Intuitively, such a paradigm takes no full consideration of the characteristics lying in domain-specific data (e.g., fine-grained nature of biological data) and so limits model capability, while mostly losing the original ability of CLIP in the general domain. In this paper, we propose a Distribution Alignment-based Language-Image Pre-Training (DALIP) method for biological data. Specifically, DALIP optimizes CLIP models by matching the similarity between feature distribution of image-text pairs instead of the original [cls] token, which can capture rich yet effective information inherent in image-text pairs as powerful representations, and so better cope with fine-grained nature of biological data. Particularly, our DALIP efficiently approximates feature distribution via its first- and second-order statistics, while presenting a Multi-head Brownian Distance Covariance (MBDC) module to acquire second-order statistics of token features efficiently. Furthermore, we collect a new dataset for plant domain (e.g., specific data in biological domain) comprising 10M plant data with 3M general-domain data (namely PlantMix-13M) according to data mixing laws. Extensive experiments show that DALIP clearly outperforms existing CLIP counterparts in biological domain, while well generalizing to remote sensing and medical imaging domains. Besides, our PlantMix-13M dataset further boosts performance of DALIP in plant domain, while preserving model ability in general domain.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 05:56:57 GMT" } ]	2025-04-03T00:00:00	[ [ "Wu", "Junjie", "" ], [ "Xie", "Jiangtao", "" ], [ "Zhang", "Zhaolin", "" ], [ "Wang", "Qilong", "" ], [ "Hu", "Qinghua", "" ], [ "Li", "Peihua", "" ], [ "Xu", "Sen", "" ] ]	TITLE: DALIP: Distribution Alignment-based Language-Image Pre-Training for Domain-Specific Data ABSTRACT: Recently, Contrastive Language-Image Pre-training (CLIP) has shown promising performance in domain-specific data (e.g., biology), and has attracted increasing research attention. Existing works generally focus on collecting extensive domain-specific data and directly tuning the original CLIP models. Intuitively, such a paradigm takes no full consideration of the characteristics lying in domain-specific data (e.g., fine-grained nature of biological data) and so limits model capability, while mostly losing the original ability of CLIP in the general domain. In this paper, we propose a Distribution Alignment-based Language-Image Pre-Training (DALIP) method for biological data. Specifically, DALIP optimizes CLIP models by matching the similarity between feature distribution of image-text pairs instead of the original [cls] token, which can capture rich yet effective information inherent in image-text pairs as powerful representations, and so better cope with fine-grained nature of biological data. Particularly, our DALIP efficiently approximates feature distribution via its first- and second-order statistics, while presenting a Multi-head Brownian Distance Covariance (MBDC) module to acquire second-order statistics of token features efficiently. Furthermore, we collect a new dataset for plant domain (e.g., specific data in biological domain) comprising 10M plant data with 3M general-domain data (namely PlantMix-13M) according to data mixing laws. Extensive experiments show that DALIP clearly outperforms existing CLIP counterparts in biological domain, while well generalizing to remote sensing and medical imaging domains. Besides, our PlantMix-13M dataset further boosts performance of DALIP in plant domain, while preserving model ability in general domain.
2504.01395	Kecen Li	Kecen Li, Chen Gong, Xiaochen Li, Yuzhong Zhao, Xinwen Hou, Tianhao Wang	From Easy to Hard: Building a Shortcut for Differentially Private Image Synthesis	Accepted at IEEE S&P (Oakland) 2025; code available at https://github.com/SunnierLee/DP-FETA	null	null	null	cs.CR cs.AI	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Differentially private (DP) image synthesis aims to generate synthetic images from a sensitive dataset, alleviating the privacy leakage concerns of organizations sharing and utilizing synthetic images. Although previous methods have significantly progressed, especially in training diffusion models on sensitive images with DP Stochastic Gradient Descent (DP-SGD), they still suffer from unsatisfactory performance. In this work, inspired by curriculum learning, we propose a two-stage DP image synthesis framework, where diffusion models learn to generate DP synthetic images from easy to hard. Unlike existing methods that directly use DP-SGD to train diffusion models, we propose an easy stage in the beginning, where diffusion models learn simple features of the sensitive images. To facilitate this easy stage, we propose to use `central images', simply aggregations of random samples of the sensitive dataset. Intuitively, although those central images do not show details, they demonstrate useful characteristics of all images and only incur minimal privacy costs, thus helping early-phase model training. We conduct experiments to present that on the average of four investigated image datasets, the fidelity and utility metrics of our synthetic images are 33.1% and 2.1% better than the state-of-the-art method.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 06:30:55 GMT" } ]	2025-04-03T00:00:00	[ [ "Li", "Kecen", "" ], [ "Gong", "Chen", "" ], [ "Li", "Xiaochen", "" ], [ "Zhao", "Yuzhong", "" ], [ "Hou", "Xinwen", "" ], [ "Wang", "Tianhao", "" ] ]	TITLE: From Easy to Hard: Building a Shortcut for Differentially Private Image Synthesis ABSTRACT: Differentially private (DP) image synthesis aims to generate synthetic images from a sensitive dataset, alleviating the privacy leakage concerns of organizations sharing and utilizing synthetic images. Although previous methods have significantly progressed, especially in training diffusion models on sensitive images with DP Stochastic Gradient Descent (DP-SGD), they still suffer from unsatisfactory performance. In this work, inspired by curriculum learning, we propose a two-stage DP image synthesis framework, where diffusion models learn to generate DP synthetic images from easy to hard. Unlike existing methods that directly use DP-SGD to train diffusion models, we propose an easy stage in the beginning, where diffusion models learn simple features of the sensitive images. To facilitate this easy stage, we propose to use `central images', simply aggregations of random samples of the sensitive dataset. Intuitively, although those central images do not show details, they demonstrate useful characteristics of all images and only incur minimal privacy costs, thus helping early-phase model training. We conduct experiments to present that on the average of four investigated image datasets, the fidelity and utility metrics of our synthetic images are 33.1% and 2.1% better than the state-of-the-art method.
2504.01400	Xingshan Zeng	Xingshan Zeng, Weiwen Liu, Xu Huang, Zezhong Wang, Lingzhi Wang, Liangyou Li, Yasheng Wang, Lifeng Shang, Xin Jiang, Ruiming Tang, Qun Liu	ToolACE-R: Tool Learning with Adaptive Self-Refinement	null	null	null	null	cs.CL cs.AI cs.LG	http://creativecommons.org/licenses/by-nc-nd/4.0/	Tool learning, which allows Large Language Models (LLMs) to leverage external tools for solving complex user tasks, has emerged as a promising avenue for extending model capabilities. However, current approaches primarily focus on data synthesis for fine-tuning LLMs to invoke tools effectively, largely ignoring how to fully stimulate the potential of the model. In this paper, we propose ToolACE-R, a novel method that introduces adaptive self-refinement for tool invocations. Our approach features a model-aware iterative training procedure that progressively incorporates more training samples based on the model's evolving capabilities. Additionally, it allows LLMs to iteratively refine their tool calls, optimizing performance without requiring external feedback. To further enhance computational efficiency, we integrate an adaptive mechanism when scaling the inference time, enabling the model to autonomously determine when to stop the refinement process. We conduct extensive experiments across several benchmark datasets, showing that ToolACE-R achieves competitive performance compared to advanced API-based models, even without any refinement. Furthermore, its performance can be further improved efficiently through adaptive self-refinement. Our results demonstrate the effectiveness of the proposed method, which is compatible with base models of various sizes, offering a promising direction for more efficient tool learning.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 06:38:56 GMT" } ]	2025-04-03T00:00:00	[ [ "Zeng", "Xingshan", "" ], [ "Liu", "Weiwen", "" ], [ "Huang", "Xu", "" ], [ "Wang", "Zezhong", "" ], [ "Wang", "Lingzhi", "" ], [ "Li", "Liangyou", "" ], [ "Wang", "Yasheng", "" ], [ "Shang", "Lifeng", "" ], [ "Jiang", "Xin", "" ], [ "Tang", "Ruiming", "" ], [ "Liu", "Qun", "" ] ]	TITLE: ToolACE-R: Tool Learning with Adaptive Self-Refinement ABSTRACT: Tool learning, which allows Large Language Models (LLMs) to leverage external tools for solving complex user tasks, has emerged as a promising avenue for extending model capabilities. However, current approaches primarily focus on data synthesis for fine-tuning LLMs to invoke tools effectively, largely ignoring how to fully stimulate the potential of the model. In this paper, we propose ToolACE-R, a novel method that introduces adaptive self-refinement for tool invocations. Our approach features a model-aware iterative training procedure that progressively incorporates more training samples based on the model's evolving capabilities. Additionally, it allows LLMs to iteratively refine their tool calls, optimizing performance without requiring external feedback. To further enhance computational efficiency, we integrate an adaptive mechanism when scaling the inference time, enabling the model to autonomously determine when to stop the refinement process. We conduct extensive experiments across several benchmark datasets, showing that ToolACE-R achieves competitive performance compared to advanced API-based models, even without any refinement. Furthermore, its performance can be further improved efficiently through adaptive self-refinement. Our results demonstrate the effectiveness of the proposed method, which is compatible with base models of various sizes, offering a promising direction for more efficient tool learning.
2504.01404	Lingxiao Tang	Lingxiao Tang, Jiakun Liu, Zhongxin Liu, Xiaohu Yang, Lingfeng Bao	LLM4SZZ: Enhancing SZZ Algorithm with Context-Enhanced Assessment on Large Language Models	null	null	null	null	cs.SE	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	The SZZ algorithm is the dominant technique for identifying bug-inducing commits and serves as a foundation for many software engineering studies, such as bug prediction and static code analysis. Researchers have proposed many variants to enhance the SZZ algorithm's performance since its introduction. The majority of them rely on static techniques or heuristic assumptions, making them easy to implement, but their performance improvements are often limited. Recently, a deep learning-based SZZ algorithm has been introduced to enhance the original SZZ algorithm. However, it requires complex preprocessing and is restricted to a single programming language. Additionally, while it enhances precision, it sacrifices recall. Furthermore, most of variants overlook crucial information, such as commit messages and patch context, and are limited to bug-fixing commits involving deleted lines. The emergence of large language models (LLMs) offers an opportunity to address these drawbacks. In this study, we investigate the strengths and limitations of LLMs and propose LLM4SZZ, which employs two approaches (i.e., rank-based identification and context-enhanced identification) to handle different types of bug-fixing commits. We determine which approach to adopt based on the LLM's ability to comprehend the bug and identify whether the bug is present in a commit. The context-enhanced identification provides the LLM with more context and requires it to find the bug-inducing commit among a set of candidate commits. In rank-based identification, we ask the LLM to select buggy statements from the bug-fixing commit and rank them based on their relevance to the root cause. Experimental results show that LLM4SZZ outperforms all baselines across three datasets, improving F1-score by 6.9% to 16.0% without significantly sacrificing recall.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 06:40:57 GMT" } ]	2025-04-03T00:00:00	[ [ "Tang", "Lingxiao", "" ], [ "Liu", "Jiakun", "" ], [ "Liu", "Zhongxin", "" ], [ "Yang", "Xiaohu", "" ], [ "Bao", "Lingfeng", "" ] ]	TITLE: LLM4SZZ: Enhancing SZZ Algorithm with Context-Enhanced Assessment on Large Language Models ABSTRACT: The SZZ algorithm is the dominant technique for identifying bug-inducing commits and serves as a foundation for many software engineering studies, such as bug prediction and static code analysis. Researchers have proposed many variants to enhance the SZZ algorithm's performance since its introduction. The majority of them rely on static techniques or heuristic assumptions, making them easy to implement, but their performance improvements are often limited. Recently, a deep learning-based SZZ algorithm has been introduced to enhance the original SZZ algorithm. However, it requires complex preprocessing and is restricted to a single programming language. Additionally, while it enhances precision, it sacrifices recall. Furthermore, most of variants overlook crucial information, such as commit messages and patch context, and are limited to bug-fixing commits involving deleted lines. The emergence of large language models (LLMs) offers an opportunity to address these drawbacks. In this study, we investigate the strengths and limitations of LLMs and propose LLM4SZZ, which employs two approaches (i.e., rank-based identification and context-enhanced identification) to handle different types of bug-fixing commits. We determine which approach to adopt based on the LLM's ability to comprehend the bug and identify whether the bug is present in a commit. The context-enhanced identification provides the LLM with more context and requires it to find the bug-inducing commit among a set of candidate commits. In rank-based identification, we ask the LLM to select buggy statements from the bug-fixing commit and rank them based on their relevance to the root cause. Experimental results show that LLM4SZZ outperforms all baselines across three datasets, improving F1-score by 6.9% to 16.0% without significantly sacrificing recall.
2504.01416	Huai Yu	Shu Han, Xubo Zhu, Ji Wu, Ximeng Cai, Wen Yang, Huai Yu, Gui-Song Xia	DF-Calib: Targetless LiDAR-Camera Calibration via Depth Flow	7 pages,3 figures, 3 figures	null	null	null	cs.RO cs.CV	http://creativecommons.org/licenses/by/4.0/	Precise LiDAR-camera calibration is crucial for integrating these two sensors into robotic systems to achieve robust perception. In applications like autonomous driving, online targetless calibration enables a prompt sensor misalignment correction from mechanical vibrations without extra targets. However, existing methods exhibit limitations in effectively extracting consistent features from LiDAR and camera data and fail to prioritize salient regions, compromising cross-modal alignment robustness. To address these issues, we propose DF-Calib, a LiDAR-camera calibration method that reformulates calibration as an intra-modality depth flow estimation problem. DF-Calib estimates a dense depth map from the camera image and completes the sparse LiDAR projected depth map, using a shared feature encoder to extract consistent depth-to-depth features, effectively bridging the 2D-3D cross-modal gap. Additionally, we introduce a reliability map to prioritize valid pixels and propose a perceptually weighted sparse flow loss to enhance depth flow estimation. Experimental results across multiple datasets validate its accuracy and generalization,with DF-Calib achieving a mean translation error of 0.635cm and rotation error of 0.045 degrees on the KITTI dataset.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 07:09:44 GMT" } ]	2025-04-03T00:00:00	[ [ "Han", "Shu", "" ], [ "Zhu", "Xubo", "" ], [ "Wu", "Ji", "" ], [ "Cai", "Ximeng", "" ], [ "Yang", "Wen", "" ], [ "Yu", "Huai", "" ], [ "Xia", "Gui-Song", "" ] ]	TITLE: DF-Calib: Targetless LiDAR-Camera Calibration via Depth Flow ABSTRACT: Precise LiDAR-camera calibration is crucial for integrating these two sensors into robotic systems to achieve robust perception. In applications like autonomous driving, online targetless calibration enables a prompt sensor misalignment correction from mechanical vibrations without extra targets. However, existing methods exhibit limitations in effectively extracting consistent features from LiDAR and camera data and fail to prioritize salient regions, compromising cross-modal alignment robustness. To address these issues, we propose DF-Calib, a LiDAR-camera calibration method that reformulates calibration as an intra-modality depth flow estimation problem. DF-Calib estimates a dense depth map from the camera image and completes the sparse LiDAR projected depth map, using a shared feature encoder to extract consistent depth-to-depth features, effectively bridging the 2D-3D cross-modal gap. Additionally, we introduce a reliability map to prioritize valid pixels and propose a perceptually weighted sparse flow loss to enhance depth flow estimation. Experimental results across multiple datasets validate its accuracy and generalization,with DF-Calib achieving a mean translation error of 0.635cm and rotation error of 0.045 degrees on the KITTI dataset.
2504.01420	Yicheng Fu	Athena Wen, Tanush Patil, Ansh Saxena, Yicheng Fu, Sean O'Brien, Kevin Zhu	FAIRE: Assessing Racial and Gender Bias in AI-Driven Resume Evaluations	null	null	null	null	cs.CL cs.AI	http://creativecommons.org/licenses/by/4.0/	In an era where AI-driven hiring is transforming recruitment practices, concerns about fairness and bias have become increasingly important. To explore these issues, we introduce a benchmark, FAIRE (Fairness Assessment In Resume Evaluation), to test for racial and gender bias in large language models (LLMs) used to evaluate resumes across different industries. We use two methods-direct scoring and ranking-to measure how model performance changes when resumes are slightly altered to reflect different racial or gender identities. Our findings reveal that while every model exhibits some degree of bias, the magnitude and direction vary considerably. This benchmark provides a clear way to examine these differences and offers valuable insights into the fairness of AI-based hiring tools. It highlights the urgent need for strategies to reduce bias in AI-driven recruitment. Our benchmark code and dataset are open-sourced at our repository: https://github.com/athenawen/FAIRE-Fairness-Assessment-In-Resume-Evaluation.git.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 07:11:30 GMT" } ]	2025-04-03T00:00:00	[ [ "Wen", "Athena", "" ], [ "Patil", "Tanush", "" ], [ "Saxena", "Ansh", "" ], [ "Fu", "Yicheng", "" ], [ "O'Brien", "Sean", "" ], [ "Zhu", "Kevin", "" ] ]	TITLE: FAIRE: Assessing Racial and Gender Bias in AI-Driven Resume Evaluations ABSTRACT: In an era where AI-driven hiring is transforming recruitment practices, concerns about fairness and bias have become increasingly important. To explore these issues, we introduce a benchmark, FAIRE (Fairness Assessment In Resume Evaluation), to test for racial and gender bias in large language models (LLMs) used to evaluate resumes across different industries. We use two methods-direct scoring and ranking-to measure how model performance changes when resumes are slightly altered to reflect different racial or gender identities. Our findings reveal that while every model exhibits some degree of bias, the magnitude and direction vary considerably. This benchmark provides a clear way to examine these differences and offers valuable insights into the fairness of AI-based hiring tools. It highlights the urgent need for strategies to reduce bias in AI-driven recruitment. Our benchmark code and dataset are open-sourced at our repository: https://github.com/athenawen/FAIRE-Fairness-Assessment-In-Resume-Evaluation.git.
2504.01428	Zhuangzhuang Chen	Zhuangzhuang Chen, Hualiang Wang, Chubin Ou, Xiaomeng Li	MuTri: Multi-view Tri-alignment for OCT to OCTA 3D Image Translation	null	null	null	null	cs.CV cs.AI	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Optical coherence tomography angiography (OCTA) shows its great importance in imaging microvascular networks by providing accurate 3D imaging of blood vessels, but it relies upon specialized sensors and expensive devices. For this reason, previous works show the potential to translate the readily available 3D Optical Coherence Tomography (OCT) images into 3D OCTA images. However, existing OCTA translation methods directly learn the mapping from the OCT domain to the OCTA domain in continuous and infinite space with guidance from only a single view, i.e., the OCTA project map, resulting in suboptimal results. To this end, we propose the multi-view Tri-alignment framework for OCT to OCTA 3D image translation in discrete and finite space, named MuTri. In the first stage, we pre-train two vector-quantized variational auto-encoder (VQ- VAE) by reconstructing 3D OCT and 3D OCTA data, providing semantic prior for subsequent multi-view guidances. In the second stage, our multi-view tri-alignment facilitates another VQVAE model to learn the mapping from the OCT domain to the OCTA domain in discrete and finite space. Specifically, a contrastive-inspired semantic alignment is proposed to maximize the mutual information with the pre-trained models from OCT and OCTA views, to facilitate codebook learning. Meanwhile, a vessel structure alignment is proposed to minimize the structure discrepancy with the pre-trained models from the OCTA project map view, benefiting from learning the detailed vessel structure information. We also collect the first large-scale dataset, namely, OCTA2024, which contains a pair of OCT and OCTA volumes from 846 subjects.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 07:28:09 GMT" } ]	2025-04-03T00:00:00	[ [ "Chen", "Zhuangzhuang", "" ], [ "Wang", "Hualiang", "" ], [ "Ou", "Chubin", "" ], [ "Li", "Xiaomeng", "" ] ]	TITLE: MuTri: Multi-view Tri-alignment for OCT to OCTA 3D Image Translation ABSTRACT: Optical coherence tomography angiography (OCTA) shows its great importance in imaging microvascular networks by providing accurate 3D imaging of blood vessels, but it relies upon specialized sensors and expensive devices. For this reason, previous works show the potential to translate the readily available 3D Optical Coherence Tomography (OCT) images into 3D OCTA images. However, existing OCTA translation methods directly learn the mapping from the OCT domain to the OCTA domain in continuous and infinite space with guidance from only a single view, i.e., the OCTA project map, resulting in suboptimal results. To this end, we propose the multi-view Tri-alignment framework for OCT to OCTA 3D image translation in discrete and finite space, named MuTri. In the first stage, we pre-train two vector-quantized variational auto-encoder (VQ- VAE) by reconstructing 3D OCT and 3D OCTA data, providing semantic prior for subsequent multi-view guidances. In the second stage, our multi-view tri-alignment facilitates another VQVAE model to learn the mapping from the OCT domain to the OCTA domain in discrete and finite space. Specifically, a contrastive-inspired semantic alignment is proposed to maximize the mutual information with the pre-trained models from OCT and OCTA views, to facilitate codebook learning. Meanwhile, a vessel structure alignment is proposed to minimize the structure discrepancy with the pre-trained models from the OCTA project map view, benefiting from learning the detailed vessel structure information. We also collect the first large-scale dataset, namely, OCTA2024, which contains a pair of OCT and OCTA volumes from 846 subjects.
2504.01431	Hao Zhu	Hao Zhu, Shengchao Yan, Jasper Hoffmann, Joschka Boedecker	Multi-convex Programming for Discrete Latent Factor Models Prototyping	null	null	null	null	math.OC cs.CE cs.LG	http://creativecommons.org/licenses/by/4.0/	Discrete latent factor models (DLFMs) are widely used in various domains such as machine learning, economics, neuroscience, psychology, etc. Currently, fitting a DLFM to some dataset relies on a customized solver for individual models, which requires lots of effort to implement and is limited to the targeted specific instance of DLFMs. In this paper, we propose a generic framework based on CVXPY, which allows users to specify and solve the fitting problem of a wide range of DLFMs, including both regression and classification models, within a very short script. Our framework is flexible and inherently supports the integration of regularization terms and constraints on the DLFM parameters and latent factors, such that the users can easily prototype the DLFM structure according to their dataset and application scenario. We introduce our open-source Python implementation and illustrate the framework in several examples.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 07:33:54 GMT" } ]	2025-04-03T00:00:00	[ [ "Zhu", "Hao", "" ], [ "Yan", "Shengchao", "" ], [ "Hoffmann", "Jasper", "" ], [ "Boedecker", "Joschka", "" ] ]	TITLE: Multi-convex Programming for Discrete Latent Factor Models Prototyping ABSTRACT: Discrete latent factor models (DLFMs) are widely used in various domains such as machine learning, economics, neuroscience, psychology, etc. Currently, fitting a DLFM to some dataset relies on a customized solver for individual models, which requires lots of effort to implement and is limited to the targeted specific instance of DLFMs. In this paper, we propose a generic framework based on CVXPY, which allows users to specify and solve the fitting problem of a wide range of DLFMs, including both regression and classification models, within a very short script. Our framework is flexible and inherently supports the integration of regularization terms and constraints on the DLFM parameters and latent factors, such that the users can easily prototype the DLFM structure according to their dataset and application scenario. We introduce our open-source Python implementation and illustrate the framework in several examples.
2504.01445	Philipp Mondorf	Philipp Mondorf, Shijia Zhou, Monica Riedler, Barbara Plank	Enabling Systematic Generalization in Abstract Spatial Reasoning through Meta-Learning for Compositionality	30 pages, 14 figures	null	null	null	cs.AI	http://creativecommons.org/licenses/by-sa/4.0/	Systematic generalization refers to the capacity to understand and generate novel combinations from known components. Despite recent progress by large language models (LLMs) across various domains, these models often fail to extend their knowledge to novel compositional scenarios, revealing notable limitations in systematic generalization. There has been an ongoing debate about whether neural networks possess the capacity for systematic generalization, with recent studies suggesting that meta-learning approaches designed for compositionality can significantly enhance this ability. However, these insights have largely been confined to linguistic problems, leaving their applicability to other tasks an open question. In this study, we extend the approach of meta-learning for compositionality to the domain of abstract spatial reasoning. To this end, we introduce $\textit{SYGAR}$-a dataset designed to evaluate the capacity of models to systematically generalize from known geometric transformations (e.g., translation, rotation) of two-dimensional objects to novel combinations of these transformations (e.g., translation+rotation). Our results show that a transformer-based encoder-decoder model, trained via meta-learning for compositionality, can systematically generalize to previously unseen transformation compositions, significantly outperforming state-of-the-art LLMs, including o3-mini, GPT-4o, and Gemini 2.0 Flash, which fail to exhibit similar systematic behavior. Our findings highlight the effectiveness of meta-learning in promoting systematicity beyond linguistic tasks, suggesting a promising direction toward more robust and generalizable models.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 07:56:39 GMT" } ]	2025-04-03T00:00:00	[ [ "Mondorf", "Philipp", "" ], [ "Zhou", "Shijia", "" ], [ "Riedler", "Monica", "" ], [ "Plank", "Barbara", "" ] ]	TITLE: Enabling Systematic Generalization in Abstract Spatial Reasoning through Meta-Learning for Compositionality ABSTRACT: Systematic generalization refers to the capacity to understand and generate novel combinations from known components. Despite recent progress by large language models (LLMs) across various domains, these models often fail to extend their knowledge to novel compositional scenarios, revealing notable limitations in systematic generalization. There has been an ongoing debate about whether neural networks possess the capacity for systematic generalization, with recent studies suggesting that meta-learning approaches designed for compositionality can significantly enhance this ability. However, these insights have largely been confined to linguistic problems, leaving their applicability to other tasks an open question. In this study, we extend the approach of meta-learning for compositionality to the domain of abstract spatial reasoning. To this end, we introduce $\textit{SYGAR}$-a dataset designed to evaluate the capacity of models to systematically generalize from known geometric transformations (e.g., translation, rotation) of two-dimensional objects to novel combinations of these transformations (e.g., translation+rotation). Our results show that a transformer-based encoder-decoder model, trained via meta-learning for compositionality, can systematically generalize to previously unseen transformation compositions, significantly outperforming state-of-the-art LLMs, including o3-mini, GPT-4o, and Gemini 2.0 Flash, which fail to exhibit similar systematic behavior. Our findings highlight the effectiveness of meta-learning in promoting systematicity beyond linguistic tasks, suggesting a promising direction toward more robust and generalizable models.
2504.01448	Hang Li	Hang Li, Shengyao Zhuang, Bevan Koopman, Guido Zuccon	LLM-VPRF: Large Language Model Based Vector Pseudo Relevance Feedback	null	null	null	null	cs.IR cs.LG	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Vector Pseudo Relevance Feedback (VPRF) has shown promising results in improving BERT-based dense retrieval systems through iterative refinement of query representations. This paper investigates the generalizability of VPRF to Large Language Model (LLM) based dense retrievers. We introduce LLM-VPRF and evaluate its effectiveness across multiple benchmark datasets, analyzing how different LLMs impact the feedback mechanism. Our results demonstrate that VPRF's benefits successfully extend to LLM architectures, establishing it as a robust technique for enhancing dense retrieval performance regardless of the underlying models. This work bridges the gap between VPRF with traditional BERT-based dense retrievers and modern LLMs, while providing insights into their future directions.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 08:02:01 GMT" } ]	2025-04-03T00:00:00	[ [ "Li", "Hang", "" ], [ "Zhuang", "Shengyao", "" ], [ "Koopman", "Bevan", "" ], [ "Zuccon", "Guido", "" ] ]	TITLE: LLM-VPRF: Large Language Model Based Vector Pseudo Relevance Feedback ABSTRACT: Vector Pseudo Relevance Feedback (VPRF) has shown promising results in improving BERT-based dense retrieval systems through iterative refinement of query representations. This paper investigates the generalizability of VPRF to Large Language Model (LLM) based dense retrievers. We introduce LLM-VPRF and evaluate its effectiveness across multiple benchmark datasets, analyzing how different LLMs impact the feedback mechanism. Our results demonstrate that VPRF's benefits successfully extend to LLM architectures, establishing it as a robust technique for enhancing dense retrieval performance regardless of the underlying models. This work bridges the gap between VPRF with traditional BERT-based dense retrievers and modern LLMs, while providing insights into their future directions.
