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arxiv-660801
|
2409.15054
|
FisheyeDepth: A Real Scale Self-Supervised Depth Estimation Model for Fisheye Camera
|
<|reference_start|>FisheyeDepth: A Real Scale Self-Supervised Depth Estimation Model for Fisheye Camera: Accurate depth estimation is crucial for 3D scene comprehension in robotics and autonomous vehicles. Fisheye cameras, known for their wide field of view, have inherent geometric benefits. However, their use in depth estimation is restricted by a scarcity of ground truth data and image distortions. We present FisheyeDepth, a self-supervised depth estimation model tailored for fisheye cameras. We incorporate a fisheye camera model into the projection and reprojection stages during training to handle image distortions, thereby improving depth estimation accuracy and training stability. Furthermore, we incorporate real-scale pose information into the geometric projection between consecutive frames, replacing the poses estimated by the conventional pose network. Essentially, this method offers the necessary physical depth for robotic tasks, and also streamlines the training and inference procedures. Additionally, we devise a multi-channel output strategy to improve robustness by adaptively fusing features at various scales, which reduces the noise from real pose data. We demonstrate the superior performance and robustness of our model in fisheye image depth estimation through evaluations on public datasets and real-world scenarios. The project website is available at: https://github.com/guoyangzhao/FisheyeDepth.<|reference_end|>
|
arxiv
|
@article{zhao2024fisheyedepth:,
title={FisheyeDepth: A Real Scale Self-Supervised Depth Estimation Model for
Fisheye Camera},
author={Guoyang Zhao, Yuxuan Liu, Weiqing Qi, Fulong Ma, Ming Liu and Jun Ma},
journal={arXiv preprint arXiv:2409.15054},
year={2024},
archivePrefix={arXiv},
eprint={2409.15054},
primaryClass={cs.CV cs.RO}
}
|
zhao2024fisheyedepth:
|
arxiv-660802
|
2409.15060
|
EMERS: Energy Meter for Recommender Systems
|
<|reference_start|>EMERS: Energy Meter for Recommender Systems: Due to recent advancements in machine learning, recommender systems use increasingly more energy for training, evaluation, and deployment. However, the recommender systems community often does not report the energy consumption of their experiments. In today's research landscape, no tools exist to easily measure the energy consumption of recommender systems experiments. To bridge this gap, we introduce EMERS, the first software library that simplifies measuring, monitoring, recording, and sharing the energy consumption of recommender systems experiments. EMERS measures energy consumption with smart power plugs and offers a user interface to monitor and compare the energy consumption of recommender systems experiments. Thereby, EMERS improves sustainability awareness and simplifies self-reporting energy consumption for recommender systems practitioners and researchers.<|reference_end|>
|
arxiv
|
@article{wegmeth2024emers:,
title={EMERS: Energy Meter for Recommender Systems},
author={Lukas Wegmeth, Tobias Vente, Alan Said and Joeran Beel},
journal={arXiv preprint arXiv:2409.15060},
year={2024},
archivePrefix={arXiv},
eprint={2409.15060},
primaryClass={cs.IR}
}
|
wegmeth2024emers:
|
arxiv-660803
|
2409.15061
|
Cloud Deployment of Large-Scale Electromagnetic Transient Simulation -- Discovery and Experiences
|
<|reference_start|>Cloud Deployment of Large-Scale Electromagnetic Transient Simulation -- Discovery and Experiences: Electromagnetic Transient (EMT) simulation starts to play a critical role in modern power system planning and operations due to large penetration of inverter based resources (IBRs). The EMT studies are computationally intensive due to very small simulation time step and complex modeling of the protection and control of IBRs. It has been challenging for the traditional on-premises computing infrastructure to meet the ever-increasing computing needs of large-scale EMT studies. This paper shares experience of ISO New England (ISO-NE) on a pilot deployment of EMT simulation in a public cloud using Amazon Web Services. The platform can successfully meet the large-scale EMT simulation computation needs in a cost-effective way while meeting cyber security and data privacy requirements.<|reference_end|>
|
arxiv
|
@article{luo2024cloud,
title={Cloud Deployment of Large-Scale Electromagnetic Transient Simulation --
Discovery and Experiences},
author={Xiaochuan Luo, Jason Ploof, Xinghao Fang, Qiang Zhang and Song Zhang},
journal={arXiv preprint arXiv:2409.15061},
year={2024},
archivePrefix={arXiv},
eprint={2409.15061},
primaryClass={eess.SY cs.SY}
}
|
luo2024cloud
|
arxiv-660804
|
2409.15066
|
A 35 GS/s 1-1 MASH VCO ADC With Second-Order Noise Shaping
|
<|reference_start|>A 35 GS/s 1-1 MASH VCO ADC With Second-Order Noise Shaping: In this work, a 3.5 GS/s voltage-controlled oscillator (VCO) analog-to-digital converter (ADC) using multi-stage noise shaping (MASH) is presented. This 28nm CMOS ADC achieves second-order noise shaping in an easily-scalable, open-loop configuration. A key enabler of the high-bandwidth MASH VCO ADC is the use of a multi-bit estimated error signal. With an OSR of 16, an SNDR of 67 dB and DR of 68 dB are achieved in 109.375 MHz bandwidth. The full-custom pseudo-analog circuits consume 9 mW, while the automatically generated digital circuits consume another 24 mW. A $\mathbf{FoM_{DR} = 163}$ dB and core area of $\mathbf{0.017\,\mathbf{mm}^2}$ are obtained.<|reference_end|>
|
arxiv
|
@article{saux2024a,
title={A 3.5 GS/s 1-1 MASH VCO ADC With Second-Order Noise Shaping},
author={Brendan Saux, Jonas Borgmans, Johan Raman and Pieter Rombouts},
journal={arXiv preprint arXiv:2409.15066},
year={2024},
doi={10.1109/TCSI.2024.3450570},
archivePrefix={arXiv},
eprint={2409.15066},
primaryClass={eess.SY cs.SY}
}
|
saux2024a
|
arxiv-660805
|
2409.15067
|
SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks
|
<|reference_start|>SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks: Federated Learning (FL) is a distributed machine learning paradigm designed for privacy-sensitive applications that run on resource-constrained devices with non-Identically and Independently Distributed (IID) data. Traditional FL frameworks adopt the client-server model with a single-level aggregation (AGR) process, where the server builds the global model by aggregating all trained local models received from client devices. However, this conventional approach encounters challenges, including susceptibility to model/data poisoning attacks. In recent years, advancements in the Internet of Things (IoT) and edge computing have enabled the development of hierarchical FL systems with a two-level AGR process running at edge and cloud servers. In this paper, we propose a Secure Hierarchical FL (SHFL) framework to address poisoning attacks in hierarchical edge networks. By aggregating trained models at the edge, SHFL employs two novel methods to address model/data poisoning attacks in the presence of client adversaries: 1) a client selection algorithm running at the edge for choosing IoT devices to participate in training, and 2) a model AGR method designed based on convex optimization theory to reduce the impact of edge models from networks with adversaries in the process of computing the global model (at the cloud level). The evaluation results reveal that compared to state-of-the-art methods, SHFL significantly increases the maximum accuracy achieved by the global model in the presence of client adversaries applying model/data poisoning attacks.<|reference_end|>
|
arxiv
|
@article{tavallaie2024shfl:,
title={SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks},
author={Omid Tavallaie, Kanchana Thilakarathna, Suranga Seneviratne, Aruna
Seneviratne, and Albert Y. Zomaya},
journal={arXiv preprint arXiv:2409.15067},
year={2024},
archivePrefix={arXiv},
eprint={2409.15067},
primaryClass={cs.LG cs.CR}
}
|
tavallaie2024shfl:
|
arxiv-660806
|
2409.15069
|
Biology and Technology Interaction: Study identifying the impact of robotic systems on fish behaviour change in industrial scale fish farms
|
<|reference_start|>Biology and Technology Interaction: Study identifying the impact of robotic systems on fish behaviour change in industrial scale fish farms: The significant growth in the aquaculture industry over the last few decades encourages new technological and robotic solutions to help improve the efficiency and safety of production. In sea-based farming of Atlantic salmon in Norway, Unmanned Underwater Vehicles (UUVs) are already being used for inspection tasks. While new methods, systems and concepts for sub-sea operations are continuously being developed, these systems generally does not take into account how their presence might impact the fish. This abstract presents an experimental study on how underwater robotic operations at fish farms in Norway can affect farmed Atlantic salmon, and how the fish behaviour changes when exposed to the robot. The abstract provides an overview of the case study, the methods of analysis, and some preliminary results.<|reference_end|>
|
arxiv
|
@article{evjemo2024biology,
title={Biology and Technology Interaction: Study identifying the impact of
robotic systems on fish behaviour change in industrial scale fish farms},
author={Linn Danielsen Evjemo and Qin Zhang and Hanne-Grete Alvheim and Herman
Bi{o}rn Amundsen and Martin F{o}re and Eleni Kelasidi},
journal={arXiv preprint arXiv:2409.15069},
year={2024},
archivePrefix={arXiv},
eprint={2409.15069},
primaryClass={cs.RO}
}
|
evjemo2024biology
|
arxiv-660807
|
2409.15072
|
Evaluating the Usability of LLMs in Threat Intelligence Enrichment
|
<|reference_start|>Evaluating the Usability of LLMs in Threat Intelligence Enrichment: Large Language Models (LLMs) have the potential to significantly enhance threat intelligence by automating the collection, preprocessing, and analysis of threat data. However, the usability of these tools is critical to ensure their effective adoption by security professionals. Despite the advanced capabilities of LLMs, concerns about their reliability, accuracy, and potential for generating inaccurate information persist. This study conducts a comprehensive usability evaluation of five LLMs ChatGPT, Gemini, Cohere, Copilot, and Meta AI focusing on their user interface design, error handling, learning curve, performance, and integration with existing tools in threat intelligence enrichment. Utilizing a heuristic walkthrough and a user study methodology, we identify key usability issues and offer actionable recommendations for improvement. Our findings aim to bridge the gap between LLM functionality and user experience, thereby promoting more efficient and accurate threat intelligence practices by ensuring these tools are user-friendly and reliable.<|reference_end|>
|
arxiv
|
@article{srikanth2024evaluating,
title={Evaluating the Usability of LLMs in Threat Intelligence Enrichment},
author={Sanchana Srikanth, Mohammad Hasanuzzaman, Farah Tasnur Meem},
journal={arXiv preprint arXiv:2409.15072},
year={2024},
archivePrefix={arXiv},
eprint={2409.15072},
primaryClass={cs.CR cs.CL cs.HC cs.LG}
}
|
srikanth2024evaluating
|
arxiv-660808
|
2409.15073
|
Exploring and Exploiting Runtime Reconfigurable Floating Point Precision in Scientific Computing: a Case Study for Solving PDEs
|
<|reference_start|>Exploring and Exploiting Runtime Reconfigurable Floating Point Precision in Scientific Computing: a Case Study for Solving PDEs: Scientific computing applications, such as computational fluid dynamics and climate modeling, typically rely on 64-bit double-precision floating-point operations, which are extremely costly in terms of computation, memory, and energy. While the machine learning community has successfully utilized low-precision computations, scientific computing remains cautious due to concerns about numerical stability. To tackle this long-standing challenge, we propose a novel approach to dynamically adjust the floating-point data precision at runtime, maintaining computational fidelity using lower bit widths. We first conduct a thorough analysis of data range distributions during scientific simulations to identify opportunities and challenges for dynamic precision adjustment. We then propose a runtime reconfigurable, flexible floating-point multiplier (R2F2), which automatically and dynamically adjusts multiplication precision based on the current operands, ensuring accurate results with lower bit widths. Our evaluation shows that 16-bit R2F2 significantly reduces error rates by 70.2\% compared to standard half-precision, with resource overhead ranging from a 5% reduction to a 7% increase and no latency overhead. In two representative scientific computing applications, R2F2, using 16 or fewer bits, can achieve the same simulation results as 32-bit precision, while standard half precision will fail. This study pioneers runtime reconfigurable arithmetic, demonstrating great potential to enhance scientific computing efficiency. Code available at https://github.com/sharc-lab/R2F2.<|reference_end|>
|
arxiv
|
@article{hao2024exploring,
title={Exploring and Exploiting Runtime Reconfigurable Floating Point Precision
in Scientific Computing: a Case Study for Solving PDEs},
author={Cong "Callie" Hao},
journal={arXiv preprint arXiv:2409.15073},
year={2024},
archivePrefix={arXiv},
eprint={2409.15073},
primaryClass={cs.AR}
}
|
hao2024exploring
|
arxiv-660809
|
2409.15076
|
Enhancing Scientific Reproducibility Through Automated BioCompute Object Creation Using Retrieval-Augmented Generation from Publications
|
<|reference_start|>Enhancing Scientific Reproducibility Through Automated BioCompute Object Creation Using Retrieval-Augmented Generation from Publications: The exponential growth in computational power and accessibility has transformed the complexity and scale of bioinformatics research, necessitating standardized documentation for transparency, reproducibility, and regulatory compliance. The IEEE BioCompute Object (BCO) standard addresses this need but faces adoption challenges due to the overhead of creating compliant documentation, especially for legacy research. This paper presents a novel approach to automate the creation of BCOs from scientific papers using Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs). We describe the development of the BCO assistant tool that leverages RAG to extract relevant information from source papers and associated code repositories, addressing key challenges such as LLM hallucination and long-context understanding. The implementation incorporates optimized retrieval processes, including a two-pass retrieval with re-ranking, and employs carefully engineered prompts for each BCO domain. We discuss the tool's architecture, extensibility, and evaluation methods, including automated and manual assessment approaches. The BCO assistant demonstrates the potential to significantly reduce the time and effort required for retroactive documentation of bioinformatics research while maintaining compliance with the standard. This approach opens avenues for AI-assisted scientific documentation and knowledge extraction from publications thereby enhancing scientific reproducibility. The BCO assistant tool and documentation is available at https://biocompute-objects.github.io/bco-rag/.<|reference_end|>
|
arxiv
|
@article{kim2024enhancing,
title={Enhancing Scientific Reproducibility Through Automated BioCompute Object
Creation Using Retrieval-Augmented Generation from Publications},
author={Sean Kim and Raja Mazumder},
journal={arXiv preprint arXiv:2409.15076},
year={2024},
archivePrefix={arXiv},
eprint={2409.15076},
primaryClass={cs.CL cs.AI q-bio.OT}
}
|
kim2024enhancing
|
arxiv-660810
|
2409.15077
|
TSCLIP: Robust CLIP Fine-Tuning for Worldwide Cross-Regional Traffic Sign Recognition
|
