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| versions
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value | probability
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1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2308.06719
|
Yiding Qiu
|
Yiding Qiu, Henrik I. Christensen
|
3D Scene Graph Prediction on Point Clouds Using Knowledge Graphs
|
accepted at CASE 2023
| null | null | null |
cs.RO cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
3D scene graph prediction is a task that aims to concurrently predict object
classes and their relationships within a 3D environment. As these environments
are primarily designed by and for humans, incorporating commonsense knowledge
regarding objects and their relationships can significantly constrain and
enhance the prediction of the scene graph. In this paper, we investigate the
application of commonsense knowledge graphs for 3D scene graph prediction on
point clouds of indoor scenes. Through experiments conducted on a real-world
indoor dataset, we demonstrate that integrating external commonsense knowledge
via the message-passing method leads to a 15.0 % improvement in scene graph
prediction accuracy with external knowledge and $7.96\%$ with internal
knowledge when compared to state-of-the-art algorithms. We also tested in the
real world with 10 frames per second for scene graph generation to show the
usage of the model in a more realistic robotics setting.
|
[
{
"version": "v1",
"created": "Sun, 13 Aug 2023 08:20:17 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Qiu",
"Yiding",
""
],
[
"Christensen",
"Henrik I.",
""
]
] |
new_dataset
| 0.973554 |
2308.06732
|
Zhiqing Wei
|
Yingying Zou, Zhiqing Wei, Yanpeng Cui, Xinyi Liu, and Zhiyong Feng
|
UD-MAC: Delay Tolerant Multiple Access Control Protocol for Unmanned
Aerial Vehicle Networks
| null | null | null | null |
cs.NI cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In unmanned aerial vehicle (UAV) networks, high-capacity data transmission is
of utmost importance for applications such as intelligent transportation, smart
cities, and forest monitoring, which rely on the mobility of UAVs to collect
and transmit large amount of data, including video and image data. Due to the
short flight time of UAVs, the network capacity will be reduced when they
return to the ground unit for charging. Hence, we suggest that UAVs can apply a
store-carry-and-forward (SCF) transmission mode to carry packets on their way
back to the ground unit for improving network throughput. In this paper, we
propose a novel protocol, named UAV delay-tolerant multiple access control
(UD-MAC), which can support different transmission modes in UAV networks. We
set a higher priority for SCF transmission and analyze the probability of being
in SCF mode to derive network throughput. The simulation results show that the
network throughput of UD-MAC is improved by 57% to 83% compared to VeMAC.
|
[
{
"version": "v1",
"created": "Sun, 13 Aug 2023 09:49:59 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Zou",
"Yingying",
""
],
[
"Wei",
"Zhiqing",
""
],
[
"Cui",
"Yanpeng",
""
],
[
"Liu",
"Xinyi",
""
],
[
"Feng",
"Zhiyong",
""
]
] |
new_dataset
| 0.998278 |
2308.06782
|
Gelei Deng
|
Gelei Deng, Yi Liu, V\'ictor Mayoral-Vilches, Peng Liu, Yuekang Li,
Yuan Xu, Tianwei Zhang, Yang Liu, Martin Pinzger, Stefan Rass
|
PentestGPT: An LLM-empowered Automatic Penetration Testing Tool
| null | null | null | null |
cs.SE cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
Penetration testing, a crucial industrial practice for ensuring system
security, has traditionally resisted automation due to the extensive expertise
required by human professionals. Large Language Models (LLMs) have shown
significant advancements in various domains, and their emergent abilities
suggest their potential to revolutionize industries. In this research, we
evaluate the performance of LLMs on real-world penetration testing tasks using
a robust benchmark created from test machines with platforms. Our findings
reveal that while LLMs demonstrate proficiency in specific sub-tasks within the
penetration testing process, such as using testing tools, interpreting outputs,
and proposing subsequent actions, they also encounter difficulties maintaining
an integrated understanding of the overall testing scenario.
In response to these insights, we introduce PentestGPT, an LLM-empowered
automatic penetration testing tool that leverages the abundant domain knowledge
inherent in LLMs. PentestGPT is meticulously designed with three
self-interacting modules, each addressing individual sub-tasks of penetration
testing, to mitigate the challenges related to context loss. Our evaluation
shows that PentestGPT not only outperforms LLMs with a task-completion increase
of 228.6\% compared to the \gptthree model among the benchmark targets but also
proves effective in tackling real-world penetration testing challenges. Having
been open-sourced on GitHub, PentestGPT has garnered over 4,700 stars and
fostered active community engagement, attesting to its value and impact in both
the academic and industrial spheres.
|
[
{
"version": "v1",
"created": "Sun, 13 Aug 2023 14:35:50 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Deng",
"Gelei",
""
],
[
"Liu",
"Yi",
""
],
[
"Mayoral-Vilches",
"Víctor",
""
],
[
"Liu",
"Peng",
""
],
[
"Li",
"Yuekang",
""
],
[
"Xu",
"Yuan",
""
],
[
"Zhang",
"Tianwei",
""
],
[
"Liu",
"Yang",
""
],
[
"Pinzger",
"Martin",
""
],
[
"Rass",
"Stefan",
""
]
] |
new_dataset
| 0.999321 |
2308.06802
|
Xiangliang Kong
|
Xiangliang Kong
|
Locally repairable convertible codes with optimal access costs
|
25 pages
| null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
Modern large-scale distributed storage systems use erasure codes to protect
against node failures with low storage overhead. In practice, the failure rate
and other factors of storage devices in the system may vary significantly over
time, and leads to changes of the ideal code parameters. To maintain the
storage efficiency, this requires the system to adjust parameters of the
currently used codes. The changing process of code parameters on encoded data
is called code conversion.
As an important class of storage codes, locally repairable codes (LRCs) can
repair any codeword symbol using a small number of other symbols. This feature
makes LRCs highly efficient for addressing single node failures in the storage
systems. In this paper, we investigate the code conversions for locally
repairable codes in the merge regime. We establish a lower bound on the access
cost of code conversion for general LRCs and propose a general construction of
LRCs that can perform code conversions with access cost matching this bound.
This construction provides a family of LRCs together with optimal conversion
process over the field of size linear in the code length.
|
[
{
"version": "v1",
"created": "Sun, 13 Aug 2023 16:09:12 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Kong",
"Xiangliang",
""
]
] |
new_dataset
| 0.99474 |
2308.06811
|
Nouran Abdalazim
|
Nouran Abdalazim, Leonardo Alchieri, Lidia Alecci, Silvia Santini
|
BiHeartS: Bilateral Heart Rate from multiple devices and body positions
for Sleep measurement Dataset
|
5 pages
| null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Sleep is the primary mean of recovery from accumulated fatigue and thus plays
a crucial role in fostering people's mental and physical well-being. Sleep
quality monitoring systems are often implemented using wearables that leverage
their sensing capabilities to provide sleep behaviour insights and
recommendations to users. Building models to estimate sleep quality from sensor
data is a challenging task, due to the variability of both physiological data,
perception of sleep quality, and the daily routine across users. This challenge
gauges the need for a comprehensive dataset that includes information about the
daily behaviour of users, physiological signals as well as the perceived sleep
quality. In this paper, we try to narrow this gap by proposing Bilateral Heart
rate from multiple devices and body positions for Sleep measurement (BiHeartS)
dataset. The dataset is collected in the wild from 10 participants for 30
consecutive nights. Both research-grade and commercial wearable devices are
included in the data collection campaign. Also, comprehensive self-reports are
collected about the sleep quality and the daily routine.
|
[
{
"version": "v1",
"created": "Sun, 13 Aug 2023 16:53:09 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Abdalazim",
"Nouran",
""
],
[
"Alchieri",
"Leonardo",
""
],
[
"Alecci",
"Lidia",
""
],
[
"Santini",
"Silvia",
""
]
] |
new_dataset
| 0.992282 |
2308.06819
|
Jo\~ao Vitorino
|
Jo\~ao Vitorino, Isabel Pra\c{c}a, Eva Maia
|
SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network
Intrusion Detection
|
31 pages, 3 tables, 6 figures, Computers and Security journal
| null | null | null |
cs.CR cs.LG cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
Machine Learning (ML) can be incredibly valuable to automate anomaly
detection and cyber-attack classification, improving the way that Network
Intrusion Detection (NID) is performed. However, despite the benefits of ML
models, they are highly susceptible to adversarial cyber-attack examples
specifically crafted to exploit them. A wide range of adversarial attacks have
been created and researchers have worked on various defense strategies to
safeguard ML models, but most were not intended for the specific constraints of
a communication network and its communication protocols, so they may lead to
unrealistic examples in the NID domain. This Systematization of Knowledge (SoK)
consolidates and summarizes the state-of-the-art adversarial learning
approaches that can generate realistic examples and could be used in real ML
development and deployment scenarios with real network traffic flows. This SoK
also describes the open challenges regarding the use of adversarial ML in the
NID domain, defines the fundamental properties that are required for an
adversarial example to be realistic, and provides guidelines for researchers to
ensure that their future experiments are adequate for a real communication
network.
|
[
{
"version": "v1",
"created": "Sun, 13 Aug 2023 17:23:36 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Vitorino",
"João",
""
],
[
"Praça",
"Isabel",
""
],
[
"Maia",
"Eva",
""
]
] |
new_dataset
| 0.994414 |
2308.06829
|
Hao Xu
|
Hao Xu, Yunqing Sun, Xiaoshuai Zhang, Erwu Liu and Chih-Lin I
|
When Web 3.0 Meets Reality: A Hyperdimensional Fractal Polytope P2P
Ecosystems
| null | null | null | null |
cs.NI cs.AR cs.CR cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
Web 3.0 opens the world of new existence of the crypto-network-entity, which
is independently defined by the public key pairs for entities and the
connection to the Web 3.0 cyberspace. In this paper, we first discover a
spacetime coordinate system based on fractal polytope in any dimensions with
discrete time offered by blockchain and consensus. Second, the novel network
entities and functions are defined to make use of hyperdimensional
deterministic switching and routing protocols and blockchain-enabled mutual
authentication. In addition to spacetime network architecture, we also define a
multi-tier identity scheme which extends the native Web 3.0
crypto-network-entity to outer cyber and physical world, offering
legal-compliant anonymity and linkability to all derived identifiers of
entities. In this way, we unify the holistic Web 3.0 network based on
persistent spacetime and its entity extension to our cyber and physical world.
|
[
{
"version": "v1",
"created": "Sun, 13 Aug 2023 18:14:45 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Xu",
"Hao",
""
],
[
"Sun",
"Yunqing",
""
],
[
"Zhang",
"Xiaoshuai",
""
],
[
"Liu",
"Erwu",
""
],
[
"I",
"Chih-Lin",
""
]
] |
new_dataset
| 0.998011 |
2308.06850
|
Laurie Williams
|
William Enck, Yasemin Acar, Michel Cukier, Alexandros Kapravelos,
Christian K\"astner, Laurie Williams
|
S3C2 Summit 2023-06: Government Secure Supply Chain Summit
|
arXiv admin note: text overlap with arXiv:2307.16557,
arXiv:2307.15642
| null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Recent years have shown increased cyber attacks targeting less secure
elements in the software supply chain and causing fatal damage to businesses
and organizations. Past well-known examples of software supply chain attacks
are the SolarWinds or log4j incidents that have affected thousands of customers
and businesses. The US government and industry are equally interested in
enhancing software supply chain security. On June 7, 2023, researchers from the
NSF-supported Secure Software Supply Chain Center (S3C2) conducted a Secure
Software Supply Chain Summit with a diverse set of 17 practitioners from 13
government agencies. The goal of the Summit was two-fold: (1) to share our
observations from our previous two summits with industry, and (2) to enable
sharing between individuals at the government agencies regarding practical
experiences and challenges with software supply chain security. For each
discussion topic, we presented our observations and take-aways from the
industry summits to spur conversation. We specifically focused on the Executive
Order 14028, software bill of materials (SBOMs), choosing new dependencies,
provenance and self-attestation, and large language models. The open
discussions enabled mutual sharing and shed light on common challenges that
government agencies see as impacting government and industry practitioners when
securing their software supply chain. In this paper, we provide a summary of
the Summit.
|
[
{
"version": "v1",
"created": "Sun, 13 Aug 2023 21:51:28 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Enck",
"William",
""
],
[
"Acar",
"Yasemin",
""
],
[
"Cukier",
"Michel",
""
],
[
"Kapravelos",
"Alexandros",
""
],
[
"Kästner",
"Christian",
""
],
[
"Williams",
"Laurie",
""
]
] |
new_dataset
| 0.999465 |
2308.06861
|
Fahimeh Fooladgar
|
Fahimeh Fooladgar, Minh Nguyen Nhat To, Parvin Mousavi, Purang
Abolmaesumi
|
Manifold DivideMix: A Semi-Supervised Contrastive Learning Framework for
Severe Label Noise
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Deep neural networks have proven to be highly effective when large amounts of
data with clean labels are available. However, their performance degrades when
training data contains noisy labels, leading to poor generalization on the test
set. Real-world datasets contain noisy label samples that either have similar
visual semantics to other classes (in-distribution) or have no semantic
relevance to any class (out-of-distribution) in the dataset. Most
state-of-the-art methods leverage ID labeled noisy samples as unlabeled data
for semi-supervised learning, but OOD labeled noisy samples cannot be used in
this way because they do not belong to any class within the dataset. Hence, in
this paper, we propose incorporating the information from all the training data
by leveraging the benefits of self-supervised training. Our method aims to
extract a meaningful and generalizable embedding space for each sample
regardless of its label. Then, we employ a simple yet effective K-nearest
neighbor method to remove portions of out-of-distribution samples. By
discarding these samples, we propose an iterative "Manifold DivideMix"
algorithm to find clean and noisy samples, and train our model in a
semi-supervised way. In addition, we propose "MixEMatch", a new algorithm for
the semi-supervised step that involves mixup augmentation at the input and
final hidden representations of the model. This will extract better
representations by interpolating both in the input and manifold spaces.
Extensive experiments on multiple synthetic-noise image benchmarks and
real-world web-crawled datasets demonstrate the effectiveness of our proposed
framework. Code is available at https://github.com/Fahim-F/ManifoldDivideMix.
|
[
{
"version": "v1",
"created": "Sun, 13 Aug 2023 23:33:33 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Fooladgar",
"Fahimeh",
""
],
[
"To",
"Minh Nguyen Nhat",
""
],
[
"Mousavi",
"Parvin",
""
],
[
"Abolmaesumi",
"Purang",
""
]
] |
new_dataset
| 0.995369 |
2308.06869
|
Shenyuan Liang
|
Shenyuan Liang, Mauricio Pamplona Segundo, Sathyanarayanan N. Aakur,
Sudeep Sarkar, Anuj Srivastava
|
Shape-Graph Matching Network (SGM-net): Registration for Statistical
Shape Analysis
| null | null | null | null |
cs.CV stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper focuses on the statistical analysis of shapes of data objects
called shape graphs, a set of nodes connected by articulated curves with
arbitrary shapes. A critical need here is a constrained registration of points
(nodes to nodes, edges to edges) across objects. This, in turn, requires
optimization over the permutation group, made challenging by differences in
nodes (in terms of numbers, locations) and edges (in terms of shapes,
placements, and sizes) across objects. This paper tackles this registration
problem using a novel neural-network architecture and involves an unsupervised
loss function developed using the elastic shape metric for curves. This
architecture results in (1) state-of-the-art matching performance and (2) an
order of magnitude reduction in the computational cost relative to baseline
approaches. We demonstrate the effectiveness of the proposed approach using
both simulated data and real-world 2D and 3D shape graphs. Code and data will
be made publicly available after review to foster research.
|
[
{
"version": "v1",
"created": "Mon, 14 Aug 2023 00:42:03 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Liang",
"Shenyuan",
""
],
[
"Segundo",
"Mauricio Pamplona",
""
],
[
"Aakur",
"Sathyanarayanan N.",
""
],
[
"Sarkar",
"Sudeep",
""
],
[
"Srivastava",
"Anuj",
""
]
] |
new_dataset
| 0.991381 |
2308.06878
|
Jiahao Liu
|
Sijia Liu, Jiahao Liu, Hansu Gu, Dongsheng Li, Tun Lu, Peng Zhang,
Ning Gu
|
AutoSeqRec: Autoencoder for Efficient Sequential Recommendation
|
10 pages, accepted by CIKM 2023
| null | null | null |
cs.IR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Sequential recommendation demonstrates the capability to recommend items by
modeling the sequential behavior of users. Traditional methods typically treat
users as sequences of items, overlooking the collaborative relationships among
them. Graph-based methods incorporate collaborative information by utilizing
the user-item interaction graph. However, these methods sometimes face
challenges in terms of time complexity and computational efficiency. To address
these limitations, this paper presents AutoSeqRec, an incremental
recommendation model specifically designed for sequential recommendation tasks.
AutoSeqRec is based on autoencoders and consists of an encoder and three
decoders within the autoencoder architecture. These components consider both
the user-item interaction matrix and the rows and columns of the item
transition matrix. The reconstruction of the user-item interaction matrix
captures user long-term preferences through collaborative filtering. In
addition, the rows and columns of the item transition matrix represent the item
out-degree and in-degree hopping behavior, which allows for modeling the user's
short-term interests. When making incremental recommendations, only the input
matrices need to be updated, without the need to update parameters, which makes
AutoSeqRec very efficient. Comprehensive evaluations demonstrate that
AutoSeqRec outperforms existing methods in terms of accuracy, while showcasing
its robustness and efficiency.
|
[
{
"version": "v1",
"created": "Mon, 14 Aug 2023 01:23:37 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Liu",
"Sijia",
""
],
[
"Liu",
"Jiahao",
""
],
[
"Gu",
"Hansu",
""
],
[
"Li",
"Dongsheng",
""
],
[
"Lu",
"Tun",
""
],
[
"Zhang",
"Peng",
""
],
[
"Gu",
"Ning",
""
]
] |
new_dataset
| 0.969647 |
2308.06891
|
Chunhao Peng
|
Chunhao Peng, Dapeng Yang, Ming Cheng, Jinghui Dai, Deyu Zhao, Li
Jiang
|
Viia-hand: a Reach-and-grasp Restoration System Integrating Voice
interaction, Computer vision and Auditory feedback for Blind Amputees
| null | null | null | null |
cs.RO cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Visual feedback plays a crucial role in the process of amputation patients
completing grasping in the field of prosthesis control. However, for blind and
visually impaired (BVI) amputees, the loss of both visual and grasping
abilities makes the "easy" reach-and-grasp task a feasible challenge. In this
paper, we propose a novel multi-sensory prosthesis system helping BVI amputees
with sensing, navigation and grasp operations. It combines modules of voice
interaction, environmental perception, grasp guidance, collaborative control,
and auditory/tactile feedback. In particular, the voice interaction module
receives user instructions and invokes other functional modules according to
the instructions. The environmental perception and grasp guidance module
obtains environmental information through computer vision, and feedbacks the
information to the user through auditory feedback modules (voice prompts and
spatial sound sources) and tactile feedback modules (vibration stimulation).
The prosthesis collaborative control module obtains the context information of
the grasp guidance process and completes the collaborative control of grasp
gestures and wrist angles of prosthesis in conjunction with the user's control
intention in order to achieve stable grasp of various objects. This paper
details a prototyping design (named viia-hand) and presents its preliminary
experimental verification on healthy subjects completing specific
reach-and-grasp tasks. Our results showed that, with the help of our new
design, the subjects were able to achieve a precise reach and reliable grasp of
the target objects in a relatively cluttered environment. Additionally, the
system is extremely user-friendly, as users can quickly adapt to it with
minimal training.
|
[
{
"version": "v1",
"created": "Mon, 14 Aug 2023 02:09:31 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Peng",
"Chunhao",
""
],
[
"Yang",
"Dapeng",
""
],
[
"Cheng",
"Ming",
""
],
[
"Dai",
"Jinghui",
""
],
[
"Zhao",
"Deyu",
""
],
[
"Jiang",
"Li",
""
]
] |
new_dataset
| 0.99685 |
2308.06911
|
Pengfei Liu
|
Pengfei Liu, Yiming Ren and Zhixiang Ren
|
GIT-Mol: A Multi-modal Large Language Model for Molecular Science with
Graph, Image, and Text
|
16 pages, 5 figures
| null | null | null |
cs.LG cs.CL q-bio.BM
|
http://creativecommons.org/licenses/by/4.0/
|
Large language models have made significant strides in natural language
processing, paving the way for innovative applications including molecular
representation and generation. However, most existing single-modality
approaches cannot capture the abundant and complex information in molecular
data. Here, we introduce GIT-Mol, a multi-modal large language model that
integrates the structure Graph, Image, and Text information, including the
Simplified Molecular Input Line Entry System (SMILES) and molecular captions.