2504.01450	Runlong Zhou	Runlong Zhou, Yi Zhang	CASCADE Your Datasets for Cross-Mode Knowledge Retrieval of Language Models	null	null	null	null	cs.LG cs.CL	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Language models often struggle with cross-mode knowledge retrieval -- the ability to access knowledge learned in one format (mode) when queried in another. We demonstrate that models trained on multiple data sources (e.g., Wikipedia and TinyStories) exhibit significantly reduced accuracy when retrieving knowledge in a format different from its original training mode. This paper quantitatively investigates this phenomenon through a controlled study of random token sequence memorization across different modes. We first explore dataset rewriting as a solution, revealing that effective cross-mode retrieval requires prohibitively extensive rewriting efforts that follow a sigmoid-like relationship. As an alternative, we propose CASCADE, a novel pretraining algorithm that uses cascading datasets with varying sequence lengths to capture knowledge at different scales. Our experiments demonstrate that CASCADE outperforms dataset rewriting approaches, even when compressed into a single model with a unified loss function. This work provides both qualitative evidence of cross-mode retrieval limitations and a practical solution to enhance language models' ability to access knowledge independently of its presentational format.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 08:02:07 GMT" } ]	2025-04-03T00:00:00	[ [ "Zhou", "Runlong", "" ], [ "Zhang", "Yi", "" ] ]	TITLE: CASCADE Your Datasets for Cross-Mode Knowledge Retrieval of Language Models ABSTRACT: Language models often struggle with cross-mode knowledge retrieval -- the ability to access knowledge learned in one format (mode) when queried in another. We demonstrate that models trained on multiple data sources (e.g., Wikipedia and TinyStories) exhibit significantly reduced accuracy when retrieving knowledge in a format different from its original training mode. This paper quantitatively investigates this phenomenon through a controlled study of random token sequence memorization across different modes. We first explore dataset rewriting as a solution, revealing that effective cross-mode retrieval requires prohibitively extensive rewriting efforts that follow a sigmoid-like relationship. As an alternative, we propose CASCADE, a novel pretraining algorithm that uses cascading datasets with varying sequence lengths to capture knowledge at different scales. Our experiments demonstrate that CASCADE outperforms dataset rewriting approaches, even when compressed into a single model with a unified loss function. This work provides both qualitative evidence of cross-mode retrieval limitations and a practical solution to enhance language models' ability to access knowledge independently of its presentational format.
2504.01451	Yongxin Ma	Jie Xu, Yongxin Ma, Yixuan Li, Xuanxuan Zhang, Jun Zhou, Shenghai Yuan, and Lihua Xie	Dynamic Initialization for LiDAR-inertial SLAM	Accepted by IEEE/ASME Transactions on Mechatronics	null	10.1109/TMECH.2025.3554878	null	cs.RO	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	The accuracy of the initial state, including initial velocity, gravity direction, and IMU biases, is critical for the initialization of LiDAR-inertial SLAM systems. Inaccurate initial values can reduce initialization speed or lead to failure. When the system faces urgent tasks, robust and fast initialization is required while the robot is moving, such as during the swift assessment of rescue environments after natural disasters, bomb disposal, and restarting LiDAR-inertial SLAM in rescue missions. However, existing initialization methods usually require the platform to remain stationary, which is ineffective when the robot is in motion. To address this issue, this paper introduces a robust and fast dynamic initialization method for LiDAR-inertial systems (D-LI-Init). This method iteratively aligns LiDAR-based odometry with IMU measurements to achieve system initialization. To enhance the reliability of the LiDAR odometry module, the LiDAR and gyroscope are tightly integrated within the ESIKF framework. The gyroscope compensates for rotational distortion in the point cloud. Translational distortion compensation occurs during the iterative update phase, resulting in the output of LiDAR-gyroscope odometry. The proposed method can initialize the system no matter the robot is moving or stationary. Experiments on public datasets and real-world environments demonstrate that the D-LI-Init algorithm can effectively serve various platforms, including vehicles, handheld devices, and UAVs. D-LI-Init completes dynamic initialization regardless of specific motion patterns. To benefit the research community, we have open-sourced our code and test datasets on GitHub.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 08:02:25 GMT" } ]	2025-04-03T00:00:00	[ [ "Xu", "Jie", "" ], [ "Ma", "Yongxin", "" ], [ "Li", "Yixuan", "" ], [ "Zhang", "Xuanxuan", "" ], [ "Zhou", "Jun", "" ], [ "Yuan", "Shenghai", "" ], [ "Xie", "Lihua", "" ] ]	TITLE: Dynamic Initialization for LiDAR-inertial SLAM ABSTRACT: The accuracy of the initial state, including initial velocity, gravity direction, and IMU biases, is critical for the initialization of LiDAR-inertial SLAM systems. Inaccurate initial values can reduce initialization speed or lead to failure. When the system faces urgent tasks, robust and fast initialization is required while the robot is moving, such as during the swift assessment of rescue environments after natural disasters, bomb disposal, and restarting LiDAR-inertial SLAM in rescue missions. However, existing initialization methods usually require the platform to remain stationary, which is ineffective when the robot is in motion. To address this issue, this paper introduces a robust and fast dynamic initialization method for LiDAR-inertial systems (D-LI-Init). This method iteratively aligns LiDAR-based odometry with IMU measurements to achieve system initialization. To enhance the reliability of the LiDAR odometry module, the LiDAR and gyroscope are tightly integrated within the ESIKF framework. The gyroscope compensates for rotational distortion in the point cloud. Translational distortion compensation occurs during the iterative update phase, resulting in the output of LiDAR-gyroscope odometry. The proposed method can initialize the system no matter the robot is moving or stationary. Experiments on public datasets and real-world environments demonstrate that the D-LI-Init algorithm can effectively serve various platforms, including vehicles, handheld devices, and UAVs. D-LI-Init completes dynamic initialization regardless of specific motion patterns. To benefit the research community, we have open-sourced our code and test datasets on GitHub.
2504.01452	Encheng Su	Encheng Su and Hu Cao and Alois Knoll	BiSeg-SAM: Weakly-Supervised Post-Processing Framework for Boosting Binary Segmentation in Segment Anything Models	2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)	null	10.1109/BIBM62325.2024.10822087	null	cs.CV cs.AI cs.LG	http://creativecommons.org/licenses/by/4.0/	Accurate segmentation of polyps and skin lesions is essential for diagnosing colorectal and skin cancers. While various segmentation methods for polyps and skin lesions using fully supervised deep learning techniques have been developed, the pixel-level annotation of medical images by doctors is both time-consuming and costly. Foundational vision models like the Segment Anything Model (SAM) have demonstrated superior performance; however, directly applying SAM to medical segmentation may not yield satisfactory results due to the lack of domain-specific medical knowledge. In this paper, we propose BiSeg-SAM, a SAM-guided weakly supervised prompting and boundary refinement network for the segmentation of polyps and skin lesions. Specifically, we fine-tune SAM combined with a CNN module to learn local features. We introduce a WeakBox with two functions: automatically generating box prompts for the SAM model and using our proposed Multi-choice Mask-to-Box (MM2B) transformation for rough mask-to-box conversion, addressing the mismatch between coarse labels and precise predictions. Additionally, we apply scale consistency (SC) loss for prediction scale alignment. Our DetailRefine module enhances boundary precision and segmentation accuracy by refining coarse predictions using a limited amount of ground truth labels. This comprehensive approach enables BiSeg-SAM to achieve excellent multi-task segmentation performance. Our method demonstrates significant superiority over state-of-the-art (SOTA) methods when tested on five polyp datasets and one skin cancer dataset.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 08:04:37 GMT" } ]	2025-04-03T00:00:00	[ [ "Su", "Encheng", "" ], [ "Cao", "Hu", "" ], [ "Knoll", "Alois", "" ] ]	TITLE: BiSeg-SAM: Weakly-Supervised Post-Processing Framework for Boosting Binary Segmentation in Segment Anything Models ABSTRACT: Accurate segmentation of polyps and skin lesions is essential for diagnosing colorectal and skin cancers. While various segmentation methods for polyps and skin lesions using fully supervised deep learning techniques have been developed, the pixel-level annotation of medical images by doctors is both time-consuming and costly. Foundational vision models like the Segment Anything Model (SAM) have demonstrated superior performance; however, directly applying SAM to medical segmentation may not yield satisfactory results due to the lack of domain-specific medical knowledge. In this paper, we propose BiSeg-SAM, a SAM-guided weakly supervised prompting and boundary refinement network for the segmentation of polyps and skin lesions. Specifically, we fine-tune SAM combined with a CNN module to learn local features. We introduce a WeakBox with two functions: automatically generating box prompts for the SAM model and using our proposed Multi-choice Mask-to-Box (MM2B) transformation for rough mask-to-box conversion, addressing the mismatch between coarse labels and precise predictions. Additionally, we apply scale consistency (SC) loss for prediction scale alignment. Our DetailRefine module enhances boundary precision and segmentation accuracy by refining coarse predictions using a limited amount of ground truth labels. This comprehensive approach enables BiSeg-SAM to achieve excellent multi-task segmentation performance. Our method demonstrates significant superiority over state-of-the-art (SOTA) methods when tested on five polyp datasets and one skin cancer dataset.
2504.01457	Ting Meng	Ting Meng, Chunyun Fu, Xiangyan Yan, Zheng Liang, Pan Ji, Jianwen Wang, Tao Huang	Deep LG-Track: An Enhanced Localization-Confidence-Guided Multi-Object Tracker	11 pages, 6 fugures	null	null	null	cs.CV	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Multi-object tracking plays a crucial role in various applications, such as autonomous driving and security surveillance. This study introduces Deep LG-Track, a novel multi-object tracker that incorporates three key enhancements to improve the tracking accuracy and robustness. First, an adaptive Kalman filter is developed to dynamically update the covariance of measurement noise based on detection confidence and trajectory disappearance. Second, a novel cost matrix is formulated to adaptively fuse motion and appearance information, leveraging localization confidence and detection confidence as weighting factors. Third, a dynamic appearance feature updating strategy is introduced, adjusting the relative weighting of historical and current appearance features based on appearance clarity and localization accuracy. Comprehensive evaluations on the MOT17 and MOT20 datasets demonstrate that the proposed Deep LG-Track consistently outperforms state-of-the-art trackers across multiple performance metrics, highlighting its effectiveness in multi-object tracking tasks.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 08:10:18 GMT" } ]	2025-04-03T00:00:00	[ [ "Meng", "Ting", "" ], [ "Fu", "Chunyun", "" ], [ "Yan", "Xiangyan", "" ], [ "Liang", "Zheng", "" ], [ "Ji", "Pan", "" ], [ "Wang", "Jianwen", "" ], [ "Huang", "Tao", "" ] ]	TITLE: Deep LG-Track: An Enhanced Localization-Confidence-Guided Multi-Object Tracker ABSTRACT: Multi-object tracking plays a crucial role in various applications, such as autonomous driving and security surveillance. This study introduces Deep LG-Track, a novel multi-object tracker that incorporates three key enhancements to improve the tracking accuracy and robustness. First, an adaptive Kalman filter is developed to dynamically update the covariance of measurement noise based on detection confidence and trajectory disappearance. Second, a novel cost matrix is formulated to adaptively fuse motion and appearance information, leveraging localization confidence and detection confidence as weighting factors. Third, a dynamic appearance feature updating strategy is introduced, adjusting the relative weighting of historical and current appearance features based on appearance clarity and localization accuracy. Comprehensive evaluations on the MOT17 and MOT20 datasets demonstrate that the proposed Deep LG-Track consistently outperforms state-of-the-art trackers across multiple performance metrics, highlighting its effectiveness in multi-object tracking tasks.
2504.01464	Akira Hatakeyama	Akira Hatakeyama, Shota Ito, Toshihiko Yanase, Naoya Ozaki	A Prefixed Patch Time Series Transformer for Two-Point Boundary Value Problems in Three-Body Problems	null	null	null	null	cs.LG	http://creativecommons.org/licenses/by/4.0/	Two-point boundary value problems for cislunar trajectories present significant challenges in circler restricted three body problem, making traditional analytical methods like Lambert's problem inapplicable. This study proposes a novel approach using a prefixed patch time series Transformer model that automates the solution of two-point boundary value problems from lunar flyby to arbitrary terminal conditions. Using prefix tokens of terminal conditions in our deep generative model enables solving boundary value problems in three-body dynamics. The training dataset consists of trajectories obtained through forward propagation rather than solving boundary value problems directly. The model demonstrates potential practical utility for preliminary trajectory design in cislunar mission scenarios.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 08:22:03 GMT" } ]	2025-04-03T00:00:00	[ [ "Hatakeyama", "Akira", "" ], [ "Ito", "Shota", "" ], [ "Yanase", "Toshihiko", "" ], [ "Ozaki", "Naoya", "" ] ]	TITLE: A Prefixed Patch Time Series Transformer for Two-Point Boundary Value Problems in Three-Body Problems ABSTRACT: Two-point boundary value problems for cislunar trajectories present significant challenges in circler restricted three body problem, making traditional analytical methods like Lambert's problem inapplicable. This study proposes a novel approach using a prefixed patch time series Transformer model that automates the solution of two-point boundary value problems from lunar flyby to arbitrary terminal conditions. Using prefix tokens of terminal conditions in our deep generative model enables solving boundary value problems in three-body dynamics. The training dataset consists of trajectories obtained through forward propagation rather than solving boundary value problems directly. The model demonstrates potential practical utility for preliminary trajectory design in cislunar mission scenarios.
2504.01470	Soumyya Kanti Datta	Soumyya Kanti Datta, Shan Jia, Siwei Lyu	Detecting Lip-Syncing Deepfakes: Vision Temporal Transformer for Analyzing Mouth Inconsistencies	null	null	null	null	cs.CV	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Deepfakes are AI-generated media in which the original content is digitally altered to create convincing but manipulated images, videos, or audio. Among the various types of deepfakes, lip-syncing deepfakes are one of the most challenging deepfakes to detect. In these videos, a person's lip movements are synthesized to match altered or entirely new audio using AI models. Therefore, unlike other types of deepfakes, the artifacts in lip-syncing deepfakes are confined to the mouth region, making them more subtle and, thus harder to discern. In this paper, we propose LIPINC-V2, a novel detection framework that leverages a combination of vision temporal transformer with multihead cross-attention to detect lip-syncing deepfakes by identifying spatiotemporal inconsistencies in the mouth region. These inconsistencies appear across adjacent frames and persist throughout the video. Our model can successfully capture both short-term and long-term variations in mouth movement, enhancing its ability to detect these inconsistencies. Additionally, we created a new lip-syncing deepfake dataset, LipSyncTIMIT, which was generated using five state-of-the-art lip-syncing models to simulate real-world scenarios. Extensive experiments on our proposed LipSyncTIMIT dataset and two other benchmark deepfake datasets demonstrate that our model achieves state-of-the-art performance. The code and the dataset are available at https://github.com/skrantidatta/LIPINC-V2 .	[ { "version": "v1", "created": "Wed, 2 Apr 2025 08:24:06 GMT" } ]	2025-04-03T00:00:00	[ [ "Datta", "Soumyya Kanti", "" ], [ "Jia", "Shan", "" ], [ "Lyu", "Siwei", "" ] ]	TITLE: Detecting Lip-Syncing Deepfakes: Vision Temporal Transformer for Analyzing Mouth Inconsistencies ABSTRACT: Deepfakes are AI-generated media in which the original content is digitally altered to create convincing but manipulated images, videos, or audio. Among the various types of deepfakes, lip-syncing deepfakes are one of the most challenging deepfakes to detect. In these videos, a person's lip movements are synthesized to match altered or entirely new audio using AI models. Therefore, unlike other types of deepfakes, the artifacts in lip-syncing deepfakes are confined to the mouth region, making them more subtle and, thus harder to discern. In this paper, we propose LIPINC-V2, a novel detection framework that leverages a combination of vision temporal transformer with multihead cross-attention to detect lip-syncing deepfakes by identifying spatiotemporal inconsistencies in the mouth region. These inconsistencies appear across adjacent frames and persist throughout the video. Our model can successfully capture both short-term and long-term variations in mouth movement, enhancing its ability to detect these inconsistencies. Additionally, we created a new lip-syncing deepfake dataset, LipSyncTIMIT, which was generated using five state-of-the-art lip-syncing models to simulate real-world scenarios. Extensive experiments on our proposed LipSyncTIMIT dataset and two other benchmark deepfake datasets demonstrate that our model achieves state-of-the-art performance. The code and the dataset are available at https://github.com/skrantidatta/LIPINC-V2 .
2504.01472	Yuejiao Su	Yuejiao Su, Yi Wang, Qiongyang Hu, Chuang Yang, and Lap-Pui Chau	ANNEXE: Unified Analyzing, Answering, and Pixel Grounding for Egocentric Interaction	Computer Vision and Pattern Recognition	null	null	null	cs.CV	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Egocentric interaction perception is one of the essential branches in investigating human-environment interaction, which lays the basis for developing next-generation intelligent systems. However, existing egocentric interaction understanding methods cannot yield coherent textual and pixel-level responses simultaneously according to user queries, which lacks flexibility for varying downstream application requirements. To comprehend egocentric interactions exhaustively, this paper presents a novel task named Egocentric Interaction Reasoning and pixel Grounding (Ego-IRG). Taking an egocentric image with the query as input, Ego-IRG is the first task that aims to resolve the interactions through three crucial steps: analyzing, answering, and pixel grounding, which results in fluent textual and fine-grained pixel-level responses. Another challenge is that existing datasets cannot meet the conditions for the Ego-IRG task. To address this limitation, this paper creates the Ego-IRGBench dataset based on extensive manual efforts, which includes over 20k egocentric images with 1.6 million queries and corresponding multimodal responses about interactions. Moreover, we design a unified ANNEXE model to generate text- and pixel-level outputs utilizing multimodal large language models, which enables a comprehensive interpretation of egocentric interactions. The experiments on the Ego-IRGBench exhibit the effectiveness of our ANNEXE model compared with other works.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 08:24:35 GMT" } ]	2025-04-03T00:00:00	[ [ "Su", "Yuejiao", "" ], [ "Wang", "Yi", "" ], [ "Hu", "Qiongyang", "" ], [ "Yang", "Chuang", "" ], [ "Chau", "Lap-Pui", "" ] ]	TITLE: ANNEXE: Unified Analyzing, Answering, and Pixel Grounding for Egocentric Interaction ABSTRACT: Egocentric interaction perception is one of the essential branches in investigating human-environment interaction, which lays the basis for developing next-generation intelligent systems. However, existing egocentric interaction understanding methods cannot yield coherent textual and pixel-level responses simultaneously according to user queries, which lacks flexibility for varying downstream application requirements. To comprehend egocentric interactions exhaustively, this paper presents a novel task named Egocentric Interaction Reasoning and pixel Grounding (Ego-IRG). Taking an egocentric image with the query as input, Ego-IRG is the first task that aims to resolve the interactions through three crucial steps: analyzing, answering, and pixel grounding, which results in fluent textual and fine-grained pixel-level responses. Another challenge is that existing datasets cannot meet the conditions for the Ego-IRG task. To address this limitation, this paper creates the Ego-IRGBench dataset based on extensive manual efforts, which includes over 20k egocentric images with 1.6 million queries and corresponding multimodal responses about interactions. Moreover, we design a unified ANNEXE model to generate text- and pixel-level outputs utilizing multimodal large language models, which enables a comprehensive interpretation of egocentric interactions. The experiments on the Ego-IRGBench exhibit the effectiveness of our ANNEXE model compared with other works.
2504.01476	Junlong Ren	Junlong Ren, Hao Wang	Enhanced Cross-modal 3D Retrieval via Tri-modal Reconstruction	ICME 2025	null	null	null	cs.CV	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Cross-modal 3D retrieval is a critical yet challenging task, aiming to achieve bi-directional retrieval between 3D and text modalities. Current methods predominantly rely on a certain 3D representation (e.g., point cloud), with few exploiting the 2D-3D consistency and complementary relationships, which constrains their performance. To bridge this gap, we propose to adopt multi-view images and point clouds to jointly represent 3D shapes, facilitating tri-modal alignment (i.e., image, point, text) for enhanced cross-modal 3D retrieval. Notably, we introduce tri-modal reconstruction to improve the generalization ability of encoders. Given point features, we reconstruct image features under the guidance of text features, and vice versa. With well-aligned point cloud and multi-view image features, we aggregate them as multimodal embeddings through fine-grained 2D-3D fusion to enhance geometric and semantic understanding. Recognizing the significant noise in current datasets where many 3D shapes and texts share similar semantics, we employ hard negative contrastive training to emphasize harder negatives with greater significance, leading to robust discriminative embeddings. Extensive experiments on the Text2Shape dataset demonstrate that our method significantly outperforms previous state-of-the-art methods in both shape-to-text and text-to-shape retrieval tasks by a substantial margin.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 08:29:42 GMT" } ]	2025-04-03T00:00:00	[ [ "Ren", "Junlong", "" ], [ "Wang", "Hao", "" ] ]	TITLE: Enhanced Cross-modal 3D Retrieval via Tri-modal Reconstruction ABSTRACT: Cross-modal 3D retrieval is a critical yet challenging task, aiming to achieve bi-directional retrieval between 3D and text modalities. Current methods predominantly rely on a certain 3D representation (e.g., point cloud), with few exploiting the 2D-3D consistency and complementary relationships, which constrains their performance. To bridge this gap, we propose to adopt multi-view images and point clouds to jointly represent 3D shapes, facilitating tri-modal alignment (i.e., image, point, text) for enhanced cross-modal 3D retrieval. Notably, we introduce tri-modal reconstruction to improve the generalization ability of encoders. Given point features, we reconstruct image features under the guidance of text features, and vice versa. With well-aligned point cloud and multi-view image features, we aggregate them as multimodal embeddings through fine-grained 2D-3D fusion to enhance geometric and semantic understanding. Recognizing the significant noise in current datasets where many 3D shapes and texts share similar semantics, we employ hard negative contrastive training to emphasize harder negatives with greater significance, leading to robust discriminative embeddings. Extensive experiments on the Text2Shape dataset demonstrate that our method significantly outperforms previous state-of-the-art methods in both shape-to-text and text-to-shape retrieval tasks by a substantial margin.
2504.01481	Fabrice Rossi	Roxane Cohen (LAMSADE), Robin David, Florian Yger (LITIS), Fabrice Rossi (CEREMADE)	Identifying Obfuscated Code through Graph-Based Semantic Analysis of Binary Code	The 13th International Conference on Complex Networks and their Applications, Dec 2024, Istabul, Turkey	null	null	null	cs.CR cs.LG stat.ML	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Protecting sensitive program content is a critical issue in various situations, ranging from legitimate use cases to unethical contexts. Obfuscation is one of the most used techniques to ensure such protection. Consequently, attackers must first detect and characterize obfuscation before launching any attack against it. This paper investigates the problem of function-level obfuscation detection using graph-based approaches, comparing algorithms, from elementary baselines to promising techniques like GNN (Graph Neural Networks), on different feature choices. We consider various obfuscation types and obfuscators, resulting in two complex datasets. Our findings demonstrate that GNNs need meaningful features that capture aspects of function semantics to outperform baselines. Our approach shows satisfactory results, especially in a challenging 11-class classification task and in a practical malware analysis example.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 08:36:27 GMT" } ]	2025-04-03T00:00:00	[ [ "Cohen", "Roxane", "", "LAMSADE" ], [ "David", "Robin", "", "LITIS" ], [ "Yger", "Florian", "", "LITIS" ], [ "Rossi", "Fabrice", "", "CEREMADE" ] ]	TITLE: Identifying Obfuscated Code through Graph-Based Semantic Analysis of Binary Code ABSTRACT: Protecting sensitive program content is a critical issue in various situations, ranging from legitimate use cases to unethical contexts. Obfuscation is one of the most used techniques to ensure such protection. Consequently, attackers must first detect and characterize obfuscation before launching any attack against it. This paper investigates the problem of function-level obfuscation detection using graph-based approaches, comparing algorithms, from elementary baselines to promising techniques like GNN (Graph Neural Networks), on different feature choices. We consider various obfuscation types and obfuscators, resulting in two complex datasets. Our findings demonstrate that GNNs need meaningful features that capture aspects of function semantics to outperform baselines. Our approach shows satisfactory results, especially in a challenging 11-class classification task and in a practical malware analysis example.
2504.01482	Qihao Ye	Qihao Ye, Xiaochuan Tian, Yuhua Zhu	A Robust Model-Based Approach for Continuous-Time Policy Evaluation with Unknown L\'evy Process Dynamics	27 pages, 9 figures	null	null	null	cs.LG cs.NA math.NA	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	This paper develops a model-based framework for continuous-time policy evaluation (CTPE) in reinforcement learning, incorporating both Brownian and L\'evy noise to model stochastic dynamics influenced by rare and extreme events. Our approach formulates the policy evaluation problem as solving a partial integro-differential equation (PIDE) for the value function with unknown coefficients. A key challenge in this setting is accurately recovering the unknown coefficients in the stochastic dynamics, particularly when driven by L\'evy processes with heavy tail effects. To address this, we propose a robust numerical approach that effectively handles both unbiased and censored trajectory datasets. This method combines maximum likelihood estimation with an iterative tail correction mechanism, improving the stability and accuracy of coefficient recovery. Additionally, we establish a theoretical bound for the policy evaluation error based on coefficient recovery error. Through numerical experiments, we demonstrate the effectiveness and robustness of our method in recovering heavy-tailed L\'evy dynamics and verify the theoretical error analysis in policy evaluation.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 08:37:14 GMT" } ]	2025-04-03T00:00:00	[ [ "Ye", "Qihao", "" ], [ "Tian", "Xiaochuan", "" ], [ "Zhu", "Yuhua", "" ] ]	TITLE: A Robust Model-Based Approach for Continuous-Time Policy Evaluation with Unknown L\'evy Process Dynamics ABSTRACT: This paper develops a model-based framework for continuous-time policy evaluation (CTPE) in reinforcement learning, incorporating both Brownian and L\'evy noise to model stochastic dynamics influenced by rare and extreme events. Our approach formulates the policy evaluation problem as solving a partial integro-differential equation (PIDE) for the value function with unknown coefficients. A key challenge in this setting is accurately recovering the unknown coefficients in the stochastic dynamics, particularly when driven by L\'evy processes with heavy tail effects. To address this, we propose a robust numerical approach that effectively handles both unbiased and censored trajectory datasets. This method combines maximum likelihood estimation with an iterative tail correction mechanism, improving the stability and accuracy of coefficient recovery. Additionally, we establish a theoretical bound for the policy evaluation error based on coefficient recovery error. Through numerical experiments, we demonstrate the effectiveness and robustness of our method in recovering heavy-tailed L\'evy dynamics and verify the theoretical error analysis in policy evaluation.