<|reference_start|>TSCLIP: Robust CLIP Fine-Tuning for Worldwide Cross-Regional Traffic Sign Recognition: Traffic sign is a critical map feature for navigation and traffic control. Nevertheless, current methods for traffic sign recognition rely on traditional deep learning models, which typically suffer from significant performance degradation considering the variations in data distribution across different regions. In this paper, we propose TSCLIP, a robust fine-tuning approach with the contrastive language-image pre-training (CLIP) model for worldwide cross-regional traffic sign recognition. We first curate a cross-regional traffic sign benchmark dataset by combining data from ten different sources. Then, we propose a prompt engineering scheme tailored to the characteristics of traffic signs, which involves specific scene descriptions and corresponding rules to generate targeted text descriptions for optimizing the model training process. During the TSCLIP fine-tuning process, we implement adaptive dynamic weight ensembling (ADWE) to seamlessly incorporate outcomes from each training iteration with the zero-shot CLIP model. This approach ensures that the model retains its ability to generalize while acquiring new knowledge about traffic signs. Our method surpasses conventional classification benchmark models in cross-regional traffic sign evaluations, and it achieves state-of-the-art performance compared to existing CLIP fine-tuning techniques. To the best knowledge of authors, TSCLIP is the first contrastive language-image model used for the worldwide cross-regional traffic sign recognition task. The project website is available at: https://github.com/guoyangzhao/TSCLIP.<|reference_end|>
|
arxiv
|
@article{zhao2024tsclip:,
title={TSCLIP: Robust CLIP Fine-Tuning for Worldwide Cross-Regional Traffic
Sign Recognition},
author={Guoyang Zhao, Fulong Ma, Weiqing Qi, Chenguang Zhang, Yuxuan Liu, Ming
Liu and Jun Ma},
journal={arXiv preprint arXiv:2409.15077},
year={2024},
archivePrefix={arXiv},
eprint={2409.15077},
primaryClass={cs.CV}
}
|
zhao2024tsclip:
|
arxiv-660811
|
2409.15080
|
Integrating Optimal Transport and Structural Inference Models for GRN Inference from Single-cell Data
|
<|reference_start|>Integrating Optimal Transport and Structural Inference Models for GRN Inference from Single-cell Data: We introduce a novel gene regulatory network (GRN) inference method that integrates optimal transport (OT) with a deep-learning structural inference model. Advances in next-generation sequencing enable detailed yet destructive gene expression assays at the single-cell level, resulting in the loss of cell evolutionary trajectories. Due to technological and cost constraints, single-cell experiments often feature cells sampled at irregular and sparse time points with a small sample size. Although trajectory-based structural inference models can accurately reveal the underlying interaction graph from observed data, their efficacy depends on the inputs of thousands of regularly sampled trajectories. The irregularly-sampled nature of single-cell data precludes the direct use of these powerful models for reconstructing GRNs. Optimal transport, a classical mathematical framework that minimize transportation costs between distributions, has shown promise in multi-omics data integration and cell fate prediction. Utilizing OT, our method constructs mappings between consecutively sampled cells to form cell-level trajectories, which are given as input to a structural inference model that recovers the GRN from single-cell data. Through case studies in two synthetic datasets, we demonstrate the feasibility of our proposed method and its promising performance over eight state-of-the-art GRN inference methods.<|reference_end|>
|
arxiv
|
@article{tong2024integrating,
title={Integrating Optimal Transport and Structural Inference Models for GRN
Inference from Single-cell Data},
author={Tsz Pan Tong, Aoran Wang, George Panagopoulos, Jun Pang},
journal={arXiv preprint arXiv:2409.15080},
year={2024},
archivePrefix={arXiv},
eprint={2409.15080},
primaryClass={cs.CE}
}
|
tong2024integrating
|
arxiv-660812
|
2409.15084
|
Depression Diagnosis Dialogue Simulation: Self-improving Psychiatrist with Tertiary Memory
|
<|reference_start|>Depression Diagnosis Dialogue Simulation: Self-improving Psychiatrist with Tertiary Memory: Mental health issues, particularly depressive disorders, present significant challenges in contemporary society, necessitating the development of effective automated diagnostic methods. This paper introduces the Agent Mental Clinic (AMC), a self-improving conversational agent system designed to enhance depression diagnosis through simulated dialogues between patient and psychiatrist agents. To enhance the dialogue quality and diagnosis accuracy, we design a psychiatrist agent consisting of a tertiary memory structure, a dialogue control and reflect plugin that acts as ``supervisor'' and a memory sampling module, fully leveraging the skills reflected by the psychiatrist agent, achieving great accuracy on depression risk and suicide risk diagnosis via conversation. Experiment results on datasets collected in real-life scenarios demonstrate that the system, simulating the procedure of training psychiatrists, can be a promising optimization method for aligning LLMs with real-life distribution in specific domains without modifying the weights of LLMs, even when only a few representative labeled cases are available.<|reference_end|>
|
arxiv
|
@article{lan2024depression,
title={Depression Diagnosis Dialogue Simulation: Self-improving Psychiatrist
with Tertiary Memory},
author={Kunyao Lan, Bingrui Jin, Zichen Zhu, Siyuan Chen, Shu Zhang, Kenny Q.
Zhu, Mengyue Wu},
journal={arXiv preprint arXiv:2409.15084},
year={2024},
archivePrefix={arXiv},
eprint={2409.15084},
primaryClass={cs.CL cs.AI cs.HC}
}
|
lan2024depression
|
arxiv-660813
|
2409.15087
|
Towards Accountable AI-Assisted Eye Disease Diagnosis: Workflow Design, External Validation, and Continual Learning
|
<|reference_start|>Towards Accountable AI-Assisted Eye Disease Diagnosis: Workflow Design, External Validation, and Continual Learning: Timely disease diagnosis is challenging due to increasing disease burdens and limited clinician availability. AI shows promise in diagnosis accuracy but faces real-world application issues due to insufficient validation in clinical workflows and diverse populations. This study addresses gaps in medical AI downstream accountability through a case study on age-related macular degeneration (AMD) diagnosis and severity classification. We designed and implemented an AI-assisted diagnostic workflow for AMD, comparing diagnostic performance with and without AI assistance among 24 clinicians from 12 institutions with real patient data sampled from the Age-Related Eye Disease Study (AREDS). Additionally, we demonstrated continual enhancement of an existing AI model by incorporating approximately 40,000 additional medical images (named AREDS2 dataset). The improved model was then systematically evaluated using both AREDS and AREDS2 test sets, as well as an external test set from Singapore. AI assistance markedly enhanced diagnostic accuracy and classification for 23 out of 24 clinicians, with the average F1-score increasing by 20% from 37.71 (Manual) to 45.52 (Manual + AI) (P-value < 0.0001), achieving an improvement of over 50% in some cases. In terms of efficiency, AI assistance reduced diagnostic times for 17 out of the 19 clinicians tracked, with time savings of up to 40%. Furthermore, a model equipped with continual learning showed robust performance across three independent datasets, recording a 29% increase in accuracy, and elevating the F1-score from 42 to 54 in the Singapore population.<|reference_end|>
|
arxiv
|
@article{chen2024towards,
title={Towards Accountable AI-Assisted Eye Disease Diagnosis: Workflow Design,
External Validation, and Continual Learning},
author={Qingyu Chen, Tiarnan D L Keenan, Elvira Agron, Alexis Allot, Emily
Guan, Bryant Duong, Amr Elsawy, Benjamin Hou, Cancan Xue, Sanjeeb Bhandari,
Geoffrey Broadhead, Chantal Cousineau-Krieger, Ellen Davis, William G
Gensheimer, David Grasic, Seema Gupta, Luis Haddock, Eleni Konstantinou,
Tania Lamba, Michele Maiberger, Dimosthenis Mantopoulos, Mitul C Mehta, Ayman
G Nahri, Mutaz AL-Nawaflh, Arnold Oshinsky, Brittany E Powell, Boonkit Purt,
Soo Shin, Hillary Stiefel, Alisa T Thavikulwat, Keith James Wroblewski, Tham
Yih Chung, Chui Ming Gemmy Cheung, Ching-Yu Cheng, Emily Y Chew, Michelle R.
Hribar, Michael F. Chiang, Zhiyong Lu},
journal={arXiv preprint arXiv:2409.15087},
year={2024},
archivePrefix={arXiv},
eprint={2409.15087},
primaryClass={eess.IV cs.CV cs.LG}
}
|
chen2024towards
|
arxiv-660814
|
2409.15088
|
AdapFair: Ensuring Continuous Fairness for Machine Learning Operations
|
<|reference_start|>AdapFair: Ensuring Continuous Fairness for Machine Learning Operations: The biases and discrimination of machine learning algorithms have attracted significant attention, leading to the development of various algorithms tailored to specific contexts. However, these solutions often fall short of addressing fairness issues inherent in machine learning operations. In this paper, we present a debiasing framework designed to find an optimal fair transformation of input data that maximally preserves data predictability. A distinctive feature of our approach is its flexibility and efficiency. It can be integrated with any downstream black-box classifiers, providing continuous fairness guarantees with minimal retraining efforts, even in the face of frequent data drifts, evolving fairness requirements, and batches of similar tasks. To achieve this, we leverage the normalizing flows to enable efficient, information-preserving data transformation, ensuring that no critical information is lost during the debiasing process. Additionally, we incorporate the Wasserstein distance as the unfairness measure to guide the optimization of data transformations. Finally, we introduce an efficient optimization algorithm with closed-formed gradient computations, making our framework scalable and suitable for dynamic, real-world environments.<|reference_end|>
|
arxiv
|
@article{huang2024adapfair:,
title={AdapFair: Ensuring Continuous Fairness for Machine Learning Operations},
author={Yinghui Huang, Zihao Tang, Xiangyu Chang},
journal={arXiv preprint arXiv:2409.15088},
year={2024},
archivePrefix={arXiv},
eprint={2409.15088},
primaryClass={cs.LG cs.CY}
}
|
huang2024adapfair:
|
arxiv-660815
|
2409.15090
|
Using Similarity to Evaluate Factual Consistency in Summaries
|
<|reference_start|>Using Similarity to Evaluate Factual Consistency in Summaries: Cutting-edge abstractive summarisers generate fluent summaries, but the factuality of the generated text is not guaranteed. Early summary factuality evaluation metrics are usually based on n-gram overlap and embedding similarity, but are reported fail to align with human annotations. Therefore, many techniques for detecting factual inconsistencies build pipelines around natural language inference (NLI) or question-answering (QA) models with additional supervised learning steps. In this paper, we revisit similarity-based metrics, showing that this failure stems from the comparison text selection and its granularity. We propose a new zero-shot factuality evaluation metric, Sentence-BERT Score (SBERTScore), which compares sentences between the summary and the source document. It outperforms widely-used word-word metrics including BERTScore and can compete with existing NLI and QA-based factuality metrics on the benchmark without needing any fine-tuning. Our experiments indicate that each technique has different strengths, with SBERTScore particularly effective in identifying correct summaries. We demonstrate how a combination of techniques is more effective in detecting various types of error.<|reference_end|>
|
arxiv
|
@article{ye2024using,
title={Using Similarity to Evaluate Factual Consistency in Summaries},
author={Yuxuan Ye, Edwin Simpson, Raul Santos Rodriguez},
journal={arXiv preprint arXiv:2409.15090},
year={2024},
archivePrefix={arXiv},
eprint={2409.15090},
primaryClass={cs.CL}
}
|
ye2024using
|
arxiv-660816
|
2409.15092
|
M2OST: Many-to-one Regression for Predicting Spatial Transcriptomics from Digital Pathology Images
|
<|reference_start|>M2OST: Many-to-one Regression for Predicting Spatial Transcriptomics from Digital Pathology Images: The advancement of Spatial Transcriptomics (ST) has facilitated the spatially-aware profiling of gene expressions based on histopathology images. Although ST data offers valuable insights into the micro-environment of tumors, its acquisition cost remains expensive. Therefore, directly predicting the ST expressions from digital pathology images is desired. Current methods usually adopt existing regression backbones along with patch-sampling for this task, which ignores the inherent multi-scale information embedded in the pyramidal data structure of digital pathology images, and wastes the inter-spot visual information crucial for accurate gene expression prediction. To address these limitations, we propose M2OST, a many-to-one regression Transformer that can accommodate the hierarchical structure of the pathology images via a decoupled multi-scale feature extractor. Unlike traditional models that are trained with one-to-one image-label pairs, M2OST uses multiple images from different levels of the digital pathology image to jointly predict the gene expressions in their common corresponding spot. Built upon our many-to-one scheme, M2OST can be easily scaled to fit different numbers of inputs, and its network structure inherently incorporates nearby inter-spot features, enhancing regression performance. We have tested M2OST on three public ST datasets and the experimental results show that M2OST can achieve state-of-the-art performance with fewer parameters and floating-point operations (FLOPs). The code will be released upon acceptance.<|reference_end|>
|
arxiv
|
@article{wang2024m2ost:,
title={M2OST: Many-to-one Regression for Predicting Spatial Transcriptomics
from Digital Pathology Images},
author={Hongyi Wang, Xiuju Du, Jing Liu, Shuyi Ouyang, Yen-Wei Chen, Lanfen
Lin},
journal={arXiv preprint arXiv:2409.15092},
year={2024},
archivePrefix={arXiv},
eprint={2409.15092},
primaryClass={cs.CV cs.AI}
}
|
wang2024m2ost:
|
arxiv-660817
|
2409.15094
|
Dynamic Pricing Algorithms for Online Set Cover
|
<|reference_start|>Dynamic Pricing Algorithms for Online Set Cover: We consider dynamic pricing algorithms as applied to the online set cover problem. In the dynamic pricing framework, we assume the standard client server model with the additional constraint that the server can only place prices over the resources they maintain, rather than authoritatively assign them. In response, incoming clients choose the resource which minimizes their disutility when taking into account these additional prices. Our main contributions are the categorization of online algorithms which can be mimicked via dynamic pricing algorithms and the identification of a strongly competitive deterministic algorithm with respect to the frequency parameter of the online set cover input.<|reference_end|>
|
arxiv
|
@article{bender2024dynamic,
title={Dynamic Pricing Algorithms for Online Set Cover},
author={Max Bender, Aum Desai, Jialin He, Oliver Thompson, Pramithas Upreti},
journal={arXiv preprint arXiv:2409.15094},
year={2024},
archivePrefix={arXiv},
eprint={2409.15094},
primaryClass={cs.DS}
}
|
bender2024dynamic
|
arxiv-660818
|
2409.15095
|
Zero-Cost Whole-Body Teleoperation for Mobile Manipulation
|