To facilitate the integration of multi-modal molecular data, we propose
GIT-Former, a novel architecture capable of mapping all modalities into a
unified latent space. Our study develops an innovative any-to-language
molecular translation strategy and achieves a 10%-15% improvement in molecular
captioning, a 5%-10% accuracy increase in property prediction, and a 20% boost
in molecule generation validity compared to baseline or single-modality models.
|
[
{
"version": "v1",
"created": "Mon, 14 Aug 2023 03:12:29 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Liu",
"Pengfei",
""
],
[
"Ren",
"Yiming",
""
],
[
"Ren",
"Zhixiang",
""
]
] |
new_dataset
| 0.999735 |
2308.06917
|
Carter Butts
|
Selena M. Livas, Scott Leo Renshaw, and Carter T. Butts
|
Calling The Dead: Resilience In The WTC Communication Networks
| null | null | null | null |
cs.SI nlin.AO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Organizations in emergency settings must cope with various sources of
disruption, most notably personnel loss. Death, incapacitation, or isolation of
individuals within an organizational communication network can impair
information passing, coordination, and connectivity, and may drive maladaptive
responses such as repeated attempts to contact lost personnel (``calling the
dead'') that themselves consume scarce resources. At the same time,
organizations may respond to such disruption by reorganizing to restore
function, a behavior that is fundamental to organizational resilience. Here, we
use empirically calibrated models of communication for 17 groups of responders
to the World Trade Center Disaster to examine the impact of exogenous removal
of personnel on communication activity and network resilience. We find that
removal of high-degree personnel and those in institutionally coordinative
roles is particularly damaging to these organizations, with specialist
responders being slower to adapt to losses. However, all organizations show
adaptations to disruption, in some cases becoming better connected and making
more complete use of personnel relative to control after experiencing losses.
|
[
{
"version": "v1",
"created": "Mon, 14 Aug 2023 03:29:02 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Livas",
"Selena M.",
""
],
[
"Renshaw",
"Scott Leo",
""
],
[
"Butts",
"Carter T.",
""
]
] |
new_dataset
| 0.998561 |
2308.06971
|
EPTCS
|
Brent A. Yorgey (Hendrix College)
|
Disco: A Functional Programming Language for Discrete Mathematics
|
In Proceedings TFPIE 2023, arXiv:2308.06110
|
EPTCS 382, 2023, pp. 64-81
|
10.4204/EPTCS.382.4
| null |
cs.PL cs.DM
|
http://creativecommons.org/licenses/by/4.0/
|
Disco is a pure, strict, statically typed functional programming language
designed to be used in the setting of a discrete mathematics course. The goals
of the language are to introduce students to functional programming concepts
early, and to enhance their learning of mathematics by providing a
computational platform for them to play with. It features
mathematically-inspired notation, property-based testing, equirecursive
algebraic types, subtyping, built-in list, bag, and finite set types, a REPL,
and student-focused documentation. Disco is implemented in Haskell, with source
code available on GitHub [https://github.com/disco-lang/disco], and interactive
web-based REPL available through replit
[https://replit.com/@BrentYorgey/Disco#README.md].
|
[
{
"version": "v1",
"created": "Mon, 14 Aug 2023 07:09:15 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Yorgey",
"Brent A.",
"",
"Hendrix College"
]
] |
new_dataset
| 0.999901 |
2308.06974
|
Xiangchao Gan
|
Jiexiong Xu, Weikun Zhao, Zhiyan Tang and Xiangchao Gan
|
A One Stop 3D Target Reconstruction and multilevel Segmentation Method
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
3D object reconstruction and multilevel segmentation are fundamental to
computer vision research. Existing algorithms usually perform 3D scene
reconstruction and target objects segmentation independently, and the
performance is not fully guaranteed due to the challenge of the 3D
segmentation. Here we propose an open-source one stop 3D target reconstruction
and multilevel segmentation framework (OSTRA), which performs segmentation on
2D images, tracks multiple instances with segmentation labels in the image
sequence, and then reconstructs labelled 3D objects or multiple parts with
Multi-View Stereo (MVS) or RGBD-based 3D reconstruction methods. We extend
object tracking and 3D reconstruction algorithms to support continuous
segmentation labels to leverage the advances in the 2D image segmentation,
especially the Segment-Anything Model (SAM) which uses the pretrained neural
network without additional training for new scenes, for 3D object segmentation.
OSTRA supports most popular 3D object models including point cloud, mesh and
voxel, and achieves high performance for semantic segmentation, instance
segmentation and part segmentation on several 3D datasets. It even surpasses
the manual segmentation in scenes with complex structures and occlusions. Our
method opens up a new avenue for reconstructing 3D targets embedded with rich
multi-scale segmentation information in complex scenes. OSTRA is available from
https://github.com/ganlab/OSTRA.
|
[
{
"version": "v1",
"created": "Mon, 14 Aug 2023 07:12:31 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Xu",
"Jiexiong",
""
],
[
"Zhao",
"Weikun",
""
],
[
"Tang",
"Zhiyan",
""
],
[
"Gan",
"Xiangchao",
""
]
] |
new_dataset
| 0.993659 |
2308.06985
|
Oren Shrout
|
Oren Shrout, Ori Nitzan, Yizhak Ben-Shabat, Ayellet Tal
|
PatchContrast: Self-Supervised Pre-training for 3D Object Detection
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Accurately detecting objects in the environment is a key challenge for
autonomous vehicles. However, obtaining annotated data for detection is
expensive and time-consuming. We introduce PatchContrast, a novel
self-supervised point cloud pre-training framework for 3D object detection. We
propose to utilize two levels of abstraction to learn discriminative
representation from unlabeled data: proposal-level and patch-level. The
proposal-level aims at localizing objects in relation to their surroundings,
whereas the patch-level adds information about the internal connections between
the object's components, hence distinguishing between different objects based
on their individual components. We demonstrate how these levels can be
integrated into self-supervised pre-training for various backbones to enhance
the downstream 3D detection task. We show that our method outperforms existing
state-of-the-art models on three commonly-used 3D detection datasets.
|
[
{
"version": "v1",
"created": "Mon, 14 Aug 2023 07:45:54 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Shrout",
"Oren",
""
],
[
"Nitzan",
"Ori",
""
],
[
"Ben-Shabat",
"Yizhak",
""
],
[
"Tal",
"Ayellet",
""
]
] |
new_dataset
| 0.984242 |
2308.07024
|
Jui-Min Hsu
|
Yu-Ting Li, Ching-Te Chiu, An-Ting Hsieh, Mao-Hsiu Hsu, Long Wenyong,
Jui-Min Hsu
|
PGT-Net: Progressive Guided Multi-task Neural Network for Small-area Wet
Fingerprint Denoising and Recognition
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Fingerprint recognition on mobile devices is an important method for identity
verification. However, real fingerprints usually contain sweat and moisture
which leads to poor recognition performance. In addition, for rolling out
slimmer and thinner phones, technology companies reduce the size of recognition
sensors by embedding them with the power button. Therefore, the limited size of
fingerprint data also increases the difficulty of recognition. Denoising the
small-area wet fingerprint images to clean ones becomes crucial to improve
recognition performance. In this paper, we propose an end-to-end trainable
progressive guided multi-task neural network (PGT-Net). The PGT-Net includes a
shared stage and specific multi-task stages, enabling the network to train
binary and non-binary fingerprints sequentially. The binary information is
regarded as guidance for output enhancement which is enriched with the ridge
and valley details. Moreover, a novel residual scaling mechanism is introduced
to stabilize the training process. Experiment results on the FW9395 and
FT-lightnoised dataset provided by FocalTech shows that PGT-Net has promising
performance on the wet-fingerprint denoising and significantly improves the
fingerprint recognition rate (FRR). On the FT-lightnoised dataset, the FRR of
fingerprint recognition can be declined from 17.75% to 4.47%. On the FW9395
dataset, the FRR of fingerprint recognition can be declined from 9.45% to
1.09%.
|
[
{
"version": "v1",
"created": "Mon, 14 Aug 2023 09:19:26 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Li",
"Yu-Ting",
""
],
[
"Chiu",
"Ching-Te",
""
],
[
"Hsieh",
"An-Ting",
""
],
[
"Hsu",
"Mao-Hsiu",
""
],
[
"Wenyong",
"Long",
""
],
[
"Hsu",
"Jui-Min",
""
]
] |
new_dataset
| 0.998606 |
2308.07026
|
Ziqi Zhou
|
Ziqi Zhou, Shengshan Hu, Minghui Li, Hangtao Zhang, Yechao Zhang, Hai
Jin
|
AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal
Contrastive Learning
|
This paper has been accepted by the ACM International Conference on
Multimedia (ACM MM '23, October 29-November 3, 2023, Ottawa, ON, Canada)
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Multimodal contrastive learning aims to train a general-purpose feature
extractor, such as CLIP, on vast amounts of raw, unlabeled paired image-text
data. This can greatly benefit various complex downstream tasks, including
cross-modal image-text retrieval and image classification. Despite its
promising prospect, the security issue of cross-modal pre-trained encoder has
not been fully explored yet, especially when the pre-trained encoder is
publicly available for commercial use.
In this work, we propose AdvCLIP, the first attack framework for generating
downstream-agnostic adversarial examples based on cross-modal pre-trained
encoders. AdvCLIP aims to construct a universal adversarial patch for a set of
natural images that can fool all the downstream tasks inheriting the victim
cross-modal pre-trained encoder. To address the challenges of heterogeneity
between different modalities and unknown downstream tasks, we first build a
topological graph structure to capture the relevant positions between target
samples and their neighbors. Then, we design a topology-deviation based
generative adversarial network to generate a universal adversarial patch. By
adding the patch to images, we minimize their embeddings similarity to
different modality and perturb the sample distribution in the feature space,
achieving unviersal non-targeted attacks. Our results demonstrate the excellent
attack performance of AdvCLIP on two types of downstream tasks across eight
datasets. We also tailor three popular defenses to mitigate AdvCLIP,
highlighting the need for new defense mechanisms to defend cross-modal
pre-trained encoders.
|
[
{
"version": "v1",
"created": "Mon, 14 Aug 2023 09:29:22 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Zhou",
"Ziqi",
""
],
[
"Hu",
"Shengshan",
""
],
[
"Li",
"Minghui",
""
],
[
"Zhang",
"Hangtao",
""
],
[
"Zhang",
"Yechao",
""
],
[
"Jin",
"Hai",
""
]
] |
new_dataset
| 0.998693 |
2308.07081
|
Jivnesh Sandhan
|
Jivnesh Sandhan, Amruta Barbadikar, Malay Maity, Pavankumar Satuluri,
Tushar Sandhan, Ravi M. Gupta, Pawan Goyal and Laxmidhar Behera
|
Aesthetics of Sanskrit Poetry from the Perspective of Computational
Linguistics: A Case Study Analysis on Siksastaka
|
15 pages
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Sanskrit poetry has played a significant role in shaping the literary and
cultural landscape of the Indian subcontinent for centuries. However, not much
attention has been devoted to uncovering the hidden beauty of Sanskrit poetry
in computational linguistics. This article explores the intersection of
Sanskrit poetry and computational linguistics by proposing a roadmap of an
interpretable framework to analyze and classify the qualities and
characteristics of fine Sanskrit poetry. We discuss the rich tradition of
Sanskrit poetry and the significance of computational linguistics in
automatically identifying the characteristics of fine poetry. The proposed
framework involves a human-in-the-loop approach that combines deterministic
aspects delegated to machines and deep semantics left to human experts. We
provide a deep analysis of Siksastaka, a Sanskrit poem, from the perspective of
6 prominent kavyashastra schools, to illustrate the proposed framework.
Additionally, we provide compound, dependency, anvaya (prose order linearised
form), meter, rasa (mood), alankar (figure of speech), and riti (writing style)
annotations for Siksastaka and a web application to illustrate the poem's
analysis and annotations. Our key contributions include the proposed framework,
the analysis of Siksastaka, the annotations and the web application for future
research. Link for interactive analysis:
https://sanskritshala.github.io/shikshastakam/
|
[
{
"version": "v1",
"created": "Mon, 14 Aug 2023 11:26:25 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Sandhan",
"Jivnesh",
""
],
[
"Barbadikar",
"Amruta",
""
],
[
"Maity",
"Malay",
""
],
[
"Satuluri",
"Pavankumar",
""
],
[
"Sandhan",
"Tushar",
""
],
[
"Gupta",
"Ravi M.",
""
],
[
"Goyal",
"Pawan",
""
],
[
"Behera",
"Laxmidhar",
""
]
] |
new_dataset
| 0.999158 |
2308.07124
|
Niklas Muennighoff
|
Niklas Muennighoff, Qian Liu, Armel Zebaze, Qinkai Zheng, Binyuan Hui,
Terry Yue Zhuo, Swayam Singh, Xiangru Tang, Leandro von Werra, Shayne Longpre
|
OctoPack: Instruction Tuning Code Large Language Models
|
57 pages (9 main), 39 figures, 16 tables
| null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Finetuning large language models (LLMs) on instructions leads to vast
performance improvements on natural language tasks. We apply instruction tuning
using code, leveraging the natural structure of Git commits, which pair code
changes with human instructions. We compile CommitPack: 4 terabytes of Git
commits across 350 programming languages. We benchmark CommitPack against other
natural and synthetic code instructions (xP3x, Self-Instruct, OASST) on the 16B
parameter StarCoder model, and achieve state-of-the-art performance among
models not trained on OpenAI outputs, on the HumanEval Python benchmark (46.2%
pass@1). We further introduce HumanEvalPack, expanding the HumanEval benchmark
to a total of 3 coding tasks (Code Repair, Code Explanation, Code Synthesis)
across 6 languages (Python, JavaScript, Java, Go, C++, Rust). Our models,
OctoCoder and OctoGeeX, achieve the best performance across HumanEvalPack among
all permissive models, demonstrating CommitPack's benefits in generalizing to a
wider set of languages and natural coding tasks. Code, models and data are
freely available at https://github.com/bigcode-project/octopack.
|
[
{
"version": "v1",
"created": "Mon, 14 Aug 2023 13:53:54 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Muennighoff",
"Niklas",
""
],
[
"Liu",
"Qian",
""
],
[
"Zebaze",
"Armel",
""
],
[
"Zheng",
"Qinkai",
""
],
[
"Hui",
"Binyuan",
""
],
[
"Zhuo",
"Terry Yue",
""
],
[
"Singh",
"Swayam",
""
],
[
"Tang",
"Xiangru",
""
],
[
"von Werra",
"Leandro",
""
],
[
"Longpre",
"Shayne",
""
]
] |
new_dataset
| 0.997854 |
2308.07153
|
Sk Aziz Ali
|
Sk Aziz Ali, Djamila Aouada, Gerd Reis, Didier Stricker
|
DELO: Deep Evidential LiDAR Odometry using Partial Optimal Transport
|
Accepted in ICCV 2023 Workshop
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Accurate, robust, and real-time LiDAR-based odometry (LO) is imperative for
many applications like robot navigation, globally consistent 3D scene map
reconstruction, or safe motion-planning. Though LiDAR sensor is known for its
precise range measurement, the non-uniform and uncertain point sampling density
induce structural inconsistencies. Hence, existing supervised and unsupervised
point set registration methods fail to establish one-to-one matching
correspondences between LiDAR frames. We introduce a novel deep learning-based
real-time (approx. 35-40ms per frame) LO method that jointly learns accurate
frame-to-frame correspondences and model's predictive uncertainty (PU) as
evidence to safe-guard LO predictions. In this work, we propose (i) partial
optimal transportation of LiDAR feature descriptor for robust LO estimation,
(ii) joint learning of predictive uncertainty while learning odometry over
driving sequences, and (iii) demonstrate how PU can serve as evidence for
necessary pose-graph optimization when LO network is either under or over
confident. We evaluate our method on KITTI dataset and show competitive
performance, even superior generalization ability over recent state-of-the-art
approaches. Source codes are available.
|
[
{
"version": "v1",
"created": "Mon, 14 Aug 2023 14:06:21 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Ali",
"Sk Aziz",
""
],
[
"Aouada",
"Djamila",
""
],
[
"Reis",
"Gerd",
""
],
[
"Stricker",
"Didier",
""
]
] |
new_dataset
| 0.988627 |
2308.07170
|
Jeremy Cochoy
|
Jeremy Cochoy
|
PitchNet: A Fully Convolutional Neural Network for Pitch Estimation
| null | null | null | null |
cs.SD cs.LG eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
In the domain of music and sound processing, pitch extraction plays a pivotal
role. This research introduces "PitchNet", a convolutional neural network
tailored for pitch extraction from the human singing voice, including acapella
performances. Integrating autocorrelation with deep learning techniques,
PitchNet aims to optimize the accuracy of pitch detection. Evaluation across
datasets comprising synthetic sounds, opera recordings, and time-stretched
vowels demonstrates its efficacy. This work paves the way for enhanced pitch
extraction in both music and voice settings.
|
[
{
"version": "v1",
"created": "Mon, 14 Aug 2023 14:26:52 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Cochoy",
"Jeremy",
""
]
] |
new_dataset
| 0.998627 |
2308.07266
|
Chandan Kumar Sheemar
|
Chandan Kumar Sheemar, Sourabh Solanki, Eva Lagunas, Jorge Querol,
Symeon Chatzinotas, and Bj\"orn Ottersten
|
Full Duplex Joint Communications and Sensing for 6G: Opportunities and
Challenges
| null | null | null | null |
cs.IT eess.SP math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
The paradigm of joint communications and sensing (JCAS) envisions a
revolutionary integration of communication and radar functionalities within a
unified hardware platform. This novel concept not only opens up unprecedented
possibilities, but also presents unique challenges. Its success is highly
dependent on efficient full-duplex (FD) operation, which has the potential to
enable simultaneous transmission and reception within the same frequency band.
While ongoing research explores the potential of JCAS, there are related
avenues of investigation that hold tremendous potential to profoundly transform
the sixth generation (6G) and beyond cellular networks. This article sheds
light on the new opportunities and challenges presented by JCAS by taking into
account the key technical challenges of FD systems. Unlike simplified JCAS
scenarios, we delve into the most comprehensive configuration, encompassing
uplink (UL) and downlink (DL) users, as well as monostatic and bistatic radars,
all harmoniously coexisting to jointly push the boundaries of both the
communications and sensing performance. The performance improvements introduced
by this advancement bring forth numerous new challenges, each meticulously
examined and expounded upon.
|
[
{
"version": "v1",
"created": "Mon, 14 Aug 2023 16:50:12 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Sheemar",
"Chandan Kumar",
""
],
[
"Solanki",
"Sourabh",
""
],
[
"Lagunas",
"Eva",
""
],
[
"Querol",
"Jorge",
""
],
[
"Chatzinotas",
"Symeon",
""
],
[
"Ottersten",
"Björn",
""
]
] |
new_dataset
| 0.998022 |
2308.07267
|
Kejia Zhang
|
Kejia Zhang, Mingyu Yang, Stephen D. J. Lang, Alistair M. McInnes,
Richard B. Sherley, Tilo Burghardt
|
Diving with Penguins: Detecting Penguins and their Prey in Animal-borne
Underwater Videos via Deep Learning
|
5 pages, 5 figures, 4 Tables, "3rd International Workshop on Camera
traps, AI, and Ecology (CamTrapAI)"
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
African penguins (Spheniscus demersus) are an endangered species. Little is
known regarding their underwater hunting strategies and associated predation
success rates, yet this is essential for guiding conservation. Modern
bio-logging technology has the potential to provide valuable insights, but
manually analysing large amounts of data from animal-borne video recorders
(AVRs) is time-consuming. In this paper, we publish an animal-borne underwater
video dataset of penguins and introduce a ready-to-deploy deep learning system
capable of robustly detecting penguins ([email protected]%) and also instances of fish
([email protected]%). We note that the detectors benefit explicitly from air-bubble
learning to improve accuracy. Extending this detector towards a dual-stream
behaviour recognition network, we also provide the first results for
identifying predation behaviour in penguin underwater videos. Whilst results
are promising, further work is required for useful applicability of predation
behaviour detection in field scenarios. In summary, we provide a highly
reliable underwater penguin detector, a fish detector, and a valuable first
attempt towards an automated visual detection of complex behaviours in a marine
predator. We publish the networks, the DivingWithPenguins video dataset,
annotations, splits, and weights for full reproducibility and immediate
usability by practitioners.
|
[
{
"version": "v1",
"created": "Mon, 14 Aug 2023 16:50:27 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Zhang",
"Kejia",
""
],
[
"Yang",
"Mingyu",
""
],
[
"Lang",
"Stephen D. J.",
""
],
[
"McInnes",
"Alistair M.",
""
],
[
"Sherley",
"Richard B.",
""
],
[
"Burghardt",
"Tilo",
""
]
] |
new_dataset
| 0.997043 |
2308.07301
|
Esteve Valls Mascar\'o
|
Esteve Valls Mascaro, Hyemin Ahn, Dongheui Lee
|
A Unified Masked Autoencoder with Patchified Skeletons for Motion
Synthesis
| null | null | null | null |
cs.CV cs.GR cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
The synthesis of human motion has traditionally been addressed through
task-dependent models that focus on specific challenges, such as predicting
future motions or filling in intermediate poses conditioned on known key-poses.
In this paper, we present a novel task-independent model called UNIMASK-M,
which can effectively address these challenges using a unified architecture.
Our model obtains comparable or better performance than the state-of-the-art in
each field. Inspired by Vision Transformers (ViTs), our UNIMASK-M model
decomposes a human pose into body parts to leverage the spatio-temporal
relationships existing in human motion. Moreover, we reformulate various
pose-conditioned motion synthesis tasks as a reconstruction problem with
different masking patterns given as input. By explicitly informing our model
about the masked joints, our UNIMASK-M becomes more robust to occlusions.
Experimental results show that our model successfully forecasts human motion on
the Human3.6M dataset. Moreover, it achieves state-of-the-art results in motion
inbetweening on the LaFAN1 dataset, particularly in long transition periods.
More information can be found on the project website
https://sites.google.com/view/estevevallsmascaro/publications/unimask-m.
|
[
{
"version": "v1",
"created": "Mon, 14 Aug 2023 17:39:44 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Mascaro",
"Esteve Valls",
""
],
[
"Ahn",
"Hyemin",
""
],
[
"Lee",
"Dongheui",
""
]
] |
new_dataset
| 0.998999 |
2308.07307
|
Yuhe Nie
|
Yuhe Nie, Shaoming Zheng, Zhan Zhuang, Xuan Song
|
Extend Wave Function Collapse to Large-Scale Content Generation
|
This paper is accepted by IEEE Conference on Games 2023 (nomination
of the Best Paper Award)
| null | null | null |
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Wave Function Collapse (WFC) is a widely used tile-based algorithm in
procedural content generation, including textures, objects, and scenes.