2504.01483	Ruiyang Liu	Siran Li, Ruiyang Liu, Chen Liu, Zhendong Wang, Gaofeng He, Yong-Lu Li, Xiaogang Jin, Huamin Wang	GarmageNet: A Dataset and Scalable Representation for Generic Garment Modeling	null	null	null	null	cs.GR cs.CV	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	High-fidelity garment modeling remains challenging due to the lack of large-scale, high-quality datasets and efficient representations capable of handling non-watertight, multi-layer geometries. In this work, we introduce Garmage, a neural-network-and-CG-friendly garment representation that seamlessly encodes the accurate geometry and sewing pattern of complex multi-layered garments as a structured set of per-panel geometry images. As a dual-2D-3D representation, Garmage achieves an unprecedented integration of 2D image-based algorithms with 3D modeling workflows, enabling high fidelity, non-watertight, multi-layered garment geometries with direct compatibility for industrial-grade simulations.Built upon this representation, we present GarmageNet, a novel generation framework capable of producing detailed multi-layered garments with body-conforming initial geometries and intricate sewing patterns, based on user prompts or existing in-the-wild sewing patterns. Furthermore, we introduce a robust stitching algorithm that recovers per-vertex stitches, ensuring seamless integration into flexible simulation pipelines for downstream editing of sewing patterns, material properties, and dynamic simulations. Finally, we release an industrial-standard, large-scale, high-fidelity garment dataset featuring detailed annotations, vertex-wise correspondences, and a robust pipeline for converting unstructured production sewing patterns into GarmageNet standard structural assets, paving the way for large-scale, industrial-grade garment generation systems.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 08:37:32 GMT" } ]	2025-04-03T00:00:00	[ [ "Li", "Siran", "" ], [ "Liu", "Ruiyang", "" ], [ "Liu", "Chen", "" ], [ "Wang", "Zhendong", "" ], [ "He", "Gaofeng", "" ], [ "Li", "Yong-Lu", "" ], [ "Jin", "Xiaogang", "" ], [ "Wang", "Huamin", "" ] ]	TITLE: GarmageNet: A Dataset and Scalable Representation for Generic Garment Modeling ABSTRACT: High-fidelity garment modeling remains challenging due to the lack of large-scale, high-quality datasets and efficient representations capable of handling non-watertight, multi-layer geometries. In this work, we introduce Garmage, a neural-network-and-CG-friendly garment representation that seamlessly encodes the accurate geometry and sewing pattern of complex multi-layered garments as a structured set of per-panel geometry images. As a dual-2D-3D representation, Garmage achieves an unprecedented integration of 2D image-based algorithms with 3D modeling workflows, enabling high fidelity, non-watertight, multi-layered garment geometries with direct compatibility for industrial-grade simulations.Built upon this representation, we present GarmageNet, a novel generation framework capable of producing detailed multi-layered garments with body-conforming initial geometries and intricate sewing patterns, based on user prompts or existing in-the-wild sewing patterns. Furthermore, we introduce a robust stitching algorithm that recovers per-vertex stitches, ensuring seamless integration into flexible simulation pipelines for downstream editing of sewing patterns, material properties, and dynamic simulations. Finally, we release an industrial-standard, large-scale, high-fidelity garment dataset featuring detailed annotations, vertex-wise correspondences, and a robust pipeline for converting unstructured production sewing patterns into GarmageNet standard structural assets, paving the way for large-scale, industrial-grade garment generation systems.
2504.01489	Changshuo Zhang	Changshuo Zhang, Xiao Zhang, Teng Shi, Jun Xu, Ji-Rong Wen	Test-Time Alignment for Tracking User Interest Shifts in Sequential Recommendation	null	null	null	null	cs.IR	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Sequential recommendation is essential in modern recommender systems, aiming to predict the next item a user may interact with based on their historical behaviors. However, real-world scenarios are often dynamic and subject to shifts in user interests. Conventional sequential recommendation models are typically trained on static historical data, limiting their ability to adapt to such shifts and resulting in significant performance degradation during testing. Recently, Test-Time Training (TTT) has emerged as a promising paradigm, enabling pre-trained models to dynamically adapt to test data by leveraging unlabeled examples during testing. However, applying TTT to effectively track and address user interest shifts in recommender systems remains an open and challenging problem. Key challenges include how to capture temporal information effectively and explicitly identifying shifts in user interests during the testing phase. To address these issues, we propose T$^2$ARec, a novel model leveraging state space model for TTT by introducing two Test-Time Alignment modules tailored for sequential recommendation, effectively capturing the distribution shifts in user interest patterns over time. Specifically, T$^2$ARec aligns absolute time intervals with model-adaptive learning intervals to capture temporal dynamics and introduce an interest state alignment mechanism to effectively and explicitly identify the user interest shifts with theoretical guarantees. These two alignment modules enable efficient and incremental updates to model parameters in a self-supervised manner during testing, enhancing predictions for online recommendation. Extensive evaluations on three benchmark datasets demonstrate that T$^2$ARec achieves state-of-the-art performance and robustly mitigates the challenges posed by user interest shifts.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 08:42:30 GMT" } ]	2025-04-03T00:00:00	[ [ "Zhang", "Changshuo", "" ], [ "Zhang", "Xiao", "" ], [ "Shi", "Teng", "" ], [ "Xu", "Jun", "" ], [ "Wen", "Ji-Rong", "" ] ]	TITLE: Test-Time Alignment for Tracking User Interest Shifts in Sequential Recommendation ABSTRACT: Sequential recommendation is essential in modern recommender systems, aiming to predict the next item a user may interact with based on their historical behaviors. However, real-world scenarios are often dynamic and subject to shifts in user interests. Conventional sequential recommendation models are typically trained on static historical data, limiting their ability to adapt to such shifts and resulting in significant performance degradation during testing. Recently, Test-Time Training (TTT) has emerged as a promising paradigm, enabling pre-trained models to dynamically adapt to test data by leveraging unlabeled examples during testing. However, applying TTT to effectively track and address user interest shifts in recommender systems remains an open and challenging problem. Key challenges include how to capture temporal information effectively and explicitly identifying shifts in user interests during the testing phase. To address these issues, we propose T$^2$ARec, a novel model leveraging state space model for TTT by introducing two Test-Time Alignment modules tailored for sequential recommendation, effectively capturing the distribution shifts in user interest patterns over time. Specifically, T$^2$ARec aligns absolute time intervals with model-adaptive learning intervals to capture temporal dynamics and introduce an interest state alignment mechanism to effectively and explicitly identify the user interest shifts with theoretical guarantees. These two alignment modules enable efficient and incremental updates to model parameters in a self-supervised manner during testing, enhancing predictions for online recommendation. Extensive evaluations on three benchmark datasets demonstrate that T$^2$ARec achieves state-of-the-art performance and robustly mitigates the challenges posed by user interest shifts.
2504.01519	Zhiyuan Tang	Zhiyuan Tang, Dong Wang, Zhikai Zhou, Yong Liu, Shen Huang, Shidong Shang	Chain of Correction for Full-text Speech Recognition with Large Language Models	null	null	null	null	cs.CL eess.AS	http://creativecommons.org/licenses/by-nc-sa/4.0/	Full-text error correction with Large Language Models (LLMs) for Automatic Speech Recognition (ASR) has gained increased attention due to its potential to correct errors across long contexts and address a broader spectrum of error types, including punctuation restoration and inverse text normalization. Nevertheless, many challenges persist, including issues related to stability, controllability, completeness, and fluency. To mitigate these challenges, this paper proposes the Chain of Correction (CoC) for full-text error correction with LLMs, which corrects errors segment by segment using pre-recognized text as guidance within a regular multi-turn chat format. The CoC also uses pre-recognized full text for context, allowing the model to better grasp global semantics and maintain a comprehensive overview of the entire content. Utilizing the open-sourced full-text error correction dataset ChFT, we fine-tune a pre-trained LLM to evaluate the performance of the CoC framework. Experimental results demonstrate that the CoC effectively corrects errors in full-text ASR outputs, significantly outperforming baseline and benchmark systems. We further analyze how to set the correction threshold to balance under-correction and over-rephrasing, extrapolate the CoC model on extremely long ASR outputs, and investigate whether other types of information can be employed to guide the error correction process.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 09:06:23 GMT" } ]	2025-04-03T00:00:00	[ [ "Tang", "Zhiyuan", "" ], [ "Wang", "Dong", "" ], [ "Zhou", "Zhikai", "" ], [ "Liu", "Yong", "" ], [ "Huang", "Shen", "" ], [ "Shang", "Shidong", "" ] ]	TITLE: Chain of Correction for Full-text Speech Recognition with Large Language Models ABSTRACT: Full-text error correction with Large Language Models (LLMs) for Automatic Speech Recognition (ASR) has gained increased attention due to its potential to correct errors across long contexts and address a broader spectrum of error types, including punctuation restoration and inverse text normalization. Nevertheless, many challenges persist, including issues related to stability, controllability, completeness, and fluency. To mitigate these challenges, this paper proposes the Chain of Correction (CoC) for full-text error correction with LLMs, which corrects errors segment by segment using pre-recognized text as guidance within a regular multi-turn chat format. The CoC also uses pre-recognized full text for context, allowing the model to better grasp global semantics and maintain a comprehensive overview of the entire content. Utilizing the open-sourced full-text error correction dataset ChFT, we fine-tune a pre-trained LLM to evaluate the performance of the CoC framework. Experimental results demonstrate that the CoC effectively corrects errors in full-text ASR outputs, significantly outperforming baseline and benchmark systems. We further analyze how to set the correction threshold to balance under-correction and over-rephrasing, extrapolate the CoC model on extremely long ASR outputs, and investigate whether other types of information can be employed to guide the error correction process.
2504.01523	Xuemeng Cai	Xuemeng Cai, Lingxiao Jiang	Adapting Knowledge Prompt Tuning for Enhanced Automated Program Repair	null	null	null	null	cs.SE	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Automated Program Repair (APR) aims to enhance software reliability by automatically generating bug-fixing patches. Recent work has improved the state-of-the-art of APR by fine-tuning pre-trained large language models (LLMs), such as CodeT5, for APR. However, the effectiveness of fine-tuning becomes weakened in data scarcity scenarios, and data scarcity can be a common issue in practice, limiting fine-tuning performance. To alleviate this limitation, this paper adapts prompt tuning for enhanced APR and conducts a comprehensive study to evaluate its effectiveness in data scarcity scenarios, using three LLMs of different sizes and six diverse datasets across four programming languages. Prompt tuning rewrites the input to a model by adding extra prompt tokens and tunes both the model and the prompts on a small dataset. These tokens provide task-specific knowledge that can improve the model for APR, which is especially critical in data scarcity scenarios. Moreover, domain knowledge has proven crucial in many code intelligence tasks, but existing studies fail to leverage domain knowledge during the prompt tuning for APR. To close this gap, we introduce knowledge prompt tuning, an approach that adapts prompt tuning with six distinct types of code- or bug-related domain knowledge for APR. Our work, to the best of our knowledge, is the first to adapt and evaluate prompt tuning and the effectiveness of code- or bug-related domain knowledge for APR, particularly under data scarcity settings. Our evaluation results demonstrate that prompt tuning with knowledge generally outperforms fine-tuning under various experimental settings, achieving an average improvement of 87.33% over fine-tuning in data scarcity scenarios.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 09:10:02 GMT" } ]	2025-04-03T00:00:00	[ [ "Cai", "Xuemeng", "" ], [ "Jiang", "Lingxiao", "" ] ]	TITLE: Adapting Knowledge Prompt Tuning for Enhanced Automated Program Repair ABSTRACT: Automated Program Repair (APR) aims to enhance software reliability by automatically generating bug-fixing patches. Recent work has improved the state-of-the-art of APR by fine-tuning pre-trained large language models (LLMs), such as CodeT5, for APR. However, the effectiveness of fine-tuning becomes weakened in data scarcity scenarios, and data scarcity can be a common issue in practice, limiting fine-tuning performance. To alleviate this limitation, this paper adapts prompt tuning for enhanced APR and conducts a comprehensive study to evaluate its effectiveness in data scarcity scenarios, using three LLMs of different sizes and six diverse datasets across four programming languages. Prompt tuning rewrites the input to a model by adding extra prompt tokens and tunes both the model and the prompts on a small dataset. These tokens provide task-specific knowledge that can improve the model for APR, which is especially critical in data scarcity scenarios. Moreover, domain knowledge has proven crucial in many code intelligence tasks, but existing studies fail to leverage domain knowledge during the prompt tuning for APR. To close this gap, we introduce knowledge prompt tuning, an approach that adapts prompt tuning with six distinct types of code- or bug-related domain knowledge for APR. Our work, to the best of our knowledge, is the first to adapt and evaluate prompt tuning and the effectiveness of code- or bug-related domain knowledge for APR, particularly under data scarcity settings. Our evaluation results demonstrate that prompt tuning with knowledge generally outperforms fine-tuning under various experimental settings, achieving an average improvement of 87.33% over fine-tuning in data scarcity scenarios.
2504.01527	Olivier Rukundo	Olivier Rukundo	Beyond Nearest Neighbor Interpolation in Data Augmentation	6 pages, 9 figures, 1 table	null	null	null	cs.CV eess.IV	http://creativecommons.org/licenses/by/4.0/	Avoiding the risk of undefined categorical labels using nearest neighbor interpolation overlooks the risk of exacerbating pixel level annotation errors in data augmentation. To simultaneously avoid these risks, the author modified convolutional neural networks data transformation functions by incorporating a modified geometric transformation function to improve the quality of augmented data by removing the reliance on nearest neighbor interpolation and integrating a mean based class filtering mechanism to handle undefined categorical labels with alternative interpolation algorithms. Experiments on semantic segmentation tasks using three medical image datasets demonstrated both qualitative and quantitative improvements with alternative interpolation algorithms.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 09:13:18 GMT" } ]	2025-04-03T00:00:00	[ [ "Rukundo", "Olivier", "" ] ]	TITLE: Beyond Nearest Neighbor Interpolation in Data Augmentation ABSTRACT: Avoiding the risk of undefined categorical labels using nearest neighbor interpolation overlooks the risk of exacerbating pixel level annotation errors in data augmentation. To simultaneously avoid these risks, the author modified convolutional neural networks data transformation functions by incorporating a modified geometric transformation function to improve the quality of augmented data by removing the reliance on nearest neighbor interpolation and integrating a mean based class filtering mechanism to handle undefined categorical labels with alternative interpolation algorithms. Experiments on semantic segmentation tasks using three medical image datasets demonstrated both qualitative and quantitative improvements with alternative interpolation algorithms.
2504.01534	Adrien Schurger-Foy	Adrien Schurger-Foy, Rafal Dariusz Kocielnik, Caglar Gulcehre, R. Michael Alvarez	Context-Aware Toxicity Detection in Multiplayer Games: Integrating Domain-Adaptive Pretraining and Match Metadata	null	null	null	null	cs.CL	http://creativecommons.org/licenses/by-nc-nd/4.0/	The detrimental effects of toxicity in competitive online video games are widely acknowledged, prompting publishers to monitor player chat conversations. This is challenging due to the context-dependent nature of toxicity, often spread across multiple messages or informed by non-textual interactions. Traditional toxicity detectors focus on isolated messages, missing the broader context needed for accurate moderation. This is especially problematic in video games, where interactions involve specialized slang, abbreviations, and typos, making it difficult for standard models to detect toxicity, especially given its rarity. We adapted RoBERTa LLM to support moderation tailored to video games, integrating both textual and non-textual context. By enhancing pretrained embeddings with metadata and addressing the unique slang and language quirks through domain adaptive pretraining, our method better captures the nuances of player interactions. Using two gaming datasets - from Defense of the Ancients 2 (DOTA 2) and Call of Duty$^\circledR$: Modern Warfare$^\circledR$III (MWIII) we demonstrate which sources of context (metadata, prior interactions...) are most useful, how to best leverage them to boost performance, and the conditions conducive to doing so. This work underscores the importance of context-aware and domain-specific approaches for proactive moderation.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 09:21:41 GMT" } ]	2025-04-03T00:00:00	[ [ "Schurger-Foy", "Adrien", "" ], [ "Kocielnik", "Rafal Dariusz", "" ], [ "Gulcehre", "Caglar", "" ], [ "Alvarez", "R. Michael", "" ] ]	TITLE: Context-Aware Toxicity Detection in Multiplayer Games: Integrating Domain-Adaptive Pretraining and Match Metadata ABSTRACT: The detrimental effects of toxicity in competitive online video games are widely acknowledged, prompting publishers to monitor player chat conversations. This is challenging due to the context-dependent nature of toxicity, often spread across multiple messages or informed by non-textual interactions. Traditional toxicity detectors focus on isolated messages, missing the broader context needed for accurate moderation. This is especially problematic in video games, where interactions involve specialized slang, abbreviations, and typos, making it difficult for standard models to detect toxicity, especially given its rarity. We adapted RoBERTa LLM to support moderation tailored to video games, integrating both textual and non-textual context. By enhancing pretrained embeddings with metadata and addressing the unique slang and language quirks through domain adaptive pretraining, our method better captures the nuances of player interactions. Using two gaming datasets - from Defense of the Ancients 2 (DOTA 2) and Call of Duty$^\circledR$: Modern Warfare$^\circledR$III (MWIII) we demonstrate which sources of context (metadata, prior interactions...) are most useful, how to best leverage them to boost performance, and the conditions conducive to doing so. This work underscores the importance of context-aware and domain-specific approaches for proactive moderation.
2504.01537	Elena Corbetta	Elena Corbetta and Thomas Bocklitz	Multi-Marker Similarity enables reduced-reference and interpretable image quality assessment in optical microscopy	24 pages, 11 figures, 1 table	null	null	null	q-bio.QM physics.optics stat.AP stat.ME	http://creativecommons.org/licenses/by/4.0/	Optical microscopy contributes to the ever-increasing progress in biological and biomedical studies, as it allows the implementation of minimally invasive experimental pipelines to translate the data of measured samples into valuable knowledge. Within these pipelines, reliable quality assessment must be ensured to validate the generated results. Image quality assessment is often applied with full-reference methods to estimate the similarity between the ground truth and the output images. However, current methods often show poor agreement with visual perception and lead to the generation of various full-reference metrics tailored to specific applications. Additionally, they rely on pixel-wise comparisons, emphasizing local intensity similarity while often overlooking comprehensive and interpretable image quality assessment. To address these issues, we have developed a multi-marker similarity method that compares standard quality markers, such as resolution, signal-to-noise ratio, contrast, and high frequency components. The method computes a similarity score between the image and the ground truth for each marker, then combines these scores into an overall similarity estimate. This provides a full-reference estimate of image quality while extracting global quality features and detecting experimental artifacts. Multi-marker similarity provides a reliable and interpretable method for image quality assessment and the generation of quality rankings. By focusing on the comparison of quality markers rather than direct image distances, the method enables reduced reference implementations, where a single field of view is used as a benchmark for multiple measurements. This opens the way for reliable automatic evaluation of big datasets, typical of large biomedical studies, when manual assessment of single images and defining the ground truth for each field of view is not feasible.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 09:23:57 GMT" } ]	2025-04-03T00:00:00	[ [ "Corbetta", "Elena", "" ], [ "Bocklitz", "Thomas", "" ] ]	TITLE: Multi-Marker Similarity enables reduced-reference and interpretable image quality assessment in optical microscopy ABSTRACT: Optical microscopy contributes to the ever-increasing progress in biological and biomedical studies, as it allows the implementation of minimally invasive experimental pipelines to translate the data of measured samples into valuable knowledge. Within these pipelines, reliable quality assessment must be ensured to validate the generated results. Image quality assessment is often applied with full-reference methods to estimate the similarity between the ground truth and the output images. However, current methods often show poor agreement with visual perception and lead to the generation of various full-reference metrics tailored to specific applications. Additionally, they rely on pixel-wise comparisons, emphasizing local intensity similarity while often overlooking comprehensive and interpretable image quality assessment. To address these issues, we have developed a multi-marker similarity method that compares standard quality markers, such as resolution, signal-to-noise ratio, contrast, and high frequency components. The method computes a similarity score between the image and the ground truth for each marker, then combines these scores into an overall similarity estimate. This provides a full-reference estimate of image quality while extracting global quality features and detecting experimental artifacts. Multi-marker similarity provides a reliable and interpretable method for image quality assessment and the generation of quality rankings. By focusing on the comparison of quality markers rather than direct image distances, the method enables reduced reference implementations, where a single field of view is used as a benchmark for multiple measurements. This opens the way for reliable automatic evaluation of big datasets, typical of large biomedical studies, when manual assessment of single images and defining the ground truth for each field of view is not feasible.
2504.01540	Rob van der Goot	Mikkel Wildner Kildeberg, Emil Allerslev Schledermann, Nicolaj Larsen, Rob van der Goot	From Sm{\o}r-re-br{\o}d to Subwords: Training LLMs on Danish, One Morpheme at a Time	null	null	null	null	cs.CL	http://creativecommons.org/licenses/by-nc-sa/4.0/	The best performing transformer-based language models use subword tokenization techniques, such as Byte-Pair-Encoding (BPE). However, these approaches often overlook linguistic principles, such as morphological segmentation, which we believe is fundamental for understanding language-specific word structure. In this study, we leverage an annotated Danish morphological dataset to train a semisupervised model for morphological segmentation, enabling the development of tokenizers optimized for Danish morphology. We evaluate four distinct tokenizers, including two custom morphological tokenizers, by analyzing their performance in morphologically segmenting Danish words. Additionally, we train two generative transformer models, \textit{CerebrasGPT-111M} and \textit{LLaMA-3.2 1B}, using these tokenizers and evaluate their downstream performance. Our findings reveal that our custom-developed tokenizers substantially enhance morphological segmentation, achieving an F1 score of 58.84, compared to 39.28 achieved by a Danish BPE tokenizer. In downstream tasks, models trained with our morphological tokenizers outperform those using BPE tokenizers across different evaluation metrics. These results highlight that incorporating Danish morphological segmentation strategies into tokenizers leads to improved performance in generative transformer models on Danish language	[ { "version": "v1", "created": "Wed, 2 Apr 2025 09:26:02 GMT" } ]	2025-04-03T00:00:00	[ [ "Kildeberg", "Mikkel Wildner", "" ], [ "Schledermann", "Emil Allerslev", "" ], [ "Larsen", "Nicolaj", "" ], [ "van der Goot", "Rob", "" ] ]	TITLE: From Sm{\o}r-re-br{\o}d to Subwords: Training LLMs on Danish, One Morpheme at a Time ABSTRACT: The best performing transformer-based language models use subword tokenization techniques, such as Byte-Pair-Encoding (BPE). However, these approaches often overlook linguistic principles, such as morphological segmentation, which we believe is fundamental for understanding language-specific word structure. In this study, we leverage an annotated Danish morphological dataset to train a semisupervised model for morphological segmentation, enabling the development of tokenizers optimized for Danish morphology. We evaluate four distinct tokenizers, including two custom morphological tokenizers, by analyzing their performance in morphologically segmenting Danish words. Additionally, we train two generative transformer models, \textit{CerebrasGPT-111M} and \textit{LLaMA-3.2 1B}, using these tokenizers and evaluate their downstream performance. Our findings reveal that our custom-developed tokenizers substantially enhance morphological segmentation, achieving an F1 score of 58.84, compared to 39.28 achieved by a Danish BPE tokenizer. In downstream tasks, models trained with our morphological tokenizers outperform those using BPE tokenizers across different evaluation metrics. These results highlight that incorporating Danish morphological segmentation strategies into tokenizers leads to improved performance in generative transformer models on Danish language
2504.01542	Amanda Myntti	Amanda Myntti, Erik Henriksson, Veronika Laippala, Sampo Pyysalo	Register Always Matters: Analysis of LLM Pretraining Data Through the Lens of Language Variation	null	null	null	null	cs.CL	http://creativecommons.org/licenses/by/4.0/	Pretraining data curation is a cornerstone in Large Language Model (LLM) development, leading to growing research on quality filtering of large web corpora. From statistical quality flags to LLM-based labeling systems, datasets are divided into categories, frequently reducing to a binary: those passing the filters deemed as valuable examples, others discarded as useless or detrimental. However, a more detailed understanding of the contribution of different kinds of texts to model performance is still largely lacking. In this article, we present the first study utilizing registers (also known as genres) - a widely used standard in corpus linguistics to model linguistic variation - to curate pretraining datasets and investigate the effect of register on the performance of LLMs. We perform comparative studies by training models with register classified data and evaluating them using standard benchmarks, and show that the register of pretraining data substantially affects model performance. We uncover surprising relationships between the pretraining material and the resulting models: using the News register results in subpar performance, and on the contrary, including the Opinion class, covering texts such as reviews and opinion blogs, is highly beneficial. While a model trained on the entire unfiltered dataset outperforms those trained on datasets limited to a single register, combining well-performing registers like How-to-Instructions, Informational Description, and Opinion leads to major improvements. Furthermore, analysis of individual benchmark results reveals key differences in the strengths and drawbacks of specific register classes as pretraining data. These findings show that register is an important explainer of model variation and can facilitate more deliberate future data selection practices.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 09:30:24 GMT" } ]	2025-04-03T00:00:00	[ [ "Myntti", "Amanda", "" ], [ "Henriksson", "Erik", "" ], [ "Laippala", "Veronika", "" ], [ "Pyysalo", "Sampo", "" ] ]	TITLE: Register Always Matters: Analysis of LLM Pretraining Data Through the Lens of Language Variation ABSTRACT: Pretraining data curation is a cornerstone in Large Language Model (LLM) development, leading to growing research on quality filtering of large web corpora. From statistical quality flags to LLM-based labeling systems, datasets are divided into categories, frequently reducing to a binary: those passing the filters deemed as valuable examples, others discarded as useless or detrimental. However, a more detailed understanding of the contribution of different kinds of texts to model performance is still largely lacking. In this article, we present the first study utilizing registers (also known as genres) - a widely used standard in corpus linguistics to model linguistic variation - to curate pretraining datasets and investigate the effect of register on the performance of LLMs. We perform comparative studies by training models with register classified data and evaluating them using standard benchmarks, and show that the register of pretraining data substantially affects model performance. We uncover surprising relationships between the pretraining material and the resulting models: using the News register results in subpar performance, and on the contrary, including the Opinion class, covering texts such as reviews and opinion blogs, is highly beneficial. While a model trained on the entire unfiltered dataset outperforms those trained on datasets limited to a single register, combining well-performing registers like How-to-Instructions, Informational Description, and Opinion leads to major improvements. Furthermore, analysis of individual benchmark results reveals key differences in the strengths and drawbacks of specific register classes as pretraining data. These findings show that register is an important explainer of model variation and can facilitate more deliberate future data selection practices.