<|reference_start|>Zero-Cost Whole-Body Teleoperation for Mobile Manipulation: Demonstration data plays a key role in learning complex behaviors and training robotic foundation models. While effective control interfaces exist for static manipulators, data collection remains cumbersome and time intensive for mobile manipulators due to their large number of degrees of freedom. While specialized hardware, avatars, or motion tracking can enable whole-body control, these approaches are either expensive, robot-specific, or suffer from the embodiment mismatch between robot and human demonstrator. In this work, we present MoMa-Teleop, a novel teleoperation method that delegates the base motions to a reinforcement learning agent, leaving the operator to focus fully on the task-relevant end-effector motions. This enables whole-body teleoperation of mobile manipulators with zero additional hardware or setup costs via standard interfaces such as joysticks or hand guidance. Moreover, the operator is not bound to a tracked workspace and can move freely with the robot over spatially extended tasks. We demonstrate that our approach results in a significant reduction in task completion time across a variety of robots and tasks. As the generated data covers diverse whole-body motions without embodiment mismatch, it enables efficient imitation learning. By focusing on task-specific end-effector motions, our approach learns skills that transfer to unseen settings, such as new obstacles or changed object positions, from as little as five demonstrations. We make code and videos available at http://moma-teleop.cs.uni-freiburg.de.<|reference_end|>
|
arxiv
|
@article{honerkamp2024zero-cost,
title={Zero-Cost Whole-Body Teleoperation for Mobile Manipulation},
author={Daniel Honerkamp, Harsh Mahesheka, Jan Ole von Hartz, Tim Welschehold,
Abhinav Valada},
journal={arXiv preprint arXiv:2409.15095},
year={2024},
archivePrefix={arXiv},
eprint={2409.15095},
primaryClass={cs.RO cs.AI}
}
|
honerkamp2024zero-cost
|
arxiv-660819
|
2409.15097
|
Efficiently Dispatching Flash Attention For Partially Filled Attention Masks
|
<|reference_start|>Efficiently Dispatching Flash Attention For Partially Filled Attention Masks: Transformers are widely used across various applications, many of which yield sparse or partially filled attention matrices. Examples include attention masks designed to reduce the quadratic complexity of attention, sequence packing techniques, and recent innovations like tree masking for fast validation in MEDUSA. Despite the inherent sparsity in these matrices, the state-of-the-art algorithm Flash Attention still processes them with quadratic complexity as though they were dense. In this paper, we introduce Binary Block Masking, a highly efficient modification that enhances Flash Attention by making it mask-aware. We further propose two optimizations: one tailored for masks with contiguous non-zero patterns and another for extremely sparse masks. Our experiments on attention masks derived from real-world scenarios demonstrate up to a 9x runtime improvement. The implementation will be publicly released to foster further research and application.<|reference_end|>
|
arxiv
|
@article{sharma2024efficiently,
title={Efficiently Dispatching Flash Attention For Partially Filled Attention
Masks},
author={Agniv Sharma and Jonas Geiping},
journal={arXiv preprint arXiv:2409.15097},
year={2024},
archivePrefix={arXiv},
eprint={2409.15097},
primaryClass={cs.LG cs.AI cs.CL}
}
|
sharma2024efficiently
|
arxiv-660820
|
2409.15100
|
Robust Federated Learning Over the Air: Combating Heavy-Tailed Noise with Median Anchored Clipping
|
<|reference_start|>Robust Federated Learning Over the Air: Combating Heavy-Tailed Noise with Median Anchored Clipping: Leveraging over-the-air computations for model aggregation is an effective approach to cope with the communication bottleneck in federated edge learning. By exploiting the superposition properties of multi-access channels, this approach facilitates an integrated design of communication and computation, thereby enhancing system privacy while reducing implementation costs. However, the inherent electromagnetic interference in radio channels often exhibits heavy-tailed distributions, giving rise to exceptionally strong noise in globally aggregated gradients that can significantly deteriorate the training performance. To address this issue, we propose a novel gradient clipping method, termed Median Anchored Clipping (MAC), to combat the detrimental effects of heavy-tailed noise. We also derive analytical expressions for the convergence rate of model training with analog over-the-air federated learning under MAC, which quantitatively demonstrates the effect of MAC on training performance. Extensive experimental results show that the proposed MAC algorithm effectively mitigates the impact of heavy-tailed noise, hence substantially enhancing system robustness.<|reference_end|>
|
arxiv
|
@article{li2024robust,
title={Robust Federated Learning Over the Air: Combating Heavy-Tailed Noise
with Median Anchored Clipping},
author={Jiaxing Li, Zihan Chen, Kai Fong Ernest Chong, Bikramjit Das, Tony Q.
S. Quek, Howard H. Yang},
journal={arXiv preprint arXiv:2409.15100},
year={2024},
archivePrefix={arXiv},
eprint={2409.15100},
primaryClass={cs.LG cs.AI}
}
|
li2024robust
|
arxiv-660821
|
2409.15101
|
GALD-SE: Guided Anisotropic Lightweight Diffusion for Efficient Speech Enhancement
|
<|reference_start|>GALD-SE: Guided Anisotropic Lightweight Diffusion for Efficient Speech Enhancement: Speech enhancement is designed to enhance the intelligibility and quality of speech across diverse noise conditions. Recently, diffusion model has gained lots of attention in speech enhancement area, achieving competitive results. Current diffusion-based methods blur the signal with isotropic Gaussian noise and recover clean speech from the prior. However, these methods often suffer from a substantial computational burden. We argue that the computational inefficiency partially stems from the oversight that speech enhancement is not purely a generative task; it primarily involves noise reduction and completion of missing information, while the clean clues in the original mixture do not need to be regenerated. In this paper, we propose a method that introduces noise with anisotropic guidance during the diffusion process, allowing the neural network to preserve clean clues within noisy recordings. This approach substantially reduces computational complexity while exhibiting robustness against various forms of noise and speech distortion. Experiments demonstrate that the proposed method achieves state-of-the-art results with only approximately 4.5 million parameters, a number significantly lower than that required by other diffusion methods. This effectively narrows the model size disparity between diffusion-based and predictive speech enhancement approaches. Additionally, the proposed method performs well in very noisy scenarios, demonstrating its potential for applications in highly challenging environments.<|reference_end|>
|
arxiv
|
@article{wang2024gald-se:,
title={GALD-SE: Guided Anisotropic Lightweight Diffusion for Efficient Speech
Enhancement},
author={Chengzhong Wang, Jianjun Gu, Dingding Yao, Junfeng Li, Yonghong Yan},
journal={arXiv preprint arXiv:2409.15101},
year={2024},
archivePrefix={arXiv},
eprint={2409.15101},
primaryClass={cs.SD eess.AS}
}
|
wang2024gald-se:
|
arxiv-660822
|
2409.15104
|
CSPS: A Communication-Efficient Sequence-Parallelism based Serving System for Transformer based Models with Long Prompts
|
<|reference_start|>CSPS: A Communication-Efficient Sequence-Parallelism based Serving System for Transformer based Models with Long Prompts: Long-sequence generative large-language model (LLM) applications have become increasingly popular. In this paper, through trace-based experiments, we found that the existing method for long sequences results in a high Time-To-First-Token (TTFT) due to sequential chunk processing, long Time-Between-Tokens (TBT) from batching long-sequence prefills and decodes, and low throughput due to constrained key-value cache (KVC) for long sequences. To address these issues, we propose two Sequence-Parallelism (SP) architectures for both tensor parallelism (TP) and non-TP. However, SP introduces two challenges: 1) network communication and computation become performance bottlenecks; 2) the latter two issues above are mitigated but not resolved, and SP's resultant KV value distribution across GPUs still requires communication for decode, increasing TBT. Hence, we propose a Communication-efficient Sparse Attention (CSA) and communication-computation-communication three-phase pipelining. We also propose SP-based decode that processes decode separately from prefill, distributes KV values of a request across different GPUs, and novelly moves Query (Q) values instead of KV values to reduce communication overhead. These methods constitute a communication-efficient Sequence-Parallelism based LLM Serving System (SPS2). Our trace-driven evaluation demonstrates that SPS2 improves the average TTFT, TBT, and response time by up to 7.5x, 1.92x, and 9.8x and improves the prefill and decode throughput by 8.2x and 5.2x while maintaining the accuracy compared to Sarathi-Serve. We distributed our source code.<|reference_end|>
|
arxiv
|
@article{zhang2024csps:,
title={CSPS: A Communication-Efficient Sequence-Parallelism based Serving
System for Transformer based Models with Long Prompts},
author={Zeyu Zhang, Haiying Shen},
journal={arXiv preprint arXiv:2409.15104},
year={2024},
archivePrefix={arXiv},
eprint={2409.15104},
primaryClass={cs.DC cs.LG}
}
|
zhang2024csps:
|
arxiv-660823
|
2409.15105
|
SPformer: A Transformer Based DRL Decision Making Method for Connected Automated Vehicles
|
<|reference_start|>SPformer: A Transformer Based DRL Decision Making Method for Connected Automated Vehicles: In mixed autonomy traffic environment, every decision made by an autonomous-driving car may have a great impact on the transportation system. Because of the complex interaction between vehicles, it is challenging to make decisions that can ensure both high traffic efficiency and safety now and futher. Connected automated vehicles (CAVs) have great potential to improve the quality of decision-making in this continuous, highly dynamic and interactive environment because of their stronger sensing and communicating ability. For multi-vehicle collaborative decision-making algorithms based on deep reinforcement learning (DRL), we need to represent the interactions between vehicles to obtain interactive features. The representation in this aspect directly affects the learning efficiency and the quality of the learned policy. To this end, we propose a CAV decision-making architecture based on transformer and reinforcement learning algorithms. A learnable policy token is used as the learning medium of the multi-vehicle joint policy, the states of all vehicles in the area of interest can be adaptively noticed in order to extract interactive features among agents. We also design an intuitive physical positional encodings, the redundant location information of which optimizes the performance of the network. Simulations show that our model can make good use of all the state information of vehicles in traffic scenario, so as to obtain high-quality driving decisions that meet efficiency and safety objectives. The comparison shows that our method significantly improves existing DRL-based multi-vehicle cooperative decision-making algorithms.<|reference_end|>
|
arxiv
|
@article{han2024spformer:,
title={SPformer: A Transformer Based DRL Decision Making Method for Connected
Automated Vehicles},
author={Ye Han, Lijun Zhang, Dejian Meng, Xingyu Hu, Yixia Lu},
journal={arXiv preprint arXiv:2409.15105},
year={2024},
archivePrefix={arXiv},
eprint={2409.15105},
primaryClass={cs.AI cs.MA cs.SY eess.SY}
}
|
han2024spformer:
|
arxiv-660824
|
2409.15107
|
The BRAVO Semantic Segmentation Challenge Results in UNCV2024
|
<|reference_start|>The BRAVO Semantic Segmentation Challenge Results in UNCV2024: We propose the unified BRAVO challenge to benchmark the reliability of semantic segmentation models under realistic perturbations and unknown out-of-distribution (OOD) scenarios. We define two categories of reliability: (1) semantic reliability, which reflects the model's accuracy and calibration when exposed to various perturbations; and (2) OOD reliability, which measures the model's ability to detect object classes that are unknown during training. The challenge attracted nearly 100 submissions from international teams representing notable research institutions. The results reveal interesting insights into the importance of large-scale pre-training and minimal architectural design in developing robust and reliable semantic segmentation models.<|reference_end|>
|
arxiv
|
@article{vu2024the,
title={The BRAVO Semantic Segmentation Challenge Results in UNCV2024},
author={Tuan-Hung Vu, Eduardo Valle, Andrei Bursuc, Tommie Kerssies, Daan de
Geus, Gijs Dubbelman, Long Qian, Bingke Zhu, Yingying Chen, Ming Tang,
Jinqiao Wang, Tom'av{s} Voj'iv{r}, Jan v{S}ochman, Jiv{r}'i Matas,
Michael Smith, Frank Ferrie, Shamik Basu, Christos Sakaridis, Luc Van Gool},
journal={arXiv preprint arXiv:2409.15107},
year={2024},
archivePrefix={arXiv},
eprint={2409.15107},
primaryClass={cs.CV cs.AI cs.LG}
}
|
vu2024the
|
arxiv-660825
|
2409.15109
|
End-User-Centric Collaborative MIMO: Performance Analysis and Proof of Concept
|
<|reference_start|>End-User-Centric Collaborative MIMO: Performance Analysis and Proof of Concept: The trend toward using increasingly large arrays of antenna elements continues. However, fitting more antennas into the limited space available on user equipment (UE) within the currently popular Frequency Range 1 spectrum presents a significant challenge. This limitation constrains the capacity scaling gains for end users, even when networks can support a higher number of antennas. To address this issue, we explore a user-centric collaborative MIMO approach, termed UE-CoMIMO, which leverages several fixed or portable devices within a personal area to form a virtually expanded antenna array. This paper develops a comprehensive mathematical framework to analyze the performance of UE-CoMIMO. Our analytical results demonstrate that UE-CoMIMO can significantly enhance the system's effective channel response within the current communication system without requiring extensive modifications. Further performance improvements can be realized by optimizing the phase shifters on the expanded antenna arrays at the collaborative devices. These findings are corroborated by ray-tracing simulations. Beyond the simulations, we implemented these collaborative devices and successfully conducted over-the-air validation in a real 5G environment, showcasing the practical potential of UE-CoMIMO. Several practical perspectives are discussed, highlighting the feasibility and benefits of this approach in real-world scenarios.<|reference_end|>
|
arxiv
|
@article{wen2024end-user-centric,
title={End-User-Centric Collaborative MIMO: Performance Analysis and Proof of
Concept},
author={Chao-Kai Wen, Yen-Cheng Chan, Tzu-Hao Huang, Hao-Jun Zeng, Fu-Kang
Wang, Lung-Sheng Tsai, and Pei-Kai Liao},
journal={arXiv preprint arXiv:2409.15109},
year={2024},
archivePrefix={arXiv},
eprint={2409.15109},
primaryClass={cs.IT eess.SP math.IT}
}
|
wen2024end-user-centric
|
arxiv-660826
|
2409.15112
|
ChatGPT as a Solver and Grader of Programming Exams written in Spanish
|
<|reference_start|>ChatGPT as a Solver and Grader of Programming Exams written in Spanish: Evaluating the capabilities of Large Language Models (LLMs) to assist teachers and students in educational tasks is receiving increasing attention. In this paper, we assess ChatGPT's capacities to solve and grade real programming exams, from an accredited BSc degree in Computer Science, written in Spanish. Our findings suggest that this AI model is only effective for solving simple coding tasks. Its proficiency in tackling complex problems or evaluating solutions authored by others are far from effective. As part of this research, we also release a new corpus of programming tasks and the corresponding prompts for solving the problems or grading the solutions. This resource can be further exploited by other research teams.<|reference_end|>
|
arxiv
|
@article{fernández-saborido2024chatgpt,
title={ChatGPT as a Solver and Grader of Programming Exams written in Spanish},
author={Pablo Fern'andez-Saborido and Marcos Fern'andez-Pichel and David E.