However, the current WFC algorithm and related research lack the ability to
generate commercialized large-scale or infinite content due to constraint
conflict and time complexity costs. This paper proposes a Nested WFC (N-WFC)
algorithm framework to reduce time complexity. To avoid conflict and
backtracking problems, we offer a complete and sub-complete tileset preparation
strategy, which requires only a small number of tiles to generate aperiodic and
deterministic infinite content. We also introduce the weight-brush system that
combines N-WFC and sub-complete tileset, proving its suitability for game
design. Our contribution addresses WFC's challenge in massive content
generation and provides a theoretical basis for implementing concrete games.
|
[
{
"version": "v1",
"created": "Mon, 14 Aug 2023 17:50:38 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Nie",
"Yuhe",
""
],
[
"Zheng",
"Shaoming",
""
],
[
"Zhuang",
"Zhan",
""
],
[
"Song",
"Xuan",
""
]
] |
new_dataset
| 0.994153 |
2308.07316
|
Alexander Martin
|
Alexander Martin and Haitian Zheng and Jie An and Jiebo Luo
|
Jurassic World Remake: Bringing Ancient Fossils Back to Life via
Zero-Shot Long Image-to-Image Translation
|
9 pages, 10 figures, ACM Multimedia 2023
| null |
10.1145/3581783.3612708
| null |
cs.CV cs.MM
|
http://creativecommons.org/licenses/by/4.0/
|
With a strong understanding of the target domain from natural language, we
produce promising results in translating across large domain gaps and bringing
skeletons back to life. In this work, we use text-guided latent diffusion
models for zero-shot image-to-image translation (I2I) across large domain gaps
(longI2I), where large amounts of new visual features and new geometry need to
be generated to enter the target domain. Being able to perform translations
across large domain gaps has a wide variety of real-world applications in
criminology, astrology, environmental conservation, and paleontology. In this
work, we introduce a new task Skull2Animal for translating between skulls and
living animals. On this task, we find that unguided Generative Adversarial
Networks (GANs) are not capable of translating across large domain gaps.
Instead of these traditional I2I methods, we explore the use of guided
diffusion and image editing models and provide a new benchmark model,
Revive-2I, capable of performing zero-shot I2I via text-prompting latent
diffusion models. We find that guidance is necessary for longI2I because, to
bridge the large domain gap, prior knowledge about the target domain is needed.
In addition, we find that prompting provides the best and most scalable
information about the target domain as classifier-guided diffusion models
require retraining for specific use cases and lack stronger constraints on the
target domain because of the wide variety of images they are trained on.
|
[
{
"version": "v1",
"created": "Mon, 14 Aug 2023 17:59:31 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Martin",
"Alexander",
""
],
[
"Zheng",
"Haitian",
""
],
[
"An",
"Jie",
""
],
[
"Luo",
"Jiebo",
""
]
] |
new_dataset
| 0.956147 |
2308.07317
|
Ariel N. Lee
|
Ariel N. Lee, Cole J. Hunter, Nataniel Ruiz
|
Platypus: Quick, Cheap, and Powerful Refinement of LLMs
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
We present $\textbf{Platypus}$, a family of fine-tuned and merged Large
Language Models (LLMs) that achieves the strongest performance and currently
stands at first place in HuggingFace's Open LLM Leaderboard as of the release
date of this work. In this work we describe (1) our curated dataset
$\textbf{Open-Platypus}$, that is a subset of other open datasets and which
$\textit{we release to the public}$ (2) our process of fine-tuning and merging
LoRA modules in order to conserve the strong prior of pretrained LLMs, while
bringing specific domain knowledge to the surface (3) our efforts in checking
for test data leaks and contamination in the training data, which can inform
future research. Specifically, the Platypus family achieves strong performance
in quantitative LLM metrics across model sizes, topping the global Open LLM
leaderboard while using just a fraction of the fine-tuning data and overall
compute that are required for other state-of-the-art fine-tuned LLMs. In
particular, a 13B Platypus model can be trained on $\textit{a single}$ A100 GPU
using 25k questions in 5 hours. This is a testament of the quality of our
Open-Platypus dataset, and opens opportunities for more improvements in the
field. Project page: https://platypus-llm.github.io
|
[
{
"version": "v1",
"created": "Mon, 14 Aug 2023 17:59:56 GMT"
}
] | 2023-08-15T00:00:00 |
[
[
"Lee",
"Ariel N.",
""
],
[
"Hunter",
"Cole J.",
""
],
[
"Ruiz",
"Nataniel",
""
]
] |
new_dataset
| 0.998222 |
2108.04486
|
Thomas Studer
|
Atefeh Rohani and Thomas Studer
|
Explicit non-normal modal logic
| null | null | null | null |
cs.LO
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Faroldi argues that deontic modals are hyperintensional and thus traditional
modal logic cannot provide an appropriate formalization of deontic situations.
To overcome this issue, we introduce novel justification logics as
hyperintensional analogues to non-normal modal logics. We establish soundness
and completeness with respect to various models and we study the problem of
realization.
|
[
{
"version": "v1",
"created": "Tue, 10 Aug 2021 07:42:56 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Jan 2022 12:06:30 GMT"
},
{
"version": "v3",
"created": "Fri, 11 Aug 2023 07:39:23 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Rohani",
"Atefeh",
""
],
[
"Studer",
"Thomas",
""
]
] |
new_dataset
| 0.995932 |
2201.09201
|
Ming Dai
|
Ming Dai and Enhui Zheng and Zhenhua Feng and Jiedong Zhuang and
Wankou Yang
|
Vision-Based UAV Self-Positioning in Low-Altitude Urban Environments
|
13 pages,8 figures
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Unmanned Aerial Vehicles (UAVs) rely on satellite systems for stable
positioning. However, due to limited satellite coverage or communication
disruptions, UAVs may lose signals from satellite-based positioning systems. In
such situations, vision-based techniques can serve as an alternative, ensuring
the self-positioning capability of UAVs. However, most of the existing datasets
are developed for the geo-localization tasks of the objects identified by UAVs,
rather than the self-positioning task of UAVs. Furthermore, the current UAV
datasets use discrete sampling on synthetic data, such as Google Maps, thereby
neglecting the crucial aspects of dense sampling and the uncertainties commonly
experienced in real-world scenarios. To address these issues, this paper
presents a new dataset, DenseUAV, which is the first publicly available dataset
designed for the UAV self-positioning task. DenseUAV adopts dense sampling on
UAV images obtained in low-altitude urban settings. In total, over 27K UAV-view
and satellite-view images of 14 university campuses are collected and
annotated, establishing a new benchmark. In terms of model development, we
first verify the superiority of Transformers over CNNs in this task. Then, we
incorporate metric learning into representation learning to enhance the
discriminative capacity of the model and to lessen the modality discrepancy.
Besides, to facilitate joint learning from both perspectives, we propose a
mutually supervised learning approach. Last, we enhance the Recall@K metric and
introduce a new measurement, SDM@K, to evaluate the performance of a trained
model from both the retrieval and localization perspectives simultaneously. As
a result, the proposed baseline method achieves a remarkable Recall@1 score of
83.05% and an SDM@1 score of 86.24% on DenseUAV. The dataset and code will be
made publicly available on https://github.com/Dmmm1997/DenseUAV.
|
[
{
"version": "v1",
"created": "Sun, 23 Jan 2022 07:18:55 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Aug 2023 18:34:17 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Dai",
"Ming",
""
],
[
"Zheng",
"Enhui",
""
],
[
"Feng",
"Zhenhua",
""
],
[
"Zhuang",
"Jiedong",
""
],
[
"Yang",
"Wankou",
""
]
] |
new_dataset
| 0.99221 |
2206.15157
|
Tim Broedermann
|
Tim Broedermann (1), Christos Sakaridis (1), Dengxin Dai (2) and Luc
Van Gool (1 and 3) ((1) ETH Zurich, (2) MPI for Informatics, (3) KU Leuven)
|
HRFuser: A Multi-resolution Sensor Fusion Architecture for 2D Object
Detection
|
IEEE International Conference on Intelligent Transportation Systems
(ITSC) 2023
| null | null | null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Besides standard cameras, autonomous vehicles typically include multiple
additional sensors, such as lidars and radars, which help acquire richer
information for perceiving the content of the driving scene. While several
recent works focus on fusing certain pairs of sensors - such as camera with
lidar or radar - by using architectural components specific to the examined
setting, a generic and modular sensor fusion architecture is missing from the
literature. In this work, we propose HRFuser, a modular architecture for
multi-modal 2D object detection. It fuses multiple sensors in a
multi-resolution fashion and scales to an arbitrary number of input modalities.
The design of HRFuser is based on state-of-the-art high-resolution networks for
image-only dense prediction and incorporates a novel multi-window
cross-attention block as the means to perform fusion of multiple modalities at
multiple resolutions. We demonstrate via extensive experiments on nuScenes and
the adverse conditions DENSE datasets that our model effectively leverages
complementary features from additional modalities, substantially improving upon
camera-only performance and consistently outperforming state-of-the-art 3D and
2D fusion methods evaluated on 2D object detection metrics. The source code is
publicly available.
|
[
{
"version": "v1",
"created": "Thu, 30 Jun 2022 09:40:05 GMT"
},
{
"version": "v2",
"created": "Thu, 15 Jun 2023 08:38:57 GMT"
},
{
"version": "v3",
"created": "Fri, 11 Aug 2023 11:06:09 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Broedermann",
"Tim",
"",
"ETH Zurich"
],
[
"Sakaridis",
"Christos",
"",
"ETH Zurich"
],
[
"Dai",
"Dengxin",
"",
"MPI for Informatics"
],
[
"Van Gool",
"Luc",
"",
"1 and 3"
]
] |
new_dataset
| 0.998083 |
2208.02993
|
Jing Tao Tang
|
Jingtao Tang, Yuan Gao, Tin Lun Lam
|
Learning to Coordinate for a Worker-Station Multi-robot System in Planar
Coverage Tasks
| null |
IEEE Robotics and Automation Letters, 7(4), 12315-12322, 2022
|
10.1109/LRA.2022.3214446
| null |
cs.RO cs.AI cs.MA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
For massive large-scale tasks, a multi-robot system (MRS) can effectively
improve efficiency by utilizing each robot's different capabilities, mobility,
and functionality. In this paper, we focus on the multi-robot coverage path
planning (mCPP) problem in large-scale planar areas with random dynamic
interferers in the environment, where the robots have limited resources. We
introduce a worker-station MRS consisting of multiple workers with limited
resources for actual work, and one station with enough resources for resource
replenishment. We aim to solve the mCPP problem for the worker-station MRS by
formulating it as a fully cooperative multi-agent reinforcement learning
problem. Then we propose an end-to-end decentralized online planning method,
which simultaneously solves coverage planning for workers and rendezvous
planning for station. Our method manages to reduce the influence of random
dynamic interferers on planning, while the robots can avoid collisions with
them. We conduct simulation and real robot experiments, and the comparison
results show that our method has competitive performance in solving the mCPP
problem for worker-station MRS in metric of task finish time.
|
[
{
"version": "v1",
"created": "Fri, 5 Aug 2022 05:36:42 GMT"
},
{
"version": "v2",
"created": "Wed, 24 Aug 2022 08:11:29 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Tang",
"Jingtao",
""
],
[
"Gao",
"Yuan",
""
],
[
"Lam",
"Tin Lun",
""
]
] |
new_dataset
| 0.986277 |
2208.12356
|
Nikolay Mikhaylovskiy
|
Eduard Zubchuk, Mikhail Arhipkin, Dmitry Menshikov, Aleksandr Karaush,
Nikolay Mikhaylovskiy
|
Lib-SibGMU -- A University Library Circulation Dataset for Recommender
Systems Developmen
|
Dataset copyright discussion
| null | null | null |
cs.IR cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We opensource under CC BY 4.0 license Lib-SibGMU - a university library
circulation dataset - for a wide research community, and benchmark major
algorithms for recommender systems on this dataset. For a recommender
architecture that consists of a vectorizer that turns the history of the books
borrowed into a vector, and a neighborhood-based recommender, trained
separately, we show that using the fastText model as a vectorizer delivers
competitive results.
|
[
{
"version": "v1",
"created": "Thu, 25 Aug 2022 22:10:18 GMT"
},
{
"version": "v2",
"created": "Fri, 11 Aug 2023 16:15:52 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Zubchuk",
"Eduard",
""
],
[
"Arhipkin",
"Mikhail",
""
],
[
"Menshikov",
"Dmitry",
""
],
[
"Karaush",
"Aleksandr",
""
],
[
"Mikhaylovskiy",
"Nikolay",
""
]
] |
new_dataset
| 0.999338 |
2211.00732
|
Haojie Pan
|
Haojie Pan, Zepeng Zhai, Yuzhou Zhang, Ruiji Fu, Ming Liu, Yangqiu
Song, Zhongyuan Wang and Bing Qin
|
Kuaipedia: a Large-scale Multi-modal Short-video Encyclopedia
| null | null | null | null |
cs.IR cs.AI cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Online encyclopedias, such as Wikipedia, have been well-developed and
researched in the last two decades. One can find any attributes or other
information of a wiki item on a wiki page edited by a community of volunteers.
However, the traditional text, images and tables can hardly express some
aspects of an wiki item. For example, when we talk about ``Shiba Inu'', one may
care more about ``How to feed it'' or ``How to train it not to protect its
food''. Currently, short-video platforms have become a hallmark in the online
world. Whether you're on TikTok, Instagram, Kuaishou, or YouTube Shorts,
short-video apps have changed how we consume and create content today. Except
for producing short videos for entertainment, we can find more and more authors
sharing insightful knowledge widely across all walks of life. These short
videos, which we call knowledge videos, can easily express any aspects (e.g.
hair or how-to-feed) consumers want to know about an item (e.g. Shiba Inu), and
they can be systematically analyzed and organized like an online encyclopedia.
In this paper, we propose Kuaipedia, a large-scale multi-modal encyclopedia
consisting of items, aspects, and short videos lined to them, which was
extracted from billions of videos of Kuaishou (Kwai), a well-known short-video
platform in China. We first collected items from multiple sources and mined
user-centered aspects from millions of users' queries to build an item-aspect
tree. Then we propose a new task called ``multi-modal item-aspect linking'' as
an expansion of ``entity linking'' to link short videos into item-aspect pairs
and build the whole short-video encyclopedia. Intrinsic evaluations show that
our encyclopedia is of large scale and highly accurate. We also conduct
sufficient extrinsic experiments to show how Kuaipedia can help fundamental
applications such as entity typing and entity linking.
|
[
{
"version": "v1",
"created": "Fri, 28 Oct 2022 12:54:30 GMT"
},
{
"version": "v2",
"created": "Thu, 3 Nov 2022 08:51:53 GMT"
},
{
"version": "v3",
"created": "Fri, 11 Aug 2023 04:00:59 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Pan",
"Haojie",
""
],
[
"Zhai",
"Zepeng",
""
],
[
"Zhang",
"Yuzhou",
""
],
[
"Fu",
"Ruiji",
""
],
[
"Liu",
"Ming",
""
],
[
"Song",
"Yangqiu",
""
],
[
"Wang",
"Zhongyuan",
""
],
[
"Qin",
"Bing",
""
]
] |
new_dataset
| 0.999684 |
2211.14260
|
Yiyu Wang
|
Yiyu Wang, Jiaqi Ge, Alexis Comber
|
An agent-based simulation model of pedestrian evacuation based on
Bayesian Nash Equilibrium
| null | null |
10.18564/jasss.5037
| null |
cs.MA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This research incorporates Bayesian game theory into pedestrian evacuation in
an agent-based model. Three pedestrian behaviours were compared: Random Follow,
Shortest Route and Bayesian Nash Equilibrium (BNE), as well as combinations of
these. The results showed that BNE pedestrians were able to evacuate more
quickly as they predict congestion levels in their next step and adjust their
directions to avoid congestion, closely matching the behaviours of evacuating
pedestrians in reality. A series of simulation experiments were conducted to
evaluate whether and how BNE affects pedestrian evacuation procedures. The
results showed that: 1) BNE has a large impact on reducing evacuation time; 2)
BNE pedestrians displayed more intelligent and efficient evacuating behaviours;
3) As the proportion of BNE users rises, average evacuation time decreases, and
average comfort level increases. A detailed description of the model and
relevant experimental results is provided in this paper. Several limitations as
well as further works are also identified.
|
[
{
"version": "v1",
"created": "Fri, 25 Nov 2022 17:41:03 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Wang",
"Yiyu",
""
],
[
"Ge",
"Jiaqi",
""
],
[
"Comber",
"Alexis",
""
]
] |
new_dataset
| 0.991284 |
2212.06817
|
Tianhe Yu
|
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph
Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog,
Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Tomas Jackson, Sally
Jesmonth, Nikhil J Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang,
Isabel Leal, Kuang-Huei Lee, Sergey Levine, Yao Lu, Utsav Malla, Deeksha
Manjunath, Igor Mordatch, Ofir Nachum, Carolina Parada, Jodilyn Peralta,
Emily Perez, Karl Pertsch, Jornell Quiambao, Kanishka Rao, Michael Ryoo,
Grecia Salazar, Pannag Sanketi, Kevin Sayed, Jaspiar Singh, Sumedh Sontakke,
Austin Stone, Clayton Tan, Huong Tran, Vincent Vanhoucke, Steve Vega, Quan
Vuong, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Tianhe Yu, Brianna Zitkovich
|
RT-1: Robotics Transformer for Real-World Control at Scale
|
See website at robotics-transformer1.github.io
| null | null | null |
cs.RO cs.AI cs.CL cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
By transferring knowledge from large, diverse, task-agnostic datasets, modern
machine learning models can solve specific downstream tasks either zero-shot or
with small task-specific datasets to a high level of performance. While this
capability has been demonstrated in other fields such as computer vision,
natural language processing or speech recognition, it remains to be shown in
robotics, where the generalization capabilities of the models are particularly
critical due to the difficulty of collecting real-world robotic data. We argue
that one of the keys to the success of such general robotic models lies with
open-ended task-agnostic training, combined with high-capacity architectures
that can absorb all of the diverse, robotic data. In this paper, we present a
model class, dubbed Robotics Transformer, that exhibits promising scalable
model properties. We verify our conclusions in a study of different model
classes and their ability to generalize as a function of the data size, model
size, and data diversity based on a large-scale data collection on real robots
performing real-world tasks. The project's website and videos can be found at
robotics-transformer1.github.io
|
[
{
"version": "v1",
"created": "Tue, 13 Dec 2022 18:55:15 GMT"
},
{
"version": "v2",
"created": "Fri, 11 Aug 2023 17:45:27 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Brohan",
"Anthony",
""
],
[
"Brown",
"Noah",
""
],
[
"Carbajal",
"Justice",
""
],
[
"Chebotar",
"Yevgen",
""
],
[
"Dabis",
"Joseph",
""
],
[
"Finn",
"Chelsea",
""
],
[
"Gopalakrishnan",
"Keerthana",
""
],
[
"Hausman",
"Karol",
""
],
[
"Herzog",
"Alex",
""
],
[
"Hsu",
"Jasmine",
""
],
[
"Ibarz",
"Julian",
""
],
[
"Ichter",
"Brian",
""
],
[
"Irpan",
"Alex",
""
],
[
"Jackson",
"Tomas",
""
],
[
"Jesmonth",
"Sally",
""
],
[
"Joshi",
"Nikhil J",
""
],
[
"Julian",
"Ryan",
""
],
[
"Kalashnikov",
"Dmitry",
""
],
[
"Kuang",
"Yuheng",
""
],
[
"Leal",
"Isabel",
""
],
[
"Lee",
"Kuang-Huei",
""
],
[
"Levine",
"Sergey",
""
],
[
"Lu",
"Yao",
""
],
[
"Malla",
"Utsav",
""
],
[
"Manjunath",
"Deeksha",
""
],
[
"Mordatch",
"Igor",
""
],
[
"Nachum",
"Ofir",
""
],
[
"Parada",
"Carolina",
""
],
[
"Peralta",
"Jodilyn",
""
],
[
"Perez",
"Emily",
""
],
[
"Pertsch",
"Karl",
""
],
[
"Quiambao",
"Jornell",
""
],
[
"Rao",
"Kanishka",
""
],
[
"Ryoo",
"Michael",
""
],
[
"Salazar",
"Grecia",
""
],
[
"Sanketi",
"Pannag",
""
],
[
"Sayed",
"Kevin",
""
],
[
"Singh",
"Jaspiar",
""
],
[
"Sontakke",
"Sumedh",
""
],
[
"Stone",
"Austin",
""
],
[
"Tan",
"Clayton",
""
],
[
"Tran",
"Huong",
""
],
[
"Vanhoucke",
"Vincent",
""
],
[
"Vega",
"Steve",
""
],
[
"Vuong",
"Quan",
""
],
[
"Xia",
"Fei",
""
],
[
"Xiao",
"Ted",
""
],
[
"Xu",
"Peng",
""
],
[
"Xu",
"Sichun",
""
],
[
"Yu",
"Tianhe",
""
],
[
"Zitkovich",
"Brianna",
""
]
] |
new_dataset
| 0.998808 |
2303.14029
|
Murali Sridharan
|
Murali Sridharan, Leevi Rantala, Mika M\"antyl\"a
|
PENTACET data -- 23 Million Contextual Code Comments and 250,000 SATD
comments
|
Accepted in MSR 2023 Tools and Data Showcase
| null | null | null |
cs.SE cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Most Self-Admitted Technical Debt (SATD) research utilizes explicit SATD
features such as 'TODO' and 'FIXME' for SATD detection. A closer look reveals
several SATD research uses simple SATD ('Easy to Find') code comments without
the contextual data (preceding and succeeding source code context). This work
addresses this gap through PENTACET (or 5C dataset) data. PENTACET is a large
Curated Contextual Code Comments per Contributor and the most extensive SATD
data. We mine 9,096 Open Source Software Java projects with a total of 435
million LOC. The outcome is a dataset with 23 million code comments, preceding
and succeeding source code context for each comment, and more than 250,000
comments labeled as SATD, including both 'Easy to Find' and 'Hard to Find'
SATD. We believe PENTACET data will further SATD research using Artificial
Intelligence techniques.
|
[
{
"version": "v1",
"created": "Fri, 24 Mar 2023 14:42:42 GMT"
},
{
"version": "v2",
"created": "Fri, 11 Aug 2023 13:40:46 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Sridharan",
"Murali",
""
],
[
"Rantala",
"Leevi",
""
],
[
"Mäntylä",
"Mika",
""
]
] |
new_dataset
| 0.999836 |
2305.10615
|
Jiatong Shi
|
Jiatong Shi, Dan Berrebbi, William Chen, Ho-Lam Chung, En-Pei Hu, Wei
Ping Huang, Xuankai Chang, Shang-Wen Li, Abdelrahman Mohamed, Hung-yi Lee,
Shinji Watanabe
|
ML-SUPERB: Multilingual Speech Universal PERformance Benchmark
|
Accepted by Interspeech
| null | null | null |
cs.SD cs.CL eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Speech processing Universal PERformance Benchmark (SUPERB) is a leaderboard
to benchmark the performance of Self-Supervised Learning (SSL) models on
various speech processing tasks. However, SUPERB largely considers English
speech in its evaluation. This paper presents multilingual SUPERB (ML-SUPERB),
covering 143 languages (ranging from high-resource to endangered), and
considering both automatic speech recognition and language identification.