2504.01547	Luca Ciampi	Luca Ciampi, Gabriele Lagani, Giuseppe Amato, Fabrizio Falchi	Semi-Supervised Biomedical Image Segmentation via Diffusion Models and Teacher-Student Co-Training	null	null	null	null	cs.CV	http://creativecommons.org/licenses/by/4.0/	Supervised deep learning for semantic segmentation has achieved excellent results in accurately identifying anatomical and pathological structures in medical images. However, it often requires large annotated training datasets, which limits its scalability in clinical settings. To address this challenge, semi-supervised learning is a well-established approach that leverages both labeled and unlabeled data. In this paper, we introduce a novel semi-supervised teacher-student framework for biomedical image segmentation, inspired by the recent success of generative models. Our approach leverages denoising diffusion probabilistic models (DDPMs) to generate segmentation masks by progressively refining noisy inputs conditioned on the corresponding images. The teacher model is first trained in an unsupervised manner using a cycle-consistency constraint based on noise-corrupted image reconstruction, enabling it to generate informative semantic masks. Subsequently, the teacher is integrated into a co-training process with a twin-student network. The student learns from ground-truth labels when available and from teacher-generated pseudo-labels otherwise, while the teacher continuously improves its pseudo-labeling capabilities. Finally, to further enhance performance, we introduce a multi-round pseudo-label generation strategy that iteratively improves the pseudo-labeling process. We evaluate our approach on multiple biomedical imaging benchmarks, spanning multiple imaging modalities and segmentation tasks. Experimental results show that our method consistently outperforms state-of-the-art semi-supervised techniques, highlighting its effectiveness in scenarios with limited annotated data. The code to replicate our experiments can be found at https://github.com/ciampluca/diffusion_semi_supervised_biomedical_image_segmentation	[ { "version": "v1", "created": "Wed, 2 Apr 2025 09:41:43 GMT" } ]	2025-04-03T00:00:00	[ [ "Ciampi", "Luca", "" ], [ "Lagani", "Gabriele", "" ], [ "Amato", "Giuseppe", "" ], [ "Falchi", "Fabrizio", "" ] ]	TITLE: Semi-Supervised Biomedical Image Segmentation via Diffusion Models and Teacher-Student Co-Training ABSTRACT: Supervised deep learning for semantic segmentation has achieved excellent results in accurately identifying anatomical and pathological structures in medical images. However, it often requires large annotated training datasets, which limits its scalability in clinical settings. To address this challenge, semi-supervised learning is a well-established approach that leverages both labeled and unlabeled data. In this paper, we introduce a novel semi-supervised teacher-student framework for biomedical image segmentation, inspired by the recent success of generative models. Our approach leverages denoising diffusion probabilistic models (DDPMs) to generate segmentation masks by progressively refining noisy inputs conditioned on the corresponding images. The teacher model is first trained in an unsupervised manner using a cycle-consistency constraint based on noise-corrupted image reconstruction, enabling it to generate informative semantic masks. Subsequently, the teacher is integrated into a co-training process with a twin-student network. The student learns from ground-truth labels when available and from teacher-generated pseudo-labels otherwise, while the teacher continuously improves its pseudo-labeling capabilities. Finally, to further enhance performance, we introduce a multi-round pseudo-label generation strategy that iteratively improves the pseudo-labeling process. We evaluate our approach on multiple biomedical imaging benchmarks, spanning multiple imaging modalities and segmentation tasks. Experimental results show that our method consistently outperforms state-of-the-art semi-supervised techniques, highlighting its effectiveness in scenarios with limited annotated data. The code to replicate our experiments can be found at https://github.com/ciampluca/diffusion_semi_supervised_biomedical_image_segmentation
2504.01557	Shujing Wang	Shujing Wang (1), Selasi Kwashie (2), Michael Bewong (3), Junwei Hu (1), Vincent M. Nofong (4), Shiqi Miao (1), Zaiwen Feng (1) ((1) Huazhong Agricultural University, Wuhan, China (2) AI & Cyber Futures Institute, Charles Sturt University, Australia (3) School of Computing, Mathematics and Engineering, Charles Sturt University, Australia (4) Department of Computer Science and Engineering, University of Mines and Technology, Ghana)	FastER: Fast On-Demand Entity Resolution in Property Graphs	null	null	null	null	cs.DB	http://creativecommons.org/licenses/by-nc-sa/4.0/	Entity resolution (ER) is the problem of identifying and linking database records that refer to the same real-world entity. Traditional ER methods use batch processing, which becomes impractical with growing data volumes due to high computational costs and lack of real-time capabilities. In many applications, users need to resolve entities for only a small portion of their data, making full data processing unnecessary -- a scenario known as "ER-on-demand". This paper proposes FastER, an efficient ER-on-demand framework for property graphs. Our approach uses graph differential dependencies (GDDs) as a knowledge encoding language to design effective filtering mechanisms that leverage both structural and attribute semantics of graphs. We construct a blocking graph from filtered subgraphs to reduce the number of candidate entity pairs requiring comparison. Additionally, FastER incorporates Progressive Profile Scheduling (PPS), allowing the system to incrementally produce results throughout the resolution process. Extensive evaluations on multiple benchmark datasets demonstrate that FastER significantly outperforms state-of-the-art ER methods in computational efficiency and real-time processing for on-demand tasks while ensuring reliability. We make FastER publicly available at: https://anonymous.4open.science/r/On_Demand_Entity_Resolution-9DFB	[ { "version": "v1", "created": "Wed, 2 Apr 2025 09:58:38 GMT" } ]	2025-04-03T00:00:00	[ [ "Wang", "Shujing", "" ], [ "Kwashie", "Selasi", "" ], [ "Bewong", "Michael", "" ], [ "Hu", "Junwei", "" ], [ "Nofong", "Vincent M.", "" ], [ "Miao", "Shiqi", "" ], [ "Feng", "Zaiwen", "" ] ]	TITLE: FastER: Fast On-Demand Entity Resolution in Property Graphs ABSTRACT: Entity resolution (ER) is the problem of identifying and linking database records that refer to the same real-world entity. Traditional ER methods use batch processing, which becomes impractical with growing data volumes due to high computational costs and lack of real-time capabilities. In many applications, users need to resolve entities for only a small portion of their data, making full data processing unnecessary -- a scenario known as "ER-on-demand". This paper proposes FastER, an efficient ER-on-demand framework for property graphs. Our approach uses graph differential dependencies (GDDs) as a knowledge encoding language to design effective filtering mechanisms that leverage both structural and attribute semantics of graphs. We construct a blocking graph from filtered subgraphs to reduce the number of candidate entity pairs requiring comparison. Additionally, FastER incorporates Progressive Profile Scheduling (PPS), allowing the system to incrementally produce results throughout the resolution process. Extensive evaluations on multiple benchmark datasets demonstrate that FastER significantly outperforms state-of-the-art ER methods in computational efficiency and real-time processing for on-demand tasks while ensuring reliability. We make FastER publicly available at: https://anonymous.4open.science/r/On_Demand_Entity_Resolution-9DFB
2504.01559	Yahui Li	Yahui Li, Zhi Zeng, Liming Pang, Guixuan Zhang, Shuwu Zhang	RealityAvatar: Towards Realistic Loose Clothing Modeling in Animatable 3D Gaussian Avatars	null	null	null	null	cs.CV	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Modeling animatable human avatars from monocular or multi-view videos has been widely studied, with recent approaches leveraging neural radiance fields (NeRFs) or 3D Gaussian Splatting (3DGS) achieving impressive results in novel-view and novel-pose synthesis. However, existing methods often struggle to accurately capture the dynamics of loose clothing, as they primarily rely on global pose conditioning or static per-frame representations, leading to oversmoothing and temporal inconsistencies in non-rigid regions. To address this, We propose RealityAvatar, an efficient framework for high-fidelity digital human modeling, specifically targeting loosely dressed avatars. Our method leverages 3D Gaussian Splatting to capture complex clothing deformations and motion dynamics while ensuring geometric consistency. By incorporating a motion trend module and a latentbone encoder, we explicitly model pose-dependent deformations and temporal variations in clothing behavior. Extensive experiments on benchmark datasets demonstrate the effectiveness of our approach in capturing fine-grained clothing deformations and motion-driven shape variations. Our method significantly enhances structural fidelity and perceptual quality in dynamic human reconstruction, particularly in non-rigid regions, while achieving better consistency across temporal frames.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 09:59:12 GMT" } ]	2025-04-03T00:00:00	[ [ "Li", "Yahui", "" ], [ "Zeng", "Zhi", "" ], [ "Pang", "Liming", "" ], [ "Zhang", "Guixuan", "" ], [ "Zhang", "Shuwu", "" ] ]	TITLE: RealityAvatar: Towards Realistic Loose Clothing Modeling in Animatable 3D Gaussian Avatars ABSTRACT: Modeling animatable human avatars from monocular or multi-view videos has been widely studied, with recent approaches leveraging neural radiance fields (NeRFs) or 3D Gaussian Splatting (3DGS) achieving impressive results in novel-view and novel-pose synthesis. However, existing methods often struggle to accurately capture the dynamics of loose clothing, as they primarily rely on global pose conditioning or static per-frame representations, leading to oversmoothing and temporal inconsistencies in non-rigid regions. To address this, We propose RealityAvatar, an efficient framework for high-fidelity digital human modeling, specifically targeting loosely dressed avatars. Our method leverages 3D Gaussian Splatting to capture complex clothing deformations and motion dynamics while ensuring geometric consistency. By incorporating a motion trend module and a latentbone encoder, we explicitly model pose-dependent deformations and temporal variations in clothing behavior. Extensive experiments on benchmark datasets demonstrate the effectiveness of our approach in capturing fine-grained clothing deformations and motion-driven shape variations. Our method significantly enhances structural fidelity and perceptual quality in dynamic human reconstruction, particularly in non-rigid regions, while achieving better consistency across temporal frames.
2504.01561	Dandan Shan	Dandan Shan and Zihan Li and Yunxiang Li and Qingde Li and Jie Tian and Qingqi Hong	STPNet: Scale-aware Text Prompt Network for Medical Image Segmentation	null	null	null	null	eess.IV cs.CV	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Accurate segmentation of lesions plays a critical role in medical image analysis and diagnosis. Traditional segmentation approaches that rely solely on visual features often struggle with the inherent uncertainty in lesion distribution and size. To address these issues, we propose STPNet, a Scale-aware Text Prompt Network that leverages vision-language modeling to enhance medical image segmentation. Our approach utilizes multi-scale textual descriptions to guide lesion localization and employs retrieval-segmentation joint learning to bridge the semantic gap between visual and linguistic modalities. Crucially, STPNet retrieves relevant textual information from a specialized medical text repository during training, eliminating the need for text input during inference while retaining the benefits of cross-modal learning. We evaluate STPNet on three datasets: COVID-Xray, COVID-CT, and Kvasir-SEG. Experimental results show that our vision-language approach outperforms state-of-the-art segmentation methods, demonstrating the effectiveness of incorporating textual semantic knowledge into medical image analysis. The code has been made publicly on https://github.com/HUANGLIZI/STPNet.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 10:01:42 GMT" } ]	2025-04-03T00:00:00	[ [ "Shan", "Dandan", "" ], [ "Li", "Zihan", "" ], [ "Li", "Yunxiang", "" ], [ "Li", "Qingde", "" ], [ "Tian", "Jie", "" ], [ "Hong", "Qingqi", "" ] ]	TITLE: STPNet: Scale-aware Text Prompt Network for Medical Image Segmentation ABSTRACT: Accurate segmentation of lesions plays a critical role in medical image analysis and diagnosis. Traditional segmentation approaches that rely solely on visual features often struggle with the inherent uncertainty in lesion distribution and size. To address these issues, we propose STPNet, a Scale-aware Text Prompt Network that leverages vision-language modeling to enhance medical image segmentation. Our approach utilizes multi-scale textual descriptions to guide lesion localization and employs retrieval-segmentation joint learning to bridge the semantic gap between visual and linguistic modalities. Crucially, STPNet retrieves relevant textual information from a specialized medical text repository during training, eliminating the need for text input during inference while retaining the benefits of cross-modal learning. We evaluate STPNet on three datasets: COVID-Xray, COVID-CT, and Kvasir-SEG. Experimental results show that our vision-language approach outperforms state-of-the-art segmentation methods, demonstrating the effectiveness of incorporating textual semantic knowledge into medical image analysis. The code has been made publicly on https://github.com/HUANGLIZI/STPNet.
2504.01577	Lirui Qi	Lirui Qi, Hongliang He, Tong Wang, Siwei Feng, Guohong Fu	Instance Migration Diffusion for Nuclear Instance Segmentation in Pathology	null	null	null	null	eess.IV cs.CV	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Nuclear instance segmentation plays a vital role in disease diagnosis within digital pathology. However, limited labeled data in pathological images restricts the overall performance of nuclear instance segmentation. To tackle this challenge, we propose a novel data augmentation framework Instance Migration Diffusion Model (IM-Diffusion), IM-Diffusion designed to generate more varied pathological images by constructing diverse nuclear layouts and internuclear spatial relationships. In detail, we introduce a Nuclear Migration Module (NMM) which constructs diverse nuclear layouts by simulating the process of nuclear migration. Building on this, we further present an Internuclear-regions Inpainting Module (IIM) to generate diverse internuclear spatial relationships by structure-aware inpainting. On the basis of the above, IM-Diffusion generates more diverse pathological images with different layouts and internuclear spatial relationships, thereby facilitating downstream tasks. Evaluation on the CoNSeP and GLySAC datasets demonstrate that the images generated by IM-Diffusion effectively enhance overall instance segmentation performance. Code will be made public later.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 10:29:31 GMT" } ]	2025-04-03T00:00:00	[ [ "Qi", "Lirui", "" ], [ "He", "Hongliang", "" ], [ "Wang", "Tong", "" ], [ "Feng", "Siwei", "" ], [ "Fu", "Guohong", "" ] ]	TITLE: Instance Migration Diffusion for Nuclear Instance Segmentation in Pathology ABSTRACT: Nuclear instance segmentation plays a vital role in disease diagnosis within digital pathology. However, limited labeled data in pathological images restricts the overall performance of nuclear instance segmentation. To tackle this challenge, we propose a novel data augmentation framework Instance Migration Diffusion Model (IM-Diffusion), IM-Diffusion designed to generate more varied pathological images by constructing diverse nuclear layouts and internuclear spatial relationships. In detail, we introduce a Nuclear Migration Module (NMM) which constructs diverse nuclear layouts by simulating the process of nuclear migration. Building on this, we further present an Internuclear-regions Inpainting Module (IIM) to generate diverse internuclear spatial relationships by structure-aware inpainting. On the basis of the above, IM-Diffusion generates more diverse pathological images with different layouts and internuclear spatial relationships, thereby facilitating downstream tasks. Evaluation on the CoNSeP and GLySAC datasets demonstrate that the images generated by IM-Diffusion effectively enhance overall instance segmentation performance. Code will be made public later.
2504.01588	Giulia Belgiovine	Luca Garello, Giulia Belgiovine, Gabriele Russo, Francesco Rea, Alessandra Sciutti	Building Knowledge from Interactions: An LLM-Based Architecture for Adaptive Tutoring and Social Reasoning	Submitted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2025	null	null	null	cs.RO cs.AI	http://creativecommons.org/licenses/by/4.0/	Integrating robotics into everyday scenarios like tutoring or physical training requires robots capable of adaptive, socially engaging, and goal-oriented interactions. While Large Language Models show promise in human-like communication, their standalone use is hindered by memory constraints and contextual incoherence. This work presents a multimodal, cognitively inspired framework that enhances LLM-based autonomous decision-making in social and task-oriented Human-Robot Interaction. Specifically, we develop an LLM-based agent for a robot trainer, balancing social conversation with task guidance and goal-driven motivation. To further enhance autonomy and personalization, we introduce a memory system for selecting, storing and retrieving experiences, facilitating generalized reasoning based on knowledge built across different interactions. A preliminary HRI user study and offline experiments with a synthetic dataset validate our approach, demonstrating the system's ability to manage complex interactions, autonomously drive training tasks, and build and retrieve contextual memories, advancing socially intelligent robotics.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 10:45:41 GMT" } ]	2025-04-03T00:00:00	[ [ "Garello", "Luca", "" ], [ "Belgiovine", "Giulia", "" ], [ "Russo", "Gabriele", "" ], [ "Rea", "Francesco", "" ], [ "Sciutti", "Alessandra", "" ] ]	TITLE: Building Knowledge from Interactions: An LLM-Based Architecture for Adaptive Tutoring and Social Reasoning ABSTRACT: Integrating robotics into everyday scenarios like tutoring or physical training requires robots capable of adaptive, socially engaging, and goal-oriented interactions. While Large Language Models show promise in human-like communication, their standalone use is hindered by memory constraints and contextual incoherence. This work presents a multimodal, cognitively inspired framework that enhances LLM-based autonomous decision-making in social and task-oriented Human-Robot Interaction. Specifically, we develop an LLM-based agent for a robot trainer, balancing social conversation with task guidance and goal-driven motivation. To further enhance autonomy and personalization, we introduce a memory system for selecting, storing and retrieving experiences, facilitating generalized reasoning based on knowledge built across different interactions. A preliminary HRI user study and offline experiments with a synthetic dataset validate our approach, demonstrating the system's ability to manage complex interactions, autonomously drive training tasks, and build and retrieve contextual memories, advancing socially intelligent robotics.
2504.01593	Martin Weigt	Francesco Calvanese, Giovanni Peinetti, Polina Pavlinova, Philippe Nghe and Martin Weigt	Integrating experimental feedback improves generative models for biological sequences	single document containing supplemental information	null	null	null	q-bio.BM physics.bio-ph q-bio.QM	http://creativecommons.org/licenses/by-nc-sa/4.0/	Generative probabilistic models have shown promise in designing artificial RNA and protein sequences but often suffer from high rates of false positives, where sequences predicted as functional fail experimental validation. To address this critical limitation, we explore the impact of reintegrating experimental feedback into the model design process. We propose a likelihood-based reintegration scheme, which we test through extensive computational experiments on both RNA and protein datasets, as well as through wet-lab experiments on the self-splicing ribozyme from the group I intron RNA family where our approach demonstrates particular efficacy. We show that integrating recent experimental data enhances the model's capacity of generating functional sequences (e.g. from 6.7\% to 63.7\% of active designs at 45 mutations). This feedback-driven approach thus provides a significant improvement in the design of biomolecular sequences by directly tackling the false-positive challenge.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 10:57:53 GMT" } ]	2025-04-03T00:00:00	[ [ "Calvanese", "Francesco", "" ], [ "Peinetti", "Giovanni", "" ], [ "Pavlinova", "Polina", "" ], [ "Nghe", "Philippe", "" ], [ "Weigt", "Martin", "" ] ]	TITLE: Integrating experimental feedback improves generative models for biological sequences ABSTRACT: Generative probabilistic models have shown promise in designing artificial RNA and protein sequences but often suffer from high rates of false positives, where sequences predicted as functional fail experimental validation. To address this critical limitation, we explore the impact of reintegrating experimental feedback into the model design process. We propose a likelihood-based reintegration scheme, which we test through extensive computational experiments on both RNA and protein datasets, as well as through wet-lab experiments on the self-splicing ribozyme from the group I intron RNA family where our approach demonstrates particular efficacy. We show that integrating recent experimental data enhances the model's capacity of generating functional sequences (e.g. from 6.7\% to 63.7\% of active designs at 45 mutations). This feedback-driven approach thus provides a significant improvement in the design of biomolecular sequences by directly tackling the false-positive challenge.
2504.01596	Jijun Xiang	Jijun Xiang, Xuan Zhu, Xianqi Wang, Yu Wang, Hong Zhang, Fei Guo, Xin Yang	DEPTHOR: Depth Enhancement from a Practical Light-Weight dToF Sensor and RGB Image	10 pages, 8 figures, 7 tables	null	null	null	cs.CV	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Depth enhancement, which uses RGB images as guidance to convert raw signals from dToF into high-precision, dense depth maps, is a critical task in computer vision. Although existing super-resolution-based methods show promising results on public datasets, they often rely on idealized assumptions like accurate region correspondences and reliable dToF inputs, overlooking calibration errors that cause misalignment and anomaly signals inherent to dToF imaging, limiting real-world applicability. To address these challenges, we propose a novel completion-based method, named DEPTHOR, featuring advances in both the training strategy and model architecture. First, we propose a method to simulate real-world dToF data from the accurate ground truth in synthetic datasets to enable noise-robust training. Second, we design a novel network that incorporates monocular depth estimation (MDE), leveraging global depth relationships and contextual information to improve prediction in challenging regions. On the ZJU-L5 dataset, our training strategy significantly enhances depth completion models, achieving results comparable to depth super-resolution methods, while our model achieves state-of-the-art results, improving Rel and RMSE by 27% and 18%, respectively. On a more challenging set of dToF samples we collected, our method outperforms SOTA methods on preliminary stereo-based GT, improving Rel and RMSE by 23% and 22%, respectively. Our Code is available at https://github.com/ShadowBbBb/Depthor	[ { "version": "v1", "created": "Wed, 2 Apr 2025 11:02:21 GMT" } ]	2025-04-03T00:00:00	[ [ "Xiang", "Jijun", "" ], [ "Zhu", "Xuan", "" ], [ "Wang", "Xianqi", "" ], [ "Wang", "Yu", "" ], [ "Zhang", "Hong", "" ], [ "Guo", "Fei", "" ], [ "Yang", "Xin", "" ] ]	TITLE: DEPTHOR: Depth Enhancement from a Practical Light-Weight dToF Sensor and RGB Image ABSTRACT: Depth enhancement, which uses RGB images as guidance to convert raw signals from dToF into high-precision, dense depth maps, is a critical task in computer vision. Although existing super-resolution-based methods show promising results on public datasets, they often rely on idealized assumptions like accurate region correspondences and reliable dToF inputs, overlooking calibration errors that cause misalignment and anomaly signals inherent to dToF imaging, limiting real-world applicability. To address these challenges, we propose a novel completion-based method, named DEPTHOR, featuring advances in both the training strategy and model architecture. First, we propose a method to simulate real-world dToF data from the accurate ground truth in synthetic datasets to enable noise-robust training. Second, we design a novel network that incorporates monocular depth estimation (MDE), leveraging global depth relationships and contextual information to improve prediction in challenging regions. On the ZJU-L5 dataset, our training strategy significantly enhances depth completion models, achieving results comparable to depth super-resolution methods, while our model achieves state-of-the-art results, improving Rel and RMSE by 27% and 18%, respectively. On a more challenging set of dToF samples we collected, our method outperforms SOTA methods on preliminary stereo-based GT, improving Rel and RMSE by 23% and 22%, respectively. Our Code is available at https://github.com/ShadowBbBb/Depthor
2504.01597	Dandan Shan	Yuehui Qiu and Dandan Shan and Yining Wang and Pei Dong and Dijia Wu and Xinnian Yang and Qingqi Hong and Dinggang Shen	A topology-preserving three-stage framework for fully-connected coronary artery extraction	null	null	null	null	eess.IV cs.CV	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Coronary artery extraction is a crucial prerequisite for computer-aided diagnosis of coronary artery disease. Accurately extracting the complete coronary tree remains challenging due to several factors, including presence of thin distal vessels, tortuous topological structures, and insufficient contrast. These issues often result in over-segmentation and under-segmentation in current segmentation methods. To address these challenges, we propose a topology-preserving three-stage framework for fully-connected coronary artery extraction. This framework includes vessel segmentation, centerline reconnection, and missing vessel reconstruction. First, we introduce a new centerline enhanced loss in the segmentation process. Second, for the broken vessel segments, we further propose a regularized walk algorithm to integrate distance, probabilities predicted by a centerline classifier, and directional cosine similarity, for reconnecting the centerlines. Third, we apply implicit neural representation and implicit modeling, to reconstruct the geometric model of the missing vessels. Experimental results show that our proposed framework outperforms existing methods, achieving Dice scores of 88.53\% and 85.07\%, with Hausdorff Distances (HD) of 1.07mm and 1.63mm on ASOCA and PDSCA datasets, respectively. Code will be available at https://github.com/YH-Qiu/CorSegRec.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 11:04:44 GMT" } ]	2025-04-03T00:00:00	[ [ "Qiu", "Yuehui", "" ], [ "Shan", "Dandan", "" ], [ "Wang", "Yining", "" ], [ "Dong", "Pei", "" ], [ "Wu", "Dijia", "" ], [ "Yang", "Xinnian", "" ], [ "Hong", "Qingqi", "" ], [ "Shen", "Dinggang", "" ] ]	TITLE: A topology-preserving three-stage framework for fully-connected coronary artery extraction ABSTRACT: Coronary artery extraction is a crucial prerequisite for computer-aided diagnosis of coronary artery disease. Accurately extracting the complete coronary tree remains challenging due to several factors, including presence of thin distal vessels, tortuous topological structures, and insufficient contrast. These issues often result in over-segmentation and under-segmentation in current segmentation methods. To address these challenges, we propose a topology-preserving three-stage framework for fully-connected coronary artery extraction. This framework includes vessel segmentation, centerline reconnection, and missing vessel reconstruction. First, we introduce a new centerline enhanced loss in the segmentation process. Second, for the broken vessel segments, we further propose a regularized walk algorithm to integrate distance, probabilities predicted by a centerline classifier, and directional cosine similarity, for reconnecting the centerlines. Third, we apply implicit neural representation and implicit modeling, to reconstruct the geometric model of the missing vessels. Experimental results show that our proposed framework outperforms existing methods, achieving Dice scores of 88.53\% and 85.07\%, with Hausdorff Distances (HD) of 1.07mm and 1.63mm on ASOCA and PDSCA datasets, respectively. Code will be available at https://github.com/YH-Qiu/CorSegRec.
2504.01602	Zihan Lin	Changshuo Zhang, Zihan Lin, Shukai Liu, Yongqi Liu, Han Li	Comment Staytime Prediction with LLM-enhanced Comment Understanding	Accepted by WWW 2025 Industry Track	null	null	null	cs.IR	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	In modern online streaming platforms, the comments section plays a critical role in enhancing the overall user experience. Understanding user behavior within the comments section is essential for comprehensive user interest modeling. A key factor of user engagement is staytime, which refers to the amount of time that users browse and post comments. Existing watchtime prediction methods struggle to adapt to staytime prediction, overlooking interactions with individual comments and their interrelation. In this paper, we present a micro-video recommendation dataset with video comments (named as KuaiComt) which is collected from Kuaishou platform. correspondingly, we propose a practical framework for comment staytime prediction with LLM-enhanced Comment Understanding (LCU). Our framework leverages the strong text comprehension capabilities of large language models (LLMs) to understand textual information of comments, while also incorporating fine-grained comment ranking signals as auxiliary tasks. The framework is two-staged: first, the LLM is fine-tuned using domain-specific tasks to bridge the video and the comments; second, we incorporate the LLM outputs into the prediction model and design two comment ranking auxiliary tasks to better understand user preference. Extensive offline experiments demonstrate the effectiveness of our framework, showing significant improvements on the task of comment staytime prediction. Additionally, online A/B testing further validates the practical benefits on industrial scenario. Our dataset KuaiComt (https://github.com/lyingCS/KuaiComt.github.io) and code for LCU (https://github.com/lyingCS/LCU) are fully released.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 11:09:18 GMT" } ]	2025-04-03T00:00:00	[ [ "Zhang", "Changshuo", "" ], [ "Lin", "Zihan", "" ], [ "Liu", "Shukai", "" ], [ "Liu", "Yongqi", "" ], [ "Li", "Han", "" ] ]	TITLE: Comment Staytime Prediction with LLM-enhanced Comment Understanding ABSTRACT: In modern online streaming platforms, the comments section plays a critical role in enhancing the overall user experience. Understanding user behavior within the comments section is essential for comprehensive user interest modeling. A key factor of user engagement is staytime, which refers to the amount of time that users browse and post comments. Existing watchtime prediction methods struggle to adapt to staytime prediction, overlooking interactions with individual comments and their interrelation. In this paper, we present a micro-video recommendation dataset with video comments (named as KuaiComt) which is collected from Kuaishou platform. correspondingly, we propose a practical framework for comment staytime prediction with LLM-enhanced Comment Understanding (LCU). Our framework leverages the strong text comprehension capabilities of large language models (LLMs) to understand textual information of comments, while also incorporating fine-grained comment ranking signals as auxiliary tasks. The framework is two-staged: first, the LLM is fine-tuned using domain-specific tasks to bridge the video and the comments; second, we incorporate the LLM outputs into the prediction model and design two comment ranking auxiliary tasks to better understand user preference. Extensive offline experiments demonstrate the effectiveness of our framework, showing significant improvements on the task of comment staytime prediction. Additionally, online A/B testing further validates the practical benefits on industrial scenario. Our dataset KuaiComt (https://github.com/lyingCS/KuaiComt.github.io) and code for LCU (https://github.com/lyingCS/LCU) are fully released.