Losada},
journal={arXiv preprint arXiv:2409.15112},
year={2024},
archivePrefix={arXiv},
eprint={2409.15112},
primaryClass={cs.AI}
}
|
fernández-saborido2024chatgpt
|
arxiv-660827
|
2409.15113
|
Inferring Scientific Cross-Document Coreference and Hierarchy with Definition-Augmented Relational Reasoning
|
<|reference_start|>Inferring Scientific Cross-Document Coreference and Hierarchy with Definition-Augmented Relational Reasoning: We address the fundamental task of inferring cross-document coreference and hierarchy in scientific texts, which has important applications in knowledge graph construction, search, recommendation and discovery. LLMs can struggle when faced with many long-tail technical concepts with nuanced variations. We present a novel method which generates context-dependent definitions of concept mentions by retrieving full-text literature, and uses the definitions to enhance detection of cross-document relations. We further generate relational definitions, which describe how two concept mentions are related or different, and design an efficient re-ranking approach to address the combinatorial explosion involved in inferring links across papers. In both fine-tuning and in-context learning settings we achieve large gains in performance. We provide analysis of generated definitions, shedding light on the relational reasoning ability of LLMs over fine-grained scientific concepts.<|reference_end|>
|
arxiv
|
@article{forer2024inferring,
title={Inferring Scientific Cross-Document Coreference and Hierarchy with
Definition-Augmented Relational Reasoning},
author={Lior Forer, Tom Hope},
journal={arXiv preprint arXiv:2409.15113},
year={2024},
archivePrefix={arXiv},
eprint={2409.15113},
primaryClass={cs.CL}
}
|
forer2024inferring
|
arxiv-660828
|
2409.15114
|
Evaluating ML Robustness in GNSS Interference Classification, Characterization \& Localization
|
<|reference_start|>Evaluating ML Robustness in GNSS Interference Classification, Characterization \& Localization: Jamming devices present a significant threat by disrupting signals from the global navigation satellite system (GNSS), compromising the robustness of accurate positioning. The detection of anomalies within frequency snapshots is crucial to counteract these interferences effectively. A critical preliminary measure involves the reliable classification of interferences and characterization and localization of jamming devices. This paper introduces an extensive dataset compromising snapshots obtained from a low-frequency antenna, capturing diverse generated interferences within a large-scale environment including controlled multipath effects. Our objective is to assess the resilience of ML models against environmental changes, such as multipath effects, variations in interference attributes, such as the interference class, bandwidth, and signal-to-noise ratio, the accuracy jamming device localization, and the constraints imposed by snapshot input lengths. By analyzing the aleatoric and epistemic uncertainties, we demonstrate the adaptness of our model in generalizing across diverse facets, thus establishing its suitability for real-world applications. https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/controlled_low_frequency<|reference_end|>
|
arxiv
|
@article{heublein2024evaluating,
title={Evaluating ML Robustness in GNSS Interference Classification,
Characterization \& Localization},
author={Lucas Heublein and Tobias Feigl and Thorsten Nowak and Alexander
R"ugamer and Christopher Mutschler and Felix Ott},
journal={arXiv preprint arXiv:2409.15114},
year={2024},
archivePrefix={arXiv},
eprint={2409.15114},
primaryClass={cs.AI}
}
|
heublein2024evaluating
|
arxiv-660829
|
2409.15117
|
Diffusion-based RGB-D Semantic Segmentation with Deformable Attention Transformer
|
<|reference_start|>Diffusion-based RGB-D Semantic Segmentation with Deformable Attention Transformer: Vision-based perception and reasoning is essential for scene understanding in any autonomous system. RGB and depth images are commonly used to capture both the semantic and geometric features of the environment. Developing methods to reliably interpret this data is critical for real-world applications, where noisy measurements are often unavoidable. In this work, we introduce a diffusion-based framework to address the RGB-D semantic segmentation problem. Additionally, we demonstrate that utilizing a Deformable Attention Transformer as the encoder to extract features from depth images effectively captures the characteristics of invalid regions in depth measurements. Our generative framework shows a greater capacity to model the underlying distribution of RGB-D images, achieving robust performance in challenging scenarios with significantly less training time compared to discriminative methods. Experimental results indicate that our approach achieves State-of-the-Art performance on both the NYUv2 and SUN-RGBD datasets in general and especially in the most challenging of their image data. Our project page will be available at https://diffusionmms.github.io/<|reference_end|>
|
arxiv
|
@article{bui2024diffusion-based,
title={Diffusion-based RGB-D Semantic Segmentation with Deformable Attention
Transformer},
author={Minh Bui and Kostas Alexis},
journal={arXiv preprint arXiv:2409.15117},
year={2024},
archivePrefix={arXiv},
eprint={2409.15117},
primaryClass={cs.CV}
}
|
bui2024diffusion-based
|
arxiv-660830
|
2409.15119
|
Log-normal Mutations and their Use in Detecting Surreptitious Fake Images
|
<|reference_start|>Log-normal Mutations and their Use in Detecting Surreptitious Fake Images: In many cases, adversarial attacks are based on specialized algorithms specifically dedicated to attacking automatic image classifiers. These algorithms perform well, thanks to an excellent ad hoc distribution of initial attacks. However, these attacks are easily detected due to their specific initial distribution. We therefore consider other black-box attacks, inspired from generic black-box optimization tools, and in particular the log-normal algorithm. We apply the log-normal method to the attack of fake detectors, and get successful attacks: importantly, these attacks are not detected by detectors specialized on classical adversarial attacks. Then, combining these attacks and deep detection, we create improved fake detectors.<|reference_end|>
|
arxiv
|
@article{labiad2024log-normal,
title={Log-normal Mutations and their Use in Detecting Surreptitious Fake
Images},
author={Ismail Labiad, Thomas B"ack, Pierre Fernandez, Laurent Najman, Tom
Sander, Furong Ye, Mariia Zameshina, Olivier Teytaud},
journal={arXiv preprint arXiv:2409.15119},
year={2024},
archivePrefix={arXiv},
eprint={2409.15119},
primaryClass={cs.AI}
}
|
labiad2024log-normal
|
arxiv-660831
|
2409.15120
|
Space-time process algebra with asynchronous communication
|
<|reference_start|>Space-time process algebra with asynchronous communication: We introduce a process algebra that concerns the timed behaviour of distributed systems with a known spatial distribution. This process algebra provides a communication mechanism that deals with the fact that a datum sent at one point in space can only be received at another point in space at the point in time that the datum reaches that point in space. The integration operator used in related process algebras to model such a communication mechanism is absent from this process algebra. This is considered an advantage because, being a variable-binding operator, the integration operator does not really fit an algebraic approach and is not founded on established metatheory.<|reference_end|>
|
arxiv
|
@article{bergstra2024space-time,
title={Space-time process algebra with asynchronous communication},
author={J. A. Bergstra, C. A. Middelburg},
journal={arXiv preprint arXiv:2409.15120},
year={2024},
archivePrefix={arXiv},
eprint={2409.15120},
primaryClass={cs.LO}
}
|
bergstra2024space-time
|
arxiv-660832
|
2409.15125
|
Detect, Describe, Discriminate: Moving Beyond VQA for MLLM Evaluation
|
<|reference_start|>Detect, Describe, Discriminate: Moving Beyond VQA for MLLM Evaluation: Visual Question Answering (VQA) with multiple choice questions enables a vision-centric evaluation of Multimodal Large Language Models (MLLMs). Although it reliably checks the existence of specific visual abilities, it is easier for the model to select an answer from multiple choices (VQA evaluation) than to generate the answer itself. In this work, we offer a novel perspective: we evaluate how well an MLLM understands a specific visual concept by its ability to uniquely describe two extremely similar images that differ only in the targeted visual concept. Specifically, we assess the ability of MLLMs to capture specific points of visual differences using self-retrieval, i.e., by retrieving the target image using its generated caption against the other image in the pair serving as the distractor. We curate 247 highly similar image pairs as part of the D3 benchmark. For each image pair, the model is prompted to: (1) Detect a specific visual difference, and (2) Describe the target image uniquely such that it (3) Discriminates the target image from the distractor. Self-retrieval within D3 enables whitebox evaluation across six different visual patterns, revealing that current models struggle to independently discern fine-grained visual differences, with open-source models failing to outperform random guess.<|reference_end|>
|
arxiv
|
@article{gaur2024detect,,
title={Detect, Describe, Discriminate: Moving Beyond VQA for MLLM Evaluation},
author={Manu Gaur, Darshan Singh S, Makarand Tapaswi},
journal={arXiv preprint arXiv:2409.15125},
year={2024},
archivePrefix={arXiv},
eprint={2409.15125},
primaryClass={cs.CV}
}
|
gaur2024detect,
|
arxiv-660833
|
2409.15126
|
UTrace: Poisoning Forensics for Private Collaborative Learning
|
<|reference_start|>UTrace: Poisoning Forensics for Private Collaborative Learning: Privacy-preserving machine learning (PPML) enables multiple data owners to contribute their data privately to a set of servers that run a secure multi-party computation (MPC) protocol to train a joint ML model. In these protocols, the input data remains private throughout the training process, and only the resulting model is made available. While this approach benefits privacy, it also exacerbates the risks of data poisoning, where compromised data owners induce undesirable model behavior by contributing malicious datasets. Existing MPC mechanisms can mitigate certain poisoning attacks, but these measures are not exhaustive. To complement existing poisoning defenses, we introduce UTrace: a framework for User-level Traceback of poisoning attacks in PPML. Utrace computes user responsibility scores using gradient similarity metrics aggregated across the most relevant samples in an owner's dataset. UTrace is effective at low poisoning rates and is resilient to poisoning attacks distributed across multiple data owners, unlike existing unlearning-based methods. We introduce methods for checkpointing gradients with low storage overhead, enabling traceback in the absence of data owners at deployment time. We also design several optimizations that reduce traceback time and communication in MPC. We provide a comprehensive evaluation of UTrace across four datasets from three data modalities (vision, text, and malware) and show its effectiveness against 10 poisoning attacks.<|reference_end|>
|
arxiv
|
@article{rose2024utrace:,
title={UTrace: Poisoning Forensics for Private Collaborative Learning},
author={Evan Rose, Hidde Lycklama, Harsh Chaudhari, Anwar Hithnawi, Alina
Oprea},
journal={arXiv preprint arXiv:2409.15126},
year={2024},
archivePrefix={arXiv},
eprint={2409.15126},
primaryClass={cs.CR cs.LG}
}
|
rose2024utrace:
|
arxiv-660834
|
2409.15127
|
Boosting Healthcare LLMs Through Retrieved Context
|
<|reference_start|>Boosting Healthcare LLMs Through Retrieved Context: Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing, and yet, their factual inaccuracies and hallucinations limits their application, particularly in critical domains like healthcare. Context retrieval methods, by introducing relevant information as input, have emerged as a crucial approach for enhancing LLM factuality and reliability. This study explores the boundaries of context retrieval methods within the healthcare domain, optimizing their components and benchmarking their performance against open and closed alternatives. Our findings reveal how open LLMs, when augmented with an optimized retrieval system, can achieve performance comparable to the biggest private solutions on established healthcare benchmarks (multiple-choice question answering). Recognizing the lack of realism of including the possible answers within the question (a setup only found in medical exams), and after assessing a strong LLM performance degradation in the absence of those options, we extend the context retrieval system in that direction. In particular, we propose OpenMedPrompt a pipeline that improves the generation of more reliable open-ended answers, moving this technology closer to practical application.<|reference_end|>
|
arxiv
|
@article{bayarri-planas2024boosting,
title={Boosting Healthcare LLMs Through Retrieved Context},
author={Jordi Bayarri-Planas and Ashwin Kumar Gururajan and Dario
Garcia-Gasulla},
journal={arXiv preprint arXiv:2409.15127},
year={2024},
archivePrefix={arXiv},
eprint={2409.15127},
primaryClass={cs.AI}
}
|
bayarri-planas2024boosting
|
arxiv-660835
|
2409.15128
|
The Number of Trials Matters in Infinite-Horizon General-Utility Markov Decision Processes
|
<|reference_start|>The Number of Trials Matters in Infinite-Horizon General-Utility Markov Decision Processes: The general-utility Markov decision processes (GUMDPs) framework generalizes the MDPs framework by considering objective functions that depend on the frequency of visitation of state-action pairs induced by a given policy. In this work, we contribute with the first analysis on the impact of the number of trials, i.e., the number of randomly sampled trajectories, in infinite-horizon GUMDPs. We show that, as opposed to standard MDPs, the number of trials plays a key-role in infinite-horizon GUMDPs and the expected performance of a given policy depends, in general, on the number of trials. We consider both discounted and average GUMDPs, where the objective function depends, respectively, on discounted and average frequencies of visitation of state-action pairs. First, we study policy evaluation under discounted GUMDPs, proving lower and upper bounds on the mismatch between the finite and infinite trials formulations for GUMDPs. Second, we address average GUMDPs, studying how different classes of GUMDPs impact the mismatch between the finite and infinite trials formulations. Third, we provide a set of empirical results to support our claims, highlighting how the number of trajectories and the structure of the underlying GUMDP influence policy evaluation.<|reference_end|>
|
arxiv
|
@article{santos2024the,
title={The Number of Trials Matters in Infinite-Horizon General-Utility Markov
Decision Processes},
author={Pedro P. Santos, Alberto Sardinha, Francisco S. Melo},
journal={arXiv preprint arXiv:2409.15128},
year={2024},
archivePrefix={arXiv},
eprint={2409.15128},
primaryClass={cs.LG}
}
|
santos2024the
|
arxiv-660836
|
2409.15130
|
CAMAL: Optimizing LSM-trees via Active Learning
|