Following the concept of SUPERB, ML-SUPERB utilizes frozen SSL features and
employs a simple framework for multilingual tasks by learning a shallow
downstream model. Similar to the SUPERB benchmark, we find speech SSL models
can significantly improve performance compared to FBANK features. Furthermore,
we find that multilingual models do not always perform better than their
monolingual counterparts. We will release ML-SUPERB as a challenge with
organized datasets and reproducible training scripts for future multilingual
representation research.
|
[
{
"version": "v1",
"created": "Thu, 18 May 2023 00:01:27 GMT"
},
{
"version": "v2",
"created": "Fri, 11 Aug 2023 17:39:21 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Shi",
"Jiatong",
""
],
[
"Berrebbi",
"Dan",
""
],
[
"Chen",
"William",
""
],
[
"Chung",
"Ho-Lam",
""
],
[
"Hu",
"En-Pei",
""
],
[
"Huang",
"Wei Ping",
""
],
[
"Chang",
"Xuankai",
""
],
[
"Li",
"Shang-Wen",
""
],
[
"Mohamed",
"Abdelrahman",
""
],
[
"Lee",
"Hung-yi",
""
],
[
"Watanabe",
"Shinji",
""
]
] |
new_dataset
| 0.998291 |
2306.02649
|
Paul Jungeblut
|
Paul Jungeblut
|
On the Complexity of Lombardi Graph Drawing
|
Appears in the Proceedings of the 31st International Symposium on
Graph Drawing and Network Visualization (GD 2023)
| null | null | null |
cs.CG cs.CC math.CO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In a Lombardi drawing of a graph the vertices are drawn as points and the
edges are drawn as circular arcs connecting their respective endpoints.
Additionally, all vertices have perfect angular resolution, i.e., all angles
incident to a vertex $v$ have size $2\pi/\mathrm{deg}(v)$. We prove that it is
$\exists\mathbb{R}$-complete to determine whether a given graph admits a
Lombardi drawing respecting a fixed cyclic ordering of the incident edges
around each vertex. In particular, this implies NP-hardness. While most
previous work studied the (non-)existence of Lombardi drawings for different
graph classes, our result is the first on the computational complexity of
finding Lombardi drawings of general graphs.
|
[
{
"version": "v1",
"created": "Mon, 5 Jun 2023 07:33:08 GMT"
},
{
"version": "v2",
"created": "Fri, 11 Aug 2023 09:38:37 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Jungeblut",
"Paul",
""
]
] |
new_dataset
| 0.987599 |
2307.03903
|
Huafeng Li
|
Huafeng Li, Le Xu, Yafei Zhang, Dapeng Tao, Zhengtao Yu
|
Adversarial Self-Attack Defense and Spatial-Temporal Relation Mining for
Visible-Infrared Video Person Re-Identification
|
11 pages,8 figures
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In visible-infrared video person re-identification (re-ID), extracting
features not affected by complex scenes (such as modality, camera views,
pedestrian pose, background, etc.) changes, and mining and utilizing motion
information are the keys to solving cross-modal pedestrian identity matching.
To this end, the paper proposes a new visible-infrared video person re-ID
method from a novel perspective, i.e., adversarial self-attack defense and
spatial-temporal relation mining. In this work, the changes of views, posture,
background and modal discrepancy are considered as the main factors that cause
the perturbations of person identity features. Such interference information
contained in the training samples is used as an adversarial perturbation. It
performs adversarial attacks on the re-ID model during the training to make the
model more robust to these unfavorable factors. The attack from the adversarial
perturbation is introduced by activating the interference information contained
in the input samples without generating adversarial samples, and it can be thus
called adversarial self-attack. This design allows adversarial attack and
defense to be integrated into one framework. This paper further proposes a
spatial-temporal information-guided feature representation network to use the
information in video sequences. The network cannot only extract the information
contained in the video-frame sequences but also use the relation of the local
information in space to guide the network to extract more robust features. The
proposed method exhibits compelling performance on large-scale cross-modality
video datasets. The source code of the proposed method will be released at
https://github.com/lhf12278/xxx.
|
[
{
"version": "v1",
"created": "Sat, 8 Jul 2023 05:03:10 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Jul 2023 09:08:49 GMT"
},
{
"version": "v3",
"created": "Fri, 11 Aug 2023 09:15:27 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Li",
"Huafeng",
""
],
[
"Xu",
"Le",
""
],
[
"Zhang",
"Yafei",
""
],
[
"Tao",
"Dapeng",
""
],
[
"Yu",
"Zhengtao",
""
]
] |
new_dataset
| 0.998953 |
2307.15483
|
Max Franke
|
Max Franke, Steffen Koch
|
Compact Phase Histograms for Guided Exploration of Periodicity
|
IEEE VIS 2023 Short Paper
| null | null | null |
cs.GR
|
http://creativecommons.org/licenses/by/4.0/
|
Periodically occurring accumulations of events or measured values are present
in many time-dependent datasets and can be of interest for analyses. The
frequency of such periodic behavior is often not known in advance, making it
difficult to detect and tedious to explore. Automated analysis methods exist,
but can be too costly for smooth, interactive analysis. We propose a compact
visual representation that reveals periodicity by showing a phase histogram for
a given period length that can be used standalone or in combination with other
linked visualizations. Our approach supports guided, interactive analyses by
suggesting other period lengths to explore, which are ranked based on two
quality measures. We further describe how the phase can be mapped to visual
representations in other views to reveal periodicity there.
|
[
{
"version": "v1",
"created": "Fri, 28 Jul 2023 11:16:28 GMT"
},
{
"version": "v2",
"created": "Fri, 11 Aug 2023 09:33:42 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Franke",
"Max",
""
],
[
"Koch",
"Steffen",
""
]
] |
new_dataset
| 0.959075 |
2308.00282
|
Hyeon Jeon
|
Hyeon Jeon, Aeri Cho, Jinhwa Jang, Soohyun Lee, Jake Hyun, Hyung-Kwon
Ko, Jaemin Jo, Jinwook Seo
|
ZADU: A Python Library for Evaluating the Reliability of Dimensionality
Reduction Embeddings
|
2023 IEEE Visualization and Visual Analytics (IEEE VIS 2023) Short
paper
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Dimensionality reduction (DR) techniques inherently distort the original
structure of input high-dimensional data, producing imperfect low-dimensional
embeddings. Diverse distortion measures have thus been proposed to evaluate the
reliability of DR embeddings. However, implementing and executing distortion
measures in practice has so far been time-consuming and tedious. To address
this issue, we present ZADU, a Python library that provides distortion
measures. ZADU is not only easy to install and execute but also enables
comprehensive evaluation of DR embeddings through three key features. First,
the library covers a wide range of distortion measures. Second, it
automatically optimizes the execution of distortion measures, substantially
reducing the running time required to execute multiple measures. Last, the
library informs how individual points contribute to the overall distortions,
facilitating the detailed analysis of DR embeddings. By simulating a real-world
scenario of optimizing DR embeddings, we verify that our optimization scheme
substantially reduces the time required to execute distortion measures.
Finally, as an application of ZADU, we present another library called ZADUVis
that allows users to easily create distortion visualizations that depict the
extent to which each region of an embedding suffers from distortions.
|
[
{
"version": "v1",
"created": "Tue, 1 Aug 2023 04:38:15 GMT"
},
{
"version": "v2",
"created": "Fri, 11 Aug 2023 04:39:33 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Jeon",
"Hyeon",
""
],
[
"Cho",
"Aeri",
""
],
[
"Jang",
"Jinhwa",
""
],
[
"Lee",
"Soohyun",
""
],
[
"Hyun",
"Jake",
""
],
[
"Ko",
"Hyung-Kwon",
""
],
[
"Jo",
"Jaemin",
""
],
[
"Seo",
"Jinwook",
""
]
] |
new_dataset
| 0.979811 |
2308.03043
|
Fatemah Almeman
|
Fatemah Almeman, Hadi Sheikhi, Luis Espinosa-Anke
|
3D-EX : A Unified Dataset of Definitions and Dictionary Examples
|
11 pages (including references pages), 9 tables, and 1 figure. This
paper is submitted to RANLP2023
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Definitions are a fundamental building block in lexicography, linguistics and
computational semantics. In NLP, they have been used for retrofitting word
embeddings or augmenting contextual representations in language models.
However, lexical resources containing definitions exhibit a wide range of
properties, which has implications in the behaviour of models trained and
evaluated on them. In this paper, we introduce 3D- EX , a dataset that aims to
fill this gap by combining well-known English resources into one centralized
knowledge repository in the form of <term, definition, example> triples. 3D- EX
is a unified evaluation framework with carefully pre-computed
train/validation/test splits to prevent memorization. We report experimental
results that suggest that this dataset could be effectively leveraged in
downstream NLP tasks. Code and data are available at
https://github.com/F-Almeman/3D-EX .
|
[
{
"version": "v1",
"created": "Sun, 6 Aug 2023 07:59:12 GMT"
},
{
"version": "v2",
"created": "Fri, 11 Aug 2023 12:07:52 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Almeman",
"Fatemah",
""
],
[
"Sheikhi",
"Hadi",
""
],
[
"Espinosa-Anke",
"Luis",
""
]
] |
new_dataset
| 0.999674 |
2308.04452
|
Faisal Haque Bappy
|
Mirza Kamrul Bashar Shuhan, Tariqul Islam, Enam Ahmed Shuvo, Faisal
Haque Bappy, Kamrul Hasan, Carlos Caicedo
|
Quarks: A Secure and Decentralized Blockchain-Based Messaging Network
| null | null |
10.1109/CSCloud-EdgeCom58631.2023.00053
| null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In last two decades, messaging systems have gained widespread popularity both
in the enterprise and consumer sectors. Many of these systems used secure
protocols like end-to-end encryption to ensure strong security in one-to-one
communication. However, the majority of them rely on centralized servers, which
allows them to use their users' personal data. Also, it allows the government
to track and regulate their citizens' activities, which poses significant
threats to "digital freedom". Also, these systems have failed to achieve
security attributes like confidentiality, integrity, and privacy for group
communications. In this paper, we present a novel blockchain-based secure
messaging system named Quarks that overcomes the security pitfalls of the
existing systems and eliminates centralized control. We have analyzed our
architecture with security models to demonstrate the system's reliability and
usability. We have developed a Proof of Concept (PoC) of the Quarks system
leveraging Distributed Ledger Technology (DLT) and conducted load testing on
that. We noticed that our PoC system achieves all the desired attributes that
are prevalent in a traditional centralized messaging scheme despite the limited
capacity of the development and testing environment. Therefore, this assures us
of the applicability of such systems in the near future if scaled up properly.
|
[
{
"version": "v1",
"created": "Sat, 5 Aug 2023 02:24:18 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Shuhan",
"Mirza Kamrul Bashar",
""
],
[
"Islam",
"Tariqul",
""
],
[
"Shuvo",
"Enam Ahmed",
""
],
[
"Bappy",
"Faisal Haque",
""
],
[
"Hasan",
"Kamrul",
""
],
[
"Caicedo",
"Carlos",
""
]
] |
new_dataset
| 0.998276 |
2308.05750
|
Alireza Shafizadeh
|
Alireza Shafizadeh, Hossein Shahbeik, Mohammad Hossein Nadian, Vijai
Kumar Gupta, Abdul-Sattar Nizami, Su Shiung Lam, Wanxi Peng, Junting Pan,
Meisam Tabatabaei, Mortaza Aghbashlo
|
Turning hazardous volatile matter compounds into fuel by catalytic steam
reforming: An evolutionary machine learning approach
| null | null |
10.1016/j.jclepro.2023.137329
| null |
cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Chemical and biomass processing systems release volatile matter compounds
into the environment daily. Catalytic reforming can convert these compounds
into valuable fuels, but developing stable and efficient catalysts is
challenging. Machine learning can handle complex relationships in big data and
optimize reaction conditions, making it an effective solution for addressing
the mentioned issues. This study is the first to develop a
machine-learning-based research framework for modeling, understanding, and
optimizing the catalytic steam reforming of volatile matter compounds. Toluene
catalytic steam reforming is used as a case study to show how chemical/textural
analyses (e.g., X-ray diffraction analysis) can be used to obtain input
features for machine learning models. Literature is used to compile a database
covering a variety of catalyst characteristics and reaction conditions. The
process is thoroughly analyzed, mechanistically discussed, modeled by six
machine learning models, and optimized using the particle swarm optimization
algorithm. Ensemble machine learning provides the best prediction performance
(R2 > 0.976) for toluene conversion and product distribution. The optimal tar
conversion (higher than 77.2%) is obtained at temperatures between 637.44 and
725.62 {\deg}C, with a steam-to-carbon molar ratio of 5.81-7.15 and a catalyst
BET surface area 476.03-638.55 m2/g. The feature importance analysis
satisfactorily reveals the effects of input descriptors on model prediction.
Operating conditions (50.9%) and catalyst properties (49.1%) are equally
important in modeling. The developed framework can expedite the search for
optimal catalyst characteristics and reaction conditions, not only for
catalytic chemical processing but also for related research areas.
|
[
{
"version": "v1",
"created": "Tue, 25 Jul 2023 16:29:07 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Shafizadeh",
"Alireza",
""
],
[
"Shahbeik",
"Hossein",
""
],
[
"Nadian",
"Mohammad Hossein",
""
],
[
"Gupta",
"Vijai Kumar",
""
],
[
"Nizami",
"Abdul-Sattar",
""
],
[
"Lam",
"Su Shiung",
""
],
[
"Peng",
"Wanxi",
""
],
[
"Pan",
"Junting",
""
],
[
"Tabatabaei",
"Meisam",
""
],
[
"Aghbashlo",
"Mortaza",
""
]
] |
new_dataset
| 0.952544 |
2308.05818
|
Unay Dorken Gallastegi
|
Unay Dorken Gallastegi, Hoover Rueda-Chacon, Martin J. Stevens, and
Vivek K Goyal
|
Absorption-Based, Passive Range Imaging from Hyperspectral Thermal
Measurements
|
15 pages, 14 figures
| null | null | null |
cs.CV eess.SP
|
http://creativecommons.org/licenses/by/4.0/
|
Passive hyperspectral long-wave infrared measurements are remarkably
informative about the surroundings, such as remote object material composition,
temperature, and range; and air temperature and gas concentrations. Remote
object material and temperature determine the spectrum of thermal radiance, and
range, air temperature, and gas concentrations determine how this spectrum is
modified by propagation to the sensor. We computationally separate these
phenomena, introducing a novel passive range imaging method based on
atmospheric absorption of ambient thermal radiance. Previously demonstrated
passive absorption-based ranging methods assume hot and highly emitting
objects. However, the temperature variation in natural scenes is usually low,
making range imaging challenging. Our method benefits from explicit
consideration of air emission and parametric modeling of atmospheric
absorption. To mitigate noise in low-contrast scenarios, we jointly estimate
range and intrinsic object properties by exploiting a variety of absorption
lines spread over the infrared spectrum. Along with Monte Carlo simulations
that demonstrate the importance of regularization, temperature differentials,
and availability of many spectral bands, we apply this method to long-wave
infrared (8--13 $\mu$m) hyperspectral image data acquired from natural scenes
with no active illumination. Range features from 15m to 150m are recovered,
with good qualitative match to unaligned lidar data.
|
[
{
"version": "v1",
"created": "Thu, 10 Aug 2023 18:35:22 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Gallastegi",
"Unay Dorken",
""
],
[
"Rueda-Chacon",
"Hoover",
""
],
[
"Stevens",
"Martin J.",
""
],
[
"Goyal",
"Vivek K",
""
]
] |
new_dataset
| 0.9963 |
2308.05820
|
Filipe Cordeiro
|
Daniel Rosa, Filipe R. Cordeiro, Ruan Carvalho, Everton Souza, Sergio
Chevtchenko, Luiz Rodrigues, Marcelo Marinho, Thales Vieira and Valmir
Macario
|
Recognizing Handwritten Mathematical Expressions of Vertical Addition
and Subtraction
|
Paper accepted at SIBGRAPI 2023
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Handwritten Mathematical Expression Recognition (HMER) is a challenging task
with many educational applications. Recent methods for HMER have been developed
for complex mathematical expressions in standard horizontal format. However,
solutions for elementary mathematical expression, such as vertical addition and
subtraction, have not been explored in the literature. This work proposes a new
handwritten elementary mathematical expression dataset composed of addition and
subtraction expressions in a vertical format. We also extended the MNIST
dataset to generate artificial images with this structure. Furthermore, we
proposed a solution for offline HMER, able to recognize vertical addition and
subtraction expressions. Our analysis evaluated the object detection algorithms
YOLO v7, YOLO v8, YOLO-NAS, NanoDet and FCOS for identifying the mathematical
symbols. We also proposed a transcription method to map the bounding boxes from
the object detection stage to a mathematical expression in the LATEX markup
sequence. Results show that our approach is efficient, achieving a high
expression recognition rate. The code and dataset are available at
https://github.com/Danielgol/HME-VAS
|
[
{
"version": "v1",
"created": "Thu, 10 Aug 2023 18:39:35 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Rosa",
"Daniel",
""
],
[
"Cordeiro",
"Filipe R.",
""
],
[
"Carvalho",
"Ruan",
""
],
[
"Souza",
"Everton",
""
],
[
"Chevtchenko",
"Sergio",
""
],
[
"Rodrigues",
"Luiz",
""
],
[
"Marinho",
"Marcelo",
""
],
[
"Vieira",
"Thales",
""
],
[
"Macario",
"Valmir",
""
]
] |
new_dataset
| 0.999459 |
2308.05821
|
Houjian Yu
|
Houjian Yu, Xibai Lou, Yang Yang, and Changhyun Choi
|
IOSG: Image-driven Object Searching and Grasping
|
Accepted to IEEE/RSJ International Conference on Intelligent Robots
(IROS 2023). Project page: https://sites.google.com/umn.edu/iosg
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
When robots retrieve specific objects from cluttered scenes, such as home and
warehouse environments, the target objects are often partially occluded or
completely hidden. Robots are thus required to search, identify a target
object, and successfully grasp it. Preceding works have relied on pre-trained
object recognition or segmentation models to find the target object. However,
such methods require laborious manual annotations to train the models and even
fail to find novel target objects. In this paper, we propose an Image-driven
Object Searching and Grasping (IOSG) approach where a robot is provided with
the reference image of a novel target object and tasked to find and retrieve
it. We design a Target Similarity Network that generates a probability map to
infer the location of the novel target. IOSG learns a hierarchical policy; the
high-level policy predicts the subtask type, whereas the low-level policies,
explorer and coordinator, generate effective push and grasp actions. The
explorer is responsible for searching the target object when it is hidden or
occluded by other objects. Once the target object is found, the coordinator
conducts target-oriented pushing and grasping to retrieve the target from the
clutter. The proposed pipeline is trained with full self-supervision in
simulation and applied to a real environment. Our model achieves a 96.0% and
94.5% task success rate on coordination and exploration tasks in simulation
respectively, and 85.0% success rate on a real robot for the search-and-grasp
task.
|
[
{
"version": "v1",
"created": "Thu, 10 Aug 2023 18:41:24 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Yu",
"Houjian",
""
],
[
"Lou",
"Xibai",
""
],
[
"Yang",
"Yang",
""
],
[
"Choi",
"Changhyun",
""
]
] |
new_dataset
| 0.960093 |
2308.05882
|
Christophe Bonneville
|
Christophe Bonneville, Youngsoo Choi, Debojyoti Ghosh, Jonathan L.
Belof
|
GPLaSDI: Gaussian Process-based Interpretable Latent Space Dynamics
Identification through Deep Autoencoder
| null | null | null | null |
cs.CE cs.LG cs.NA math.NA
|
http://creativecommons.org/licenses/by/4.0/
|
Numerically solving partial differential equations (PDEs) can be challenging
and computationally expensive. This has led to the development of reduced-order
models (ROMs) that are accurate but faster than full order models (FOMs).