2504.01605	Renda Han	Renda Han, Guangzhen Yao, Wenxin Zhang, Yu Li, Wen Xin, Huajie Lei, Mengfei Li, Zeyu Zhang, Chengze Du, and Yahe Tian	Multi-Relation Graph-Kernel Strengthen Network for Graph-Level Clustering	null	null	null	null	cs.LG	http://creativecommons.org/licenses/by/4.0/	Graph-level clustering is a fundamental task of data mining, aiming at dividing unlabeled graphs into distinct groups. However, existing deep methods that are limited by pooling have difficulty extracting diverse and complex graph structure features, while traditional graph kernel methods rely on exhaustive substructure search, unable to adaptive handle multi-relational data. This limitation hampers producing robust and representative graph-level embeddings. To address this issue, we propose a novel Multi-Relation Graph-Kernel Strengthen Network for Graph-Level Clustering (MGSN), which integrates multi-relation modeling with graph kernel techniques to fully leverage their respective advantages. Specifically, MGSN constructs multi-relation graphs to capture diverse semantic relationships between nodes and graphs, which employ graph kernel methods to extract graph similarity features, enriching the representation space. Moreover, a relation-aware representation refinement strategy is designed, which adaptively aligns multi-relation information across views while enhancing graph-level features through a progressive fusion process. Extensive experiments on multiple benchmark datasets demonstrate the superiority of MGSN over state-of-the-art methods. The results highlight its ability to leverage multi-relation structures and graph kernel features, establishing a new paradigm for robust graph-level clustering.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 11:17:15 GMT" } ]	2025-04-03T00:00:00	[ [ "Han", "Renda", "" ], [ "Yao", "Guangzhen", "" ], [ "Zhang", "Wenxin", "" ], [ "Li", "Yu", "" ], [ "Xin", "Wen", "" ], [ "Lei", "Huajie", "" ], [ "Li", "Mengfei", "" ], [ "Zhang", "Zeyu", "" ], [ "Du", "Chengze", "" ], [ "Tian", "Yahe", "" ] ]	TITLE: Multi-Relation Graph-Kernel Strengthen Network for Graph-Level Clustering ABSTRACT: Graph-level clustering is a fundamental task of data mining, aiming at dividing unlabeled graphs into distinct groups. However, existing deep methods that are limited by pooling have difficulty extracting diverse and complex graph structure features, while traditional graph kernel methods rely on exhaustive substructure search, unable to adaptive handle multi-relational data. This limitation hampers producing robust and representative graph-level embeddings. To address this issue, we propose a novel Multi-Relation Graph-Kernel Strengthen Network for Graph-Level Clustering (MGSN), which integrates multi-relation modeling with graph kernel techniques to fully leverage their respective advantages. Specifically, MGSN constructs multi-relation graphs to capture diverse semantic relationships between nodes and graphs, which employ graph kernel methods to extract graph similarity features, enriching the representation space. Moreover, a relation-aware representation refinement strategy is designed, which adaptively aligns multi-relation information across views while enhancing graph-level features through a progressive fusion process. Extensive experiments on multiple benchmark datasets demonstrate the superiority of MGSN over state-of-the-art methods. The results highlight its ability to leverage multi-relation structures and graph kernel features, establishing a new paradigm for robust graph-level clustering.
2504.01619	Hao Wu	Hao Wu, Hao Wang, Ruochong Li, Xuran Ma, Hui Xiong	3DBonsai: Structure-Aware Bonsai Modeling Using Conditioned 3D Gaussian Splatting	Accepted by ICME 2025	null	null	null	cs.CV	http://creativecommons.org/licenses/by/4.0/	Recent advancements in text-to-3D generation have shown remarkable results by leveraging 3D priors in combination with 2D diffusion. However, previous methods utilize 3D priors that lack detailed and complex structural information, limiting them to generating simple objects and presenting challenges for creating intricate structures such as bonsai. In this paper, we propose 3DBonsai, a novel text-to-3D framework for generating 3D bonsai with complex structures. Technically, we first design a trainable 3D space colonization algorithm to produce bonsai structures, which are then enhanced through random sampling and point cloud augmentation to serve as the 3D Gaussian priors. We introduce two bonsai generation pipelines with distinct structural levels: fine structure conditioned generation, which initializes 3D Gaussians using a 3D structure prior to produce detailed and complex bonsai, and coarse structure conditioned generation, which employs a multi-view structure consistency module to align 2D and 3D structures. Moreover, we have compiled a unified 2D and 3D Chinese-style bonsai dataset. Our experimental results demonstrate that 3DBonsai significantly outperforms existing methods, providing a new benchmark for structure-aware 3D bonsai generation.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 11:27:02 GMT" } ]	2025-04-03T00:00:00	[ [ "Wu", "Hao", "" ], [ "Wang", "Hao", "" ], [ "Li", "Ruochong", "" ], [ "Ma", "Xuran", "" ], [ "Xiong", "Hui", "" ] ]	TITLE: 3DBonsai: Structure-Aware Bonsai Modeling Using Conditioned 3D Gaussian Splatting ABSTRACT: Recent advancements in text-to-3D generation have shown remarkable results by leveraging 3D priors in combination with 2D diffusion. However, previous methods utilize 3D priors that lack detailed and complex structural information, limiting them to generating simple objects and presenting challenges for creating intricate structures such as bonsai. In this paper, we propose 3DBonsai, a novel text-to-3D framework for generating 3D bonsai with complex structures. Technically, we first design a trainable 3D space colonization algorithm to produce bonsai structures, which are then enhanced through random sampling and point cloud augmentation to serve as the 3D Gaussian priors. We introduce two bonsai generation pipelines with distinct structural levels: fine structure conditioned generation, which initializes 3D Gaussians using a 3D structure prior to produce detailed and complex bonsai, and coarse structure conditioned generation, which employs a multi-view structure consistency module to align 2D and 3D structures. Moreover, we have compiled a unified 2D and 3D Chinese-style bonsai dataset. Our experimental results demonstrate that 3DBonsai significantly outperforms existing methods, providing a new benchmark for structure-aware 3D bonsai generation.
2504.01627	Lena Schmidt	Lena Schmidt, Oshin Sharma, Chris Marshall, Sonia Garcia Gonzalez Moral	Horizon Scans can be accelerated using novel information retrieval and artificial intelligence tools	null	null	null	null	cs.IR cs.AI cs.CL	http://creativecommons.org/licenses/by/4.0/	Introduction: Horizon scanning in healthcare assesses early signals of innovation, crucial for timely adoption. Current horizon scanning faces challenges in efficient information retrieval and analysis, especially from unstructured sources like news, presenting a need for innovative tools. Methodology: The study introduces SCANAR and AIDOC, open-source Python-based tools designed to improve horizon scanning. SCANAR automates the retrieval and processing of news articles, offering functionalities such as de-duplication and unsupervised relevancy ranking. AIDOC aids filtration by leveraging AI to reorder textual data based on relevancy, employing neural networks for semantic similarity, and subsequently prioritizing likely relevant entries for human review. Results: Twelve internal datasets from horizon scans and four external benchmarking datasets were used. SCANAR improved retrieval efficiency by automating processes previously dependent on manual labour. AIDOC displayed work-saving potential, achieving around 62% reduction in manual review efforts at 95% recall. Comparative analysis with benchmarking data showed AIDOC's performance was similar to existing systematic review automation tools, though performance varied depending on dataset characteristics. A smaller case-study on our news datasets shows the potential of ensembling large language models within the active-learning process for faster detection of relevant articles across news datasets. Conclusion: The validation indicates that SCANAR and AIDOC show potential to enhance horizon scanning efficiency by streamlining data retrieval and prioritisation. These tools may alleviate methodological limitations and allow broader, swifter horizon scans. Further studies are suggested to optimize these models and to design new workflows and validation processes that integrate large language models.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 11:33:08 GMT" } ]	2025-04-03T00:00:00	[ [ "Schmidt", "Lena", "" ], [ "Sharma", "Oshin", "" ], [ "Marshall", "Chris", "" ], [ "Moral", "Sonia Garcia Gonzalez", "" ] ]	TITLE: Horizon Scans can be accelerated using novel information retrieval and artificial intelligence tools ABSTRACT: Introduction: Horizon scanning in healthcare assesses early signals of innovation, crucial for timely adoption. Current horizon scanning faces challenges in efficient information retrieval and analysis, especially from unstructured sources like news, presenting a need for innovative tools. Methodology: The study introduces SCANAR and AIDOC, open-source Python-based tools designed to improve horizon scanning. SCANAR automates the retrieval and processing of news articles, offering functionalities such as de-duplication and unsupervised relevancy ranking. AIDOC aids filtration by leveraging AI to reorder textual data based on relevancy, employing neural networks for semantic similarity, and subsequently prioritizing likely relevant entries for human review. Results: Twelve internal datasets from horizon scans and four external benchmarking datasets were used. SCANAR improved retrieval efficiency by automating processes previously dependent on manual labour. AIDOC displayed work-saving potential, achieving around 62% reduction in manual review efforts at 95% recall. Comparative analysis with benchmarking data showed AIDOC's performance was similar to existing systematic review automation tools, though performance varied depending on dataset characteristics. A smaller case-study on our news datasets shows the potential of ensembling large language models within the active-learning process for faster detection of relevant articles across news datasets. Conclusion: The validation indicates that SCANAR and AIDOC show potential to enhance horizon scanning efficiency by streamlining data retrieval and prioritisation. These tools may alleviate methodological limitations and allow broader, swifter horizon scans. Further studies are suggested to optimize these models and to design new workflows and validation processes that integrate large language models.
2504.01647	Tobias Fischer	Tobias Fischer and Samuel Rota Bul\`o and Yung-Hsu Yang and Nikhil Varma Keetha and Lorenzo Porzi and Norman M\"uller and Katja Schwarz and Jonathon Luiten and Marc Pollefeys and Peter Kontschieder	FlowR: Flowing from Sparse to Dense 3D Reconstructions	Project page is available at https://tobiasfshr.github.io/pub/flowr	null	null	null	cs.CV	http://creativecommons.org/licenses/by-sa/4.0/	3D Gaussian splatting enables high-quality novel view synthesis (NVS) at real-time frame rates. However, its quality drops sharply as we depart from the training views. Thus, dense captures are needed to match the high-quality expectations of some applications, e.g. Virtual Reality (VR). However, such dense captures are very laborious and expensive to obtain. Existing works have explored using 2D generative models to alleviate this requirement by distillation or generating additional training views. These methods are often conditioned only on a handful of reference input views and thus do not fully exploit the available 3D information, leading to inconsistent generation results and reconstruction artifacts. To tackle this problem, we propose a multi-view, flow matching model that learns a flow to connect novel view renderings from possibly sparse reconstructions to renderings that we expect from dense reconstructions. This enables augmenting scene captures with novel, generated views to improve reconstruction quality. Our model is trained on a novel dataset of 3.6M image pairs and can process up to 45 views at 540x960 resolution (91K tokens) on one H100 GPU in a single forward pass. Our pipeline consistently improves NVS in sparse- and dense-view scenarios, leading to higher-quality reconstructions than prior works across multiple, widely-used NVS benchmarks.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 11:57:01 GMT" } ]	2025-04-03T00:00:00	[ [ "Fischer", "Tobias", "" ], [ "Bulò", "Samuel Rota", "" ], [ "Yang", "Yung-Hsu", "" ], [ "Keetha", "Nikhil Varma", "" ], [ "Porzi", "Lorenzo", "" ], [ "Müller", "Norman", "" ], [ "Schwarz", "Katja", "" ], [ "Luiten", "Jonathon", "" ], [ "Pollefeys", "Marc", "" ], [ "Kontschieder", "Peter", "" ] ]	TITLE: FlowR: Flowing from Sparse to Dense 3D Reconstructions ABSTRACT: 3D Gaussian splatting enables high-quality novel view synthesis (NVS) at real-time frame rates. However, its quality drops sharply as we depart from the training views. Thus, dense captures are needed to match the high-quality expectations of some applications, e.g. Virtual Reality (VR). However, such dense captures are very laborious and expensive to obtain. Existing works have explored using 2D generative models to alleviate this requirement by distillation or generating additional training views. These methods are often conditioned only on a handful of reference input views and thus do not fully exploit the available 3D information, leading to inconsistent generation results and reconstruction artifacts. To tackle this problem, we propose a multi-view, flow matching model that learns a flow to connect novel view renderings from possibly sparse reconstructions to renderings that we expect from dense reconstructions. This enables augmenting scene captures with novel, generated views to improve reconstruction quality. Our model is trained on a novel dataset of 3.6M image pairs and can process up to 45 views at 540x960 resolution (91K tokens) on one H100 GPU in a single forward pass. Our pipeline consistently improves NVS in sparse- and dense-view scenarios, leading to higher-quality reconstructions than prior works across multiple, widely-used NVS benchmarks.
2504.01648	Haosheng Li	Haosheng Li, Yuecong Xu, Junjie Chen, Kemi Ding	ProtoGuard-guided PROPEL: Class-Aware Prototype Enhancement and Progressive Labeling for Incremental 3D Point Cloud Segmentation	null	null	null	null	cs.CV	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	3D point cloud semantic segmentation technology has been widely used. However, in real-world scenarios, the environment is evolving. Thus, offline-trained segmentation models may lead to catastrophic forgetting of previously seen classes. Class-incremental learning (CIL) is designed to address the problem of catastrophic forgetting. While point clouds are common, we observe high similarity and unclear boundaries between different classes. Meanwhile, they are known to be imbalanced in class distribution. These lead to issues including misclassification between similar classes and the long-tail problem, which have not been adequately addressed in previous CIL methods. We thus propose ProtoGuard and PROPEL (Progressive Refinement Of PsEudo-Labels). In the base-class training phase, ProtoGuard maintains geometric and semantic prototypes for each class, which are combined into prototype features using an attention mechanism. In the novel-class training phase, PROPEL inherits the base feature extractor and classifier, guiding pseudo-label propagation and updates based on density distribution and semantic similarity. Extensive experiments show that our approach achieves remarkable results on both the S3DIS and ScanNet datasets, improving the mIoU of 3D point cloud segmentation by a maximum of 20.39% under the 5-step CIL scenario on S3DIS.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 11:58:04 GMT" } ]	2025-04-03T00:00:00	[ [ "Li", "Haosheng", "" ], [ "Xu", "Yuecong", "" ], [ "Chen", "Junjie", "" ], [ "Ding", "Kemi", "" ] ]	TITLE: ProtoGuard-guided PROPEL: Class-Aware Prototype Enhancement and Progressive Labeling for Incremental 3D Point Cloud Segmentation ABSTRACT: 3D point cloud semantic segmentation technology has been widely used. However, in real-world scenarios, the environment is evolving. Thus, offline-trained segmentation models may lead to catastrophic forgetting of previously seen classes. Class-incremental learning (CIL) is designed to address the problem of catastrophic forgetting. While point clouds are common, we observe high similarity and unclear boundaries between different classes. Meanwhile, they are known to be imbalanced in class distribution. These lead to issues including misclassification between similar classes and the long-tail problem, which have not been adequately addressed in previous CIL methods. We thus propose ProtoGuard and PROPEL (Progressive Refinement Of PsEudo-Labels). In the base-class training phase, ProtoGuard maintains geometric and semantic prototypes for each class, which are combined into prototype features using an attention mechanism. In the novel-class training phase, PROPEL inherits the base feature extractor and classifier, guiding pseudo-label propagation and updates based on density distribution and semantic similarity. Extensive experiments show that our approach achieves remarkable results on both the S3DIS and ScanNet datasets, improving the mIoU of 3D point cloud segmentation by a maximum of 20.39% under the 5-step CIL scenario on S3DIS.
2504.01660	James Trayford Dr	James W. Trayford, Samantha Youles, Chris Harrison, Rose Shepherd, Nicolas Bonne	STRAUSS: Sonification Tools & Resources for Analysis Using Sound Synthesis	4 pages, linking to documentation on ReadTheDocs (https://strauss.readthedocs.io/en/latest/)	null	null	null	astro-ph.IM physics.data-an	http://creativecommons.org/licenses/by/4.0/	Sonification, or conveying data using non-verbal audio, is a relatively niche but growing approach for presenting data across multiple specialist domains including astronomy, climate science, and beyond. The STRAUSS Python package aims to provide such a tool, which builds upon previous approaches to provide a powerful means to explore different ways of expressing data, with fine control over the output audio and its format. STRAUSS is a free, open source (FOSS) package, designed to allow flexible and effective sonification to be integrated into data workflows, in analogy to widely used visualisation packages. The remit of STRAUSS is broad; it is intended to be able to bridge between ad-hoc solutions for sonifying very particular datasets, and highly technical compositional and sound-design tools that are not optimised for sonification, or may have a steep learning curve. The code offers a range of approaches to sonification for a variety of contexts (e.g. science education, science communication, technical data analysis, etc). To this end, STRAUSS is packaged with a number of examples of different sonification approaches, and preset configurations to support "low-barrier, high-ceiling" approach. STRAUSS has been used to produce both educational resources and analysis tools.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 12:12:30 GMT" } ]	2025-04-03T00:00:00	[ [ "Trayford", "James W.", "" ], [ "Youles", "Samantha", "" ], [ "Harrison", "Chris", "" ], [ "Shepherd", "Rose", "" ], [ "Bonne", "Nicolas", "" ] ]	TITLE: STRAUSS: Sonification Tools & Resources for Analysis Using Sound Synthesis ABSTRACT: Sonification, or conveying data using non-verbal audio, is a relatively niche but growing approach for presenting data across multiple specialist domains including astronomy, climate science, and beyond. The STRAUSS Python package aims to provide such a tool, which builds upon previous approaches to provide a powerful means to explore different ways of expressing data, with fine control over the output audio and its format. STRAUSS is a free, open source (FOSS) package, designed to allow flexible and effective sonification to be integrated into data workflows, in analogy to widely used visualisation packages. The remit of STRAUSS is broad; it is intended to be able to bridge between ad-hoc solutions for sonifying very particular datasets, and highly technical compositional and sound-design tools that are not optimised for sonification, or may have a steep learning curve. The code offers a range of approaches to sonification for a variety of contexts (e.g. science education, science communication, technical data analysis, etc). To this end, STRAUSS is packaged with a number of examples of different sonification approaches, and preset configurations to support "low-barrier, high-ceiling" approach. STRAUSS has been used to produce both educational resources and analysis tools.
2504.01666	Sarah Alyami	Sarah Alyami and Hamzah Luqman	CLIP-SLA: Parameter-Efficient CLIP Adaptation for Continuous Sign Language Recognition	null	null	null	null	cs.CV	http://creativecommons.org/licenses/by/4.0/	Continuous sign language recognition (CSLR) focuses on interpreting and transcribing sequences of sign language gestures in videos. In this work, we propose CLIP sign language adaptation (CLIP-SLA), a novel CSLR framework that leverages the powerful pre-trained visual encoder from the CLIP model to sign language tasks through parameter-efficient fine-tuning (PEFT). We introduce two variants, SLA-Adapter and SLA-LoRA, which integrate PEFT modules into the CLIP visual encoder, enabling fine-tuning with minimal trainable parameters. The effectiveness of the proposed frameworks is validated on four datasets: Phoenix2014, Phoenix2014-T, CSL-Daily, and Isharah-500, where both CLIP-SLA variants outperformed several SOTA models with fewer trainable parameters. Extensive ablation studies emphasize the effectiveness and flexibility of the proposed methods with different vision-language models for CSLR. These findings showcase the potential of adapting large-scale pre-trained models for scalable and efficient CSLR, which pave the way for future advancements in sign language understanding.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 12:15:33 GMT" } ]	2025-04-03T00:00:00	[ [ "Alyami", "Sarah", "" ], [ "Luqman", "Hamzah", "" ] ]	TITLE: CLIP-SLA: Parameter-Efficient CLIP Adaptation for Continuous Sign Language Recognition ABSTRACT: Continuous sign language recognition (CSLR) focuses on interpreting and transcribing sequences of sign language gestures in videos. In this work, we propose CLIP sign language adaptation (CLIP-SLA), a novel CSLR framework that leverages the powerful pre-trained visual encoder from the CLIP model to sign language tasks through parameter-efficient fine-tuning (PEFT). We introduce two variants, SLA-Adapter and SLA-LoRA, which integrate PEFT modules into the CLIP visual encoder, enabling fine-tuning with minimal trainable parameters. The effectiveness of the proposed frameworks is validated on four datasets: Phoenix2014, Phoenix2014-T, CSL-Daily, and Isharah-500, where both CLIP-SLA variants outperformed several SOTA models with fewer trainable parameters. Extensive ablation studies emphasize the effectiveness and flexibility of the proposed methods with different vision-language models for CSLR. These findings showcase the potential of adapting large-scale pre-trained models for scalable and efficient CSLR, which pave the way for future advancements in sign language understanding.
2504.01676	Jingyang Zhu	Yuanming Shi, Jingyang Zhu, Chunxiao Jiang, Linling Kuang, and Khaled B. Letaief	Satellite Edge Artificial Intelligence with Large Models: Architectures and Technologies	15 pages, 5 figures; submitted to SCIENCE CHINA Information Sciences for possible publication	null	null	null	cs.LG cs.DC cs.NI eess.SP	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Driven by the growing demand for intelligent remote sensing applications, large artificial intelligence (AI) models pre-trained on large-scale unlabeled datasets and fine-tuned for downstream tasks have significantly improved learning performance for various downstream tasks due to their generalization capabilities. However, many specific downstream tasks, such as extreme weather nowcasting (e.g., downburst and tornado), disaster monitoring, and battlefield surveillance, require real-time data processing. Traditional methods via transferring raw data to ground stations for processing often cause significant issues in terms of latency and trustworthiness. To address these challenges, satellite edge AI provides a paradigm shift from ground-based to on-board data processing by leveraging the integrated communication-and-computation capabilities in space computing power networks (Space-CPN), thereby enhancing the timeliness, effectiveness, and trustworthiness for remote sensing downstream tasks. Moreover, satellite edge large AI model (LAM) involves both the training (i.e., fine-tuning) and inference phases, where a key challenge lies in developing computation task decomposition principles to support scalable LAM deployment in resource-constrained space networks with time-varying topologies. In this article, we first propose a satellite federated fine-tuning architecture to split and deploy the modules of LAM over space and ground networks for efficient LAM fine-tuning. We then introduce a microservice-empowered satellite edge LAM inference architecture that virtualizes LAM components into lightweight microservices tailored for multi-task multimodal inference. Finally, we discuss the future directions for enhancing the efficiency and scalability of satellite edge LAM, including task-oriented communication, brain-inspired computing, and satellite edge AI network optimization.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 12:25:57 GMT" } ]	2025-04-03T00:00:00	[ [ "Shi", "Yuanming", "" ], [ "Zhu", "Jingyang", "" ], [ "Jiang", "Chunxiao", "" ], [ "Kuang", "Linling", "" ], [ "Letaief", "Khaled B.", "" ] ]	TITLE: Satellite Edge Artificial Intelligence with Large Models: Architectures and Technologies ABSTRACT: Driven by the growing demand for intelligent remote sensing applications, large artificial intelligence (AI) models pre-trained on large-scale unlabeled datasets and fine-tuned for downstream tasks have significantly improved learning performance for various downstream tasks due to their generalization capabilities. However, many specific downstream tasks, such as extreme weather nowcasting (e.g., downburst and tornado), disaster monitoring, and battlefield surveillance, require real-time data processing. Traditional methods via transferring raw data to ground stations for processing often cause significant issues in terms of latency and trustworthiness. To address these challenges, satellite edge AI provides a paradigm shift from ground-based to on-board data processing by leveraging the integrated communication-and-computation capabilities in space computing power networks (Space-CPN), thereby enhancing the timeliness, effectiveness, and trustworthiness for remote sensing downstream tasks. Moreover, satellite edge large AI model (LAM) involves both the training (i.e., fine-tuning) and inference phases, where a key challenge lies in developing computation task decomposition principles to support scalable LAM deployment in resource-constrained space networks with time-varying topologies. In this article, we first propose a satellite federated fine-tuning architecture to split and deploy the modules of LAM over space and ground networks for efficient LAM fine-tuning. We then introduce a microservice-empowered satellite edge LAM inference architecture that virtualizes LAM components into lightweight microservices tailored for multi-task multimodal inference. Finally, we discuss the future directions for enhancing the efficiency and scalability of satellite edge LAM, including task-oriented communication, brain-inspired computing, and satellite edge AI network optimization.
2504.01689	Noam Elata Mr	Noam Elata, Hyungjin Chung, Jong Chul Ye, Tomer Michaeli, Michael Elad	InvFussion: Bridging Supervised and Zero-shot Diffusion for Inverse Problems	null	null	null	null	cs.CV	http://creativecommons.org/licenses/by/4.0/	Diffusion Models have demonstrated remarkable capabilities in handling inverse problems, offering high-quality posterior-sampling-based solutions. Despite significant advances, a fundamental trade-off persists, regarding the way the conditioned synthesis is employed: Training-based methods achieve high quality results, while zero-shot approaches trade this with flexibility. This work introduces a framework that combines the best of both worlds -- the strong performance of supervised approaches and the flexibility of zero-shot methods. This is achieved through a novel architectural design that seamlessly integrates the degradation operator directly into the denoiser. In each block, our proposed architecture applies the degradation operator on the network activations and conditions the output using the attention mechanism, enabling adaptation to diverse degradation scenarios while maintaining high performance. Our work demonstrates the versatility of the proposed architecture, operating as a general MMSE estimator, a posterior sampler, or a Neural Posterior Principal Component estimator. This flexibility enables a wide range of downstream tasks, highlighting the broad applicability of our framework. The proposed modification of the denoiser network offers a versatile, accurate, and computationally efficient solution, demonstrating the advantages of dedicated network architectures for complex inverse problems. Experimental results on the FFHQ and ImageNet datasets demonstrate state-of-the-art posterior-sampling performance, surpassing both training-based and zero-shot alternatives.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 12:40:57 GMT" } ]	2025-04-03T00:00:00	[ [ "Elata", "Noam", "" ], [ "Chung", "Hyungjin", "" ], [ "Ye", "Jong Chul", "" ], [ "Michaeli", "Tomer", "" ], [ "Elad", "Michael", "" ] ]	TITLE: InvFussion: Bridging Supervised and Zero-shot Diffusion for Inverse Problems ABSTRACT: Diffusion Models have demonstrated remarkable capabilities in handling inverse problems, offering high-quality posterior-sampling-based solutions. Despite significant advances, a fundamental trade-off persists, regarding the way the conditioned synthesis is employed: Training-based methods achieve high quality results, while zero-shot approaches trade this with flexibility. This work introduces a framework that combines the best of both worlds -- the strong performance of supervised approaches and the flexibility of zero-shot methods. This is achieved through a novel architectural design that seamlessly integrates the degradation operator directly into the denoiser. In each block, our proposed architecture applies the degradation operator on the network activations and conditions the output using the attention mechanism, enabling adaptation to diverse degradation scenarios while maintaining high performance. Our work demonstrates the versatility of the proposed architecture, operating as a general MMSE estimator, a posterior sampler, or a Neural Posterior Principal Component estimator. This flexibility enables a wide range of downstream tasks, highlighting the broad applicability of our framework. The proposed modification of the denoiser network offers a versatile, accurate, and computationally efficient solution, demonstrating the advantages of dedicated network architectures for complex inverse problems. Experimental results on the FFHQ and ImageNet datasets demonstrate state-of-the-art posterior-sampling performance, surpassing both training-based and zero-shot alternatives.