<|reference_start|>CAMAL: Optimizing LSM-trees via Active Learning: We use machine learning to optimize LSM-tree structure, aiming to reduce the cost of processing various read/write operations. We introduce a new approach Camal, which boasts the following features: (1) ML-Aided: Camal is the first attempt to apply active learning to tune LSM-tree based key-value stores. The learning process is coupled with traditional cost models to improve the training process; (2) Decoupled Active Learning: backed by rigorous analysis, Camal adopts active learning paradigm based on a decoupled tuning of each parameter, which further accelerates the learning process; (3) Easy Extrapolation: Camal adopts an effective mechanism to incrementally update the model with the growth of the data size; (4) Dynamic Mode: Camal is able to tune LSM-tree online under dynamically changing workloads; (5) Significant System Improvement: By integrating Camal into a full system RocksDB, the system performance improves by 28% on average and up to 8x compared to a state-of-the-art RocksDB design.<|reference_end|>
|
arxiv
|
@article{yu2024camal:,
title={CAMAL: Optimizing LSM-trees via Active Learning},
author={Weiping Yu, Siqiang Luo, Zihao Yu, Gao Cong},
journal={arXiv preprint arXiv:2409.15130},
year={2024},
archivePrefix={arXiv},
eprint={2409.15130},
primaryClass={cs.DB cs.AI cs.LG}
}
|
yu2024camal:
|
arxiv-660837
|
2409.15132
|
FusionRF: High-Fidelity Satellite Neural Radiance Fields from Multispectral and Panchromatic Acquisitions
|
<|reference_start|>FusionRF: High-Fidelity Satellite Neural Radiance Fields from Multispectral and Panchromatic Acquisitions: We introduce FusionRF, a novel neural rendering terrain reconstruction method from optically unprocessed satellite imagery. While previous methods depend on external pansharpening methods to fuse low resolution multispectral imagery and high resolution panchromatic imagery, FusionRF directly performs reconstruction based on optically unprocessed acquisitions with no prior knowledge. This is accomplished through the addition of a sharpening kernel which models the resolution loss in multispectral images. Additionally, novel modal embeddings allow the model to perform image fusion as a bottleneck to novel view synthesis. We evaluate our method on multispectral and panchromatic satellite images from the WorldView-3 satellite in various locations, and FusionRF outperforms previous State-of-The-Art methods in depth reconstruction on unprocessed imagery, renders sharp training and novel views, and retains multi-spectral information.<|reference_end|>
|
arxiv
|
@article{sprintson2024fusionrf:,
title={FusionRF: High-Fidelity Satellite Neural Radiance Fields from
Multispectral and Panchromatic Acquisitions},
author={Michael Sprintson, Rama Chellappa, and Cheng Peng},
journal={arXiv preprint arXiv:2409.15132},
year={2024},
archivePrefix={arXiv},
eprint={2409.15132},
primaryClass={cs.CV eess.IV}
}
|
sprintson2024fusionrf:
|
arxiv-660838
|
2409.15133
|
Don't Use LLMs to Make Relevance Judgments
|
<|reference_start|>Don't Use LLMs to Make Relevance Judgments: Making the relevance judgments for a TREC-style test collection can be complex and expensive. A typical TREC track usually involves a team of six contractors working for 2-4 weeks. Those contractors need to be trained and monitored. Software has to be written to support recording relevance judgments correctly and efficiently. The recent advent of large language models that produce astoundingly human-like flowing text output in response to a natural language prompt has inspired IR researchers to wonder how those models might be used in the relevance judgment collection process. At the ACM SIGIR 2024 conference, a workshop ``LLM4Eval'' provided a venue for this work, and featured a data challenge activity where participants reproduced TREC deep learning track judgments, as was done by Thomas et al (arXiv:2408.08896, arXiv:2309.10621). I was asked to give a keynote at the workshop, and this paper presents that keynote in article form. The bottom-line-up-front message is, don't use LLMs to create relevance judgments for TREC-style evaluations.<|reference_end|>
|
arxiv
|
@article{soboroff2024don't,
title={Don't Use LLMs to Make Relevance Judgments},
author={Ian Soboroff},
journal={arXiv preprint arXiv:2409.15133},
year={2024},
archivePrefix={arXiv},
eprint={2409.15133},
primaryClass={cs.IR}
}
|
soboroff2024don't
|
arxiv-660839
|
2409.15135
|
Controllable Traffic Simulation through LLM-Guided Hierarchical Chain-of-Thought Reasoning
|
<|reference_start|>Controllable Traffic Simulation through LLM-Guided Hierarchical Chain-of-Thought Reasoning: Evaluating autonomous driving systems in complex and diverse traffic scenarios through controllable simulation is essential to ensure their safety and reliability. However, existing traffic simulation methods face challenges in their controllability. To address this, this paper proposes a novel diffusion-based and LLM-enhanced traffic simulation framework. Our approach incorporates a unique chain-of-thought (CoT) mechanism, which systematically examines the hierarchical structure of traffic elements and guides LLMs to thoroughly analyze traffic scenario descriptions step by step, enhancing their understanding of complex situations. Furthermore, we propose a Frenet-frame-based cost function framework that provides LLMs with geometrically meaningful quantities, improving their grasp of spatial relationships in a scenario and enabling more accurate cost function generation. Experiments on the Waymo Open Motion Dataset (WOMD) demonstrate that our method handles more intricate descriptions, generates a broader range of scenarios in a controllable manner, and outperforms existing diffusion-based methods in terms of efficiency.<|reference_end|>
|
arxiv
|
@article{liu2024controllable,
title={Controllable Traffic Simulation through LLM-Guided Hierarchical
Chain-of-Thought Reasoning},
author={Zhiyuan Liu, Leheng Li, Yuning Wang, Haotian Lin, Zhizhe Liu, Lei He,
Jianqiang Wang},
journal={arXiv preprint arXiv:2409.15135},
year={2024},
archivePrefix={arXiv},
eprint={2409.15135},
primaryClass={cs.RO}
}
|
liu2024controllable
|
arxiv-660840
|
2409.15137
|
Data governance: A Critical Foundation for Data Driven Decision-Making in Operations and Supply Chains
|
<|reference_start|>Data governance: A Critical Foundation for Data Driven Decision-Making in Operations and Supply Chains: In the context of Industry 4.0, the manufacturing sector is increasingly facing the challenge of data usability, which is becoming a widespread phenomenon and a new contemporary concern. In response, Data Governance (DG) emerges as a viable avenue to address data challenges. This study aims to call attention on DG research in the field of operations and supply chain management (OSCM). Based on literature research, we investigate research gaps in academia. Built upon three case studies, we exanimated and analyzed real life data issues in the industry. Four types of cause related to data issues were found: 1) human factors, 2) lack of written rules and regulations, 3) ineffective technological hardware and software, and 4) lack of resources. Subsequently, a three-pronged research framework was suggested. This paper highlights the urgency for research on DG in OSCM, outlines a research pathway for fellow scholars, and offers guidance to industry in the design and implementation of DG strategies.<|reference_end|>
|
arxiv
|
@article{li2024data,
title={Data governance: A Critical Foundation for Data Driven Decision-Making
in Operations and Supply Chains},
author={Xuejiao Li, Yang Cheng, Charles M{o}ller},
journal={arXiv preprint arXiv:2409.15137},
year={2024},
archivePrefix={arXiv},
eprint={2409.15137},
primaryClass={cs.DB cs.CY}
}
|
li2024data
|
arxiv-660841
|
2409.15139
|
The Top Manifold Connectedness of Quantum Control Landscapes
|
<|reference_start|>The Top Manifold Connectedness of Quantum Control Landscapes: The control of quantum systems has been proven to possess trap-free optimization landscapes under the satisfaction of proper assumptions. However, many details of the landscape geometry and their influence on search efficiency still need to be fully understood. This paper numerically explores the path-connectedness of globally optimal control solutions forming the top manifold of the landscape. We randomly sample a plurality of optimal controls in the top manifold to assess the existence of a continuous path at the top of the landscape that connects two arbitrary optimal solutions. It is shown that for different quantum control objectives including state-to-state transition probabilities, observable expectation values and unitary transformations, such a continuous path can be readily found, implying that these top manifolds are fundamentally path-connected. The significance of the latter conjecture lies in seeking locations in the top manifold where an ancillary objective can also be optimized while maintaining the full optimality of the original objective that defined the landscape.<|reference_end|>
|
arxiv
|
@article{fan2024the,
title={The Top Manifold Connectedness of Quantum Control Landscapes},
author={Yidian Fan, Re-Bing Wu, Tak-San Ho, Gaurav V. Bhole and Herschel
Rabitz},
journal={arXiv preprint arXiv:2409.15139},
year={2024},
archivePrefix={arXiv},
eprint={2409.15139},
primaryClass={quant-ph cs.SY eess.SY}
}
|
fan2024the
|
arxiv-660842
|
2409.15140
|
Bisection Width, Discrepancy, and Eigenvalues of Hypergraphs
|
<|reference_start|>Bisection Width, Discrepancy, and Eigenvalues of Hypergraphs: A celebrated result of Alon from 1993 states that any $d$-regular graph on $n$ vertices (where $d=O(n^{1/9})$) has a bisection with at most $\frac{dn}{2}(\frac{1}{2}-\Omega(\frac{1}{\sqrt{d}}))$ edges, and this is optimal. Recently, this result was greatly extended by R\"aty, Sudakov, and Tomon. We build on the ideas of the latter, and use a semidefinite programming inspired approach to prove the following variant for hypergraphs: every $r$-uniform $d$-regular hypergraph on $n$ vertices (where $d\ll n^{1/2}$) has a bisection of size at most $$\frac{dn}{r}\left(1-\frac{1}{2^{r-1}}-\frac{c}{\sqrt{d}}\right),$$ for some $c=c(r)>0$. This bound is the best possible up to the precise value of $c$. Moreover, a bisection achieving this bound can be found by a polynomial-time randomized algorithm. The minimum bisection is closely related to discrepancy. We also prove sharp bounds on the discrepancy and so called positive discrepancy of hypergraphs, extending results of Bollob\'as and Scott. Furthermore, we discuss implications about Alon-Boppana type bounds. We show that if $H$ is an $r$-uniform $d$-regular hypergraph, then certain notions of second largest eigenvalue $\lambda_2$ associated with the adjacency tensor satisfy $\lambda_2\geq \Omega_r(\sqrt{d})$, improving results of Li and Mohar.<|reference_end|>
|
arxiv
|
@article{räty2024bisection,
title={Bisection Width, Discrepancy, and Eigenvalues of Hypergraphs},
author={Eero R"aty, Istv'an Tomon},
journal={arXiv preprint arXiv:2409.15140},
year={2024},
archivePrefix={arXiv},
eprint={2409.15140},
primaryClass={math.CO cs.CC}
}
|
räty2024bisection
|
arxiv-660843
|
2409.15142
|
Critical Node Detection in Temporal Social Networks, Based on Global and Semi-local Centrality Measures
|
<|reference_start|>Critical Node Detection in Temporal Social Networks, Based on Global and Semi-local Centrality Measures: Nodes that play strategic roles in networks are called critical or influential nodes. For example, in an epidemic, we can control the infection spread by isolating critical nodes; in marketing, we can use certain nodes as the initial spreaders aiming to reach the largest part of the network, or they can be selected for removal in targeted attacks to maximise the fragmentation of the network. In this study, we focus on critical node detection in temporal networks. We propose three new measures to identify the critical nodes in temporal networks: the temporal supracycle ratio, temporal semi-local integration, and temporal semi-local centrality. We analyse the performance of these measures based on their effect on the SIR epidemic model in three scenarios: isolating the influential nodes when an epidemic happens, using the influential nodes as seeds of the epidemic, or removing them to analyse the robustness of the network. We compare the results with existing centrality measures, particularly temporal betweenness, temporal centrality, and temporal degree deviation. The results show that the introduced measures help identify influential nodes more accurately. The proposed methods can be used to detect nodes that need to be isolated to reduce the spread of an epidemic or as initial nodes to speedup dissemination of information.<|reference_end|>
|
arxiv
|
@article{farahi2024critical,
title={Critical Node Detection in Temporal Social Networks, Based on Global and
Semi-local Centrality Measures},
author={Zahra Farahi, Ali Kamandi, Rooholah Abedian, Luis Enrique Correa Rocha},
journal={arXiv preprint arXiv:2409.15142},
year={2024},
archivePrefix={arXiv},
eprint={2409.15142},
primaryClass={physics.soc-ph cs.SI}
}
|
farahi2024critical
|
arxiv-660844
|
2409.15143
|
Designing an Interpretable Interface for Contextual Bandits
|
<|reference_start|>Designing an Interpretable Interface for Contextual Bandits: Contextual bandits have become an increasingly popular solution for personalized recommender systems. Despite their growing use, the interpretability of these systems remains a significant challenge, particularly for the often non-expert operators tasked with ensuring their optimal performance. In this paper, we address this challenge by designing a new interface to explain to domain experts the underlying behaviour of a bandit. Central is a metric we term "value gain", a measure derived from off-policy evaluation to quantify the real-world impact of sub-components within a bandit. We conduct a qualitative user study to evaluate the effectiveness of our interface. Our findings suggest that by carefully balancing technical rigour with accessible presentation, it is possible to empower non-experts to manage complex machine learning systems. We conclude by outlining guiding principles that other researchers should consider when building similar such interfaces in future.<|reference_end|>
|
arxiv
|
@article{maher2024designing,
title={Designing an Interpretable Interface for Contextual Bandits},
author={Andrew Maher, Matia Gobbo, Lancelot Lachartre, Subash Prabanantham,
Rowan Swiers and Puli Liyanagama},
journal={arXiv preprint arXiv:2409.15143},
year={2024},
archivePrefix={arXiv},
eprint={2409.15143},
primaryClass={cs.LG stat.ML}
}
|
maher2024designing
|
arxiv-660845
|
2409.15146
|
COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Models
|
<|reference_start|>COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Models: Leveraging the powerful reasoning capabilities of large language models (LLMs), recent LLM-based robot task planning methods yield promising results. However, they mainly focus on single or multiple homogeneous robots on simple tasks. Practically, complex long-horizon tasks always require collaborations among multiple heterogeneous robots especially with more complex action spaces, which makes these tasks more challenging. To this end, we propose COHERENT, a novel LLM-based task planning framework for collaboration of heterogeneous multi-robot systems including quadrotors, robotic dogs, and robotic arms. Specifically, a Proposal-Execution-Feedback-Adjustment (PEFA) mechanism is designed to decompose and assign actions for individual robots, where a centralized task assigner makes a task planning proposal to decompose the complex task into subtasks, and then assigns subtasks to robot executors. Each robot executor selects a feasible action to implement the assigned subtask and reports self-reflection feedback to the task assigner for plan adjustment. The PEFA loops until the task is completed. Moreover, we create a challenging heterogeneous multi-robot task planning benchmark encompassing 100 complex long-horizon tasks. The experimental results show that our work surpasses the previous methods by a large margin in terms of success rate and execution efficiency. The experimental videos, code, and benchmark are released at https://github.com/MrKeee/COHERENT.<|reference_end|>