Recently, machine learning advances have enabled the creation of non-linear
projection methods, such as Latent Space Dynamics Identification (LaSDI). LaSDI
maps full-order PDE solutions to a latent space using autoencoders and learns
the system of ODEs governing the latent space dynamics. By interpolating and
solving the ODE system in the reduced latent space, fast and accurate ROM
predictions can be made by feeding the predicted latent space dynamics into the
decoder. In this paper, we introduce GPLaSDI, a novel LaSDI-based framework
that relies on Gaussian process (GP) for latent space ODE interpolations. Using
GPs offers two significant advantages. First, it enables the quantification of
uncertainty over the ROM predictions. Second, leveraging this prediction
uncertainty allows for efficient adaptive training through a greedy selection
of additional training data points. This approach does not require prior
knowledge of the underlying PDEs. Consequently, GPLaSDI is inherently
non-intrusive and can be applied to problems without a known PDE or its
residual. We demonstrate the effectiveness of our approach on the Burgers
equation, Vlasov equation for plasma physics, and a rising thermal bubble
problem. Our proposed method achieves between 200 and 100,000 times speed-up,
with up to 7% relative error.
|
[
{
"version": "v1",
"created": "Thu, 10 Aug 2023 23:54:12 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Bonneville",
"Christophe",
""
],
[
"Choi",
"Youngsoo",
""
],
[
"Ghosh",
"Debojyoti",
""
],
[
"Belof",
"Jonathan L.",
""
]
] |
new_dataset
| 0.986859 |
2308.05884
|
Alpin Dale
|
Tear Gosling, Alpin Dale, Yinhe Zheng
|
PIPPA: A Partially Synthetic Conversational Dataset
|
13 pages, 5 figures
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-sa/4.0/
|
With the emergence of increasingly powerful large language models, there is a
burgeoning interest in leveraging these models for casual conversation and
role-play applications. However, existing conversational and role-playing
datasets often fail to capture the diverse and nuanced interactions typically
exhibited by real-world role-play participants. To address this limitation and
contribute to the rapidly growing field, we introduce a partially-synthetic
dataset named PIPPA (Personal Interaction Pairs between People and AI). PIPPA
is a result of a community-driven crowdsourcing effort involving a group of
role-play enthusiasts. The dataset comprises over 1 million utterances that are
distributed across 26,000 conversation sessions and provides a rich resource
for researchers and AI developers to explore and refine conversational AI
systems in the context of role-play scenarios.
|
[
{
"version": "v1",
"created": "Fri, 11 Aug 2023 00:33:26 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Gosling",
"Tear",
""
],
[
"Dale",
"Alpin",
""
],
[
"Zheng",
"Yinhe",
""
]
] |
new_dataset
| 0.999641 |
2308.05921
|
Ryugo Morita
|
Ryugo Morita, Zhiqiang Zhang, Jinjia Zhou
|
BATINet: Background-Aware Text to Image Synthesis and Manipulation
Network
|
Accepted to ICIP2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Background-Induced Text2Image (BIT2I) aims to generate foreground content
according to the text on the given background image. Most studies focus on
generating high-quality foreground content, although they ignore the
relationship between the two contents. In this study, we analyzed a novel
Background-Aware Text2Image (BAT2I) task in which the generated content matches
the input background. We proposed a Background-Aware Text to Image synthesis
and manipulation Network (BATINet), which contains two key components: Position
Detect Network (PDN) and Harmonize Network (HN). The PDN detects the most
plausible position of the text-relevant object in the background image. The HN
harmonizes the generated content referring to background style information.
Finally, we reconstructed the generation network, which consists of the
multi-GAN and attention module to match more user preferences. Moreover, we can
apply BATINet to text-guided image manipulation. It solves the most challenging
task of manipulating the shape of an object. We demonstrated through
qualitative and quantitative evaluations on the CUB dataset that the proposed
model outperforms other state-of-the-art methods.
|
[
{
"version": "v1",
"created": "Fri, 11 Aug 2023 03:22:33 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Morita",
"Ryugo",
""
],
[
"Zhang",
"Zhiqiang",
""
],
[
"Zhou",
"Jinjia",
""
]
] |
new_dataset
| 0.999721 |
2308.05938
|
Xing Lan
|
Xing Lan, Jiayi Lyu, Hanyu Jiang, Kun Dong, Zehai Niu, Yi Zhang, Jian
Xue
|
FoodSAM: Any Food Segmentation
|
Code is available at https://github.com/jamesjg/FoodSAM
| null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we explore the zero-shot capability of the Segment Anything
Model (SAM) for food image segmentation. To address the lack of class-specific
information in SAM-generated masks, we propose a novel framework, called
FoodSAM. This innovative approach integrates the coarse semantic mask with
SAM-generated masks to enhance semantic segmentation quality. Besides, we
recognize that the ingredients in food can be supposed as independent
individuals, which motivated us to perform instance segmentation on food
images. Furthermore, FoodSAM extends its zero-shot capability to encompass
panoptic segmentation by incorporating an object detector, which renders
FoodSAM to effectively capture non-food object information. Drawing inspiration
from the recent success of promptable segmentation, we also extend FoodSAM to
promptable segmentation, supporting various prompt variants. Consequently,
FoodSAM emerges as an all-encompassing solution capable of segmenting food
items at multiple levels of granularity. Remarkably, this pioneering framework
stands as the first-ever work to achieve instance, panoptic, and promptable
segmentation on food images. Extensive experiments demonstrate the feasibility
and impressing performance of FoodSAM, validating SAM's potential as a
prominent and influential tool within the domain of food image segmentation. We
release our code at https://github.com/jamesjg/FoodSAM.
|
[
{
"version": "v1",
"created": "Fri, 11 Aug 2023 04:42:10 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Lan",
"Xing",
""
],
[
"Lyu",
"Jiayi",
""
],
[
"Jiang",
"Hanyu",
""
],
[
"Dong",
"Kun",
""
],
[
"Niu",
"Zehai",
""
],
[
"Zhang",
"Yi",
""
],
[
"Xue",
"Jian",
""
]
] |
new_dataset
| 0.999314 |
2308.05939
|
Dominic Maggio
|
Dominic Maggio, Courtney Mario, Luca Carlone
|
VERF: Runtime Monitoring of Pose Estimation with Neural Radiance Fields
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
We present VERF, a collection of two methods (VERF-PnP and VERF-Light) for
providing runtime assurance on the correctness of a camera pose estimate of a
monocular camera without relying on direct depth measurements. We leverage the
ability of NeRF (Neural Radiance Fields) to render novel RGB perspectives of a
scene. We only require as input the camera image whose pose is being estimated,
an estimate of the camera pose we want to monitor, and a NeRF model containing
the scene pictured by the camera. We can then predict if the pose estimate is
within a desired distance from the ground truth and justify our prediction with
a level of confidence. VERF-Light does this by rendering a viewpoint with NeRF
at the estimated pose and estimating its relative offset to the sensor image up
to scale. Since scene scale is unknown, the approach renders another auxiliary
image and reasons over the consistency of the optical flows across the three
images. VERF-PnP takes a different approach by rendering a stereo pair of
images with NeRF and utilizing the Perspective-n-Point (PnP) algorithm. We
evaluate both methods on the LLFF dataset, on data from a Unitree A1 quadruped
robot, and on data collected from Blue Origin's sub-orbital New Shepard rocket
to demonstrate the effectiveness of the proposed pose monitoring method across
a range of scene scales. We also show monitoring can be completed in under half
a second on a 3090 GPU.
|
[
{
"version": "v1",
"created": "Fri, 11 Aug 2023 04:43:31 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Maggio",
"Dominic",
""
],
[
"Mario",
"Courtney",
""
],
[
"Carlone",
"Luca",
""
]
] |
new_dataset
| 0.996696 |
2308.05950
|
Sumit Patel
|
Ras Dwivedi, Sumit Patel, Prof. Sandeep Shukla
|
Blockchain-Based Transferable Digital Rights of Land
|
5 pages, Paper presented in https://easychair.org/cfp/ICSF2023
| null | null | null |
cs.DC cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
Land, being a scarce and valuable resource, is in high demand, especially in
densely populated areas of older cities. Development authorities require land
for infrastructure projects and other amenities, while landowners hold onto
their land for both its usage and its financial value. Transferable Development
Rights (TDRs) serve as a mechanism to separate the development rights
associated with the land from the physical land itself. Development authorities
acquire the land by offering compensation in the form of TDRs, which hold
monetary value. In this paper, we present the tokenization of development
rights, focusing on the implementation in collaboration with a development
authority. While there have been previous implementations of land tokenization,
we believe our approach is the first to tokenize development rights
specifically. Our implementation addresses practical challenges related to
record-keeping, ground verification of land, and the unique identification of
stakeholders. We ensure the accurate evaluation of development rights by
incorporating publicly available circle rates, which consider the ground
development of the land and its surrounding areas.
|
[
{
"version": "v1",
"created": "Fri, 11 Aug 2023 05:50:40 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Dwivedi",
"Ras",
""
],
[
"Patel",
"Sumit",
""
],
[
"Shukla",
"Prof. Sandeep",
""
]
] |
new_dataset
| 0.998695 |
2308.05960
|
Zhiwei Liu
|
Zhiwei Liu, Weiran Yao, Jianguo Zhang, Le Xue, Shelby Heinecke,
Rithesh Murthy, Yihao Feng, Zeyuan Chen, Juan Carlos Niebles, Devansh Arpit,
Ran Xu, Phil Mui, Huan Wang, Caiming Xiong, Silvio Savarese
|
BOLAA: Benchmarking and Orchestrating LLM-augmented Autonomous Agents
|
Preprint
| null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
The massive successes of large language models (LLMs) encourage the emerging
exploration of LLM-augmented Autonomous Agents (LAAs). An LAA is able to
generate actions with its core LLM and interact with environments, which
facilitates the ability to resolve complex tasks by conditioning on past
interactions such as observations and actions. Since the investigation of LAA
is still very recent, limited explorations are available. Therefore, we provide
a comprehensive comparison of LAA in terms of both agent architectures and LLM
backbones. Additionally, we propose a new strategy to orchestrate multiple LAAs
such that each labor LAA focuses on one type of action, \textit{i.e.} BOLAA,
where a controller manages the communication among multiple agents. We conduct
simulations on both decision-making and multi-step reasoning environments,
which comprehensively justify the capacity of LAAs. Our performance results
provide quantitative suggestions for designing LAA architectures and the
optimal choice of LLMs, as well as the compatibility of both. We release our
implementation code of LAAs to the public at
\url{https://github.com/salesforce/BOLAA}.
|
[
{
"version": "v1",
"created": "Fri, 11 Aug 2023 06:37:54 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Liu",
"Zhiwei",
""
],
[
"Yao",
"Weiran",
""
],
[
"Zhang",
"Jianguo",
""
],
[
"Xue",
"Le",
""
],
[
"Heinecke",
"Shelby",
""
],
[
"Murthy",
"Rithesh",
""
],
[
"Feng",
"Yihao",
""
],
[
"Chen",
"Zeyuan",
""
],
[
"Niebles",
"Juan Carlos",
""
],
[
"Arpit",
"Devansh",
""
],
[
"Xu",
"Ran",
""
],
[
"Mui",
"Phil",
""
],
[
"Wang",
"Huan",
""
],
[
"Xiong",
"Caiming",
""
],
[
"Savarese",
"Silvio",
""
]
] |
new_dataset
| 0.999716 |
2308.05992
|
Inhyuk Oh
|
In Hyuk Oh, Ju Won Seo, Jin Sung Kim, and Chung Choo Chung
|
Reachable Set-based Path Planning for Automated Vertical Parking System
|
8 pages, 10 figures, conference. This is the Accepted Manuscript
version of an article accepted for publication in [IEEE International
Conference on Intelligent Transportation Systems ITSC 2023]. IOP Publishing
Ltd is not responsible for any errors or omissions in this version of the
manuscript or any version derived from it. No information about DOI has been
posted yet
| null | null | null |
cs.RO cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper proposes a local path planning method with a reachable set for
Automated vertical Parking Systems (APS). First, given a parking lot layout
with a goal position, we define an intermediate pose for the APS to accomplish
reverse parking with a single maneuver, i.e., without changing the gear shift.
Then, we introduce a reachable set which is a set of points consisting of the
grid points of all possible intermediate poses. Once the APS approaches the
goal position, it must select an intermediate pose in the reachable set. A
minimization problem was formulated and solved to choose the intermediate pose.
We performed various scenarios with different parking lot conditions. We used
the Hybrid-A* algorithm for the global path planning to move the vehicle from
the starting pose to the intermediate pose and utilized clothoid-based local
path planning to move from the intermediate pose to the goal pose.
Additionally, we designed a controller to follow the generated path and
validated its tracking performance. It was confirmed that the tracking error in
the mean root square for the lateral position was bounded within 0.06m and for
orientation within 0.01rad.
|
[
{
"version": "v1",
"created": "Fri, 11 Aug 2023 07:59:13 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Oh",
"In Hyuk",
""
],
[
"Seo",
"Ju Won",
""
],
[
"Kim",
"Jin Sung",
""
],
[
"Chung",
"Chung Choo",
""
]
] |
new_dataset
| 0.975864 |
2308.06007
|
Soumya Prakash Dash
|
Soumya P. Dash and Aryan Kaushik
|
RIS-Assisted 6G Wireless Communications: A Novel Statistical Framework
in the Presence of Direct Channel
|
5 pages
| null | null | null |
cs.IT eess.SP math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A RIS-assisted wireless communication system in the presence of a direct
communication path between the transceiver pair is considered in this paper.
The transmitter-RIS and the RIS-receiver channels follow independent Nakagami-m
distributions, and the direct channel between the transceiver pair follows a
Rayleigh distribution. Considering this system model, the statistics of the
composite channel for the RIS-assisted communication system are derived in
terms of obtaining novel expressions for the probability density functions for
the magnitude and the phase of the communication channel. The correctness of
the analytical framework is verified via Monte Carlo simulations, and the
effects of the shape parameters of the channels and the number of reflecting
elements in the RIS on the randomness of the composite channel are studied via
numerical results.
|
[
{
"version": "v1",
"created": "Fri, 11 Aug 2023 08:26:48 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Dash",
"Soumya P.",
""
],
[
"Kaushik",
"Aryan",
""
]
] |
new_dataset
| 0.993604 |
2308.06076
|
Haoyu Wang
|
Haoyu Wang, Haozhe Wu, Junliang Xing, Jia Jia
|
Versatile Face Animator: Driving Arbitrary 3D Facial Avatar in RGBD
Space
|
Accepted by ACM MM2023
| null |
10.1145/3581783.3612065
| null |
cs.CV cs.MM
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Creating realistic 3D facial animation is crucial for various applications in
the movie production and gaming industry, especially with the burgeoning demand
in the metaverse. However, prevalent methods such as blendshape-based
approaches and facial rigging techniques are time-consuming, labor-intensive,
and lack standardized configurations, making facial animation production
challenging and costly. In this paper, we propose a novel self-supervised
framework, Versatile Face Animator, which combines facial motion capture with
motion retargeting in an end-to-end manner, eliminating the need for
blendshapes or rigs. Our method has the following two main characteristics: 1)
we propose an RGBD animation module to learn facial motion from raw RGBD videos
by hierarchical motion dictionaries and animate RGBD images rendered from 3D
facial mesh coarse-to-fine, enabling facial animation on arbitrary 3D
characters regardless of their topology, textures, blendshapes, and rigs; and
2) we introduce a mesh retarget module to utilize RGBD animation to create 3D
facial animation by manipulating facial mesh with controller transformations,
which are estimated from dense optical flow fields and blended together with
geodesic-distance-based weights. Comprehensive experiments demonstrate the
effectiveness of our proposed framework in generating impressive 3D facial
animation results, highlighting its potential as a promising solution for the
cost-effective and efficient production of facial animation in the metaverse.
|
[
{
"version": "v1",
"created": "Fri, 11 Aug 2023 11:29:01 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Wang",
"Haoyu",
""
],
[
"Wu",
"Haozhe",
""
],
[
"Xing",
"Junliang",
""
],
[
"Jia",
"Jia",
""
]
] |
new_dataset
| 0.998933 |
2308.06082
|
Manish Kumar
|
Manish Kumar
|
Security of XCB and HCTR
|
M.Tech Dissertation. Indian Statistical Institute, Kolkata, July 2018
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Tweakable Enciphering Scheme (TES) is a length preserving scheme which
provides confidentiality and admissible integrity. XCB (Extended Code Book) is
a TES which was introduced in 2004. In 2007, it was modified and security bound
was provided. Later, these two versions were referred to as XCBv1 and XCBv2
respectively. XCBv2 was proposed as the IEEE-std 1619.2 2010 for encryption of
sector oriented storage media. In 2013, first time Security bound of XCBv1 was
given and XCBv2's security bound was enhanced. A constant of $2^{22}$ appears
in the security bounds of the XCBv1 and XCBv2.
We showed that this constant of $2^{22}$ can be reduced to $2^{5}$. Further,
we modified the XCB (MXCB) scheme such that it gives better security bound
compared to the present XCB scheme. We also analyzed some weak keys attack on
XCB and a type of TES known as HCTR (proposed in 2005). We performed
distinguishing attack and the hash key recovery attack on HCTR. Next, we
analyzed the dependency of the two different keys in HCTR.
|
[
{
"version": "v1",
"created": "Fri, 11 Aug 2023 11:45:09 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Kumar",
"Manish",
""
]
] |
new_dataset
| 0.980525 |
2308.06113
|
Maximilian Kaul
|
Maximilian Kaul, Alexander K\"uchler, Christian Banse
|
A Uniform Representation of Classical and Quantum Source Code for Static
Code Analysis
|
2023 IEEE International Conference on Quantum Computing and
Engineering (QCE)
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The emergence of quantum computing raises the question of how to identify
(security-relevant) programming errors during development. However, current
static code analysis tools fail to model information specific to quantum
computing. In this paper, we identify this information and propose to extend
classical code analysis tools accordingly. Among such tools, we identify the
Code Property Graph to be very well suited for this task as it can be easily
extended with quantum computing specific information. For our proof of concept,
we implemented a tool which includes information from the quantum world in the
graph and demonstrate its ability to analyze source code written in Qiskit and
OpenQASM. Our tool brings together the information from the classical and
quantum world, enabling analysis across both domains. By combining all relevant
information into a single detailed analysis, this powerful tool can facilitate
tackling future quantum source code analysis challenges.
|
[
{
"version": "v1",
"created": "Fri, 11 Aug 2023 13:03:32 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Kaul",
"Maximilian",
""
],
[
"Küchler",
"Alexander",
""
],
[
"Banse",
"Christian",
""
]
] |
new_dataset
| 0.951168 |
2308.06173
|
Amira Guesmi
|
Amira Guesmi, Muhammad Abdullah Hanif, Bassem Ouni, and Muhammed
Shafique
|
Physical Adversarial Attacks For Camera-based Smart Systems: Current
Trends, Categorization, Applications, Research Challenges, and Future Outlook
| null | null | null | null |
cs.CR cs.AI cs.CV cs.LG cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we present a comprehensive survey of the current trends
focusing specifically on physical adversarial attacks. We aim to provide a
thorough understanding of the concept of physical adversarial attacks,
analyzing their key characteristics and distinguishing features. Furthermore,
we explore the specific requirements and challenges associated with executing
attacks in the physical world. Our article delves into various physical
adversarial attack methods, categorized according to their target tasks in
different applications, including classification, detection, face recognition,
semantic segmentation and depth estimation. We assess the performance of these
attack methods in terms of their effectiveness, stealthiness, and robustness.
We examine how each technique strives to ensure the successful manipulation of
DNNs while mitigating the risk of detection and withstanding real-world
distortions. Lastly, we discuss the current challenges and outline potential
future research directions in the field of physical adversarial attacks. We
highlight the need for enhanced defense mechanisms, the exploration of novel
attack strategies, the evaluation of attacks in different application domains,
and the establishment of standardized benchmarks and evaluation criteria for
physical adversarial attacks. Through this comprehensive survey, we aim to
provide a valuable resource for researchers, practitioners, and policymakers to
gain a holistic understanding of physical adversarial attacks in computer
vision and facilitate the development of robust and secure DNN-based systems.
|
[
{
"version": "v1",
"created": "Fri, 11 Aug 2023 15:02:19 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Guesmi",
"Amira",
""
],
[
"Hanif",
"Muhammad Abdullah",
""
],
[
"Ouni",
"Bassem",
""
],
[
"Shafique",
"Muhammed",
""
]
] |
new_dataset
| 0.984806 |
2308.06241
|
Mohammad Maksood Akhter
|
Mohammad Maksood Akhter, Devpriya Kanojia
|
Covid-19 Public Sentiment Analysis for Indian Tweets Classification
| null | null | null | null |
cs.CL cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
When any extraordinary event takes place in the world wide area, it is the
social media that acts as the fastest carrier of the news along with the
consequences dealt with that event. One can gather much information through
social networks regarding the sentiments, behavior, and opinions of the people.