2504.01692	Isabella Cama	Isabella Cama, Alejandro Guzm\'an, Cristina Campi, Michele Piana, Karim Lekadir, Sara Garbarino, Oliver D\'iaz	Segmentation variability and radiomics stability for predicting Triple-Negative Breast Cancer subtype using Magnetic Resonance Imaging	22 pages, 7 figures	null	null	null	stat.AP cs.AI	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Most papers caution against using predictive models for disease stratification based on unselected radiomic features, as these features are affected by contouring variability. Instead, they advocate for the use of the Intraclass Correlation Coefficient (ICC) as a measure of stability for feature selection. However, the direct effect of segmentation variability on the predictive models is rarely studied. This study investigates the impact of segmentation variability on feature stability and predictive performance in radiomics-based prediction of Triple-Negative Breast Cancer (TNBC) subtype using Magnetic Resonance Imaging. A total of 244 images from the Duke dataset were used, with segmentation variability introduced through modifications of manual segmentations. For each mask, explainable radiomic features were selected using the Shapley Additive exPlanations method and used to train logistic regression models. Feature stability across segmentations was assessed via ICC, Pearson's correlation, and reliability scores quantifying the relationship between feature stability and segmentation variability. Results indicate that segmentation accuracy does not significantly impact predictive performance. While incorporating peritumoral information may reduce feature reproducibility, it does not diminish feature predictive capability. Moreover, feature selection in predictive models is not inherently tied to feature stability with respect to segmentation, suggesting that an overreliance on ICC or reliability scores for feature selection might exclude valuable predictive features.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 12:48:01 GMT" } ]	2025-04-03T00:00:00	[ [ "Cama", "Isabella", "" ], [ "Guzmán", "Alejandro", "" ], [ "Campi", "Cristina", "" ], [ "Piana", "Michele", "" ], [ "Lekadir", "Karim", "" ], [ "Garbarino", "Sara", "" ], [ "Díaz", "Oliver", "" ] ]	TITLE: Segmentation variability and radiomics stability for predicting Triple-Negative Breast Cancer subtype using Magnetic Resonance Imaging ABSTRACT: Most papers caution against using predictive models for disease stratification based on unselected radiomic features, as these features are affected by contouring variability. Instead, they advocate for the use of the Intraclass Correlation Coefficient (ICC) as a measure of stability for feature selection. However, the direct effect of segmentation variability on the predictive models is rarely studied. This study investigates the impact of segmentation variability on feature stability and predictive performance in radiomics-based prediction of Triple-Negative Breast Cancer (TNBC) subtype using Magnetic Resonance Imaging. A total of 244 images from the Duke dataset were used, with segmentation variability introduced through modifications of manual segmentations. For each mask, explainable radiomic features were selected using the Shapley Additive exPlanations method and used to train logistic regression models. Feature stability across segmentations was assessed via ICC, Pearson's correlation, and reliability scores quantifying the relationship between feature stability and segmentation variability. Results indicate that segmentation accuracy does not significantly impact predictive performance. While incorporating peritumoral information may reduce feature reproducibility, it does not diminish feature predictive capability. Moreover, feature selection in predictive models is not inherently tied to feature stability with respect to segmentation, suggesting that an overreliance on ICC or reliability scores for feature selection might exclude valuable predictive features.
2504.01708	Petr Vanc	Petr Vanc, Karla Stepanova	TransforMerger: Transformer-based Voice-Gesture Fusion for Robust Human-Robot Communication	8 pages, 7 figures	null	null	null	cs.RO cs.HC cs.LG	http://creativecommons.org/licenses/by-nc-sa/4.0/	As human-robot collaboration advances, natural and flexible communication methods are essential for effective robot control. Traditional methods relying on a single modality or rigid rules struggle with noisy or misaligned data as well as with object descriptions that do not perfectly fit the predefined object names (e.g. 'Pick that red object'). We introduce TransforMerger, a transformer-based reasoning model that infers a structured action command for robotic manipulation based on fused voice and gesture inputs. Our approach merges multimodal data into a single unified sentence, which is then processed by the language model. We employ probabilistic embeddings to handle uncertainty and we integrate contextual scene understanding to resolve ambiguous references (e.g., gestures pointing to multiple objects or vague verbal cues like "this"). We evaluate TransforMerger in simulated and real-world experiments, demonstrating its robustness to noise, misalignment, and missing information. Our results show that TransforMerger outperforms deterministic baselines, especially in scenarios requiring more contextual knowledge, enabling more robust and flexible human-robot communication. Code and datasets are available at: http://imitrob.ciirc.cvut.cz/publications/transformerger.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 13:15:59 GMT" } ]	2025-04-03T00:00:00	[ [ "Vanc", "Petr", "" ], [ "Stepanova", "Karla", "" ] ]	TITLE: TransforMerger: Transformer-based Voice-Gesture Fusion for Robust Human-Robot Communication ABSTRACT: As human-robot collaboration advances, natural and flexible communication methods are essential for effective robot control. Traditional methods relying on a single modality or rigid rules struggle with noisy or misaligned data as well as with object descriptions that do not perfectly fit the predefined object names (e.g. 'Pick that red object'). We introduce TransforMerger, a transformer-based reasoning model that infers a structured action command for robotic manipulation based on fused voice and gesture inputs. Our approach merges multimodal data into a single unified sentence, which is then processed by the language model. We employ probabilistic embeddings to handle uncertainty and we integrate contextual scene understanding to resolve ambiguous references (e.g., gestures pointing to multiple objects or vague verbal cues like "this"). We evaluate TransforMerger in simulated and real-world experiments, demonstrating its robustness to noise, misalignment, and missing information. Our results show that TransforMerger outperforms deterministic baselines, especially in scenarios requiring more contextual knowledge, enabling more robust and flexible human-robot communication. Code and datasets are available at: http://imitrob.ciirc.cvut.cz/publications/transformerger.
2504.01738	Philip Lippmann	Philip Lippmann and Jie Yang	Style over Substance: Distilled Language Models Reason Via Stylistic Replication	null	null	null	null	cs.CL cs.AI	http://creativecommons.org/licenses/by-nc-sa/4.0/	Specialized reasoning language models (RLMs) have demonstrated that scaling test-time computation through detailed reasoning traces significantly enhances performance. Although these traces effectively facilitate knowledge distillation into smaller, instruction-tuned models, the precise nature of transferred reasoning remains unclear. In this study, we investigate to what extent distilled models internalize replicated stylistic patterns during reasoning. To this end, we systematically analyze reasoning traces, identifying structural and lexical patterns that characterize successful reasoning. We then introduce two new datasets -- a dataset of emergent reasoning traces and a synthetic dataset explicitly constructed to replicate these stylistic patterns -- to precisely examine their influence on distilled models' reasoning capabilities. We find that models trained on the synthetic traces achieve comparable performance, indicating that distilled reasoning abilities rely significantly on surface-level patterns. Surprisingly, we observe an increase in performance even when the synthetic traces are altered to lead to the wrong answer. Our findings highlight how stylistic patterns can be leveraged to efficiently enhance LM reasoning across diverse model families.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 13:50:20 GMT" } ]	2025-04-03T00:00:00	[ [ "Lippmann", "Philip", "" ], [ "Yang", "Jie", "" ] ]	TITLE: Style over Substance: Distilled Language Models Reason Via Stylistic Replication ABSTRACT: Specialized reasoning language models (RLMs) have demonstrated that scaling test-time computation through detailed reasoning traces significantly enhances performance. Although these traces effectively facilitate knowledge distillation into smaller, instruction-tuned models, the precise nature of transferred reasoning remains unclear. In this study, we investigate to what extent distilled models internalize replicated stylistic patterns during reasoning. To this end, we systematically analyze reasoning traces, identifying structural and lexical patterns that characterize successful reasoning. We then introduce two new datasets -- a dataset of emergent reasoning traces and a synthetic dataset explicitly constructed to replicate these stylistic patterns -- to precisely examine their influence on distilled models' reasoning capabilities. We find that models trained on the synthetic traces achieve comparable performance, indicating that distilled reasoning abilities rely significantly on surface-level patterns. Surprisingly, we observe an increase in performance even when the synthetic traces are altered to lead to the wrong answer. Our findings highlight how stylistic patterns can be leveraged to efficiently enhance LM reasoning across diverse model families.
2504.01740	Neville Kenneth Kitson	Neville K. Kitson, Anthony C. Constantinou	Stable Structure Learning with HC-Stable and Tabu-Stable Algorithms	null	null	null	null	cs.LG	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Many Bayesian Network structure learning algorithms are unstable, with the learned graph sensitive to arbitrary dataset artifacts, such as the ordering of columns (i.e., variable order). PC-Stable attempts to address this issue for the widely-used PC algorithm, prompting researchers to use the "stable" version instead. However, this problem seems to have been overlooked for score-based algorithms. In this study, we show that some widely-used score-based algorithms, as well as hybrid and constraint-based algorithms, including PC-Stable, suffer from the same issue. We propose a novel solution for score-based greedy hill-climbing that eliminates instability by determining a stable node order, leading to consistent results regardless of variable ordering. Two implementations, HC-Stable and Tabu-Stable, are introduced. Tabu-Stable achieves the highest BIC scores across all networks, and the highest accuracy for categorical networks. These results highlight the importance of addressing instability in structure learning and provide a robust and practical approach for future applications. This extends the scope and impact of our previous work presented at Probabilistic Graphical Models 2024 by incorporating continuous variables. The implementation, along with usage instructions, is freely available on GitHub at https://github.com/causal-iq/discovery.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 13:51:44 GMT" } ]	2025-04-03T00:00:00	[ [ "Kitson", "Neville K.", "" ], [ "Constantinou", "Anthony C.", "" ] ]	TITLE: Stable Structure Learning with HC-Stable and Tabu-Stable Algorithms ABSTRACT: Many Bayesian Network structure learning algorithms are unstable, with the learned graph sensitive to arbitrary dataset artifacts, such as the ordering of columns (i.e., variable order). PC-Stable attempts to address this issue for the widely-used PC algorithm, prompting researchers to use the "stable" version instead. However, this problem seems to have been overlooked for score-based algorithms. In this study, we show that some widely-used score-based algorithms, as well as hybrid and constraint-based algorithms, including PC-Stable, suffer from the same issue. We propose a novel solution for score-based greedy hill-climbing that eliminates instability by determining a stable node order, leading to consistent results regardless of variable ordering. Two implementations, HC-Stable and Tabu-Stable, are introduced. Tabu-Stable achieves the highest BIC scores across all networks, and the highest accuracy for categorical networks. These results highlight the importance of addressing instability in structure learning and provide a robust and practical approach for future applications. This extends the scope and impact of our previous work presented at Probabilistic Graphical Models 2024 by incorporating continuous variables. The implementation, along with usage instructions, is freely available on GitHub at https://github.com/causal-iq/discovery.
2504.01757	Eduardo Fernandes Montesuma	Eduardo Fernandes Montesuma	KD$^{2}$M: An unifying framework for feature knowledge distillation	8 pages, 2 figures, 1 table, under review	null	null	null	stat.ML cs.LG	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Knowledge Distillation (KD) seeks to transfer the knowledge of a teacher, towards a student neural net. This process is often done by matching the networks' predictions (i.e., their output), but, recently several works have proposed to match the distributions of neural nets' activations (i.e., their features), a process known as \emph{distribution matching}. In this paper, we propose an unifying framework, Knowledge Distillation through Distribution Matching (KD$^{2}$M), which formalizes this strategy. Our contributions are threefold. We i) provide an overview of distribution metrics used in distribution matching, ii) benchmark on computer vision datasets, and iii) derive new theoretical results for KD.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 14:14:46 GMT" } ]	2025-04-03T00:00:00	[ [ "Montesuma", "Eduardo Fernandes", "" ] ]	TITLE: KD$^{2}$M: An unifying framework for feature knowledge distillation ABSTRACT: Knowledge Distillation (KD) seeks to transfer the knowledge of a teacher, towards a student neural net. This process is often done by matching the networks' predictions (i.e., their output), but, recently several works have proposed to match the distributions of neural nets' activations (i.e., their features), a process known as \emph{distribution matching}. In this paper, we propose an unifying framework, Knowledge Distillation through Distribution Matching (KD$^{2}$M), which formalizes this strategy. Our contributions are threefold. We i) provide an overview of distribution metrics used in distribution matching, ii) benchmark on computer vision datasets, and iii) derive new theoretical results for KD.
2504.01764	Mingrui Ye	Mingrui Ye, Lianping Yang, Hegui Zhu, Zenghao Zheng, Xin Wang, Yantao Lo	Dual-stream Transformer-GCN Model with Contextualized Representations Learning for Monocular 3D Human Pose Estimation	null	null	null	null	cs.CV cs.AI	http://creativecommons.org/licenses/by/4.0/	This paper introduces a novel approach to monocular 3D human pose estimation using contextualized representation learning with the Transformer-GCN dual-stream model. Monocular 3D human pose estimation is challenged by depth ambiguity, limited 3D-labeled training data, imbalanced modeling, and restricted model generalization. To address these limitations, our work introduces a groundbreaking motion pre-training method based on contextualized representation learning. Specifically, our method involves masking 2D pose features and utilizing a Transformer-GCN dual-stream model to learn high-dimensional representations through a self-distillation setup. By focusing on contextualized representation learning and spatial-temporal modeling, our approach enhances the model's ability to understand spatial-temporal relationships between postures, resulting in superior generalization. Furthermore, leveraging the Transformer-GCN dual-stream model, our approach effectively balances global and local interactions in video pose estimation. The model adaptively integrates information from both the Transformer and GCN streams, where the GCN stream effectively learns local relationships between adjacent key points and frames, while the Transformer stream captures comprehensive global spatial and temporal features. Our model achieves state-of-the-art performance on two benchmark datasets, with an MPJPE of 38.0mm and P-MPJPE of 31.9mm on Human3.6M, and an MPJPE of 15.9mm on MPI-INF-3DHP. Furthermore, visual experiments on public datasets and in-the-wild videos demonstrate the robustness and generalization capabilities of our approach.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 14:17:57 GMT" } ]	2025-04-03T00:00:00	[ [ "Ye", "Mingrui", "" ], [ "Yang", "Lianping", "" ], [ "Zhu", "Hegui", "" ], [ "Zheng", "Zenghao", "" ], [ "Wang", "Xin", "" ], [ "Lo", "Yantao", "" ] ]	TITLE: Dual-stream Transformer-GCN Model with Contextualized Representations Learning for Monocular 3D Human Pose Estimation ABSTRACT: This paper introduces a novel approach to monocular 3D human pose estimation using contextualized representation learning with the Transformer-GCN dual-stream model. Monocular 3D human pose estimation is challenged by depth ambiguity, limited 3D-labeled training data, imbalanced modeling, and restricted model generalization. To address these limitations, our work introduces a groundbreaking motion pre-training method based on contextualized representation learning. Specifically, our method involves masking 2D pose features and utilizing a Transformer-GCN dual-stream model to learn high-dimensional representations through a self-distillation setup. By focusing on contextualized representation learning and spatial-temporal modeling, our approach enhances the model's ability to understand spatial-temporal relationships between postures, resulting in superior generalization. Furthermore, leveraging the Transformer-GCN dual-stream model, our approach effectively balances global and local interactions in video pose estimation. The model adaptively integrates information from both the Transformer and GCN streams, where the GCN stream effectively learns local relationships between adjacent key points and frames, while the Transformer stream captures comprehensive global spatial and temporal features. Our model achieves state-of-the-art performance on two benchmark datasets, with an MPJPE of 38.0mm and P-MPJPE of 31.9mm on Human3.6M, and an MPJPE of 15.9mm on MPI-INF-3DHP. Furthermore, visual experiments on public datasets and in-the-wild videos demonstrate the robustness and generalization capabilities of our approach.
2504.01790	Sveinung Ohrem	Sveinung Johan Ohrem, Bent Haugal{\o}kken, Eleni Kelasidi	SOLAQUA: SINTEF Ocean Large Aquaculture Robotics Dataset	null	null	null	null	cs.RO cs.CV	http://creativecommons.org/licenses/by-sa/4.0/	This paper presents a dataset gathered with an underwater robot in a sea-based aquaculture setting. Data was gathered from an operational fish farm and includes data from sensors such as the Waterlinked A50 DVL, the Nortek Nucleus 1000 DVL, Sonardyne Micro Ranger 2 USBL, Sonoptix Mulitbeam Sonar, mono and stereo cameras, and vehicle sensor data such as power usage, IMU, pressure, temperature, and more. Data acquisition is performed during both manual and autonomous traversal of the net pen structure. The collected vision data is of undamaged nets with some fish and marine growth presence, and it is expected that both the research community and the aquaculture industry will benefit greatly from the utilization of the proposed SOLAQUA dataset.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 14:58:16 GMT" } ]	2025-04-03T00:00:00	[ [ "Ohrem", "Sveinung Johan", "" ], [ "Haugaløkken", "Bent", "" ], [ "Kelasidi", "Eleni", "" ] ]	TITLE: SOLAQUA: SINTEF Ocean Large Aquaculture Robotics Dataset ABSTRACT: This paper presents a dataset gathered with an underwater robot in a sea-based aquaculture setting. Data was gathered from an operational fish farm and includes data from sensors such as the Waterlinked A50 DVL, the Nortek Nucleus 1000 DVL, Sonardyne Micro Ranger 2 USBL, Sonoptix Mulitbeam Sonar, mono and stereo cameras, and vehicle sensor data such as power usage, IMU, pressure, temperature, and more. Data acquisition is performed during both manual and autonomous traversal of the net pen structure. The collected vision data is of undamaged nets with some fish and marine growth presence, and it is expected that both the research community and the aquaculture industry will benefit greatly from the utilization of the proposed SOLAQUA dataset.
2504.01792	Limeng Qiao	Limeng Qiao, Yiyang Gan, Bairui Wang, Jie Qin, Shuang Xu, Siqi Yang, Lin Ma	UniViTAR: Unified Vision Transformer with Native Resolution	null	null	null	null	cs.CV	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Conventional Vision Transformer simplifies visual modeling by standardizing input resolutions, often disregarding the variability of natural visual data and compromising spatial-contextual fidelity. While preliminary explorations have superficially investigated native resolution modeling, existing approaches still lack systematic analysis from a visual representation perspective. To bridge this gap, we introduce UniViTAR, a family of homogeneous vision foundation models tailored for unified visual modality and native resolution scenario in the era of multimodal. Our framework first conducts architectural upgrades to the vanilla paradigm by integrating multiple advanced components. Building upon these improvements, a progressive training paradigm is introduced, which strategically combines two core mechanisms: (1) resolution curriculum learning, transitioning from fixed-resolution pretraining to native resolution tuning, thereby leveraging ViT's inherent adaptability to variable-length sequences, and (2) visual modality adaptation via inter-batch image-video switching, which balances computational efficiency with enhanced temporal reasoning. In parallel, a hybrid training framework further synergizes sigmoid-based contrastive loss with feature distillation from a frozen teacher model, thereby accelerating early-stage convergence. Finally, trained exclusively on public datasets, externsive experiments across multiple model scales from 0.3B to 1B demonstrate its effectiveness.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 14:59:39 GMT" } ]	2025-04-03T00:00:00	[ [ "Qiao", "Limeng", "" ], [ "Gan", "Yiyang", "" ], [ "Wang", "Bairui", "" ], [ "Qin", "Jie", "" ], [ "Xu", "Shuang", "" ], [ "Yang", "Siqi", "" ], [ "Ma", "Lin", "" ] ]	TITLE: UniViTAR: Unified Vision Transformer with Native Resolution ABSTRACT: Conventional Vision Transformer simplifies visual modeling by standardizing input resolutions, often disregarding the variability of natural visual data and compromising spatial-contextual fidelity. While preliminary explorations have superficially investigated native resolution modeling, existing approaches still lack systematic analysis from a visual representation perspective. To bridge this gap, we introduce UniViTAR, a family of homogeneous vision foundation models tailored for unified visual modality and native resolution scenario in the era of multimodal. Our framework first conducts architectural upgrades to the vanilla paradigm by integrating multiple advanced components. Building upon these improvements, a progressive training paradigm is introduced, which strategically combines two core mechanisms: (1) resolution curriculum learning, transitioning from fixed-resolution pretraining to native resolution tuning, thereby leveraging ViT's inherent adaptability to variable-length sequences, and (2) visual modality adaptation via inter-batch image-video switching, which balances computational efficiency with enhanced temporal reasoning. In parallel, a hybrid training framework further synergizes sigmoid-based contrastive loss with feature distillation from a frozen teacher model, thereby accelerating early-stage convergence. Finally, trained exclusively on public datasets, externsive experiments across multiple model scales from 0.3B to 1B demonstrate its effectiveness.
2504.01803	Javier Pastor-Galindo	Felipe S\'anchez Gonz\'alez, Javier Pastor-Galindo, Jos\'e A. Ruip\'erez-Valiente	DISINFOX: an open-source threat exchange platform serving intelligence on disinformation and influence operations	null	null	null	null	cs.CR	http://creativecommons.org/licenses/by/4.0/	This paper introduces DISINFOX, an open-source threat intelligence exchange platform for the structured collection, management, and dissemination of disinformation incidents and influence operations. Analysts can upload and correlate information manipulation and interference incidents, while clients can access and analyze the data through an interactive web interface or programmatically via a public API. This facilitates integration with other vendors, providing a unified view of cybersecurity and disinformation events. The solution is fully containerized using Docker, comprising a web-based frontend for user interaction, a backend REST API for managing core functionalities, and a public API for structured data retrieval, enabling seamless integration with existing Cyber Threat Intelligence (CTI) workflows. In particular, DISINFOX models the incidents through DISARM Tactics, Techniques, and Procedures (TTPs), a MITRE ATT&CK-like framework for disinformation, with a custom data model based on the Structured Threat Information eXpression (STIX2) standard. As an open-source solution, DISINFOX provides a reproducible and extensible hub for researchers, analysts, and policymakers seeking to enhance the detection, investigation, and mitigation of disinformation threats. The intelligence generated from a custom dataset has been tested and utilized by a local instance of OpenCTI, a mature CTI platform, via a custom-built connector, validating the platform with the exchange of more than 100 disinformation incidents.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 15:11:43 GMT" } ]	2025-04-03T00:00:00	[ [ "González", "Felipe Sánchez", "" ], [ "Pastor-Galindo", "Javier", "" ], [ "Ruipérez-Valiente", "José A.", "" ] ]	TITLE: DISINFOX: an open-source threat exchange platform serving intelligence on disinformation and influence operations ABSTRACT: This paper introduces DISINFOX, an open-source threat intelligence exchange platform for the structured collection, management, and dissemination of disinformation incidents and influence operations. Analysts can upload and correlate information manipulation and interference incidents, while clients can access and analyze the data through an interactive web interface or programmatically via a public API. This facilitates integration with other vendors, providing a unified view of cybersecurity and disinformation events. The solution is fully containerized using Docker, comprising a web-based frontend for user interaction, a backend REST API for managing core functionalities, and a public API for structured data retrieval, enabling seamless integration with existing Cyber Threat Intelligence (CTI) workflows. In particular, DISINFOX models the incidents through DISARM Tactics, Techniques, and Procedures (TTPs), a MITRE ATT&CK-like framework for disinformation, with a custom data model based on the Structured Threat Information eXpression (STIX2) standard. As an open-source solution, DISINFOX provides a reproducible and extensible hub for researchers, analysts, and policymakers seeking to enhance the detection, investigation, and mitigation of disinformation threats. The intelligence generated from a custom dataset has been tested and utilized by a local instance of OpenCTI, a mature CTI platform, via a custom-built connector, validating the platform with the exchange of more than 100 disinformation incidents.
2504.01805	Kun Ouyang	Kun Ouyang	Spatial-R1: Enhancing MLLMs in Video Spatial Reasoning	null	null	null	null	cs.CV	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Enhancing the spatial reasoning capabilities of Multi-modal Large Language Models (MLLMs) for video understanding is crucial yet challenging. We present Spatial-R1, a targeted approach involving two key contributions: the curation of SR, a new video spatial reasoning dataset from ScanNet with automatically generated QA pairs across seven task types, and the application of Task-Specific Group Relative Policy Optimization (GRPO) for fine-tuning. By training the Qwen2.5-VL-7B-Instruct model on SR using GRPO, Spatial-R1 significantly advances performance on the VSI-Bench benchmark, achieving a 7.4\% gain over the baseline and outperforming strong contemporary models. This work validates the effectiveness of specialized data curation and optimization techniques for improving complex spatial reasoning in video MLLMs.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 15:12:17 GMT" } ]	2025-04-03T00:00:00	[ [ "Ouyang", "Kun", "" ] ]	TITLE: Spatial-R1: Enhancing MLLMs in Video Spatial Reasoning ABSTRACT: Enhancing the spatial reasoning capabilities of Multi-modal Large Language Models (MLLMs) for video understanding is crucial yet challenging. We present Spatial-R1, a targeted approach involving two key contributions: the curation of SR, a new video spatial reasoning dataset from ScanNet with automatically generated QA pairs across seven task types, and the application of Task-Specific Group Relative Policy Optimization (GRPO) for fine-tuning. By training the Qwen2.5-VL-7B-Instruct model on SR using GRPO, Spatial-R1 significantly advances performance on the VSI-Bench benchmark, achieving a 7.4\% gain over the baseline and outperforming strong contemporary models. This work validates the effectiveness of specialized data curation and optimization techniques for improving complex spatial reasoning in video MLLMs.