|
arxiv
|
@article{liu2024coherent:,
title={COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large
Language Models},
author={Kehui Liu, Zixin Tang, Dong Wang, Zhigang Wang, Bin Zhao, Xuelong Li},
journal={arXiv preprint arXiv:2409.15146},
year={2024},
archivePrefix={arXiv},
eprint={2409.15146},
primaryClass={cs.RO cs.AI}
}
|
liu2024coherent:
|
arxiv-660846
|
2409.15147
|
Modeling a demographic problem using the Leslie matrix
|
<|reference_start|>Modeling a demographic problem using the Leslie matrix: The application of Leslie matrices in demographic research is considered in this paper. The Leslie matrix is first proposed in the 1940s and gained popularity in the mid-1960s, becoming fundamental tool for predicting population dynamics. The Leslie matrix allows to categorize individuals based on various attributes and calculate the expected population sizes for various demographic categories in subsequent time intervals. The universality of the Leslie matrix extends to diverse life cycles in plants and animals, making it ubiquitous tool in non-human species. In the paper is presented detailed application of Leslie matrices to the problem of the two countries, demonstrating their practical value in solving real demographic problems. In conclusion, the Leslie matrix remains a cornerstone of demographic analysis, reflecting the complexity of population dynamics and providing a robust framework for understanding the intricate interplay of factors shaping human society. Its enduring relevance and adaptability make it an essential component in the toolkit of demographers and ecologists.<|reference_end|>
|
arxiv
|
@article{malafeyev2024modeling,
title={Modeling a demographic problem using the Leslie matrix},
author={O.A. Malafeyev, T.R. Nabiev, N.D. Redinskikh},
journal={arXiv preprint arXiv:2409.15147},
year={2024},
archivePrefix={arXiv},
eprint={2409.15147},
primaryClass={math.OC cs.CY physics.soc-ph}
}
|
malafeyev2024modeling
|
arxiv-660847
|
2409.15148
|
Bounded-confidence opinion models with random-time interactions
|
<|reference_start|>Bounded-confidence opinion models with random-time interactions: In models of opinion dynamics, the opinions of individual agents evolve with time. One type of opinion model is a bounded-confidence model (BCM), in which opinions take continuous values and interacting agents compromise their opinions with each other if those opinions are sufficiently similar. In studies of BCMs, it is typically assumed that interactions between agents occur at deterministic times. This assumption neglects an inherent element of randomness in social systems. In this paper, we study BCMs on networks and allow agents to interact at random times. To incorporate random-time interactions, we use renewal processes to determine social interactions, which can follow arbitrary waiting-time distributions (WTDs). We establish connections between these random-time-interaction BCMs and deterministic-time-interaction BCMs. We find that BCMs with Markovian WTDs have consistent statistical properties on different networks but that the statistical properties of BCMs with non-Markovian WTDs depend on network structure.<|reference_end|>
|
arxiv
|
@article{chu2024bounded-confidence,
title={Bounded-confidence opinion models with random-time interactions},
author={Weiqi Chu and Mason A Porter},
journal={arXiv preprint arXiv:2409.15148},
year={2024},
archivePrefix={arXiv},
eprint={2409.15148},
primaryClass={physics.soc-ph cs.SI math.DS math.PR}
}
|
chu2024bounded-confidence
|
arxiv-660848
|
2409.15150
|
Racing the Market: An Industry Support Analysis for Pricing-Driven DevOps in SaaS
|
<|reference_start|>Racing the Market: An Industry Support Analysis for Pricing-Driven DevOps in SaaS: The SaaS paradigm has popularized the usage of pricings, allowing providers to offer customers a wide range of subscription possibilities. This creates a vast configuration space for users, enabling them to choose the features and support guarantees that best suit their needs. Regardless of the reasons why changes in these pricings are made, the frequency of changes within the elements of pricings continues to increase. Therefore, for those responsible for the development and operation of SaaS, it would be ideal to minimize the time required to transfer changes in SaaS pricing to the software and underlying infrastructure, without compromising the quality and reliability.% of the service; %i.e., this development and operation should be Pricing-Driven. This work explores the support offered by the industry for this need. By modeling over 150 pricings from 30 different SaaS over six years, we reveal that the configuration space grows exponentially with the number of add-ons and linearly with the number of plans. We also evaluate 21 different feature toggling solutions, finding that feature toggling, particularly permission toggles, is a promising technique for enabling rapid adaptation to pricing changes. Our results suggest that developing automated solutions with minimal human intervention could effectively reduce the time-to-market for SaaS updates driven by pricing changes, especially with the adoption of a standard for serializing pricings.<|reference_end|>
|
arxiv
|
@article{garcia-fernández2024racing,
title={Racing the Market: An Industry Support Analysis for Pricing-Driven
DevOps in SaaS},
author={Alejandro Garcia-Fern'andez, Jos'e Antonio Parejo, Francisco Javier
Cavero, Antonio Ruiz-Cort'es},
journal={arXiv preprint arXiv:2409.15150},
year={2024},
archivePrefix={arXiv},
eprint={2409.15150},
primaryClass={cs.SE}
}
|
garcia-fernández2024racing
|
arxiv-660849
|
2409.15151
|
Workload Distribution with Rateless Encoding: A Low-Latency Computation Offloading Method within Edge Networks
|
<|reference_start|>Workload Distribution with Rateless Encoding: A Low-Latency Computation Offloading Method within Edge Networks: This paper introduces REDC, a comprehensive strategy for offloading computational tasks within mobile Edge Networks (EN) to Distributed Computing (DC) after Rateless Encoding (RE). Despite the efficiency, reliability, and scalability advantages of distributed computing in ENs, straggler-induced latencies and failures pose significant challenges. Coded distributed computing has gained attention for its efficient redundancy computing, alleviating the impact of stragglers. Yet, current research predominantly focuses on tolerating a predefined number of stragglers with minimal encoding redundancy. Furthermore, nodes within edge networks are characterized by their inherent heterogeneity in computation, communication, and storage capacities, and unpredictable straggler effects and failures. To our knowledge, existing encoding offloading approaches lack a systematic design and unified consideration of these characteristics. REDC addresses these issues by adaptively encoding tasks, then distributing the workload based on node variations. In the face of unpredictability failures, the rateless encoding adaptation provides resilience to dynamic straggler effects. Considering the node heterogeneity and system status, tasks are offloaded to optimal subset "valid" nodes. Load distribution decisions are made based on updates to queuing theory modeling through state feedback. The REDC framework is applicable to EN by improving resource utilization and reducing task sequence execution delays. Experimental results demonstrate our method's effectiveness and resilient performance, maintaining efficacy even in the presence of unstable nodes.<|reference_end|>
|
arxiv
|
@article{guo2023workload,
title={Workload Distribution with Rateless Encoding: A Low-Latency Computation
Offloading Method within Edge Networks},
author={Zhongfu Guo, Xinsheng Ji, Wei You, Yu Zhao, Bai Yi, Lingwei Wang},
journal={arXiv preprint arXiv:2409.15151},
year={2023},
archivePrefix={arXiv},
eprint={2409.15151},
primaryClass={cs.DC}
}
|
guo2023workload
|
arxiv-660850
|
2409.15152
|
Predicting Expert Evaluations in Software Code Reviews
|
<|reference_start|>Predicting Expert Evaluations in Software Code Reviews: Manual code reviews are an essential but time-consuming part of software development, often leading reviewers to prioritize technical issues while skipping valuable assessments. This paper presents an algorithmic model that automates aspects of code review typically avoided due to their complexity or subjectivity, such as assessing coding time, implementation time, and code complexity. Instead of replacing manual reviews, our model adds insights that help reviewers focus on more impactful tasks. Calibrated using expert evaluations, the model predicts key metrics from code commits with strong correlations to human judgments (r = 0.82 for coding time, r = 0.86 for implementation time). By automating these assessments, we reduce the burden on human reviewers and ensure consistent analysis of time-consuming areas, offering a scalable solution alongside manual reviews. This research shows how automated tools can enhance code reviews by addressing overlooked tasks, supporting data-driven decisions and improving the review process.<|reference_end|>
|
arxiv
|
@article{denisov-blanch2024predicting,
title={Predicting Expert Evaluations in Software Code Reviews},
author={Yegor Denisov-Blanch, Igor Ciobanu, Simon Obstbaum, Michal Kosinski},
journal={arXiv preprint arXiv:2409.15152},
year={2024},
archivePrefix={arXiv},
eprint={2409.15152},
primaryClass={cs.SE}
}
|
denisov-blanch2024predicting
|
arxiv-660851
|
2409.15154
|
RMCBench: Benchmarking Large Language Models' Resistance to Malicious Code
|
<|reference_start|>RMCBench: Benchmarking Large Language Models' Resistance to Malicious Code: The emergence of Large Language Models (LLMs) has significantly influenced various aspects of software development activities. Despite their benefits, LLMs also pose notable risks, including the potential to generate harmful content and being abused by malicious developers to create malicious code. Several previous studies have focused on the ability of LLMs to resist the generation of harmful content that violates human ethical standards, such as biased or offensive content. However, there is no research evaluating the ability of LLMs to resist malicious code generation. To fill this gap, we propose RMCBench, the first benchmark comprising 473 prompts designed to assess the ability of LLMs to resist malicious code generation. This benchmark employs two scenarios: a text-to-code scenario, where LLMs are prompted with descriptions to generate code, and a code-to-code scenario, where LLMs translate or complete existing malicious code. Based on RMCBench, we conduct an empirical study on 11 representative LLMs to assess their ability to resist malicious code generation. Our findings indicate that current LLMs have a limited ability to resist malicious code generation with an average refusal rate of 40.36% in text-to-code scenario and 11.52% in code-to-code scenario. The average refusal rate of all LLMs in RMCBench is only 28.71%; ChatGPT-4 has a refusal rate of only 35.73%. We also analyze the factors that affect LLMs' ability to resist malicious code generation and provide implications for developers to enhance model robustness.<|reference_end|>
|
arxiv
|
@article{chen2024rmcbench:,
title={RMCBench: Benchmarking Large Language Models' Resistance to Malicious
Code},
author={Jiachi Chen, Qingyuan Zhong, Yanlin Wang, Kaiwen Ning, Yongkun Liu,
Zenan Xu, Zhe Zhao, Ting Chen, and Zibin Zheng},
journal={arXiv preprint arXiv:2409.15154},
year={2024},
doi={10.1145/3691620.3695480},
archivePrefix={arXiv},
eprint={2409.15154},
primaryClass={cs.SE cs.AI}
}
|
chen2024rmcbench:
|
arxiv-660852
|
2409.15155
|
MAR-DTN: Metal Artifact Reduction using Domain Transformation Network for Radiotherapy Planning
|
<|reference_start|>MAR-DTN: Metal Artifact Reduction using Domain Transformation Network for Radiotherapy Planning: For the planning of radiotherapy treatments for head and neck cancers, Computed Tomography (CT) scans of the patients are typically employed. However, in patients with head and neck cancer, the quality of standard CT scans generated using kilo-Voltage (kVCT) tube potentials is severely degraded by streak artifacts occurring in the presence of metallic implants such as dental fillings. Some radiotherapy devices offer the possibility of acquiring Mega-Voltage CT (MVCT) for daily patient setup verification, due to the higher energy of X-rays used, MVCT scans are almost entirely free from artifacts making them more suitable for radiotherapy treatment planning. In this study, we leverage the advantages of kVCT scans with those of MVCT scans (artifact-free). We propose a deep learning-based approach capable of generating artifact-free MVCT images from acquired kVCT images. The outcome offers the benefits of artifact-free MVCT images with enhanced soft tissue contrast, harnessing valuable information obtained through kVCT technology for precise therapy calibration. Our proposed method employs UNet-inspired model, and is compared with adversarial learning and transformer networks. This first and unique approach achieves remarkable success, with PSNR of 30.02 dB across the entire patient volume and 27.47 dB in artifact-affected regions exclusively. It is worth noting that the PSNR calculation excludes the background, concentrating solely on the region of interest.<|reference_end|>
|
arxiv
|
@article{serrano-antón2024mar-dtn:,
title={MAR-DTN: Metal Artifact Reduction using Domain Transformation Network
for Radiotherapy Planning},
author={Bel'en Serrano-Ant'on, Mubashara Rehman, Niki Martinel, Michele
Avanzo, Riccardo Spizzo, Giuseppe Fanetti, Alberto P. Mu~nuzuri, Christian
Micheloni},
journal={arXiv preprint arXiv:2409.15155},
year={2024},
archivePrefix={arXiv},
eprint={2409.15155},
primaryClass={eess.IV cs.AI cs.CV}
}
|
serrano-antón2024mar-dtn:
|
arxiv-660853
|
2409.15156
|
Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling
|
<|reference_start|>Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling: The remarkable success of large language pretraining and the discovery of scaling laws signify a paradigm shift in machine learning. Notably, the primary objective has evolved from minimizing generalization error to reducing approximation error, and the most effective strategy has transitioned from regularization (in a broad sense) to scaling up models. This raises a critical question: Do the established principles that proved successful in the generalization-centric era remain valid in this new era of scaling? This paper examines several influential regularization-based principles that may no longer hold true in the scaling-centric, large language model (LLM) era. These principles include explicit L2 regularization and implicit regularization through small batch sizes and large learning rates. Additionally, we identify a new phenomenon termed ``scaling law crossover,'' where two scaling curves intersect at a certain scale, implying that methods effective at smaller scales may not generalize to larger ones. Together, these observations highlight two fundamental questions within this new paradigm: $\bullet$ Guiding Principles for Scaling: If regularization is no longer the primary guiding principle for model design, what new principles are emerging to guide scaling? $\bullet$ Model Comparison at Scale: How to reliably and effectively compare models at the scale where only a single experiment is feasible?<|reference_end|>
|
arxiv
|
@article{xiao2024rethinking,
title={Rethinking Conventional Wisdom in Machine Learning: From Generalization
to Scaling},
author={Lechao Xiao},
journal={arXiv preprint arXiv:2409.15156},
year={2024},
archivePrefix={arXiv},
eprint={2409.15156},