In this paper, we focus mainly on sentiment analysis of twitter data of India
which comprises of COVID-19 tweets. We show how Twitter data has been extracted
and then run sentimental analysis queries on it. This is helpful to analyze the
information in the tweets where opinions are highly unstructured,
heterogeneous, and are either positive or negative or neutral in some cases.
|
[
{
"version": "v1",
"created": "Tue, 1 Aug 2023 09:29:55 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Akhter",
"Mohammad Maksood",
""
],
[
"Kanojia",
"Devpriya",
""
]
] |
new_dataset
| 0.984991 |
2308.06248
|
Robin Hesse
|
Robin Hesse, Simone Schaub-Meyer, Stefan Roth
|
FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of
Explainable AI Methods
|
Accepted at ICCV 2023. Code: https://github.com/visinf/funnybirds
| null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The field of explainable artificial intelligence (XAI) aims to uncover the
inner workings of complex deep neural models. While being crucial for
safety-critical domains, XAI inherently lacks ground-truth explanations, making
its automatic evaluation an unsolved problem. We address this challenge by
proposing a novel synthetic vision dataset, named FunnyBirds, and accompanying
automatic evaluation protocols. Our dataset allows performing semantically
meaningful image interventions, e.g., removing individual object parts, which
has three important implications. First, it enables analyzing explanations on a
part level, which is closer to human comprehension than existing methods that
evaluate on a pixel level. Second, by comparing the model output for inputs
with removed parts, we can estimate ground-truth part importances that should
be reflected in the explanations. Third, by mapping individual explanations
into a common space of part importances, we can analyze a variety of different
explanation types in a single common framework. Using our tools, we report
results for 24 different combinations of neural models and XAI methods,
demonstrating the strengths and weaknesses of the assessed methods in a fully
automatic and systematic manner.
|
[
{
"version": "v1",
"created": "Fri, 11 Aug 2023 17:29:02 GMT"
}
] | 2023-08-14T00:00:00 |
[
[
"Hesse",
"Robin",
""
],
[
"Schaub-Meyer",
"Simone",
""
],
[
"Roth",
"Stefan",
""
]
] |
new_dataset
| 0.999252 |
2112.02399
|
Longtian Qiu
|
Longtian Qiu, Renrui Zhang, Ziyu Guo, Ziyao Zeng, Zilu Guo, Yafeng Li,
Guangnan Zhang
|
VT-CLIP: Enhancing Vision-Language Models with Visual-guided Texts
| null | null | null | null |
cs.CV cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Contrastive Language-Image Pre-training (CLIP) has drawn increasing attention
recently for its transferable visual representation learning. However, due to
the semantic gap within datasets, CLIP's pre-trained image-text alignment
becomes sub-optimal on downstream tasks, which severely harms its transferring
performance. To better adapt the cross-modality embedding space, we propose to
enhance CLIP via Visual-guided Texts, named VT-CLIP. Specifically, we guide
textual features of different categories to adaptively explore informative
regions on the image and aggregate visual features by attention mechanisms. In
this way, the texts become visual-guided, namely, more semantically correlated
with downstream images, which greatly benefits the category-wise matching
process. In few-shot settings, we evaluate our VT-CLIP on 11 well-known
classification datasets to demonstrate its effectiveness.
|
[
{
"version": "v1",
"created": "Sat, 4 Dec 2021 18:34:24 GMT"
},
{
"version": "v2",
"created": "Thu, 3 Nov 2022 08:23:13 GMT"
},
{
"version": "v3",
"created": "Thu, 10 Aug 2023 15:31:54 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Qiu",
"Longtian",
""
],
[
"Zhang",
"Renrui",
""
],
[
"Guo",
"Ziyu",
""
],
[
"Zeng",
"Ziyao",
""
],
[
"Guo",
"Zilu",
""
],
[
"Li",
"Yafeng",
""
],
[
"Zhang",
"Guangnan",
""
]
] |
new_dataset
| 0.969662 |
2201.08157
|
Johannes Hertrich
|
Fabian Altekr\"uger, Johannes Hertrich
|
WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for
Superresolution
| null |
SIAM Journal on Imaging Sciences, vol. 16(3), pp. 1033-1067, 2023
|
10.1137/22M1496542
| null |
cs.CV cs.LG eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Exploiting image patches instead of whole images have proved to be a powerful
approach to tackle various problems in image processing. Recently, Wasserstein
patch priors (WPP), which are based on the comparison of the patch
distributions of the unknown image and a reference image, were successfully
used as data-driven regularizers in the variational formulation of
superresolution. However, for each input image, this approach requires the
solution of a non-convex minimization problem which is computationally costly.
In this paper, we propose to learn two kind of neural networks in an
unsupervised way based on WPP loss functions. First, we show how convolutional
neural networks (CNNs) can be incorporated. Once the network, called WPPNet, is
learned, it can be very efficiently applied to any input image. Second, we
incorporate conditional normalizing flows to provide a tool for uncertainty
quantification. Numerical examples demonstrate the very good performance of
WPPNets for superresolution in various image classes even if the forward
operator is known only approximately.
|
[
{
"version": "v1",
"created": "Thu, 20 Jan 2022 13:04:19 GMT"
},
{
"version": "v2",
"created": "Thu, 5 May 2022 10:42:47 GMT"
},
{
"version": "v3",
"created": "Thu, 5 Jan 2023 10:09:10 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Altekrüger",
"Fabian",
""
],
[
"Hertrich",
"Johannes",
""
]
] |
new_dataset
| 0.99226 |
2202.02270
|
Jonatan Langlet
|
Jonatan Langlet, Ran Ben Basat, Gabriele Oliaro, Michael Mitzenmacher,
Minlan Yu, Gianni Antichi
|
Direct Telemetry Access
|
As appearing in the proceedings of ACM SIGCOMM'23
| null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Fine-grained network telemetry is becoming a modern datacenter standard and
is the basis of essential applications such as congestion control, load
balancing, and advanced troubleshooting. As network size increases and
telemetry gets more fine-grained, there is a tremendous growth in the amount of
data needed to be reported from switches to collectors to enable network-wide
view. As a consequence, it is progressively hard to scale data collection
systems.
We introduce Direct Telemetry Access (DTA), a solution optimized for
aggregating and moving hundreds of millions of reports per second from switches
into queryable data structures in collectors' memory. DTA is lightweight and it
is able to greatly reduce overheads at collectors. DTA is built on top of RDMA,
and we propose novel and expressive reporting primitives to allow easy
integration with existing state-of-the-art telemetry mechanisms such as INT or
Marple.
We show that DTA significantly improves telemetry collection rates. For
example, when used with INT, it can collect and aggregate over 400M reports per
second with a single server, improving over the Atomic MultiLog by up to $16$x.
|
[
{
"version": "v1",
"created": "Fri, 4 Feb 2022 17:55:09 GMT"
},
{
"version": "v2",
"created": "Wed, 21 Sep 2022 10:28:03 GMT"
},
{
"version": "v3",
"created": "Thu, 10 Aug 2023 10:11:07 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Langlet",
"Jonatan",
""
],
[
"Basat",
"Ran Ben",
""
],
[
"Oliaro",
"Gabriele",
""
],
[
"Mitzenmacher",
"Michael",
""
],
[
"Yu",
"Minlan",
""
],
[
"Antichi",
"Gianni",
""
]
] |
new_dataset
| 0.95822 |
2202.03026
|
Xiaokang Chen
|
Xiaokang Chen, Mingyu Ding, Xiaodi Wang, Ying Xin, Shentong Mo, Yunhao
Wang, Shumin Han, Ping Luo, Gang Zeng, Jingdong Wang
|
Context Autoencoder for Self-Supervised Representation Learning
|
Accepted by International Journal of Computer Vision (IJCV)
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We present a novel masked image modeling (MIM) approach, context autoencoder
(CAE), for self-supervised representation pretraining. We pretrain an encoder
by making predictions in the encoded representation space. The pretraining
tasks include two tasks: masked representation prediction - predict the
representations for the masked patches, and masked patch reconstruction -
reconstruct the masked patches. The network is an encoder-regressor-decoder
architecture: the encoder takes the visible patches as input; the regressor
predicts the representations of the masked patches, which are expected to be
aligned with the representations computed from the encoder, using the
representations of visible patches and the positions of visible and masked
patches; the decoder reconstructs the masked patches from the predicted encoded
representations. The CAE design encourages the separation of learning the
encoder (representation) from completing the pertaining tasks: masked
representation prediction and masked patch reconstruction tasks, and making
predictions in the encoded representation space empirically shows the benefit
to representation learning. We demonstrate the effectiveness of our CAE through
superior transfer performance in downstream tasks: semantic segmentation,
object detection and instance segmentation, and classification. The code will
be available at https://github.com/Atten4Vis/CAE.
|
[
{
"version": "v1",
"created": "Mon, 7 Feb 2022 09:33:45 GMT"
},
{
"version": "v2",
"created": "Mon, 30 May 2022 08:42:10 GMT"
},
{
"version": "v3",
"created": "Thu, 10 Aug 2023 11:01:14 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Chen",
"Xiaokang",
""
],
[
"Ding",
"Mingyu",
""
],
[
"Wang",
"Xiaodi",
""
],
[
"Xin",
"Ying",
""
],
[
"Mo",
"Shentong",
""
],
[
"Wang",
"Yunhao",
""
],
[
"Han",
"Shumin",
""
],
[
"Luo",
"Ping",
""
],
[
"Zeng",
"Gang",
""
],
[
"Wang",
"Jingdong",
""
]
] |
new_dataset
| 0.976985 |
2206.09973
|
Gleb Kalachev
|
Gleb Kalachev, Pavel Panteleev
|
Two-sided Robustly Testable Codes
|
26 pages, 3 figures
| null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
We show that the tensor product of two random linear codes is robustly
testable with high probability. This implies that one can obtain pairs of
linear codes such that their product and the product of their dual codes are
simultaneously robustly testable. Such two-sided robustly testable codes (with
a much weaker form of robustness) were the key ingredient in the recent
constructions of asymptotically good quantum LDPC codes, which ensured their
linear minimum distance. We hope that the existence of such codes with a
stronger form of robustness, shown here, can be used to simplify the proofs and
provide better distance bounds in these constructions. We also give new very
simple examples of non-robustly testable codes. We show that if the
parity-checks of two codes are mutually orthogonal, then their product is not
robustly testable. In particular, this implies that the product of a code with
its dual can never be robustly testable. We also study a property of a
collection of linear codes called product-expansion, which can be viewed as a
coboundary expansion of the cochain complex naturally associated with the
product of these codes. We show that this property is related with the robust
testability and the agreement testability of the products of codes.
|
[
{
"version": "v1",
"created": "Mon, 20 Jun 2022 19:28:57 GMT"
},
{
"version": "v2",
"created": "Sun, 2 Jul 2023 15:58:35 GMT"
},
{
"version": "v3",
"created": "Thu, 10 Aug 2023 17:12:09 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Kalachev",
"Gleb",
""
],
[
"Panteleev",
"Pavel",
""
]
] |
new_dataset
| 0.99945 |
2207.02157
|
Kumar Vijay Mishra
|
Tong Wei, Linlong Wu, Kumar Vijay Mishra and M. R. Bhavani Shankar
|
Multi-IRS-Aided Doppler-Tolerant Wideband DFRC System
|
16 pages, 8 figures, 2 tables
| null | null | null |
cs.IT eess.SP math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Intelligent reflecting surface (IRS) is recognized as an enabler of future
dual-function radar-communications (DFRC) by improving spectral efficiency,
coverage, parameter estimation, and interference suppression. Prior studies on
IRS-aided DFRC focus either on narrowband processing, single-IRS deployment,
static targets, non-clutter scenario, or on the under-utilized line-of-sight
(LoS) and non-line-of-sight (NLoS) paths. In this paper, we address the
aforementioned shortcomings by optimizing a wideband DFRC system comprising
multiple IRSs and a dual-function base station that jointly processes the LoS
and NLoS wideband multi-carrier signals to improve both the communications SINR
and the radar SINR in the presence of a moving target and clutter. We formulate
the transmit, {receive} and IRS beamformer design as the maximization of the
worst-case radar signal-to-interference-plus-noise ratio (SINR) subject to
transmit power and communications SINR. We tackle this nonconvex problem under
the alternating optimization framework, where the subproblems are solved by a
combination of Dinkelbach algorithm, consensus alternating direction method of
multipliers, and Riemannian steepest decent. Our numerical experiments show
that the proposed multi-IRS-aided wideband DFRC provides over $4$ dB radar SINR
and $31.7$\% improvement in target detection over a single-IRS system.
|
[
{
"version": "v1",
"created": "Tue, 5 Jul 2022 16:22:03 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Aug 2023 06:03:16 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Wei",
"Tong",
""
],
[
"Wu",
"Linlong",
""
],
[
"Mishra",
"Kumar Vijay",
""
],
[
"Shankar",
"M. R. Bhavani",
""
]
] |
new_dataset
| 0.996701 |
2209.04278
|
Rajitha de Silva
|
Rajitha de Silva, Grzegorz Cielniak, Gang Wang, Junfeng Gao
|
Deep learning-based Crop Row Detection for Infield Navigation of
Agri-Robots
|
Published in Journal of Field Robotics:
https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.22238
| null | null | null |
cs.CV cs.AI cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Autonomous navigation in agricultural environments is challenged by varying
field conditions that arise in arable fields. State-of-the-art solutions for
autonomous navigation in such environments require expensive hardware such as
RTK-GNSS. This paper presents a robust crop row detection algorithm that
withstands such field variations using inexpensive cameras. Existing datasets
for crop row detection does not represent all the possible field variations. A
dataset of sugar beet images was created representing 11 field variations
comprised of multiple grow stages, light levels, varying weed densities, curved
crop rows and discontinuous crop rows. The proposed pipeline segments the crop
rows using a deep learning-based method and employs the predicted segmentation
mask for extraction of the central crop using a novel central crop row
selection algorithm. The novel crop row detection algorithm was tested for crop
row detection performance and the capability of visual servoing along a crop
row. The visual servoing-based navigation was tested on a realistic simulation
scenario with the real ground and plant textures. Our algorithm demonstrated
robust vision-based crop row detection in challenging field conditions
outperforming the baseline.
|
[
{
"version": "v1",
"created": "Fri, 9 Sep 2022 12:47:24 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Aug 2023 15:19:34 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"de Silva",
"Rajitha",
""
],
[
"Cielniak",
"Grzegorz",
""
],
[
"Wang",
"Gang",
""
],
[
"Gao",
"Junfeng",
""
]
] |
new_dataset
| 0.99508 |
2209.14408
|
Rowan Dempster
|
Eddy Zhou, Alex Zhuang, Alikasim Budhwani, Rowan Dempster, Quanquan
Li, Mohammad Al-Sharman, Derek Rayside, and William Melek
|
RALACs: Action Recognition in Autonomous Vehicles using Interaction
Encoding and Optical Flow
| null | null | null | null |
cs.CV cs.LG cs.RO
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
When applied to autonomous vehicle (AV) settings, action recognition can
enhance an environment model's situational awareness. This is especially
prevalent in scenarios where traditional geometric descriptions and heuristics
in AVs are insufficient. However, action recognition has traditionally been
studied for humans, and its limited adaptability to noisy, un-clipped,
un-pampered, raw RGB data has limited its application in other fields. To push
for the advancement and adoption of action recognition into AVs, this work
proposes a novel two-stage action recognition system, termed RALACs. RALACs
formulates the problem of action recognition for road scenes, and bridges the
gap between it and the established field of human action recognition. This work
shows how attention layers can be useful for encoding the relations across
agents, and stresses how such a scheme can be class-agnostic. Furthermore, to
address the dynamic nature of agents on the road, RALACs constructs a novel
approach to adapting Region of Interest (ROI) Alignment to agent tracks for
downstream action classification. Finally, our scheme also considers the
problem of active agent detection, and utilizes a novel application of fusing
optical flow maps to discern relevant agents in a road scene. We show that our
proposed scheme can outperform the baseline on the ICCV2021 Road Challenge
dataset and by deploying it on a real vehicle platform, we provide preliminary
insight to the usefulness of action recognition in decision making.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 20:36:49 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Aug 2023 18:30:48 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Zhou",
"Eddy",
""
],
[
"Zhuang",
"Alex",
""
],
[
"Budhwani",
"Alikasim",
""
],
[
"Dempster",
"Rowan",
""
],
[
"Li",
"Quanquan",
""
],
[
"Al-Sharman",
"Mohammad",
""
],
[
"Rayside",
"Derek",
""
],
[
"Melek",
"William",
""
]
] |
new_dataset
| 0.998025 |
2303.13381
|
Wouter Jansen
|
Wouter Jansen, Erik Verreycken, Anthony Schenck, Jean-Edouard
Blanquart, Connor Verhulst, Nico Huebel, Jan Steckel
|
Cosys-AirSim: A Real-Time Simulation Framework Expanded for Complex
Industrial Applications
|
Presented at Annual Modeling and Simulation Conference, ANNSIM 2023,
https://ieeexplore.ieee.org/abstract/document/10155352
| null | null | null |
cs.RO eess.SP
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Within academia and industry, there has been a need for expansive simulation
frameworks that include model-based simulation of sensors, mobile vehicles, and
the environment around them. To this end, the modular, real-time, and
open-source AirSim framework has been a popular community-built system that
fulfills some of those needs. However, the framework required adding systems to
serve some complex industrial applications, including designing and testing new
sensor modalities, Simultaneous Localization And Mapping (SLAM), autonomous
navigation algorithms, and transfer learning with machine learning models. In
this work, we discuss the modification and additions to our open-source version
of the AirSim simulation framework, including new sensor modalities, vehicle
types, and methods to generate realistic environments with changeable objects
procedurally. Furthermore, we show the various applications and use cases the
framework can serve.
|
[
{
"version": "v1",
"created": "Thu, 23 Mar 2023 15:48:28 GMT"
},
{
"version": "v2",
"created": "Tue, 28 Mar 2023 13:27:16 GMT"
},
{
"version": "v3",
"created": "Thu, 10 Aug 2023 11:15:48 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Jansen",
"Wouter",
""
],
[
"Verreycken",
"Erik",
""
],
[
"Schenck",
"Anthony",
""
],
[
"Blanquart",
"Jean-Edouard",
""
],
[
"Verhulst",
"Connor",
""
],
[
"Huebel",
"Nico",
""
],
[
"Steckel",
"Jan",
""
]
] |
new_dataset
| 0.999307 |
2304.14520
|
Alexander Kyuroson
|
Alexander Kyuroson, Niklas Dahlquist, Nikolaos Stathoulopoulos,
Vignesh Kottayam Viswanathan, Anton Koval and George Nikolakopoulos
|
Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol
Particles for Frontier Exploration
|
Accepted in the 31st Mediterranean Conference on Control and
Automation [MED2023]
| null |
10.1109/MED59994.2023.10185906
| null |
cs.RO cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Algorithms for autonomous navigation in environments without Global
Navigation Satellite System (GNSS) coverage mainly rely on onboard perception
systems. These systems commonly incorporate sensors like cameras and Light
Detection and Rangings (LiDARs), the performance of which may degrade in the
presence of aerosol particles. Thus, there is a need of fusing acquired data
from these sensors with data from Radio Detection and Rangings (RADARs) which
can penetrate through such particles. Overall, this will improve the
performance of localization and collision avoidance algorithms under such
environmental conditions. This paper introduces a multimodal dataset from the
harsh and unstructured underground environment with aerosol particles. A
detailed description of the onboard sensors and the environment, where the
dataset is collected are presented to enable full evaluation of acquired data.
Furthermore, the dataset contains synchronized raw data measurements from all
onboard sensors in Robot Operating System (ROS) format to facilitate the
evaluation of navigation, and localization algorithms in such environments. In
contrast to the existing datasets, the focus of this paper is not only to
capture both temporal and spatial data diversities but also to present the
impact of harsh conditions on captured data. Therefore, to validate the
dataset, a preliminary comparison of odometry from onboard LiDARs is presented.
|
[
{
"version": "v1",
"created": "Thu, 27 Apr 2023 20:21:18 GMT"
},
{
"version": "v2",
"created": "Wed, 21 Jun 2023 09:56:57 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Kyuroson",
"Alexander",
""
],
[
"Dahlquist",
"Niklas",
""
],
[
"Stathoulopoulos",
"Nikolaos",
""
],
[
"Viswanathan",
"Vignesh Kottayam",
""
],
[
"Koval",
"Anton",
""
],
[
"Nikolakopoulos",
"George",
""
]
] |
new_dataset
| 0.999686 |
2306.10634
|
Kai Li
|
Kai Li, Darren Lee, Shixuan Guan
|
Understanding the Cryptocurrency Free Giveaway Scam Disseminated on
Twitter Lists
|
9 pages, 5 figures
| null | null | null |
cs.CR cs.SI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This paper presents a comprehensive analysis of the cryptocurrency free
giveaway scam disseminated in a new distribution channel, Twitter lists. To
collect and detect the scam in this channel, unlike existing scam detection
systems that rely on manual effort, this paper develops a fully automated scam
detection system, \textit{GiveawayScamHunter}, to continuously collect lists
from Twitter and utilize a Nature-Language-Processing (NLP) model to
automatically detect the free giveaway scam and extract the scam cryptocurrency
address.
By running \textit{GiveawayScamHunter} from June 2022 to June 2023, we
detected 95,111 free giveaway scam lists on Twitter that were created by
thousands of Twitter accounts. Through analyzing the list creator accounts, our
work reveals that scammers have combined different strategies to spread the
scam, including compromising popular accounts and creating spam accounts on
Twitter. Our analysis result shows that 43.9\% of spam accounts still remain
active as of this writing. Furthermore, we collected 327 free giveaway domains
and 121 new scam cryptocurrency addresses. By tracking the transactions of the
scam cryptocurrency addresses, this work uncovers that over 365 victims have
been attacked by the scam, resulting in an estimated financial loss of 872K
USD.
Overall, this work sheds light on the tactics, scale, and impact of free
giveaway scams disseminated on Twitter lists, emphasizing the urgent need for
effective detection and prevention mechanisms to protect social media users
from such fraudulent activity.
|
[
{
"version": "v1",
"created": "Sun, 18 Jun 2023 20:10:54 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Aug 2023 17:50:57 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Li",
"Kai",
""
],
[
"Lee",
"Darren",
""
],
[
"Guan",
"Shixuan",
""
]
] |
new_dataset
| 0.997964 |
2306.11029
|
Delong Chen
|
Fan Liu, Delong Chen, Zhangqingyun Guan, Xiaocong Zhou, Jiale Zhu, Jun
Zhou
|
RemoteCLIP: A Vision Language Foundation Model for Remote Sensing
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
General-purpose foundation models have become increasingly important in the
field of artificial intelligence. While self-supervised learning (SSL) and
Masked Image Modeling (MIM) have led to promising results in building such
foundation models for remote sensing, these models primarily learn low-level
features, require annotated data for fine-tuning, and not applicable for
retrieval and zero-shot applications due to the lack of language understanding.
In response to these limitations, we propose RemoteCLIP, the first
vision-language foundation model for remote sensing that aims to learn robust
visual features with rich semantics, as well as aligned text embeddings for
seamless downstream application. To address the scarcity of pre-training data,
we leverage data scaling, converting heterogeneous annotations based on
Box-to-Caption (B2C) and Mask-to-Box (M2B) conversion, and further
incorporating UAV imagery, resulting a 12xlarger pretraining dataset.