2504.01833	Sumuk Shashidhar	Sumuk Shashidhar, Cl\'ementine Fourrier, Alina Lozovskia, Thomas Wolf, Gokhan Tur, Dilek Hakkani-T\"ur	YourBench: Easy Custom Evaluation Sets for Everyone	null	null	null	null	cs.CL cs.AI	http://creativecommons.org/licenses/by/4.0/	Evaluating large language models (LLMs) effectively remains a critical bottleneck, as traditional static benchmarks suffer from saturation and contamination, while human evaluations are costly and slow. This hinders timely or domain-specific assessment, crucial for real-world applications. We introduce YourBench, a novel, open-source framework that addresses these limitations by enabling dynamic, automated generation of reliable, up-to-date, and domain-tailored benchmarks cheaply and without manual annotation, directly from user-provided documents. We demonstrate its efficacy by replicating 7 diverse MMLU subsets using minimal source text, achieving this for under 15 USD in total inference costs while perfectly preserving the relative model performance rankings (Spearman Rho = 1) observed on the original benchmark. To ensure that YourBench generates data grounded in provided input instead of relying on posterior parametric knowledge in models, we also introduce Tempora-0325, a novel dataset of over 7K diverse documents, published exclusively after March 2025. Our comprehensive analysis spans 26 SoTA models from 7 major families across varying scales (3-671B parameters) to validate the quality of generated evaluations through rigorous algorithmic checks (e.g., citation grounding) and human assessments. We release the YourBench library, the Tempora-0325 dataset, 150k+ question answer pairs based on Tempora and all evaluation and inference traces to facilitate reproducible research and empower the community to generate bespoke benchmarks on demand, fostering more relevant and trustworthy LLM evaluation.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 15:40:24 GMT" } ]	2025-04-03T00:00:00	[ [ "Shashidhar", "Sumuk", "" ], [ "Fourrier", "Clémentine", "" ], [ "Lozovskia", "Alina", "" ], [ "Wolf", "Thomas", "" ], [ "Tur", "Gokhan", "" ], [ "Hakkani-Tür", "Dilek", "" ] ]	TITLE: YourBench: Easy Custom Evaluation Sets for Everyone ABSTRACT: Evaluating large language models (LLMs) effectively remains a critical bottleneck, as traditional static benchmarks suffer from saturation and contamination, while human evaluations are costly and slow. This hinders timely or domain-specific assessment, crucial for real-world applications. We introduce YourBench, a novel, open-source framework that addresses these limitations by enabling dynamic, automated generation of reliable, up-to-date, and domain-tailored benchmarks cheaply and without manual annotation, directly from user-provided documents. We demonstrate its efficacy by replicating 7 diverse MMLU subsets using minimal source text, achieving this for under 15 USD in total inference costs while perfectly preserving the relative model performance rankings (Spearman Rho = 1) observed on the original benchmark. To ensure that YourBench generates data grounded in provided input instead of relying on posterior parametric knowledge in models, we also introduce Tempora-0325, a novel dataset of over 7K diverse documents, published exclusively after March 2025. Our comprehensive analysis spans 26 SoTA models from 7 major families across varying scales (3-671B parameters) to validate the quality of generated evaluations through rigorous algorithmic checks (e.g., citation grounding) and human assessments. We release the YourBench library, the Tempora-0325 dataset, 150k+ question answer pairs based on Tempora and all evaluation and inference traces to facilitate reproducible research and empower the community to generate bespoke benchmarks on demand, fostering more relevant and trustworthy LLM evaluation.
2504.01838	Nusrat Munia	Nusrat Munia and Abdullah-Al-Zubaer Imran	Prompting Medical Vision-Language Models to Mitigate Diagnosis Bias by Generating Realistic Dermoscopic Images	Paper accepted at International Symposium on Biomedical Imaging (ISBI 2025)	null	null	null	cs.CV	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Artificial Intelligence (AI) in skin disease diagnosis has improved significantly, but a major concern is that these models frequently show biased performance across subgroups, especially regarding sensitive attributes such as skin color. To address these issues, we propose a novel generative AI-based framework, namely, Dermatology Diffusion Transformer (DermDiT), which leverages text prompts generated via Vision Language Models and multimodal text-image learning to generate new dermoscopic images. We utilize large vision language models to generate accurate and proper prompts for each dermoscopic image which helps to generate synthetic images to improve the representation of underrepresented groups (patient, disease, etc.) in highly imbalanced datasets for clinical diagnoses. Our extensive experimentation showcases the large vision language models providing much more insightful representations, that enable DermDiT to generate high-quality images. Our code is available at https://github.com/Munia03/DermDiT	[ { "version": "v1", "created": "Wed, 2 Apr 2025 15:44:12 GMT" } ]	2025-04-03T00:00:00	[ [ "Munia", "Nusrat", "" ], [ "Imran", "Abdullah-Al-Zubaer", "" ] ]	TITLE: Prompting Medical Vision-Language Models to Mitigate Diagnosis Bias by Generating Realistic Dermoscopic Images ABSTRACT: Artificial Intelligence (AI) in skin disease diagnosis has improved significantly, but a major concern is that these models frequently show biased performance across subgroups, especially regarding sensitive attributes such as skin color. To address these issues, we propose a novel generative AI-based framework, namely, Dermatology Diffusion Transformer (DermDiT), which leverages text prompts generated via Vision Language Models and multimodal text-image learning to generate new dermoscopic images. We utilize large vision language models to generate accurate and proper prompts for each dermoscopic image which helps to generate synthetic images to improve the representation of underrepresented groups (patient, disease, etc.) in highly imbalanced datasets for clinical diagnoses. Our extensive experimentation showcases the large vision language models providing much more insightful representations, that enable DermDiT to generate high-quality images. Our code is available at https://github.com/Munia03/DermDiT
2504.01850	Ali Al-Kaswan	Ali Al-Kaswan, Sebastian Deatc, Beg\"um Ko\c{c}, Arie van Deursen, Maliheh Izadi	Code Red! On the Harmfulness of Applying Off-the-shelf Large Language Models to Programming Tasks	FSE'25 Technical Track	null	null	null	cs.SE cs.AI	http://creativecommons.org/licenses/by/4.0/	Nowadays, developers increasingly rely on solutions powered by Large Language Models (LLM) to assist them with their coding tasks. This makes it crucial to align these tools with human values to prevent malicious misuse. In this paper, we propose a comprehensive framework for assessing the potential harmfulness of LLMs within the software engineering domain. We begin by developing a taxonomy of potentially harmful software engineering scenarios and subsequently, create a dataset of prompts based on this taxonomy. To systematically assess the responses, we design and validate an automatic evaluator that classifies the outputs of a variety of LLMs both open-source and closed-source models, as well as general-purpose and code-specific LLMs. Furthermore, we investigate the impact of models size, architecture family, and alignment strategies on their tendency to generate harmful content. The results show significant disparities in the alignment of various LLMs for harmlessness. We find that some models and model families, such as Openhermes, are more harmful than others and that code-specific models do not perform better than their general-purpose counterparts. Notably, some fine-tuned models perform significantly worse than their base-models due to their design choices. On the other side, we find that larger models tend to be more helpful and are less likely to respond with harmful information. These results highlight the importance of targeted alignment strategies tailored to the unique challenges of software engineering tasks and provide a foundation for future work in this critical area.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 16:00:14 GMT" } ]	2025-04-03T00:00:00	[ [ "Al-Kaswan", "Ali", "" ], [ "Deatc", "Sebastian", "" ], [ "Koç", "Begüm", "" ], [ "van Deursen", "Arie", "" ], [ "Izadi", "Maliheh", "" ] ]	TITLE: Code Red! On the Harmfulness of Applying Off-the-shelf Large Language Models to Programming Tasks ABSTRACT: Nowadays, developers increasingly rely on solutions powered by Large Language Models (LLM) to assist them with their coding tasks. This makes it crucial to align these tools with human values to prevent malicious misuse. In this paper, we propose a comprehensive framework for assessing the potential harmfulness of LLMs within the software engineering domain. We begin by developing a taxonomy of potentially harmful software engineering scenarios and subsequently, create a dataset of prompts based on this taxonomy. To systematically assess the responses, we design and validate an automatic evaluator that classifies the outputs of a variety of LLMs both open-source and closed-source models, as well as general-purpose and code-specific LLMs. Furthermore, we investigate the impact of models size, architecture family, and alignment strategies on their tendency to generate harmful content. The results show significant disparities in the alignment of various LLMs for harmlessness. We find that some models and model families, such as Openhermes, are more harmful than others and that code-specific models do not perform better than their general-purpose counterparts. Notably, some fine-tuned models perform significantly worse than their base-models due to their design choices. On the other side, we find that larger models tend to be more helpful and are less likely to respond with harmful information. These results highlight the importance of targeted alignment strategies tailored to the unique challenges of software engineering tasks and provide a foundation for future work in this critical area.
2504.01857	Zhiwei Yu	Zhiwei Yu, Tuo Li, Changhong Wang, Hui Chen, Lang Zhou	Cross-Lingual Consistency: A Novel Inference Framework for Advancing Reasoning in Large Language Models	null	null	null	null	cs.CL cs.AI	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Chain-of-thought (CoT) has emerged as a critical mechanism for enhancing reasoning capabilities in large language models (LLMs), with self-consistency demonstrating notable promise in boosting performance. However, inherent linguistic biases in multilingual training corpora frequently cause semantic drift and logical inconsistencies, especially in sub-10B parameter LLMs handling complex inference tasks. To overcome these constraints, we propose the Cross-Lingual Consistency (CLC) framework, an innovative inference paradigm that integrates multilingual reasoning paths through majority voting to elevate LLMs' reasoning capabilities. Empirical evaluations on the CMATH dataset reveal CLC's superiority over the conventional self-consistency method, delivering 9.5%, 6.5%, and 6.0% absolute accuracy gains for DeepSeek-Math-7B-Instruct, Qwen2.5-Math-7B-Instruct, and Gemma2-9B-Instruct respectively. Expanding CLC's linguistic scope to 11 diverse languages implies two synergistic benefits: 1) neutralizing linguistic biases in multilingual training corpora through multilingual ensemble voting, 2) escaping monolingual reasoning traps by exploring the broader multilingual solution space. This dual benefits empirically enables more globally optimal reasoning paths compared to monolingual self-consistency baselines, as evidenced by the 4.1%-18.5% accuracy gains using Gemma2-9B-Instruct on the MGSM dataset.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 16:09:39 GMT" } ]	2025-04-03T00:00:00	[ [ "Yu", "Zhiwei", "" ], [ "Li", "Tuo", "" ], [ "Wang", "Changhong", "" ], [ "Chen", "Hui", "" ], [ "Zhou", "Lang", "" ] ]	TITLE: Cross-Lingual Consistency: A Novel Inference Framework for Advancing Reasoning in Large Language Models ABSTRACT: Chain-of-thought (CoT) has emerged as a critical mechanism for enhancing reasoning capabilities in large language models (LLMs), with self-consistency demonstrating notable promise in boosting performance. However, inherent linguistic biases in multilingual training corpora frequently cause semantic drift and logical inconsistencies, especially in sub-10B parameter LLMs handling complex inference tasks. To overcome these constraints, we propose the Cross-Lingual Consistency (CLC) framework, an innovative inference paradigm that integrates multilingual reasoning paths through majority voting to elevate LLMs' reasoning capabilities. Empirical evaluations on the CMATH dataset reveal CLC's superiority over the conventional self-consistency method, delivering 9.5%, 6.5%, and 6.0% absolute accuracy gains for DeepSeek-Math-7B-Instruct, Qwen2.5-Math-7B-Instruct, and Gemma2-9B-Instruct respectively. Expanding CLC's linguistic scope to 11 diverse languages implies two synergistic benefits: 1) neutralizing linguistic biases in multilingual training corpora through multilingual ensemble voting, 2) escaping monolingual reasoning traps by exploring the broader multilingual solution space. This dual benefits empirically enables more globally optimal reasoning paths compared to monolingual self-consistency baselines, as evidenced by the 4.1%-18.5% accuracy gains using Gemma2-9B-Instruct on the MGSM dataset.
2504.01861	Yeong Gwang Son	Yeong Gwang Son, Seunghwan Um, Juyong Hong, Tat Hieu Bui, and Hyouk Ryeol Choi	Corner-Grasp: Multi-Action Grasp Detection and Active Gripper Adaptation for Grasping in Cluttered Environments	11 pages, 14 figures	null	null	null	cs.RO cs.LG	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Robotic grasping is an essential capability, playing a critical role in enabling robots to physically interact with their surroundings. Despite extensive research, challenges remain due to the diverse shapes and properties of target objects, inaccuracies in sensing, and potential collisions with the environment. In this work, we propose a method for effectively grasping in cluttered bin-picking environments where these challenges intersect. We utilize a multi-functional gripper that combines both suction and finger grasping to handle a wide range of objects. We also present an active gripper adaptation strategy to minimize collisions between the gripper hardware and the surrounding environment by actively leveraging the reciprocating suction cup and reconfigurable finger motion. To fully utilize the gripper's capabilities, we built a neural network that detects suction and finger grasp points from a single input RGB-D image. This network is trained using a larger-scale synthetic dataset generated from simulation. In addition to this, we propose an efficient approach to constructing a real-world dataset that facilitates grasp point detection on various objects with diverse characteristics. Experiment results show that the proposed method can grasp objects in cluttered bin-picking scenarios and prevent collisions with environmental constraints such as a corner of the bin. Our proposed method demonstrated its effectiveness in the 9th Robotic Grasping and Manipulation Competition (RGMC) held at ICRA 2024.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 16:12:28 GMT" } ]	2025-04-03T00:00:00	[ [ "Son", "Yeong Gwang", "" ], [ "Um", "Seunghwan", "" ], [ "Hong", "Juyong", "" ], [ "Bui", "Tat Hieu", "" ], [ "Choi", "Hyouk Ryeol", "" ] ]	TITLE: Corner-Grasp: Multi-Action Grasp Detection and Active Gripper Adaptation for Grasping in Cluttered Environments ABSTRACT: Robotic grasping is an essential capability, playing a critical role in enabling robots to physically interact with their surroundings. Despite extensive research, challenges remain due to the diverse shapes and properties of target objects, inaccuracies in sensing, and potential collisions with the environment. In this work, we propose a method for effectively grasping in cluttered bin-picking environments where these challenges intersect. We utilize a multi-functional gripper that combines both suction and finger grasping to handle a wide range of objects. We also present an active gripper adaptation strategy to minimize collisions between the gripper hardware and the surrounding environment by actively leveraging the reciprocating suction cup and reconfigurable finger motion. To fully utilize the gripper's capabilities, we built a neural network that detects suction and finger grasp points from a single input RGB-D image. This network is trained using a larger-scale synthetic dataset generated from simulation. In addition to this, we propose an efficient approach to constructing a real-world dataset that facilitates grasp point detection on various objects with diverse characteristics. Experiment results show that the proposed method can grasp objects in cluttered bin-picking scenarios and prevent collisions with environmental constraints such as a corner of the bin. Our proposed method demonstrated its effectiveness in the 9th Robotic Grasping and Manipulation Competition (RGMC) held at ICRA 2024.
2504.01863	Mark Smucker	Mark D. Smucker and Houmaan Chamani	Extending MovieLens-32M to Provide New Evaluation Objectives	Our extension to MovieLens-32M is available for researchers at https://uwaterlooir.github.io/datasets/ml-32m-extension	null	null	null	cs.IR	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Offline evaluation of recommender systems has traditionally treated the problem as a machine learning problem. In the classic case of recommending movies, where the user has provided explicit ratings of which movies they like and don't like, each user's ratings are split into test and train sets, and the evaluation task becomes to predict the held out test data using the training data. This machine learning style of evaluation makes the objective to recommend the movies that a user has watched and rated highly, which is not the same task as helping the user find movies that they would enjoy if they watched them. This mismatch in objective between evaluation and task is a compromise to avoid the cost of asking a user to evaluate recommendations by watching each movie. As a resource available for download, we offer an extension to the MovieLens-32M dataset that provides for new evaluation objectives. Our primary objective is to predict the movies that a user would be interested in watching, i.e. predict their watchlist. To construct this extension, we recruited MovieLens users, collected their profiles, made recommendations with a diverse set of algorithms, pooled the recommendations, and had the users assess the pools. Notably, we found that the traditional machine learning style of evaluation ranks the Popular algorithm, which recommends movies based on total number of ratings in the system, in the middle of the twenty-two recommendation runs we used to build the pools. In contrast, when we rank the runs by users' interest in watching movies, we find that recommending popular movies as a recommendation algorithm becomes one of the worst performing runs. It appears that by asking users to assess their personal recommendations, we can alleviate the popularity bias issues created by using information retrieval effectiveness measures for the evaluation of recommender systems.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 16:15:46 GMT" } ]	2025-04-03T00:00:00	[ [ "Smucker", "Mark D.", "" ], [ "Chamani", "Houmaan", "" ] ]	TITLE: Extending MovieLens-32M to Provide New Evaluation Objectives ABSTRACT: Offline evaluation of recommender systems has traditionally treated the problem as a machine learning problem. In the classic case of recommending movies, where the user has provided explicit ratings of which movies they like and don't like, each user's ratings are split into test and train sets, and the evaluation task becomes to predict the held out test data using the training data. This machine learning style of evaluation makes the objective to recommend the movies that a user has watched and rated highly, which is not the same task as helping the user find movies that they would enjoy if they watched them. This mismatch in objective between evaluation and task is a compromise to avoid the cost of asking a user to evaluate recommendations by watching each movie. As a resource available for download, we offer an extension to the MovieLens-32M dataset that provides for new evaluation objectives. Our primary objective is to predict the movies that a user would be interested in watching, i.e. predict their watchlist. To construct this extension, we recruited MovieLens users, collected their profiles, made recommendations with a diverse set of algorithms, pooled the recommendations, and had the users assess the pools. Notably, we found that the traditional machine learning style of evaluation ranks the Popular algorithm, which recommends movies based on total number of ratings in the system, in the middle of the twenty-two recommendation runs we used to build the pools. In contrast, when we rank the runs by users' interest in watching movies, we find that recommending popular movies as a recommendation algorithm becomes one of the worst performing runs. It appears that by asking users to assess their personal recommendations, we can alleviate the popularity bias issues created by using information retrieval effectiveness measures for the evaluation of recommender systems.
2504.01875	Ben Keslaki	Ben Keslaki	Architect Your Landscape Approach (AYLA) for Optimizations in Deep Learning	null	null	null	null	cs.LG	http://creativecommons.org/licenses/by/4.0/	Stochastic Gradient Descent (SGD) and its variants, such as ADAM, are foundational to deep learning optimization, adjusting model parameters using fixed or adaptive learning rates based on loss function gradients. However, these methods often face challenges in balancing adaptability and efficiency in non-convex, high-dimensional settings. This paper introduces AYLA, a novel optimization technique that enhances training dynamics through loss function transformations. By applying a tunable power-law transformation, AYLA preserves critical points while scaling loss values to amplify gradient sensitivity, accelerating convergence. We further propose a dynamic (effective) learning rate that adapts to the transformed loss, improving optimization efficiency. Empirical tests on finding minimum of a synthetic non-convex polynomial, a non-convex curve-fitting dataset, and digit classification (MNIST) demonstrate that AYLA surpasses SGD and ADAM in convergence speed and stability. This approach redefines the loss landscape for better optimization outcomes, offering a promising advancement for deep neural networks and can be applied to any optimization method and potentially improve the performance of it.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 16:31:39 GMT" } ]	2025-04-03T00:00:00	[ [ "Keslaki", "Ben", "" ] ]	TITLE: Architect Your Landscape Approach (AYLA) for Optimizations in Deep Learning ABSTRACT: Stochastic Gradient Descent (SGD) and its variants, such as ADAM, are foundational to deep learning optimization, adjusting model parameters using fixed or adaptive learning rates based on loss function gradients. However, these methods often face challenges in balancing adaptability and efficiency in non-convex, high-dimensional settings. This paper introduces AYLA, a novel optimization technique that enhances training dynamics through loss function transformations. By applying a tunable power-law transformation, AYLA preserves critical points while scaling loss values to amplify gradient sensitivity, accelerating convergence. We further propose a dynamic (effective) learning rate that adapts to the transformed loss, improving optimization efficiency. Empirical tests on finding minimum of a synthetic non-convex polynomial, a non-convex curve-fitting dataset, and digit classification (MNIST) demonstrate that AYLA surpasses SGD and ADAM in convergence speed and stability. This approach redefines the loss landscape for better optimization outcomes, offering a promising advancement for deep neural networks and can be applied to any optimization method and potentially improve the performance of it.
2504.01879	Tushar Kataria	Abhilash Shankarampeta, Harsh Mahajan, Tushar Kataria, Dan Roth, Vivek Gupta	TransientTables: Evaluating LLMs' Reasoning on Temporally Evolving Semi-structured Tables	19 Pages. 21 Tables, 1 figure	null	null	null	cs.CL cs.CV cs.IR	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	Humans continuously make new discoveries, and understanding temporal sequence of events leading to these breakthroughs is essential for advancing science and society. This ability to reason over time allows us to identify future steps and understand the effects of financial and political decisions on our lives. However, large language models (LLMs) are typically trained on static datasets, limiting their ability to perform effective temporal reasoning. To assess the temporal reasoning capabilities of LLMs, we present the TRANSIENTTABLES dataset, which comprises 3,971 questions derived from over 14,000 tables, spanning 1,238 entities across multiple time periods. We introduce a template-based question-generation pipeline that harnesses LLMs to refine both templates and questions. Additionally, we establish baseline results using state-of-the-art LLMs to create a benchmark. We also introduce novel modeling strategies centered around task decomposition, enhancing LLM performance.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 16:34:43 GMT" } ]	2025-04-03T00:00:00	[ [ "Shankarampeta", "Abhilash", "" ], [ "Mahajan", "Harsh", "" ], [ "Kataria", "Tushar", "" ], [ "Roth", "Dan", "" ], [ "Gupta", "Vivek", "" ] ]	TITLE: TransientTables: Evaluating LLMs' Reasoning on Temporally Evolving Semi-structured Tables ABSTRACT: Humans continuously make new discoveries, and understanding temporal sequence of events leading to these breakthroughs is essential for advancing science and society. This ability to reason over time allows us to identify future steps and understand the effects of financial and political decisions on our lives. However, large language models (LLMs) are typically trained on static datasets, limiting their ability to perform effective temporal reasoning. To assess the temporal reasoning capabilities of LLMs, we present the TRANSIENTTABLES dataset, which comprises 3,971 questions derived from over 14,000 tables, spanning 1,238 entities across multiple time periods. We introduce a template-based question-generation pipeline that harnesses LLMs to refine both templates and questions. Additionally, we establish baseline results using state-of-the-art LLMs to create a benchmark. We also introduce novel modeling strategies centered around task decomposition, enhancing LLM performance.
2504.01882	Diego Cajaraville-Aboy	Diego Cajaraville-Aboy, Marta Moure-Garrido, Carlos Beis-Penedo, Carlos Garcia-Rubio, Rebeca P. D\'iaz-Redondo, Celeste Campo, Ana Fern\'andez-Vilas, and Manuel Fern\'andez-Veiga	CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection	15 pages, 8 figures, 4 tables	null	null	null	cs.LG	http://creativecommons.org/licenses/by-nc-nd/4.0/	The use of DNS over HTTPS (DoH) tunneling by an attacker to hide malicious activity within encrypted DNS traffic poses a serious threat to network security, as it allows malicious actors to bypass traditional monitoring and intrusion detection systems while evading detection by conventional traffic analysis techniques. Machine Learning (ML) techniques can be used to detect DoH tunnels; however, their effectiveness relies on large datasets containing both benign and malicious traffic. Sharing such datasets across entities is challenging due to privacy concerns. In this work, we propose CO-DEFEND (Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection), a Decentralized Federated Learning (DFL) framework that enables multiple entities to collaboratively train a classification machine learning model while preserving data privacy and enhancing resilience against single points of failure. The proposed DFL framework, which is scalable and privacy-preserving, is based on a federation process that allows multiple entities to train online their local models using incoming DoH flows in real time as they are processed by the entity. In addition, we adapt four classical machine learning algorithms, Support Vector Machines (SVM), Logistic Regression (LR), Decision Trees (DT), and Random Forest (RF), for federated scenarios, comparing their results with more computationally complex alternatives such as neural networks. We compare our proposed method by using the dataset CIRA-CIC-DoHBrw-2020 with existing machine learning approaches to demonstrate its effectiveness in detecting malicious DoH tunnels and the benefits it brings.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 16:40:01 GMT" } ]	2025-04-03T00:00:00	[ [ "Cajaraville-Aboy", "Diego", "" ], [ "Moure-Garrido", "Marta", "" ], [ "Beis-Penedo", "Carlos", "" ], [ "Garcia-Rubio", "Carlos", "" ], [ "Díaz-Redondo", "Rebeca P.", "" ], [ "Campo", "Celeste", "" ], [ "Fernández-Vilas", "Ana", "" ], [ "Fernández-Veiga", "Manuel", "" ] ]	TITLE: CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection ABSTRACT: The use of DNS over HTTPS (DoH) tunneling by an attacker to hide malicious activity within encrypted DNS traffic poses a serious threat to network security, as it allows malicious actors to bypass traditional monitoring and intrusion detection systems while evading detection by conventional traffic analysis techniques. Machine Learning (ML) techniques can be used to detect DoH tunnels; however, their effectiveness relies on large datasets containing both benign and malicious traffic. Sharing such datasets across entities is challenging due to privacy concerns. In this work, we propose CO-DEFEND (Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection), a Decentralized Federated Learning (DFL) framework that enables multiple entities to collaboratively train a classification machine learning model while preserving data privacy and enhancing resilience against single points of failure. The proposed DFL framework, which is scalable and privacy-preserving, is based on a federation process that allows multiple entities to train online their local models using incoming DoH flows in real time as they are processed by the entity. In addition, we adapt four classical machine learning algorithms, Support Vector Machines (SVM), Logistic Regression (LR), Decision Trees (DT), and Random Forest (RF), for federated scenarios, comparing their results with more computationally complex alternatives such as neural networks. We compare our proposed method by using the dataset CIRA-CIC-DoHBrw-2020 with existing machine learning approaches to demonstrate its effectiveness in detecting malicious DoH tunnels and the benefits it brings.
2504.01901	Haochen Wang	Haochen Wang and Yucheng Zhao and Tiancai Wang and Haoqiang Fan and Xiangyu Zhang and Zhaoxiang Zhang	Ross3D: Reconstructive Visual Instruction Tuning with 3D-Awareness	null	null	null	null	cs.CV cs.AI cs.CL cs.RO	http://creativecommons.org/licenses/by/4.0/	The rapid development of Large Multimodal Models (LMMs) for 2D images and videos has spurred efforts to adapt these models for interpreting 3D scenes. However, the absence of large-scale 3D vision-language datasets has posed a significant obstacle. To address this issue, typical approaches focus on injecting 3D awareness into 2D LMMs by designing 3D input-level scene representations. This work provides a new perspective. We introduce reconstructive visual instruction tuning with 3D-awareness (Ross3D), which integrates 3D-aware visual supervision into the training procedure. Specifically, it incorporates cross-view and global-view reconstruction. The former requires reconstructing masked views by aggregating overlapping information from other views. The latter aims to aggregate information from all available views to recover Bird's-Eye-View images, contributing to a comprehensive overview of the entire scene. Empirically, Ross3D achieves state-of-the-art performance across various 3D scene understanding benchmarks. More importantly, our semi-supervised experiments demonstrate significant potential in leveraging large amounts of unlabeled 3D vision-only data.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 16:59:55 GMT" } ]	2025-04-03T00:00:00	[ [ "Wang", "Haochen", "" ], [ "Zhao", "Yucheng", "" ], [ "Wang", "Tiancai", "" ], [ "Fan", "Haoqiang", "" ], [ "Zhang", "Xiangyu", "" ], [ "Zhang", "Zhaoxiang", "" ] ]	TITLE: Ross3D: Reconstructive Visual Instruction Tuning with 3D-Awareness ABSTRACT: The rapid development of Large Multimodal Models (LMMs) for 2D images and videos has spurred efforts to adapt these models for interpreting 3D scenes. However, the absence of large-scale 3D vision-language datasets has posed a significant obstacle. To address this issue, typical approaches focus on injecting 3D awareness into 2D LMMs by designing 3D input-level scene representations. This work provides a new perspective. We introduce reconstructive visual instruction tuning with 3D-awareness (Ross3D), which integrates 3D-aware visual supervision into the training procedure. Specifically, it incorporates cross-view and global-view reconstruction. The former requires reconstructing masked views by aggregating overlapping information from other views. The latter aims to aggregate information from all available views to recover Bird's-Eye-View images, contributing to a comprehensive overview of the entire scene. Empirically, Ross3D achieves state-of-the-art performance across various 3D scene understanding benchmarks. More importantly, our semi-supervised experiments demonstrate significant potential in leveraging large amounts of unlabeled 3D vision-only data.