primaryClass={cs.LG stat.ML}
}
|
xiao2024rethinking
|
arxiv-660854
|
2409.15157
|
LoVA: Long-form Video-to-Audio Generation
|
<|reference_start|>LoVA: Long-form Video-to-Audio Generation: Video-to-audio (V2A) generation is important for video editing and post-processing, enabling the creation of semantics-aligned audio for silent video. However, most existing methods focus on generating short-form audio for short video segment (less than 10 seconds), while giving little attention to the scenario of long-form video inputs. For current UNet-based diffusion V2A models, an inevitable problem when handling long-form audio generation is the inconsistencies within the final concatenated audio. In this paper, we first highlight the importance of long-form V2A problem. Besides, we propose LoVA, a novel model for Long-form Video-to-Audio generation. Based on the Diffusion Transformer (DiT) architecture, LoVA proves to be more effective at generating long-form audio compared to existing autoregressive models and UNet-based diffusion models. Extensive objective and subjective experiments demonstrate that LoVA achieves comparable performance on 10-second V2A benchmark and outperforms all other baselines on a benchmark with long-form video input.<|reference_end|>
|
arxiv
|
@article{cheng2024lova:,
title={LoVA: Long-form Video-to-Audio Generation},
author={Xin Cheng, Xihua Wang, Yihan Wu, Yuyue Wang and Ruihua Song},
journal={arXiv preprint arXiv:2409.15157},
year={2024},
archivePrefix={arXiv},
eprint={2409.15157},
primaryClass={cs.SD cs.MM eess.AS}
}
|
cheng2024lova:
|
arxiv-660855
|
2409.15158
|
Automatic Feature Learning for Essence: a Case Study on Car Sequencing
|
<|reference_start|>Automatic Feature Learning for Essence: a Case Study on Car Sequencing: Constraint modelling languages such as Essence offer a means to describe combinatorial problems at a high-level, i.e., without committing to detailed modelling decisions for a particular solver or solving paradigm. Given a problem description written in Essence, there are multiple ways to translate it to a low-level constraint model. Choosing the right combination of a low-level constraint model and a target constraint solver can have significant impact on the effectiveness of the solving process. Furthermore, the choice of the best combination of constraint model and solver can be instance-dependent, i.e., there may not exist a single combination that works best for all instances of the same problem. In this paper, we consider the task of building machine learning models to automatically select the best combination for a problem instance. A critical part of the learning process is to define instance features, which serve as input to the selection model. Our contribution is automatic learning of instance features directly from the high-level representation of a problem instance using a language model. We evaluate the performance of our approach using the Essence modelling language with a case study involving the car sequencing problem.<|reference_end|>
|
arxiv
|
@article{pellegrino2024automatic,
title={Automatic Feature Learning for Essence: a Case Study on Car Sequencing},
author={Alessio Pellegrino, "Ozg"ur Akg"un, Nguyen Dang, Zeynep Kiziltan,
Ian Miguel},
journal={arXiv preprint arXiv:2409.15158},
year={2024},
archivePrefix={arXiv},
eprint={2409.15158},
primaryClass={cs.AI}
}
|
pellegrino2024automatic
|
arxiv-660856
|
2409.15159
|
DeepCloth-ROB$^2_\textQS$P&P: Towards a Robust Robot Deployment for Quasi-Static Pick-and-Place Cloth-Shaping Neural Controllers
|
<|reference_start|>DeepCloth-ROB$^2_\textQS$P&P: Towards a Robust Robot Deployment for Quasi-Static Pick-and-Place Cloth-Shaping Neural Controllers: The fidelity gap between simulation-trained vision-based data-driven cloth neural controllers and real-world operation impedes reliable deployment of methods from simulation into physical trials. Real-world grasping errors, such as misgrasping and multilayer grasping, degrade their performance; additionally, some fabrics made of synthetic material also tend to stick to the commonly employed Franka Emika Panda's original gripper. Different approaches adopted various strategies to resolve these problems, further complicating real-world comparison between state-of-the-art methods. We propose DeepCloth-ROB$^2_{\text{QS}}$P&P with a simulation-to-reality transfer strategy Towel-Sim2Real and a cloth grasping protocol to consider and mitigate these grasping errors for robustly deploying quasi-static pick-and-place neural controllers in cloth shaping and demonstrate its generalisability across different deep-learning methods, fabric contexts and robot platforms. Our approach allows us to compare multiple neural controllers in a real environment for the first time, offering valuable insights to the cloth manipulation community.<|reference_end|>
|
arxiv
|
@article{kadi2024deepcloth-rob$^2_{\text{qs}}$p&p:,
title={DeepCloth-ROB$^2_{\text{QS}}$P&P: Towards a Robust Robot Deployment for
Quasi-Static Pick-and-Place Cloth-Shaping Neural Controllers},
author={Halid Abdulrahim Kadi, Jose Alex Chandy, Luis Figueredo, Kasim
Terzi'c, Praminda Caleb-Solly},
journal={arXiv preprint arXiv:2409.15159},
year={2024},
archivePrefix={arXiv},
eprint={2409.15159},
primaryClass={cs.RO cs.AI}
}
|
kadi2024deepcloth-rob$^2_{\text{qs}}$p&p:
|
arxiv-660857
|
2409.15161
|
A Gated Residual Kolmogorov-Arnold Networks for Mixtures of Experts
|
<|reference_start|>A Gated Residual Kolmogorov-Arnold Networks for Mixtures of Experts: This paper introduces KAMoE, a novel Mixture of Experts (MoE) framework based on Gated Residual Kolmogorov-Arnold Networks (GRKAN). We propose GRKAN as an alternative to the traditional gating function, aiming to enhance efficiency and interpretability in MoE modeling. Through extensive experiments on digital asset markets and real estate valuation, we demonstrate that KAMoE consistently outperforms traditional MoE architectures across various tasks and model types. Our results show that GRKAN exhibits superior performance compared to standard Gating Residual Networks, particularly in LSTM-based models for sequential tasks. We also provide insights into the trade-offs between model complexity and performance gains in MoE and KAMoE architectures.<|reference_end|>
|
arxiv
|
@article{inzirillo2024a,
title={A Gated Residual Kolmogorov-Arnold Networks for Mixtures of Experts},
author={Hugo Inzirillo and Remi Genet},
journal={arXiv preprint arXiv:2409.15161},
year={2024},
archivePrefix={arXiv},
eprint={2409.15161},
primaryClass={cs.LG cs.NE}
}
|
inzirillo2024a
|
arxiv-660858
|
2409.15163
|
Lessons Learned on Information Retrieval in Electronic Health Records: A Comparison of Embedding Models and Pooling Strategies
|
<|reference_start|>Lessons Learned on Information Retrieval in Electronic Health Records: A Comparison of Embedding Models and Pooling Strategies: Objective: Applying large language models (LLMs) to the clinical domain is challenging due to the context-heavy nature of processing medical records. Retrieval-augmented generation (RAG) offers a solution by facilitating reasoning over large text sources. However, there are many parameters to optimize in just the retrieval system alone. This paper presents an ablation study exploring how different embedding models and pooling methods affect information retrieval for the clinical domain. Methods: Evaluating on three retrieval tasks on two electronic health record (EHR) data sources, we compared seven models, including medical- and general-domain models, specialized encoder embedding models, and off-the-shelf decoder LLMs. We also examine the choice of embedding pooling strategy for each model, independently on the query and the text to retrieve. Results: We found that the choice of embedding model significantly impacts retrieval performance, with BGE, a comparatively small general-domain model, consistently outperforming all others, including medical-specific models. However, our findings also revealed substantial variability across datasets and query text phrasings. We also determined the best pooling methods for each of these models to guide future design of retrieval systems. Discussion: The choice of embedding model, pooling strategy, and query formulation can significantly impact retrieval performance and the performance of these models on other public benchmarks does not necessarily transfer to new domains. Further studies such as this one are vital for guiding empirically-grounded development of retrieval frameworks, such as in the context of RAG, for the clinical domain.<|reference_end|>
|
arxiv
|
@article{myers2024lessons,
title={Lessons Learned on Information Retrieval in Electronic Health Records: A
Comparison of Embedding Models and Pooling Strategies},
author={Skatje Myers, Timothy A. Miller, Yanjun Gao, Matthew M. Churpek, Anoop
Mayampurath, Dmitriy Dligach, Majid Afshar},
journal={arXiv preprint arXiv:2409.15163},
year={2024},
archivePrefix={arXiv},
eprint={2409.15163},
primaryClass={cs.CL cs.IR}
}
|
myers2024lessons
|
arxiv-660859
|
2409.15164
|
Reliable and Secure Communications Through Compact Ultra-Massive Antenna Arrays
|
<|reference_start|>Reliable and Secure Communications Through Compact Ultra-Massive Antenna Arrays: Compact Ultramassive Antenna Array (CUMA) is a pioneering paradigm that leverages the flexibility of the Fluid Antenna System (FAS) to enable a simple multiple access scheme for massive connectivity without the need for precoding, power control at the base station or interference mitigation in each user's equipment. In order to overcome the mathematical intricacy required to analyze their performance, we use an asymptotic matching approach to relax such complexity with a remarkable accuracy. First, we analyze the performance of the CUMA network in terms of the outage probability (OP) and the ergodic rate (ER), deriving simple and highly accurate closed-form approximations to the channel statistics. Then, we evaluate the potential of the CUMA scheme to provide secure multi-user communications from a physical layer security perspective. Leveraging a tight approximation to the signal-to-interference-ratio (SIR) distribution, we derive closed-form expressions for the secrecy outage probability (SOP). We observe that the baseline CUMA (without side information processing) exhibits limited performance when eavesdroppers are equipped with a CUMA of the same type. To improve their secure performance, we suggest that a simple imperfect interference cancellation mechanism at the legitimate receiver may substantially increase the secrecy performance. Monte Carlo simulations validate our approximations and demonstrate their accuracy under different CUMA-based scenarios.<|reference_end|>
|
arxiv
|
@article{vega-sánchez2024reliable,
title={Reliable and Secure Communications Through Compact Ultra-Massive Antenna
Arrays},
author={Jos'e David Vega-S'anchez, Henry Ramiro Carvajal Mora, Nathaly
Ver'onica Orozco Garz'on, and F. J. L'opez-Mart'inez},
journal={arXiv preprint arXiv:2409.15164},
year={2024},
archivePrefix={arXiv},
eprint={2409.15164},
primaryClass={cs.IT eess.SP math.IT}
}
|
vega-sánchez2024reliable
|
arxiv-660860
|
2409.15165
|
Two-Level preconditioning method for solving saddle point systems in contact computation
|
<|reference_start|>Two-Level preconditioning method for solving saddle point systems in contact computation: In contact mechanics computation, the constraint conditions on the contact surfaces are typically enforced by the Lagrange multiplier method, resulting in a saddle point system. Given that the saddle point matrix is indefinite, solving these systems presents significant challenges. For a two-dimensional tied contact problem, an efficient two-level preconditioning method is developed. This method utilizes physical quantities for coarsening, introducing two types of interpolation operators and corresponding smoothing algorithms. Additionally, the constructed coarse grid operator exhibits symmetry and positive definiteness, adequately reflecting the contact constraints. Numerical results show the effectiveness of the method.<|reference_end|>
|
arxiv
|
@article{duan2024two-level,
title={Two-Level preconditioning method for solving saddle point systems in
contact computation},
author={Xiaoyu Duan, Hengbin An},
journal={arXiv preprint arXiv:2409.15165},
year={2024},
archivePrefix={arXiv},
eprint={2409.15165},
primaryClass={math.NA cs.NA}
}
|
duan2024two-level
|
arxiv-660861
|
2409.15166
|
Harmonic Path Integral Diffusion
|
<|reference_start|>Harmonic Path Integral Diffusion: In this manuscript, we present a novel approach for sampling from a continuous multivariate probability distribution, which may either be explicitly known (up to a normalization factor) or represented via empirical samples. Our method constructs a time-dependent bridge from a delta function centered at the origin of the state space at $t=0$, optimally transforming it into the target distribution at $t=1$. We formulate this as a Stochastic Optimal Control problem of the Path Integral Control type, with a cost function comprising (in its basic form) a quadratic control term, a quadratic state term, and a terminal constraint. This framework, which we refer to as Harmonic Path Integral Diffusion (H-PID), leverages an analytical solution through a mapping to an auxiliary quantum harmonic oscillator in imaginary time. The H-PID framework results in a set of efficient sampling algorithms, without the incorporation of Neural Networks. The algorithms are validated on two standard use cases: a mixture of Gaussians over a grid and images from CIFAR-10. We contrast these algorithms with other sampling methods, particularly simulated annealing and path integral sampling, highlighting their advantages in terms of analytical control, accuracy, and computational efficiency on benchmark problems. Additionally, we extend the methodology to more general cases where the underlying stochastic differential equation includes an external deterministic, possibly non-conservative force, and where the cost function incorporates a gauge potential term. These extensions open up new possibilities for applying our framework to a broader range of statistics specific to applications.<|reference_end|>
|
arxiv
|
@article{behjoo2024harmonic,
title={Harmonic Path Integral Diffusion},
author={Hamidreza Behjoo, Michael Chertkov},
journal={arXiv preprint arXiv:2409.15166},
year={2024},
archivePrefix={arXiv},
eprint={2409.15166},
primaryClass={stat.ML cs.LG stat.CO}
}
|
behjoo2024harmonic
|
arxiv-660862
|
2409.15167
|
Data-driven model discovery with Kolmogorov-Arnold networks
|
<|reference_start|>Data-driven model discovery with Kolmogorov-Arnold networks: Data-driven model discovery of complex dynamical systems is typically done using sparse optimization, but it has a fundamental limitation: sparsity in that the underlying governing equations of the system contain only a small number of elementary mathematical terms. Examples where sparse optimization fails abound, such as the classic Ikeda or optical-cavity map in nonlinear dynamics and a large variety of ecosystems. Exploiting the recently articulated Kolmogorov-Arnold networks, we develop a general model-discovery framework for any dynamical systems including those that do not satisfy the sparsity condition. In particular, we demonstrate non-uniqueness in that a large number of approximate models of the system can be found which generate the same invariant set with the correct statistics such as the Lyapunov exponents and Kullback-Leibler divergence. An analogy to shadowing of numerical trajectories in chaotic systems is pointed out.<|reference_end|>
|
arxiv
|
@article{moradi2024data-driven,
title={Data-driven model discovery with Kolmogorov-Arnold networks},
author={Mohammadamin Moradi, Shirin Panahi, Erik M. Bollt, and Ying-Cheng Lai},