RemoteCLIP can be applied to a variety of downstream tasks, including zero-shot
image classification, linear probing, k-NN classification, few-shot
classification, image-text retrieval, and object counting. Evaluations on 16
datasets, including a newly introduced RemoteCount benchmark to test the object
counting ability, show that RemoteCLIP consistently outperforms baseline
foundation models across different model scales. Impressively, RemoteCLIP
outperform previous SoTA by 9.14% mean recall on RSICD dataset and by 8.92% on
RSICD dataset. For zero-shot classification, our RemoteCLIP outperform CLIP
baseline by up to 6.39% average accuracy on 12 downstream datasets.Pretrained
models is available at https://github.com/ChenDelong1999/RemoteCLIP .
|
[
{
"version": "v1",
"created": "Mon, 19 Jun 2023 15:46:41 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Aug 2023 02:05:45 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Liu",
"Fan",
""
],
[
"Chen",
"Delong",
""
],
[
"Guan",
"Zhangqingyun",
""
],
[
"Zhou",
"Xiaocong",
""
],
[
"Zhu",
"Jiale",
""
],
[
"Zhou",
"Jun",
""
]
] |
new_dataset
| 0.999614 |
2307.14527
|
Thomas Manzini
|
Thomas Manzini, Robin Murphy
|
Open Problems in Computer Vision for Wilderness SAR and The Search for
Patricia Wu-Murad
|
10 pages, 10 figures
| null | null | null |
cs.CV cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper details the challenges in applying two computer vision systems, an
EfficientDET supervised learning model and the unsupervised RX spectral
classifier, to 98.9 GB of drone imagery from the Wu-Murad wilderness search and
rescue (WSAR) effort in Japan and identifies 3 directions for future research.
There have been at least 19 proposed approaches and 3 datasets aimed at
locating missing persons in drone imagery, but only 3 approaches (2
unsupervised and 1 of an unknown structure) are referenced in the literature as
having been used in an actual WSAR operation. Of these proposed approaches, the
EfficientDET architecture and the unsupervised spectral RX classifier were
selected as the most appropriate for this setting. The EfficientDET model was
applied to the HERIDAL dataset and despite achieving performance that is
statistically equivalent to the state-of-the-art, the model fails to translate
to the real world in terms of false positives (e.g., identifying tree limbs and
rocks as people), and false negatives (e.g., failing to identify members of the
search team). The poor results in practice for algorithms that showed good
results on datasets suggest 3 areas of future research: more realistic datasets
for wilderness SAR, computer vision models that are capable of seamlessly
handling the variety of imagery that can be collected during actual WSAR
operations, and better alignment on performance measures.
|
[
{
"version": "v1",
"created": "Wed, 26 Jul 2023 22:09:29 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Aug 2023 01:46:11 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Manzini",
"Thomas",
""
],
[
"Murphy",
"Robin",
""
]
] |
new_dataset
| 0.98733 |
2308.03463
|
Zhongjie Duan
|
Zhongjie Duan, Lizhou You, Chengyu Wang, Cen Chen, Ziheng Wu, Weining
Qian, Jun Huang
|
DiffSynth: Latent In-Iteration Deflickering for Realistic Video
Synthesis
|
9 pages, 6 figures
| null | null | null |
cs.CV cs.MM
|
http://creativecommons.org/licenses/by/4.0/
|
In recent years, diffusion models have emerged as the most powerful approach
in image synthesis. However, applying these models directly to video synthesis
presents challenges, as it often leads to noticeable flickering contents.
Although recently proposed zero-shot methods can alleviate flicker to some
extent, we still struggle to generate coherent videos. In this paper, we
propose DiffSynth, a novel approach that aims to convert image synthesis
pipelines to video synthesis pipelines. DiffSynth consists of two key
components: a latent in-iteration deflickering framework and a video
deflickering algorithm. The latent in-iteration deflickering framework applies
video deflickering to the latent space of diffusion models, effectively
preventing flicker accumulation in intermediate steps. Additionally, we propose
a video deflickering algorithm, named patch blending algorithm, that remaps
objects in different frames and blends them together to enhance video
consistency. One of the notable advantages of DiffSynth is its general
applicability to various video synthesis tasks, including text-guided video
stylization, fashion video synthesis, image-guided video stylization, video
restoring, and 3D rendering. In the task of text-guided video stylization, we
make it possible to synthesize high-quality videos without cherry-picking. The
experimental results demonstrate the effectiveness of DiffSynth. All videos can
be viewed on our project page. Source codes will also be released.
|
[
{
"version": "v1",
"created": "Mon, 7 Aug 2023 10:41:52 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Aug 2023 07:54:55 GMT"
},
{
"version": "v3",
"created": "Thu, 10 Aug 2023 02:26:16 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Duan",
"Zhongjie",
""
],
[
"You",
"Lizhou",
""
],
[
"Wang",
"Chengyu",
""
],
[
"Chen",
"Cen",
""
],
[
"Wu",
"Ziheng",
""
],
[
"Qian",
"Weining",
""
],
[
"Huang",
"Jun",
""
]
] |
new_dataset
| 0.996588 |
2308.04313
|
Jan Egger
|
Jan Egger, Christina Gsaxner, Xiaojun Chen, Jiang Bian, Jens Kleesiek,
Behrus Puladi
|
Apple Vision Pro for Healthcare: "The Ultimate Display"? -- Entering the
Wonderland of Precision
|
This is a Preprint under CC BY. This work was supported by NIH/NIAID
R01AI172875, NIH/NCATS UL1 TR001427, the REACT-EU project KITE and enFaced
2.0 (FWF KLI 1044). B. Puladi was funded by the Medical Faculty of the RWTH
Aachen University as part of the Clinician Scientist Program. C. Gsaxner was
funded by the Advanced Research Opportunities Program from the RWTH Aachen
University
| null | null | null |
cs.AI cs.GR cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
At the Worldwide Developers Conference (WWDC) in June 2023, Apple introduced
the Vision Pro. The Vision Pro is a Mixed Reality (MR) headset, more
specifically it is a Virtual Reality (VR) device with an additional Video
See-Through (VST) capability. The VST capability turns the Vision Pro also into
an Augmented Reality (AR) device. The AR feature is enabled by streaming the
real world via cameras to the (VR) screens in front of the user's eyes. This is
of course not unique and similar to other devices, like the Varjo XR-3.
Nevertheless, the Vision Pro has some interesting features, like an inside-out
screen that can show the headset wearers' eyes to "outsiders" or a button on
the top, called "Digital Crown", that allows you to seamlessly blend digital
content with your physical space by turning it. In addition, it is untethered,
except for the cable to the battery, which makes the headset more agile,
compared to the Varjo XR-3. This could actually come closer to the "Ultimate
Display", which Ivan Sutherland had already sketched in 1965. Not available to
the public yet, like the Ultimate Display, we want to take a look into the
crystal ball in this perspective to see if it can overcome some clinical
challenges that - especially - AR still faces in the medical domain, but also
go beyond and discuss if the Vision Pro could support clinicians in essential
tasks to spend more time with their patients.
|
[
{
"version": "v1",
"created": "Tue, 8 Aug 2023 15:01:51 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Aug 2023 13:34:57 GMT"
},
{
"version": "v3",
"created": "Thu, 10 Aug 2023 05:03:39 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Egger",
"Jan",
""
],
[
"Gsaxner",
"Christina",
""
],
[
"Chen",
"Xiaojun",
""
],
[
"Bian",
"Jiang",
""
],
[
"Kleesiek",
"Jens",
""
],
[
"Puladi",
"Behrus",
""
]
] |
new_dataset
| 0.992309 |
2308.04995
|
Fadi Boutros
|
Fadi Boutros, Jonas Henry Grebe, Arjan Kuijper, Naser Damer
|
IDiff-Face: Synthetic-based Face Recognition through Fizzy
Identity-Conditioned Diffusion Models
|
Accepted at ICCV2023
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The availability of large-scale authentic face databases has been crucial to
the significant advances made in face recognition research over the past
decade. However, legal and ethical concerns led to the recent retraction of
many of these databases by their creators, raising questions about the
continuity of future face recognition research without one of its key
resources. Synthetic datasets have emerged as a promising alternative to
privacy-sensitive authentic data for face recognition development. However,
recent synthetic datasets that are used to train face recognition models suffer
either from limitations in intra-class diversity or cross-class (identity)
discrimination, leading to less optimal accuracies, far away from the
accuracies achieved by models trained on authentic data. This paper targets
this issue by proposing IDiff-Face, a novel approach based on conditional
latent diffusion models for synthetic identity generation with realistic
identity variations for face recognition training. Through extensive
evaluations, our proposed synthetic-based face recognition approach pushed the
limits of state-of-the-art performances, achieving, for example, 98.00%
accuracy on the Labeled Faces in the Wild (LFW) benchmark, far ahead from the
recent synthetic-based face recognition solutions with 95.40% and bridging the
gap to authentic-based face recognition with 99.82% accuracy.
|
[
{
"version": "v1",
"created": "Wed, 9 Aug 2023 14:48:31 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Aug 2023 10:43:53 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Boutros",
"Fadi",
""
],
[
"Grebe",
"Jonas Henry",
""
],
[
"Kuijper",
"Arjan",
""
],
[
"Damer",
"Naser",
""
]
] |
new_dataset
| 0.998657 |
2308.05179
|
Md Simul Hasan Talukder
|
Md. Simul Hasan Talukder, Mohammad Raziuddin Chowdhury, Md Sakib Ullah
Sourav, Abdullah Al Rakin, Shabbir Ahmed Shuvo, Rejwan Bin Sulaiman, Musarrat
Saberin Nipun, Muntarin Islam, Mst Rumpa Islam, Md Aminul Islam, Zubaer Haque
|
JutePestDetect: An Intelligent Approach for Jute Pest Identification
Using Fine-Tuned Transfer Learning
|
29 Pages, 7 Tables, 7 Figures, 5 Appendix
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-sa/4.0/
|
In certain Asian countries, Jute is one of the primary sources of income and
Gross Domestic Product (GDP) for the agricultural sector. Like many other
crops, Jute is prone to pest infestations, and its identification is typically
made visually in countries like Bangladesh, India, Myanmar, and China. In
addition, this method is time-consuming, challenging, and somewhat imprecise,
which poses a substantial financial risk. To address this issue, the study
proposes a high-performing and resilient transfer learning (TL) based
JutePestDetect model to identify jute pests at the early stage. Firstly, we
prepared jute pest dataset containing 17 classes and around 380 photos per pest
class, which were evaluated after manual and automatic pre-processing and
cleaning, such as background removal and resizing. Subsequently, five prominent
pre-trained models -DenseNet201, InceptionV3, MobileNetV2, VGG19, and ResNet50
were selected from a previous study to design the JutePestDetect model. Each
model was revised by replacing the classification layer with a global average
pooling layer and incorporating a dropout layer for regularization. To evaluate
the models performance, various metrics such as precision, recall, F1 score,
ROC curve, and confusion matrix were employed. These analyses provided
additional insights for determining the efficacy of the models. Among them, the
customized regularized DenseNet201-based proposed JutePestDetect model
outperformed the others, achieving an impressive accuracy of 99%. As a result,
our proposed method and strategy offer an enhanced approach to pest
identification in the case of Jute, which can significantly benefit farmers
worldwide.
|
[
{
"version": "v1",
"created": "Sun, 28 May 2023 15:51:35 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Talukder",
"Md. Simul Hasan",
""
],
[
"Chowdhury",
"Mohammad Raziuddin",
""
],
[
"Sourav",
"Md Sakib Ullah",
""
],
[
"Rakin",
"Abdullah Al",
""
],
[
"Shuvo",
"Shabbir Ahmed",
""
],
[
"Sulaiman",
"Rejwan Bin",
""
],
[
"Nipun",
"Musarrat Saberin",
""
],
[
"Islam",
"Muntarin",
""
],
[
"Islam",
"Mst Rumpa",
""
],
[
"Islam",
"Md Aminul",
""
],
[
"Haque",
"Zubaer",
""
]
] |
new_dataset
| 0.998547 |
2308.05184
|
John Chung
|
John Joon Young Chung, Eytan Adar
|
PromptPaint: Steering Text-to-Image Generation Through Paint Medium-like
Interactions
|
Accepted to UIST2023
| null |
10.1145/3586183.3606777
| null |
cs.HC cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
While diffusion-based text-to-image (T2I) models provide a simple and
powerful way to generate images, guiding this generation remains a challenge.
For concepts that are difficult to describe through language, users may
struggle to create prompts. Moreover, many of these models are built as
end-to-end systems, lacking support for iterative shaping of the image. In
response, we introduce PromptPaint, which combines T2I generation with
interactions that model how we use colored paints. PromptPaint allows users to
go beyond language to mix prompts that express challenging concepts. Just as we
iteratively tune colors through layered placements of paint on a physical
canvas, PromptPaint similarly allows users to apply different prompts to
different canvas areas and times of the generative process. Through a set of
studies, we characterize different approaches for mixing prompts, design
trade-offs, and socio-technical challenges for generative models. With
PromptPaint we provide insight into future steerable generative tools.
|
[
{
"version": "v1",
"created": "Wed, 9 Aug 2023 18:41:11 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Chung",
"John Joon Young",
""
],
[
"Adar",
"Eytan",
""
]
] |
new_dataset
| 0.997915 |
2308.05207
|
Orestis Papadigenopoulos
|
Vineet Goyal, Salal Humair, Orestis Papadigenopoulos, Assaf Zeevi
|
MNL-Prophet: Sequential Assortment Selection under Uncertainty
| null | null | null | null |
cs.DS cs.GT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Due to numerous applications in retail and (online) advertising the problem
of assortment selection has been widely studied under many combinations of
discrete choice models and feasibility constraints. In many situations,
however, an assortment of products has to be constructed gradually and without
accurate knowledge of all possible alternatives; in such cases, existing
offline approaches become inapplicable. We consider a stochastic variant of the
assortment selection problem, where the parameters that determine the revenue
and (relative) demand of each item are jointly drawn from some known
item-specific distribution. The items are observed sequentially in an arbitrary
and unknown order; upon observing the realized parameters of each item, the
decision-maker decides irrevocably whether to include it in the constructed
assortment, or forfeit it forever. The objective is to maximize the expected
total revenue of the constructed assortment, relative to that of an offline
algorithm which foresees all the parameter realizations and computes the
optimal assortment. We provide simple threshold-based online policies for the
unconstrained and cardinality-constrained versions of the problem under a
natural class of substitutable choice models; as we show, our policies are
(worst-case) optimal under the celebrated Multinomial Logit choice model. We
extend our results to the case of knapsack constraints and discuss interesting
connections to the Prophet Inequality problem, which is already subsumed by our
setting.
|
[
{
"version": "v1",
"created": "Wed, 9 Aug 2023 20:02:59 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Goyal",
"Vineet",
""
],
[
"Humair",
"Salal",
""
],
[
"Papadigenopoulos",
"Orestis",
""
],
[
"Zeevi",
"Assaf",
""
]
] |
new_dataset
| 0.989393 |
2308.05219
|
Elizabeth Hou
|
Elizabeth M. Hou, Gregory Castanon
|
Decoding Layer Saliency in Language Transformers
| null | null | null | null |
cs.CL cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we introduce a strategy for identifying textual saliency in
large-scale language models applied to classification tasks. In visual networks
where saliency is more well-studied, saliency is naturally localized through
the convolutional layers of the network; however, the same is not true in
modern transformer-stack networks used to process natural language. We adapt
gradient-based saliency methods for these networks, propose a method for
evaluating the degree of semantic coherence of each layer, and demonstrate
consistent improvement over numerous other methods for textual saliency on
multiple benchmark classification datasets. Our approach requires no additional
training or access to labelled data, and is comparatively very computationally
efficient.
|
[
{
"version": "v1",
"created": "Wed, 9 Aug 2023 20:53:22 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Hou",
"Elizabeth M.",
""
],
[
"Castanon",
"Gregory",
""
]
] |
new_dataset
| 0.986865 |
2308.05221
|
Hangjie Shi
|
Hangjie Shi, Leslie Ball, Govind Thattai, Desheng Zhang, Lucy Hu,
Qiaozi Gao, Suhaila Shakiah, Xiaofeng Gao, Aishwarya Padmakumar, Bofei Yang,
Cadence Chung, Dinakar Guthy, Gaurav Sukhatme, Karthika Arumugam, Matthew
Wen, Osman Ipek, Patrick Lange, Rohan Khanna, Shreyas Pansare, Vasu Sharma,
Chao Zhang, Cris Flagg, Daniel Pressel, Lavina Vaz, Luke Dai, Prasoon Goyal,
Sattvik Sahai, Shaohua Liu, Yao Lu, Anna Gottardi, Shui Hu, Yang Liu, Dilek
Hakkani-Tur, Kate Bland, Heather Rocker, James Jeun, Yadunandana Rao, Michael
Johnston, Akshaya Iyengar, Arindam Mandal, Prem Natarajan, Reza Ghanadan
|
Alexa, play with robot: Introducing the First Alexa Prize SimBot
Challenge on Embodied AI
| null | null | null | null |
cs.HC cs.AI cs.RO
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The Alexa Prize program has empowered numerous university students to
explore, experiment, and showcase their talents in building conversational
agents through challenges like the SocialBot Grand Challenge and the TaskBot
Challenge. As conversational agents increasingly appear in multimodal and
embodied contexts, it is important to explore the affordances of conversational
interaction augmented with computer vision and physical embodiment. This paper
describes the SimBot Challenge, a new challenge in which university teams
compete to build robot assistants that complete tasks in a simulated physical
environment. This paper provides an overview of the SimBot Challenge, which
included both online and offline challenge phases. We describe the
infrastructure and support provided to the teams including Alexa Arena, the
simulated environment, and the ML toolkit provided to teams to accelerate their
building of vision and language models. We summarize the approaches the
participating teams took to overcome research challenges and extract key
lessons learned. Finally, we provide analysis of the performance of the
competing SimBots during the competition.
|
[
{
"version": "v1",
"created": "Wed, 9 Aug 2023 20:56:56 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Shi",
"Hangjie",
""
],
[
"Ball",
"Leslie",
""
],
[
"Thattai",
"Govind",
""
],
[
"Zhang",
"Desheng",
""
],
[
"Hu",
"Lucy",
""
],
[
"Gao",
"Qiaozi",
""
],
[
"Shakiah",
"Suhaila",
""
],
[
"Gao",
"Xiaofeng",
""
],
[
"Padmakumar",
"Aishwarya",
""
],
[
"Yang",
"Bofei",
""
],
[
"Chung",
"Cadence",
""
],
[
"Guthy",
"Dinakar",
""
],
[
"Sukhatme",
"Gaurav",
""
],
[
"Arumugam",
"Karthika",
""
],
[
"Wen",
"Matthew",
""
],
[
"Ipek",
"Osman",
""
],
[
"Lange",
"Patrick",
""
],
[
"Khanna",
"Rohan",
""
],
[
"Pansare",
"Shreyas",
""
],
[
"Sharma",
"Vasu",
""
],
[
"Zhang",
"Chao",
""
],
[
"Flagg",
"Cris",
""
],
[
"Pressel",
"Daniel",
""
],
[
"Vaz",
"Lavina",
""
],
[
"Dai",
"Luke",
""
],
[
"Goyal",
"Prasoon",
""
],
[
"Sahai",
"Sattvik",
""
],
[
"Liu",
"Shaohua",
""
],
[
"Lu",
"Yao",
""
],
[
"Gottardi",
"Anna",
""
],
[
"Hu",
"Shui",
""
],
[
"Liu",
"Yang",
""
],
[
"Hakkani-Tur",
"Dilek",
""
],
[
"Bland",
"Kate",
""
],
[
"Rocker",
"Heather",
""
],
[
"Jeun",
"James",
""
],
[
"Rao",
"Yadunandana",
""
],
[
"Johnston",
"Michael",
""
],
[
"Iyengar",
"Akshaya",
""
],
[
"Mandal",
"Arindam",
""
],
[
"Natarajan",
"Prem",
""
],
[
"Ghanadan",
"Reza",
""
]
] |
new_dataset
| 0.996865 |
2308.05264
|
Soumyaroop Nandi
|
Soumyaroop Nandi, Prem Natarajan, Wael Abd-Almageed
|
TrainFors: A Large Benchmark Training Dataset for Image Manipulation
Detection and Localization
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The evaluation datasets and metrics for image manipulation detection and
localization (IMDL) research have been standardized. But the training dataset
for such a task is still nonstandard. Previous researchers have used
unconventional and deviating datasets to train neural networks for detecting
image forgeries and localizing pixel maps of manipulated regions. For a fair
comparison, the training set, test set, and evaluation metrics should be
persistent. Hence, comparing the existing methods may not seem fair as the
results depend heavily on the training datasets as well as the model
architecture. Moreover, none of the previous works release the synthetic
training dataset used for the IMDL task. We propose a standardized benchmark
training dataset for image splicing, copy-move forgery, removal forgery, and
image enhancement forgery. Furthermore, we identify the problems with the
existing IMDL datasets and propose the required modifications. We also train
the state-of-the-art IMDL methods on our proposed TrainFors1 dataset for a fair
evaluation and report the actual performance of these methods under similar
conditions.
|
[
{
"version": "v1",
"created": "Thu, 10 Aug 2023 00:26:34 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Nandi",
"Soumyaroop",
""
],
[
"Natarajan",
"Prem",
""
],
[
"Abd-Almageed",
"Wael",
""
]
] |
new_dataset
| 0.99858 |
2308.05278
|
Jo\~ao Carneiro
|
Paulo Trezentos, Ricardo Capote, Tiago Teodoro, Jo\~ao Carneiro
|
DCM: A Developers Certification Model for Mobile Ecosystems
|
8 pages, 4 figures
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This article introduces a distributed model of trust for app developers in
Android and iOS mobile ecosystems. The model aims to allow the co-existence of
multiple app stores and distribution channels while retaining a high level of
safety for mobile device users and minimum changes to current mobile operating
systems. The Developers Certification Model (DCM) is a trust model for Android
and iOS that aims to distinguish legit applications from security threats to
user safeness by answering the question: "is the developer of this app
trustable"? It proposes security by design, where safety relies on a chain of
trust mapping real-world levels of trust across organizations. For the
technical implementation, DCM is heavily inspired by SSL/TLS certification
protocol, as a proven model that has been working for over 30 years.