2504.01903	Zijun Wang	Zijun Wang, Haoqin Tu, Yuhan Wang, Juncheng Wu, Jieru Mei, Brian R. Bartoldson, Bhavya Kailkhura, Cihang Xie	STAR-1: Safer Alignment of Reasoning LLMs with 1K Data	null	null	null	null	cs.CL cs.AI	http://creativecommons.org/licenses/by/4.0/	This paper introduces STAR-1, a high-quality, just-1k-scale safety dataset specifically designed for large reasoning models (LRMs) like DeepSeek-R1. Built on three core principles -- diversity, deliberative reasoning, and rigorous filtering -- STAR-1 aims to address the critical needs for safety alignment in LRMs. Specifically, we begin by integrating existing open-source safety datasets from diverse sources. Then, we curate safety policies to generate policy-grounded deliberative reasoning samples. Lastly, we apply a GPT-4o-based safety scoring system to select training examples aligned with best practices. Experimental results show that fine-tuning LRMs with STAR-1 leads to an average 40% improvement in safety performance across four benchmarks, while only incurring a marginal decrease (e.g., an average of 1.1%) in reasoning ability measured across five reasoning tasks. Extensive ablation studies further validate the importance of our design principles in constructing STAR-1 and analyze its efficacy across both LRMs and traditional LLMs. Our project page is https://ucsc-vlaa.github.io/STAR-1.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 17:04:04 GMT" } ]	2025-04-03T00:00:00	[ [ "Wang", "Zijun", "" ], [ "Tu", "Haoqin", "" ], [ "Wang", "Yuhan", "" ], [ "Wu", "Juncheng", "" ], [ "Mei", "Jieru", "" ], [ "Bartoldson", "Brian R.", "" ], [ "Kailkhura", "Bhavya", "" ], [ "Xie", "Cihang", "" ] ]	TITLE: STAR-1: Safer Alignment of Reasoning LLMs with 1K Data ABSTRACT: This paper introduces STAR-1, a high-quality, just-1k-scale safety dataset specifically designed for large reasoning models (LRMs) like DeepSeek-R1. Built on three core principles -- diversity, deliberative reasoning, and rigorous filtering -- STAR-1 aims to address the critical needs for safety alignment in LRMs. Specifically, we begin by integrating existing open-source safety datasets from diverse sources. Then, we curate safety policies to generate policy-grounded deliberative reasoning samples. Lastly, we apply a GPT-4o-based safety scoring system to select training examples aligned with best practices. Experimental results show that fine-tuning LRMs with STAR-1 leads to an average 40% improvement in safety performance across four benchmarks, while only incurring a marginal decrease (e.g., an average of 1.1%) in reasoning ability measured across five reasoning tasks. Extensive ablation studies further validate the importance of our design principles in constructing STAR-1 and analyze its efficacy across both LRMs and traditional LLMs. Our project page is https://ucsc-vlaa.github.io/STAR-1.
2504.01916	Mothilal Asokan	Mothilal Asokan, Kebin Wu, Fatima Albreiki	FineLIP: Extending CLIP's Reach via Fine-Grained Alignment with Longer Text Inputs	null	null	null	null	cs.CV cs.AI cs.CL	http://creativecommons.org/licenses/by/4.0/	As a pioneering vision-language model, CLIP (Contrastive Language-Image Pre-training) has achieved significant success across various domains and a wide range of downstream vision-language tasks. However, the text encoders in popular CLIP models are limited to processing only 77 text tokens, which constrains their ability to effectively handle longer, detail-rich captions. Additionally, CLIP models often struggle to effectively capture detailed visual and textual information, which hampers their performance on tasks that require fine-grained analysis. To address these limitations, we present a novel approach, \textbf{FineLIP}, that extends the capabilities of CLIP. FineLIP enhances cross-modal text-image mapping by incorporating \textbf{Fine}-grained alignment with \textbf{L}onger text input within the CL\textbf{IP}-style framework. FineLIP first extends the positional embeddings to handle longer text, followed by the dynamic aggregation of local image and text tokens. The aggregated results are then used to enforce fine-grained token-to-token cross-modal alignment. We validate our model on datasets with long, detailed captions across two tasks: zero-shot cross-modal retrieval and text-to-image generation. Quantitative and qualitative experimental results demonstrate the effectiveness of FineLIP, outperforming existing state-of-the-art approaches. Furthermore, comprehensive ablation studies validate the benefits of key design elements within FineLIP.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 17:19:59 GMT" } ]	2025-04-03T00:00:00	[ [ "Asokan", "Mothilal", "" ], [ "Wu", "Kebin", "" ], [ "Albreiki", "Fatima", "" ] ]	TITLE: FineLIP: Extending CLIP's Reach via Fine-Grained Alignment with Longer Text Inputs ABSTRACT: As a pioneering vision-language model, CLIP (Contrastive Language-Image Pre-training) has achieved significant success across various domains and a wide range of downstream vision-language tasks. However, the text encoders in popular CLIP models are limited to processing only 77 text tokens, which constrains their ability to effectively handle longer, detail-rich captions. Additionally, CLIP models often struggle to effectively capture detailed visual and textual information, which hampers their performance on tasks that require fine-grained analysis. To address these limitations, we present a novel approach, \textbf{FineLIP}, that extends the capabilities of CLIP. FineLIP enhances cross-modal text-image mapping by incorporating \textbf{Fine}-grained alignment with \textbf{L}onger text input within the CL\textbf{IP}-style framework. FineLIP first extends the positional embeddings to handle longer text, followed by the dynamic aggregation of local image and text tokens. The aggregated results are then used to enforce fine-grained token-to-token cross-modal alignment. We validate our model on datasets with long, detailed captions across two tasks: zero-shot cross-modal retrieval and text-to-image generation. Quantitative and qualitative experimental results demonstrate the effectiveness of FineLIP, outperforming existing state-of-the-art approaches. Furthermore, comprehensive ablation studies validate the benefits of key design elements within FineLIP.
2504.01921	Harsh Vardhan	Harsh Vardhan, Xiaofan Yu, Tajana Rosing, Arya Mazumdar	Client Selection in Federated Learning with Data Heterogeneity and Network Latencies	null	null	null	null	cs.LG stat.ML	http://creativecommons.org/licenses/by/4.0/	Federated learning (FL) is a distributed machine learning paradigm where multiple clients conduct local training based on their private data, then the updated models are sent to a central server for global aggregation. The practical convergence of FL is challenged by multiple factors, with the primary hurdle being the heterogeneity among clients. This heterogeneity manifests as data heterogeneity concerning local data distribution and latency heterogeneity during model transmission to the server. While prior research has introduced various efficient client selection methods to alleviate the negative impacts of either of these heterogeneities individually, efficient methods to handle real-world settings where both these heterogeneities exist simultaneously do not exist. In this paper, we propose two novel theoretically optimal client selection schemes that can handle both these heterogeneities. Our methods involve solving simple optimization problems every round obtained by minimizing the theoretical runtime to convergence. Empirical evaluations on 9 datasets with non-iid data distributions, 2 practical delay distributions, and non-convex neural network models demonstrate that our algorithms are at least competitive to and at most 20 times better than best existing baselines.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 17:31:15 GMT" } ]	2025-04-03T00:00:00	[ [ "Vardhan", "Harsh", "" ], [ "Yu", "Xiaofan", "" ], [ "Rosing", "Tajana", "" ], [ "Mazumdar", "Arya", "" ] ]	TITLE: Client Selection in Federated Learning with Data Heterogeneity and Network Latencies ABSTRACT: Federated learning (FL) is a distributed machine learning paradigm where multiple clients conduct local training based on their private data, then the updated models are sent to a central server for global aggregation. The practical convergence of FL is challenged by multiple factors, with the primary hurdle being the heterogeneity among clients. This heterogeneity manifests as data heterogeneity concerning local data distribution and latency heterogeneity during model transmission to the server. While prior research has introduced various efficient client selection methods to alleviate the negative impacts of either of these heterogeneities individually, efficient methods to handle real-world settings where both these heterogeneities exist simultaneously do not exist. In this paper, we propose two novel theoretically optimal client selection schemes that can handle both these heterogeneities. Our methods involve solving simple optimization problems every round obtained by minimizing the theoretical runtime to convergence. Empirical evaluations on 9 datasets with non-iid data distributions, 2 practical delay distributions, and non-convex neural network models demonstrate that our algorithms are at least competitive to and at most 20 times better than best existing baselines.
2504.01922	Zhaoyang Cao	Zhaoyang Cao, John Nguyen, Reza Zafarani	Is Less Really More? Fake News Detection with Limited Information	null	null	null	null	cs.IR	http://creativecommons.org/licenses/by/4.0/	The threat that online fake news and misinformation pose to democracy, justice, public confidence, and especially to vulnerable populations, has led to a sharp increase in the need for fake news detection and intervention. Whether multi-modal or pure text-based, most fake news detection methods depend on textual analysis of entire articles. However, these fake news detection methods come with certain limitations. For instance, fake news detection methods that rely on full text can be computationally inefficient, demand large amounts of training data to achieve competitive accuracy, and may lack robustness across different datasets. This is because fake news datasets have strong variations in terms of the level and types of information they provide; where some can include large paragraphs of text with images and metadata, others can be a few short sentences. Perhaps if one could only use minimal information to detect fake news, fake news detection methods could become more robust and resilient to the lack of information. We aim to overcome these limitations by detecting fake news using systematically selected, limited information that is both effective and capable of delivering robust, promising performance. We propose a framework called SLIM Systematically-selected Limited Information) for fake news detection. In SLIM, we quantify the amount of information by introducing information-theoretic measures. SLIM leverages limited information to achieve performance in fake news detection comparable to that of state-of-the-art obtained using the full text. Furthermore, by combining various types of limited information, SLIM can perform even better while significantly reducing the quantity of information required for training compared to state-of-the-art language model-based fake news detection techniques.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 17:32:37 GMT" } ]	2025-04-03T00:00:00	[ [ "Cao", "Zhaoyang", "" ], [ "Nguyen", "John", "" ], [ "Zafarani", "Reza", "" ] ]	TITLE: Is Less Really More? Fake News Detection with Limited Information ABSTRACT: The threat that online fake news and misinformation pose to democracy, justice, public confidence, and especially to vulnerable populations, has led to a sharp increase in the need for fake news detection and intervention. Whether multi-modal or pure text-based, most fake news detection methods depend on textual analysis of entire articles. However, these fake news detection methods come with certain limitations. For instance, fake news detection methods that rely on full text can be computationally inefficient, demand large amounts of training data to achieve competitive accuracy, and may lack robustness across different datasets. This is because fake news datasets have strong variations in terms of the level and types of information they provide; where some can include large paragraphs of text with images and metadata, others can be a few short sentences. Perhaps if one could only use minimal information to detect fake news, fake news detection methods could become more robust and resilient to the lack of information. We aim to overcome these limitations by detecting fake news using systematically selected, limited information that is both effective and capable of delivering robust, promising performance. We propose a framework called SLIM Systematically-selected Limited Information) for fake news detection. In SLIM, we quantify the amount of information by introducing information-theoretic measures. SLIM leverages limited information to achieve performance in fake news detection comparable to that of state-of-the-art obtained using the full text. Furthermore, by combining various types of limited information, SLIM can perform even better while significantly reducing the quantity of information required for training compared to state-of-the-art language model-based fake news detection techniques.
2504.01925	Haykel Snoussi	Haykel Snoussi and Davood Karimi	Equivariant Spherical CNNs for Accurate Fiber Orientation Distribution Estimation in Neonatal Diffusion MRI with Reduced Acquisition Time	null	null	null	null	cs.CV cs.AI	http://creativecommons.org/licenses/by/4.0/	Early and accurate assessment of brain microstructure using diffusion Magnetic Resonance Imaging (dMRI) is crucial for identifying neurodevelopmental disorders in neonates, but remains challenging due to low signal-to-noise ratio (SNR), motion artifacts, and ongoing myelination. In this study, we propose a rotationally equivariant Spherical Convolutional Neural Network (sCNN) framework tailored for neonatal dMRI. We predict the Fiber Orientation Distribution (FOD) from multi-shell dMRI signals acquired with a reduced set of gradient directions (30% of the full protocol), enabling faster and more cost-effective acquisitions. We train and evaluate the performance of our sCNN using real data from 43 neonatal dMRI datasets provided by the Developing Human Connectome Project (dHCP). Our results demonstrate that the sCNN achieves significantly lower mean squared error (MSE) and higher angular correlation coefficient (ACC) compared to a Multi-Layer Perceptron (MLP) baseline, indicating improved accuracy in FOD estimation. Furthermore, tractography results based on the sCNN-predicted FODs show improved anatomical plausibility, coverage, and coherence compared to those from the MLP. These findings highlight that sCNNs, with their inherent rotational equivariance, offer a promising approach for accurate and clinically efficient dMRI analysis, paving the way for improved diagnostic capabilities and characterization of early brain development.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 17:36:51 GMT" } ]	2025-04-03T00:00:00	[ [ "Snoussi", "Haykel", "" ], [ "Karimi", "Davood", "" ] ]	TITLE: Equivariant Spherical CNNs for Accurate Fiber Orientation Distribution Estimation in Neonatal Diffusion MRI with Reduced Acquisition Time ABSTRACT: Early and accurate assessment of brain microstructure using diffusion Magnetic Resonance Imaging (dMRI) is crucial for identifying neurodevelopmental disorders in neonates, but remains challenging due to low signal-to-noise ratio (SNR), motion artifacts, and ongoing myelination. In this study, we propose a rotationally equivariant Spherical Convolutional Neural Network (sCNN) framework tailored for neonatal dMRI. We predict the Fiber Orientation Distribution (FOD) from multi-shell dMRI signals acquired with a reduced set of gradient directions (30% of the full protocol), enabling faster and more cost-effective acquisitions. We train and evaluate the performance of our sCNN using real data from 43 neonatal dMRI datasets provided by the Developing Human Connectome Project (dHCP). Our results demonstrate that the sCNN achieves significantly lower mean squared error (MSE) and higher angular correlation coefficient (ACC) compared to a Multi-Layer Perceptron (MLP) baseline, indicating improved accuracy in FOD estimation. Furthermore, tractography results based on the sCNN-predicted FODs show improved anatomical plausibility, coverage, and coherence compared to those from the MLP. These findings highlight that sCNNs, with their inherent rotational equivariance, offer a promising approach for accurate and clinically efficient dMRI analysis, paving the way for improved diagnostic capabilities and characterization of early brain development.
2504.01930	Washington Cunha	Washington Cunha, Leonardo Rocha, Marcos Andr\'e Gon\c{c}alves	A thorough benchmark of automatic text classification: From traditional approaches to large language models	7 pages, 2 figures, 3 tables	null	null	null	cs.CL cs.AI	http://creativecommons.org/licenses/by/4.0/	Automatic text classification (ATC) has experienced remarkable advancements in the past decade, best exemplified by recent small and large language models (SLMs and LLMs), leveraged by Transformer architectures. Despite recent effectiveness improvements, a comprehensive cost-benefit analysis investigating whether the effectiveness gains of these recent approaches compensate their much higher costs when compared to more traditional text classification approaches such as SVMs and Logistic Regression is still missing in the literature. In this context, this work's main contributions are twofold: (i) we provide a scientifically sound comparative analysis of the cost-benefit of twelve traditional and recent ATC solutions including five open LLMs, and (ii) a large benchmark comprising {22 datasets}, including sentiment analysis and topic classification, with their (train-validation-test) partitions based on folded cross-validation procedures, along with documentation, and code. The release of code, data, and documentation enables the community to replicate experiments and advance the field in a more scientifically sound manner. Our comparative experimental results indicate that LLMs outperform traditional approaches (up to 26%-7.1% on average) and SLMs (up to 4.9%-1.9% on average) in terms of effectiveness. However, LLMs incur significantly higher computational costs due to fine-tuning, being, on average 590x and 8.5x slower than traditional methods and SLMs, respectively. Results suggests the following recommendations: (1) LLMs for applications that require the best possible effectiveness and can afford the costs; (2) traditional methods such as Logistic Regression and SVM for resource-limited applications or those that cannot afford the cost of tuning large LLMs; and (3) SLMs like Roberta for near-optimal effectiveness-efficiency trade-off.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 17:40:08 GMT" } ]	2025-04-03T00:00:00	[ [ "Cunha", "Washington", "" ], [ "Rocha", "Leonardo", "" ], [ "Gonçalves", "Marcos André", "" ] ]	TITLE: A thorough benchmark of automatic text classification: From traditional approaches to large language models ABSTRACT: Automatic text classification (ATC) has experienced remarkable advancements in the past decade, best exemplified by recent small and large language models (SLMs and LLMs), leveraged by Transformer architectures. Despite recent effectiveness improvements, a comprehensive cost-benefit analysis investigating whether the effectiveness gains of these recent approaches compensate their much higher costs when compared to more traditional text classification approaches such as SVMs and Logistic Regression is still missing in the literature. In this context, this work's main contributions are twofold: (i) we provide a scientifically sound comparative analysis of the cost-benefit of twelve traditional and recent ATC solutions including five open LLMs, and (ii) a large benchmark comprising {22 datasets}, including sentiment analysis and topic classification, with their (train-validation-test) partitions based on folded cross-validation procedures, along with documentation, and code. The release of code, data, and documentation enables the community to replicate experiments and advance the field in a more scientifically sound manner. Our comparative experimental results indicate that LLMs outperform traditional approaches (up to 26%-7.1% on average) and SLMs (up to 4.9%-1.9% on average) in terms of effectiveness. However, LLMs incur significantly higher computational costs due to fine-tuning, being, on average 590x and 8.5x slower than traditional methods and SLMs, respectively. Results suggests the following recommendations: (1) LLMs for applications that require the best possible effectiveness and can afford the costs; (2) traditional methods such as Logistic Regression and SVM for resource-limited applications or those that cannot afford the cost of tuning large LLMs; and (3) SLMs like Roberta for near-optimal effectiveness-efficiency trade-off.
2504.01943	Wasi Uddin Ahmad	Wasi Uddin Ahmad, Sean Narenthiran, Somshubra Majumdar, Aleksander Ficek, Siddhartha Jain, Jocelyn Huang, Vahid Noroozi, Boris Ginsburg	OpenCodeReasoning: Advancing Data Distillation for Competitive Coding	Work in progress	null	null	null	cs.CL	http://creativecommons.org/licenses/by/4.0/	Since the advent of reasoning-based large language models, many have found great success from distilling reasoning capabilities into student models. Such techniques have significantly bridged the gap between reasoning and standard LLMs on coding tasks. Despite this, much of the progress on distilling reasoning models remains locked behind proprietary datasets or lacks details on data curation, filtering and subsequent training. To address this, we construct a superior supervised fine-tuning (SFT) dataset that we use to achieve state-of-the-art coding capability results in models of various sizes. Our distilled models use only SFT to achieve 61.8% on LiveCodeBench and 24.6% on CodeContests, surpassing alternatives trained with reinforcement learning. We then perform analysis on the data sources used to construct our dataset, the impact of code execution filtering, and the importance of instruction/solution diversity. We observe that execution filtering negatively affected benchmark accuracy, leading us to prioritize instruction diversity over solution correctness. Finally, we also analyze the token efficiency and reasoning patterns utilized by these models. We will open-source these datasets and distilled models to the community.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 17:50:31 GMT" } ]	2025-04-03T00:00:00	[ [ "Ahmad", "Wasi Uddin", "" ], [ "Narenthiran", "Sean", "" ], [ "Majumdar", "Somshubra", "" ], [ "Ficek", "Aleksander", "" ], [ "Jain", "Siddhartha", "" ], [ "Huang", "Jocelyn", "" ], [ "Noroozi", "Vahid", "" ], [ "Ginsburg", "Boris", "" ] ]	TITLE: OpenCodeReasoning: Advancing Data Distillation for Competitive Coding ABSTRACT: Since the advent of reasoning-based large language models, many have found great success from distilling reasoning capabilities into student models. Such techniques have significantly bridged the gap between reasoning and standard LLMs on coding tasks. Despite this, much of the progress on distilling reasoning models remains locked behind proprietary datasets or lacks details on data curation, filtering and subsequent training. To address this, we construct a superior supervised fine-tuning (SFT) dataset that we use to achieve state-of-the-art coding capability results in models of various sizes. Our distilled models use only SFT to achieve 61.8% on LiveCodeBench and 24.6% on CodeContests, surpassing alternatives trained with reinforcement learning. We then perform analysis on the data sources used to construct our dataset, the impact of code execution filtering, and the importance of instruction/solution diversity. We observe that execution filtering negatively affected benchmark accuracy, leading us to prioritize instruction diversity over solution correctness. Finally, we also analyze the token efficiency and reasoning patterns utilized by these models. We will open-source these datasets and distilled models to the community.
2504.01947	Daniel Becking	Daniel Becking, Ingo Friese, Karsten M\"uller, Thomas Buchholz, Mandy Galkow-Schneider, Wojciech Samek, Detlev Marpe	Efficient Federated Learning Tiny Language Models for Mobile Network Feature Prediction	Accepted at 2025 EuCNC & 6G Summit Poster Session	null	null	null	cs.LG cs.AI cs.DC eess.SP	http://creativecommons.org/licenses/by-nc-sa/4.0/	In telecommunications, Autonomous Networks (ANs) automatically adjust configurations based on specific requirements (e.g., bandwidth) and available resources. These networks rely on continuous monitoring and intelligent mechanisms for self-optimization, self-repair, and self-protection, nowadays enhanced by Neural Networks (NNs) to enable predictive modeling and pattern recognition. Here, Federated Learning (FL) allows multiple AN cells - each equipped with NNs - to collaboratively train models while preserving data privacy. However, FL requires frequent transmission of large neural data and thus an efficient, standardized compression strategy for reliable communication. To address this, we investigate NNCodec, a Fraunhofer implementation of the ISO/IEC Neural Network Coding (NNC) standard, within a novel FL framework that integrates tiny language models (TLMs) for various mobile network feature prediction (e.g., ping, SNR or band frequency). Our experimental results on the Berlin V2X dataset demonstrate that NNCodec achieves transparent compression (i.e., negligible performance loss) while reducing communication overhead to below 1%, showing the effectiveness of combining NNC with FL in collaboratively learned autonomous mobile networks.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 17:54:06 GMT" } ]	2025-04-03T00:00:00	[ [ "Becking", "Daniel", "" ], [ "Friese", "Ingo", "" ], [ "Müller", "Karsten", "" ], [ "Buchholz", "Thomas", "" ], [ "Galkow-Schneider", "Mandy", "" ], [ "Samek", "Wojciech", "" ], [ "Marpe", "Detlev", "" ] ]	TITLE: Efficient Federated Learning Tiny Language Models for Mobile Network Feature Prediction ABSTRACT: In telecommunications, Autonomous Networks (ANs) automatically adjust configurations based on specific requirements (e.g., bandwidth) and available resources. These networks rely on continuous monitoring and intelligent mechanisms for self-optimization, self-repair, and self-protection, nowadays enhanced by Neural Networks (NNs) to enable predictive modeling and pattern recognition. Here, Federated Learning (FL) allows multiple AN cells - each equipped with NNs - to collaboratively train models while preserving data privacy. However, FL requires frequent transmission of large neural data and thus an efficient, standardized compression strategy for reliable communication. To address this, we investigate NNCodec, a Fraunhofer implementation of the ISO/IEC Neural Network Coding (NNC) standard, within a novel FL framework that integrates tiny language models (TLMs) for various mobile network feature prediction (e.g., ping, SNR or band frequency). Our experimental results on the Berlin V2X dataset demonstrate that NNCodec achieves transparent compression (i.e., negligible performance loss) while reducing communication overhead to below 1%, showing the effectiveness of combining NNC with FL in collaboratively learned autonomous mobile networks.
2504.01951	Ciro Beneduce	Massimiliano Luca, Ciro Beneduce, Bruno Lepri, Jacopo Staiano	The LLM Wears Prada: Analysing Gender Bias and Stereotypes through Online Shopping Data	null	null	null	null	cs.AI cs.CL cs.CY	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	With the wide and cross-domain adoption of Large Language Models, it becomes crucial to assess to which extent the statistical correlations in training data, which underlie their impressive performance, hide subtle and potentially troubling biases. Gender bias in LLMs has been widely investigated from the perspectives of works, hobbies, and emotions typically associated with a specific gender. In this study, we introduce a novel perspective. We investigate whether LLMs can predict an individual's gender based solely on online shopping histories and whether these predictions are influenced by gender biases and stereotypes. Using a dataset of historical online purchases from users in the United States, we evaluate the ability of six LLMs to classify gender and we then analyze their reasoning and products-gender co-occurrences. Results indicate that while models can infer gender with moderate accuracy, their decisions are often rooted in stereotypical associations between product categories and gender. Furthermore, explicit instructions to avoid bias reduce the certainty of model predictions, but do not eliminate stereotypical patterns. Our findings highlight the persistent nature of gender biases in LLMs and emphasize the need for robust bias-mitigation strategies.	[ { "version": "v1", "created": "Wed, 2 Apr 2025 17:56:08 GMT" } ]	2025-04-03T00:00:00	[ [ "Luca", "Massimiliano", "" ], [ "Beneduce", "Ciro", "" ], [ "Lepri", "Bruno", "" ], [ "Staiano", "Jacopo", "" ] ]	TITLE: The LLM Wears Prada: Analysing Gender Bias and Stereotypes through Online Shopping Data ABSTRACT: With the wide and cross-domain adoption of Large Language Models, it becomes crucial to assess to which extent the statistical correlations in training data, which underlie their impressive performance, hide subtle and potentially troubling biases. Gender bias in LLMs has been widely investigated from the perspectives of works, hobbies, and emotions typically associated with a specific gender. In this study, we introduce a novel perspective. We investigate whether LLMs can predict an individual's gender based solely on online shopping histories and whether these predictions are influenced by gender biases and stereotypes. Using a dataset of historical online purchases from users in the United States, we evaluate the ability of six LLMs to classify gender and we then analyze their reasoning and products-gender co-occurrences. Results indicate that while models can infer gender with moderate accuracy, their decisions are often rooted in stereotypical associations between product categories and gender. Furthermore, explicit instructions to avoid bias reduce the certainty of model predictions, but do not eliminate stereotypical patterns. Our findings highlight the persistent nature of gender biases in LLMs and emphasize the need for robust bias-mitigation strategies.

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