|
[
{
"version": "v1",
"created": "Thu, 10 Aug 2023 01:44:45 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Trezentos",
"Paulo",
""
],
[
"Capote",
"Ricardo",
""
],
[
"Teodoro",
"Tiago",
""
],
[
"Carneiro",
"João",
""
]
] |
new_dataset
| 0.999576 |
2308.05334
|
Dabin Kim
|
Dabin Kim, Matthias Pezzutto, Luca Schenato, and H. Jin Kim
|
Visibility-Constrained Control of Multirotor via Reference Governor
|
8 pages, 6 figures, Accepted to 62nd IEEE Conference on Decision and
Control (CDC 2023)
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
For safe vision-based control applications, perception-related constraints
have to be satisfied in addition to other state constraints. In this paper, we
deal with the problem where a multirotor equipped with a camera needs to
maintain the visibility of a point of interest while tracking a reference given
by a high-level planner. We devise a method based on reference governor that,
differently from existing solutions, is able to enforce control-level
visibility constraints with theoretically assured feasibility. To this end, we
design a new type of reference governor for linear systems with polynomial
constraints which is capable of handling time-varying references. The proposed
solution is implemented online for the real-time multirotor control with
visibility constraints and validated with simulations and an actual hardware
experiment.
|
[
{
"version": "v1",
"created": "Thu, 10 Aug 2023 04:48:34 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Kim",
"Dabin",
""
],
[
"Pezzutto",
"Matthias",
""
],
[
"Schenato",
"Luca",
""
],
[
"Kim",
"H. Jin",
""
]
] |
new_dataset
| 0.99401 |
2308.05336
|
Mehrnoush ShamsFard
|
Vahide Tajalli, Fateme Kalantari and Mehrnoush Shamsfard
|
Developing an Informal-Formal Persian Corpus
|
16 pages, 1 Figure and 3 tables
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Informal language is a style of spoken or written language frequently used in
casual conversations, social media, weblogs, emails and text messages. In
informal writing, the language faces some lexical and/or syntactic changes
varying among different languages. Persian is one of the languages with many
differences between its formal and informal styles of writing, thus developing
informal language processing tools for this language seems necessary. Such a
converter needs a large aligned parallel corpus of colloquial-formal sentences
which can be useful for linguists to extract a regulated grammar and
orthography for colloquial Persian as is done for the formal language. In this
paper we explain our methodology in building a parallel corpus of 50,000
sentence pairs with alignments in the word/phrase level. The sentences were
attempted to cover almost all kinds of lexical and syntactic changes between
informal and formal Persian, therefore both methods of exploring and collecting
from the different resources of informal scripts and following the phonological
and morphological patterns of changes were applied to find as much instances as
possible. The resulting corpus has about 530,000 alignments and a dictionary
containing 49,397 word and phrase pairs.
|
[
{
"version": "v1",
"created": "Thu, 10 Aug 2023 04:57:34 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Tajalli",
"Vahide",
""
],
[
"Kalantari",
"Fateme",
""
],
[
"Shamsfard",
"Mehrnoush",
""
]
] |
new_dataset
| 0.998659 |
2308.05344
|
Alvaro Fernandez-Quilez
|
Alvaro Fernandez-Quilez, Tobias Nordstr\"om, Fredrik J\"aderling,
Svein Reidar Kjosavik and Martin Eklund
|
Prostate Age Gap (PAG): An MRI surrogate marker of aging for prostate
cancer detection
|
Under review
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Background: Prostate cancer (PC) MRI-based risk calculators are commonly
based on biological (e.g. PSA), MRI markers (e.g. volume), and patient age.
Whilst patient age measures the amount of years an individual has existed,
biological age (BA) might better reflect the physiology of an individual.
However, surrogates from prostate MRI and linkage with clinically significant
PC (csPC) remain to be explored. Purpose: To obtain and evaluate Prostate Age
Gap (PAG) as an MRI marker tool for csPC risk. Study type: Retrospective.
Population: A total of 7243 prostate MRI slices from 468 participants who had
undergone prostate biopsies. A deep learning model was trained on 3223 MRI
slices cropped around the gland from 81 low-grade PC (ncsPC, Gleason score <=6)
and 131 negative cases and tested on the remaining 256 participants.
Assessment: Chronological age was defined as the age of the participant at the
time of the visit and used to train the deep learning model to predict the age
of the patient. Following, we obtained PAG, defined as the model predicted age
minus the patient's chronological age. Multivariate logistic regression models
were used to estimate the association through odds ratio (OR) and predictive
value of PAG and compared against PSA levels and PI-RADS>=3. Statistical tests:
T-test, Mann-Whitney U test, Permutation test and ROC curve analysis. Results:
The multivariate adjusted model showed a significant difference in the odds of
clinically significant PC (csPC, Gleason score >=7) (OR =3.78, 95% confidence
interval (CI):2.32-6.16, P <.001). PAG showed a better predictive ability when
compared to PI-RADS>=3 and adjusted by other risk factors, including PSA
levels: AUC =0.981 vs AUC =0.704, p<.001. Conclusion: PAG was significantly
associated with the risk of clinically significant PC and outperformed other
well-established PC risk factors.
|
[
{
"version": "v1",
"created": "Thu, 10 Aug 2023 05:20:25 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Fernandez-Quilez",
"Alvaro",
""
],
[
"Nordström",
"Tobias",
""
],
[
"Jäderling",
"Fredrik",
""
],
[
"Kjosavik",
"Svein Reidar",
""
],
[
"Eklund",
"Martin",
""
]
] |
new_dataset
| 0.986352 |
2308.05355
|
Xinquan Yang
|
Xinquan Yang and Jinheng Xie and Xuechen Li and Xuguang Li and Linlin
Shen and Yongqiang Deng
|
TCSloT: Text Guided 3D Context and Slope Aware Triple Network for Dental
Implant Position Prediction
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In implant prosthesis treatment, the surgical guide of implant is used to
ensure accurate implantation. However, such design heavily relies on the manual
location of the implant position. When deep neural network has been proposed to
assist the dentist in locating the implant position, most of them take a single
slice as input, which do not fully explore 3D contextual information and
ignoring the influence of implant slope. In this paper, we design a Text Guided
3D Context and Slope Aware Triple Network (TCSloT) which enables the perception
of contextual information from multiple adjacent slices and awareness of
variation of implant slopes. A Texture Variation Perception (TVP) module is
correspondingly elaborated to process the multiple slices and capture the
texture variation among slices and a Slope-Aware Loss (SAL) is proposed to
dynamically assign varying weights for the regression head. Additionally, we
design a conditional text guidance (CTG) module to integrate the text condition
(i.e., left, middle and right) from the CLIP for assisting the implant position
prediction. Extensive experiments on a dental implant dataset through five-fold
cross-validation demonstrated that the proposed TCSloT achieves superior
performance than existing methods.
|
[
{
"version": "v1",
"created": "Thu, 10 Aug 2023 05:51:21 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Yang",
"Xinquan",
""
],
[
"Xie",
"Jinheng",
""
],
[
"Li",
"Xuechen",
""
],
[
"Li",
"Xuguang",
""
],
[
"Shen",
"Linlin",
""
],
[
"Deng",
"Yongqiang",
""
]
] |
new_dataset
| 0.998959 |
2308.05358
|
Guozhang Liu
|
Guozhang Liu, Baochai Peng, Ting Liu, Pan Zhang, Mengke Yuan, Chaoran
Lu, Ningning Cao, Sen Zhang, Simin Huang, Tao Wang
|
Fine-grained building roof instance segmentation based on domain adapted
pretraining and composite dual-backbone
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The diversity of building architecture styles of global cities situated on
various landforms, the degraded optical imagery affected by clouds and shadows,
and the significant inter-class imbalance of roof types pose challenges for
designing a robust and accurate building roof instance segmentor. To address
these issues, we propose an effective framework to fulfill semantic
interpretation of individual buildings with high-resolution optical satellite
imagery. Specifically, the leveraged domain adapted pretraining strategy and
composite dual-backbone greatly facilitates the discriminative feature
learning. Moreover, new data augmentation pipeline, stochastic weight averaging
(SWA) training and instance segmentation based model ensemble in testing are
utilized to acquire additional performance boost. Experiment results show that
our approach ranks in the first place of the 2023 IEEE GRSS Data Fusion Contest
(DFC) Track 1 test phase ($mAP_{50}$:50.6\%). Note-worthily, we have also
explored the potential of multimodal data fusion with both optical satellite
imagery and SAR data.
|
[
{
"version": "v1",
"created": "Thu, 10 Aug 2023 05:54:57 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Liu",
"Guozhang",
""
],
[
"Peng",
"Baochai",
""
],
[
"Liu",
"Ting",
""
],
[
"Zhang",
"Pan",
""
],
[
"Yuan",
"Mengke",
""
],
[
"Lu",
"Chaoran",
""
],
[
"Cao",
"Ningning",
""
],
[
"Zhang",
"Sen",
""
],
[
"Huang",
"Simin",
""
],
[
"Wang",
"Tao",
""
]
] |
new_dataset
| 0.95726 |
2308.05386
|
Fuqiang Zhao
|
Fuqiang Zhao, Bidan Huang, Mingchang Li, Mengde Li, Zhongtao Fu, Ziwei
Lei, Miao Li
|
A novel tactile palm for robotic object manipulation
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Tactile sensing is of great importance during human hand usage such as object
exploration, grasping and manipulation. Different types of tactile sensors have
been designed during the past decades, which are mainly focused on either the
fingertips for grasping or the upper-body for human-robot interaction. In this
paper, a novel soft tactile sensor has been designed to mimic the functionality
of human palm that can estimate the contact state of different objects. The
tactile palm mainly consists of three parts including an electrode array, a
soft cover skin and the conductive sponge. The design principle are described
in details, with a number of experiments showcasing the effectiveness of the
proposed design.
|
[
{
"version": "v1",
"created": "Thu, 10 Aug 2023 07:03:15 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Zhao",
"Fuqiang",
""
],
[
"Huang",
"Bidan",
""
],
[
"Li",
"Mingchang",
""
],
[
"Li",
"Mengde",
""
],
[
"Fu",
"Zhongtao",
""
],
[
"Lei",
"Ziwei",
""
],
[
"Li",
"Miao",
""
]
] |
new_dataset
| 0.999314 |
2308.05387
|
Guozhang Liu
|
Chaoran Lu, Ningning Cao, Pan Zhang, Ting Liu, Baochai Peng, Guozhang
Liu, Mengke Yuan, Sen Zhang, Simin Huang, Tao Wang
|
HGDNet: A Height-Hierarchy Guided Dual-Decoder Network for Single View
Building Extraction and Height Estimation
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Unifying the correlative single-view satellite image building extraction and
height estimation tasks indicates a promising way to share representations and
acquire generalist model for large-scale urban 3D reconstruction. However, the
common spatial misalignment between building footprints and
stereo-reconstructed nDSM height labels incurs degraded performance on both
tasks. To address this issue, we propose a Height-hierarchy Guided Dual-decoder
Network (HGDNet) to estimate building height. Under the guidance of synthesized
discrete height-hierarchy nDSM, auxiliary height-hierarchical building
extraction branch enhance the height estimation branch with implicit
constraints, yielding an accuracy improvement of more than 6% on the DFC 2023
track2 dataset. Additional two-stage cascade architecture is adopted to achieve
more accurate building extraction. Experiments on the DFC 2023 Track 2 dataset
shows the superiority of the proposed method in building height estimation
({\delta}1:0.8012), instance extraction (AP50:0.7730), and the final average
score 0.7871 ranks in the first place in test phase.
|
[
{
"version": "v1",
"created": "Thu, 10 Aug 2023 07:03:32 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Lu",
"Chaoran",
""
],
[
"Cao",
"Ningning",
""
],
[
"Zhang",
"Pan",
""
],
[
"Liu",
"Ting",
""
],
[
"Peng",
"Baochai",
""
],
[
"Liu",
"Guozhang",
""
],
[
"Yuan",
"Mengke",
""
],
[
"Zhang",
"Sen",
""
],
[
"Huang",
"Simin",
""
],
[
"Wang",
"Tao",
""
]
] |
new_dataset
| 0.952959 |
2308.05441
|
Hao Liang
|
Hao Liang, Pietro Perona and Guha Balakrishnan
|
Benchmarking Algorithmic Bias in Face Recognition: An Experimental
Approach Using Synthetic Faces and Human Evaluation
|
accepted to iccv2023; 18 figures
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose an experimental method for measuring bias in face recognition
systems. Existing methods to measure bias depend on benchmark datasets that are
collected in the wild and annotated for protected (e.g., race, gender) and
non-protected (e.g., pose, lighting) attributes. Such observational datasets
only permit correlational conclusions, e.g., "Algorithm A's accuracy is
different on female and male faces in dataset X.". By contrast, experimental
methods manipulate attributes individually and thus permit causal conclusions,
e.g., "Algorithm A's accuracy is affected by gender and skin color."
Our method is based on generating synthetic faces using a neural face
generator, where each attribute of interest is modified independently while
leaving all other attributes constant. Human observers crucially provide the
ground truth on perceptual identity similarity between synthetic image pairs.
We validate our method quantitatively by evaluating race and gender biases of
three research-grade face recognition models. Our synthetic pipeline reveals
that for these algorithms, accuracy is lower for Black and East Asian
population subgroups. Our method can also quantify how perceptual changes in
attributes affect face identity distances reported by these models. Our large
synthetic dataset, consisting of 48,000 synthetic face image pairs (10,200
unique synthetic faces) and 555,000 human annotations (individual attributes
and pairwise identity comparisons) is available to researchers in this
important area.
|
[
{
"version": "v1",
"created": "Thu, 10 Aug 2023 08:57:31 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Liang",
"Hao",
""
],
[
"Perona",
"Pietro",
""
],
[
"Balakrishnan",
"Guha",
""
]
] |
new_dataset
| 0.957962 |
2308.05459
|
Changkun Liu
|
Changkun Liu, Yukun Zhao, Tristan Braud
|
KS-APR: Keyframe Selection for Robust Absolute Pose Regression
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Markerless Mobile Augmented Reality (AR) aims to anchor digital content in
the physical world without using specific 2D or 3D objects. Absolute Pose
Regressors (APR) are end-to-end machine learning solutions that infer the
device's pose from a single monocular image. Thanks to their low computation
cost, they can be directly executed on the constrained hardware of mobile AR
devices. However, APR methods tend to yield significant inaccuracies for input
images that are too distant from the training set. This paper introduces
KS-APR, a pipeline that assesses the reliability of an estimated pose with
minimal overhead by combining the inference results of the APR and the prior
images in the training set. Mobile AR systems tend to rely upon visual-inertial
odometry to track the relative pose of the device during the experience. As
such, KS-APR favours reliability over frequency, discarding unreliable poses.
This pipeline can integrate most existing APR methods to improve accuracy by
filtering unreliable images with their pose estimates. We implement the
pipeline on three types of APR models on indoor and outdoor datasets. The
median error on position and orientation is reduced for all models, and the
proportion of large errors is minimized across datasets. Our method enables
state-of-the-art APRs such as DFNetdm to outperform single-image and sequential
APR methods. These results demonstrate the scalability and effectiveness of
KS-APR for visual localization tasks that do not require one-shot decisions.
|
[
{
"version": "v1",
"created": "Thu, 10 Aug 2023 09:32:20 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Liu",
"Changkun",
""
],
[
"Zhao",
"Yukun",
""
],
[
"Braud",
"Tristan",
""
]
] |
new_dataset
| 0.987144 |
2308.05472
|
Mengfan Zheng
|
Mengfan Zheng and Cong Ling
|
PAC Codes for Source and Joint Source-Channel Coding
|
6 pages, 6 figures. Submitted to GC 2023 Workshop - Channel Coding
Beyond 5G
| null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
Polarization-adjusted convolutional (PAC) codes, as a concatenated coding
scheme based on polar codes, is able to approach the finite-length bound of
binary-input AWGN channel at short blocklengths. In this paper, we extend PAC
codes to the fields of source coding and joint source-channel coding and show
that they can also approach the corresponding finite-length bounds at short
blocklengths.
|
[
{
"version": "v1",
"created": "Thu, 10 Aug 2023 09:55:56 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Zheng",
"Mengfan",
""
],
[
"Ling",
"Cong",
""
]
] |
new_dataset
| 0.999662 |
2308.05480
|
Yuming Chen
|
Yuming Chen, Xinbin Yuan, Ruiqi Wu, Jiabao Wang, Qibin Hou, Ming-Ming
Cheng
|
YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time
Object Detection
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We aim at providing the object detection community with an efficient and
performant object detector, termed YOLO-MS. The core design is based on a
series of investigations on how convolutions with different kernel sizes affect
the detection performance of objects at different scales. The outcome is a new
strategy that can strongly enhance multi-scale feature representations of
real-time object detectors. To verify the effectiveness of our strategy, we
build a network architecture, termed YOLO-MS. We train our YOLO-MS on the MS
COCO dataset from scratch without relying on any other large-scale datasets,
like ImageNet, or pre-trained weights. Without bells and whistles, our YOLO-MS
outperforms the recent state-of-the-art real-time object detectors, including
YOLO-v7 and RTMDet, when using a comparable number of parameters and FLOPs.
Taking the XS version of YOLO-MS as an example, with only 4.5M learnable
parameters and 8.7G FLOPs, it can achieve an AP score of 43%+ on MS COCO, which
is about 2%+ higher than RTMDet with the same model size. Moreover, our work
can also be used as a plug-and-play module for other YOLO models. Typically,
our method significantly improves the AP of YOLOv8 from 37%+ to 40%+ with even
fewer parameters and FLOPs. Code is available at
https://github.com/FishAndWasabi/YOLO-MS.
|
[
{
"version": "v1",
"created": "Thu, 10 Aug 2023 10:12:27 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Chen",
"Yuming",
""
],
[
"Yuan",
"Xinbin",
""
],
[
"Wu",
"Ruiqi",
""
],
[
"Wang",
"Jiabao",
""
],
[
"Hou",
"Qibin",
""
],
[
"Cheng",
"Ming-Ming",
""
]
] |
new_dataset
| 0.989274 |
2308.05515
|
Udugama Vithanage Bavantha Lakshan Udugama
|
U.V.B.L. Udugama, G. Vosselman, F. Nex
|
Mono-hydra: Real-time 3D scene graph construction from monocular camera
input with IMU
|
7 pages, 5 figures, GSW 2023 conference paper
| null | null | null |
cs.RO cs.AI
|
http://creativecommons.org/licenses/by-sa/4.0/
|
The ability of robots to autonomously navigate through 3D environments
depends on their comprehension of spatial concepts, ranging from low-level
geometry to high-level semantics, such as objects, places, and buildings. To
enable such comprehension, 3D scene graphs have emerged as a robust tool for
representing the environment as a layered graph of concepts and their
relationships. However, building these representations using monocular vision
systems in real-time remains a difficult task that has not been explored in
depth. This paper puts forth a real-time spatial perception system Mono-Hydra,
combining a monocular camera and an IMU sensor setup, focusing on indoor
scenarios. However, the proposed approach is adaptable to outdoor applications,
offering flexibility in its potential uses. The system employs a suite of deep
learning algorithms to derive depth and semantics. It uses a robocentric
visual-inertial odometry (VIO) algorithm based on square-root information,
thereby ensuring consistent visual odometry with an IMU and a monocular camera.
This system achieves sub-20 cm error in real-time processing at 15 fps,
enabling real-time 3D scene graph construction using a laptop GPU (NVIDIA
3080). This enhances decision-making efficiency and effectiveness in simple
camera setups, augmenting robotic system agility. We make Mono-Hydra publicly
available at: https://github.com/UAV-Centre-ITC/Mono_Hydra
|
[
{
"version": "v1",
"created": "Thu, 10 Aug 2023 11:58:38 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Udugama",
"U. V. B. L.",
""
],
[
"Vosselman",
"G.",
""
],
[
"Nex",
"F.",
""
]
] |
new_dataset
| 0.999524 |
2308.05521
|
Christian Dietrich
|
Christian Dietrich and Tim-Marek Thomas and Matthias Mnich
|
Checkpoint Placement for Systematic Fault-Injection Campaigns
|
Preprint for accepted version at ICCAD'23
| null | null | null |
cs.AR
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Shrinking hardware structures and decreasing operating voltages lead to an
increasing number of transient hardware faults,which thus become a core problem
to consider for safety-critical systems. Here, systematic fault injection (FI),
where one program-under-test is systematically stressed with faults, provides
an in-depth resilience analysis in the presence of faults. However, FI
campaigns require many independent injection experiments and, combined, long
run times, especially if we aim for a high coverage of the fault space. One
cost factor is the forwarding phase, which is the time required to bring the
system-under test into the fault-free state at injection time. One common
technique to speed up the forwarding are checkpoints of the fault-free system
state at fixed points in time.
In this paper, we show that the placement of checkpoints has a significant
influence on the required forwarding cycles, especially if we place faults
non-uniformly on the time axis. For this, we discuss the checkpoint-selection
problem in general, formalize it as a maximum-weight reward path problem in
graphs, propose an ILP formulation and a dynamic programming algorithm that
find the optimal solution, and provide a heuristic checkpoint-selection method
based on a genetic algorithm. Applied to the MiBench benchmark suite, our
approach consistently reduces the forward-phase cycles by at least 88 percent
and up to 99.934 percent when placing 16 checkpoints.
|
[
{
"version": "v1",
"created": "Thu, 10 Aug 2023 12:03:54 GMT"
}
] | 2023-08-11T00:00:00 |
[
[
"Dietrich",
"Christian",
""
],
[
"Thomas",
"Tim-Marek",
""
],
[
"Mnich",
"Matthias",
""
]
] |
new_dataset
| 0.985516 |
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