journal={arXiv preprint arXiv:2409.15167},
year={2024},
archivePrefix={arXiv},
eprint={2409.15167},
primaryClass={cs.LG math.DS nlin.CD physics.data-an}
}
|
moradi2024data-driven
|
arxiv-660863
|
2409.15168
|
Adaptive Learning via a Negative Selection Strategy for Few-Shot Bioacoustic Event Detection
|
<|reference_start|>Adaptive Learning via a Negative Selection Strategy for Few-Shot Bioacoustic Event Detection: Although the Prototypical Network (ProtoNet) has demonstrated effectiveness in few-shot biological event detection, two persistent issues remain. Firstly, there is difficulty in constructing a representative negative prototype due to the absence of explicitly annotated negative samples. Secondly, the durations of the target biological vocalisations vary across tasks, making it challenging for the model to consistently yield optimal results across all tasks. To address these issues, we propose a novel adaptive learning framework with an adaptive learning loss to guide classifier updates. Additionally, we propose a negative selection strategy to construct a more representative negative prototype for ProtoNet. All experiments ware performed on the DCASE 2023 TASK5 few-shot bioacoustic event detection dataset. The results show that our proposed method achieves an F-measure of 0.703, an improvement of 12.84%.<|reference_end|>
|
arxiv
|
@article{chen2024adaptive,
title={Adaptive Learning via a Negative Selection Strategy for Few-Shot
Bioacoustic Event Detection},
author={Yaxiong Chen, Xueping Zhang, Yunfei Zi, Shengwu Xiong},
journal={arXiv preprint arXiv:2409.15168},
year={2024},
archivePrefix={arXiv},
eprint={2409.15168},
primaryClass={cs.SD eess.AS}
}
|
chen2024adaptive
|
arxiv-660864
|
2409.15169
|
CamLoPA: A Hidden Wireless Camera Localization Framework via Signal Propagation Path Analysis
|
<|reference_start|>CamLoPA: A Hidden Wireless Camera Localization Framework via Signal Propagation Path Analysis: Hidden wireless cameras pose significant privacy threats, necessitating effective detection and localization methods. However, existing solutions often require spacious activity areas, expensive specialized devices, or pre-collected training data, limiting their practical deployment. To address these limitations, we introduce CamLoPA, a training-free wireless camera detection and localization framework that operates with minimal activity space constraints using low-cost commercial-off-the-shelf (COTS) devices. CamLoPA can achieve detection and localization in just 45 seconds of user activities with a Raspberry Pi board. During this short period, it analyzes the causal relationship between the wireless traffic and user movement to detect the presence of a snooping camera. Upon detection, CamLoPA employs a novel azimuth location model based on wireless signal propagation path analysis. Specifically, this model leverages the time ratio of user paths crossing the First Fresnel Zone (FFZ) to determine the azimuth angle of the camera. Then CamLoPA refines the localization by identifying the camera's quadrant. We evaluate CamLoPA across various devices and environments, demonstrating that it achieves 95.37% snooping camera detection accuracy and an average localization error of 17.23, under the significantly reduced activity space requirements. Our demo are available at https://www.youtube.com/watch?v=GKam04FzeM4.<|reference_end|>
|
arxiv
|
@article{zhang2024camlopa:,
title={CamLoPA: A Hidden Wireless Camera Localization Framework via Signal
Propagation Path Analysis},
author={Xiang Zhang, Jie Zhang, Zehua Ma, Jinyang Huang, Meng Li, Huan Yan,
Peng Zhao, Zijian Zhang, Qing Guo, Tianwei Zhang, Bin Liu, Nenghai Yu},
journal={arXiv preprint arXiv:2409.15169},
year={2024},
archivePrefix={arXiv},
eprint={2409.15169},
primaryClass={cs.CR cs.HC}
}
|
zhang2024camlopa:
|
arxiv-660865
|
2409.15171
|
Hybrid Drawing Solutions in AR Bitmap-to-Vector Techniques on 3D Surfaces
|
<|reference_start|>Hybrid Drawing Solutions in AR Bitmap-to-Vector Techniques on 3D Surfaces: Recent advancements in augmented reality and virtual reality have significantly enhanced workflows for drawing 3D objects. Despite these technological strides, existing AR tools often lack the necessary precision and struggle to maintain quality when scaled, posing challenges for larger-scale drawing tasks. This paper introduces a novel AR tool that uniquely integrates bitmap drawing and vectorization techniques. This integration allows engineers to perform rapid, real-time drawings directly on 3D models, with the capability to vectorize the data for scalable accuracy and editable points, ensuring no loss in fidelity when modifying or resizing the drawings. We conducted user studies involving professional engineers, designers, and contractors to evaluate the tool's integration into existing workflows, its usability, and its impact on project outcomes. The results demonstrate that our enhancements significantly improve the efficiency of drawing processes. Specifically, the ability to perform quick, editable, and scalable drawings directly on 3D models not only enhances productivity but also ensures adaptability across various project sizes and complexities.<|reference_end|>
|
arxiv
|
@article{ding2024hybrid,
title={Hybrid Drawing Solutions in AR Bitmap-to-Vector Techniques on 3D
Surfaces},
author={Pengcheng Ding, Yedian Cheng, Mirjana Prpa},
journal={arXiv preprint arXiv:2409.15171},
year={2024},
archivePrefix={arXiv},
eprint={2409.15171},
primaryClass={cs.GR}
}
|
ding2024hybrid
|
arxiv-660866
|
2409.15172
|
Skills Made to Order: Efficient Acquisition of Robot Cooking Skills Guided by Multiple Forms of Internet Data
|
<|reference_start|>Skills Made to Order: Efficient Acquisition of Robot Cooking Skills Guided by Multiple Forms of Internet Data: This study explores the utility of various internet data sources to select among a set of template robot behaviors to perform skills. Learning contact-rich skills involving tool use from internet data sources has typically been challenging due to the lack of physical information such as contact existence, location, areas, and force in this data. Prior works have generally used internet data and foundation models trained on this data to generate low-level robot behavior. We hypothesize that these data and models may be better suited to selecting among a set of basic robot behaviors to perform these contact-rich skills. We explore three methods of template selection: querying large language models, comparing video of robot execution to retrieved human video using features from a pretrained video encoder common in prior work, and performing the same comparison using features from an optic flow encoder trained on internet data. Our results show that LLMs are surprisingly capable template selectors despite their lack of visual information, optical flow encoding significantly outperforms video encoders trained with an order of magnitude more data, and important synergies exist between various forms of internet data for template selection. By exploiting these synergies, we create a template selector using multiple forms of internet data that achieves a 79\% success rate on a set of 16 different cooking skills involving tool-use.<|reference_end|>
|
arxiv
|
@article{verghese2024skills,
title={Skills Made to Order: Efficient Acquisition of Robot Cooking Skills
Guided by Multiple Forms of Internet Data},
author={Mrinal Verghese, Christopher Atkeson},
journal={arXiv preprint arXiv:2409.15172},
year={2024},
archivePrefix={arXiv},
eprint={2409.15172},
primaryClass={cs.RO cs.AI cs.LG}
}
|
verghese2024skills
|
arxiv-660867
|
2409.15173
|
Recommendation with Generative Models
|
<|reference_start|>Recommendation with Generative Models: Generative models are a class of AI models capable of creating new instances of data by learning and sampling from their statistical distributions. In recent years, these models have gained prominence in machine learning due to the development of approaches such as generative adversarial networks (GANs), variational autoencoders (VAEs), and transformer-based architectures such as GPT. These models have applications across various domains, such as image generation, text synthesis, and music composition. In recommender systems, generative models, referred to as Gen-RecSys, improve the accuracy and diversity of recommendations by generating structured outputs, text-based interactions, and multimedia content. By leveraging these capabilities, Gen-RecSys can produce more personalized, engaging, and dynamic user experiences, expanding the role of AI in eCommerce, media, and beyond. Our book goes beyond existing literature by offering a comprehensive understanding of generative models and their applications, with a special focus on deep generative models (DGMs) and their classification. We introduce a taxonomy that categorizes DGMs into three types: ID-driven models, large language models (LLMs), and multimodal models. Each category addresses unique technical and architectural advancements within its respective research area. This taxonomy allows researchers to easily navigate developments in Gen-RecSys across domains such as conversational AI and multimodal content generation. Additionally, we examine the impact and potential risks of generative models, emphasizing the importance of robust evaluation frameworks.<|reference_end|>
|
arxiv
|
@article{deldjoo2024recommendation,
title={Recommendation with Generative Models},
author={Yashar Deldjoo, Zhankui He, Julian McAuley, Anton Korikov, Scott
Sanner, Arnau Ramisa, Rene Vidal, Maheswaran Sathiamoorthy, Atoosa
Kasrizadeh, Silvia Milano, Francesco Ricci},
journal={arXiv preprint arXiv:2409.15173},
year={2024},
archivePrefix={arXiv},
eprint={2409.15173},
primaryClass={cs.IR}
}
|
deldjoo2024recommendation
|
arxiv-660868
|
2409.15174
|
Terrain-Aware Model Predictive Control of Heterogeneous Bipedal and Aerial Robot Coordination for Search and Rescue Tasks
|
<|reference_start|>Terrain-Aware Model Predictive Control of Heterogeneous Bipedal and Aerial Robot Coordination for Search and Rescue Tasks: Humanoid robots offer significant advantages for search and rescue tasks, thanks to their capability to traverse rough terrains and perform transportation tasks. In this study, we present a task and motion planning framework for search and rescue operations using a heterogeneous robot team composed of humanoids and aerial robots. We propose a terrain-aware Model Predictive Controller (MPC) that incorporates terrain elevation gradients learned using Gaussian processes (GP). This terrain-aware MPC generates safe navigation paths for the bipedal robots to traverse rough terrain while minimizing terrain slopes, and it directs the quadrotors to perform aerial search and mapping tasks. The rescue subjects' locations are estimated by a target belief GP, which is updated online during the map exploration. A high-level planner for task allocation is designed by encoding the navigation tasks using syntactically cosafe Linear Temporal Logic (scLTL), and a consensus-based algorithm is designed for task assignment of individual robots. We evaluate the efficacy of our planning framework in simulation in an uncertain environment with various terrains and random rescue subject placements.<|reference_end|>
|
arxiv
|
@article{shamsah2024terrain-aware,
title={Terrain-Aware Model Predictive Control of Heterogeneous Bipedal and
Aerial Robot Coordination for Search and Rescue Tasks},
author={Abdulaziz Shamsah, Jesse Jiang, Ziwon Yoon, Samuel Coogan and Ye Zhao},
journal={arXiv preprint arXiv:2409.15174},
year={2024},
archivePrefix={arXiv},
eprint={2409.15174},
primaryClass={cs.RO}
}
|
shamsah2024terrain-aware
|
arxiv-660869
|
2409.15175
|
Generalized Logistic Maps and Convergence
|
<|reference_start|>Generalized Logistic Maps and Convergence: We treat three cubic recurrences, two of which generalize the famous iterated map $x \mapsto x (1-x)$ from discrete chaos theory. A feature of each asymptotic series developed here is a constant, dependent on the initial condition but otherwise intrinsic to the function at hand.<|reference_end|>
|
arxiv
|
@article{finch2024generalized,
title={Generalized Logistic Maps and Convergence},
author={Steven Finch},
journal={arXiv preprint arXiv:2409.15175},
year={2024},
archivePrefix={arXiv},
eprint={2409.15175},
primaryClass={math.DS cs.DM math.CO}
}
|
finch2024generalized
|
arxiv-660870
|
2409.15176
|
SpikeGS: Learning 3D Gaussian Fields from Continuous Spike Stream
|
<|reference_start|>SpikeGS: Learning 3D Gaussian Fields from Continuous Spike Stream: A spike camera is a specialized high-speed visual sensor that offers advantages such as high temporal resolution and high dynamic range compared to conventional frame cameras.These features provide the camera with significant advantages in many computer vision tasks. However, the tasks of novel view synthesis based on spike cameras remain underdeveloped. Although there are existing methods for learning neural radiance fields from spike stream, they either lack robustness in extremely noisy, low-quality lighting conditions or suffer from high computational complexity due to the deep fully connected neural networks and ray marching rendering strategies used in neural radiance fields, making it difficult to recover fine texture details. In contrast, the latest advancements in 3DGS have achieved high-quality real-time rendering by optimizing the point cloud representation into Gaussian ellipsoids. Building on this, we introduce SpikeGS, the method to learn 3D Gaussian fields solely from spike stream. We designed a differentiable spike stream rendering framework based on 3DGS, incorporating noise embedding and spiking neurons. By leveraging the multi-view consistency of 3DGS and the tile-based multi-threaded parallel rendering mechanism, we achieved high-quality real-time rendering results. Additionally, we introduced a spike rendering loss function that generalizes under varying illumination conditions. Our method can reconstruct view synthesis results with fine texture details from a continuous spike stream captured by a moving spike camera, while demonstrating high robustness in extremely noisy low-light scenarios. Experimental results on both real and synthetic datasets demonstrate that our method surpasses existing approaches in terms of rendering quality and speed. Our code will be available at https://github.com/520jz/SpikeGS.<|reference_end|>
|
arxiv
|
@article{yu2024spikegs:,
title={SpikeGS: Learning 3D Gaussian Fields from Continuous Spike Stream},
author={Jinze Yu, Xin Peng, Zhengda Lu, Laurent Kneip, Yiqun Wang},
journal={arXiv preprint arXiv:2409.15176},
year={2024},
archivePrefix={arXiv},
eprint={2409.15176},
primaryClass={cs.CV}
}
|
yu2024spikegs:
|
arxiv-660871
|
2409.15179
|
MIMAFace: Face Animation via Motion-Identity Modulated Appearance Feature Learning
|
<|reference_start|>MIMAFace: Face Animation via Motion-Identity Modulated Appearance Feature Learning: Current diffusion-based face animation methods generally adopt a ReferenceNet (a copy of U-Net) and a large amount of curated self-acquired data to learn appearance features, as robust appearance features are vital for ensuring temporal stability. However, when trained on public datasets, the results often exhibit a noticeable performance gap in image quality and temporal consistency. To address this issue, we meticulously examine the essential appearance features in the facial animation tasks, which include motion-agnostic (e.g., clothing, background) and motion-related (e.g., facial details) texture components, along with high-level discriminative identity features. Drawing from this analysis, we introduce a Motion-Identity Modulated Appearance Learning Module (MIA) that modulates CLIP features at both motion and identity levels. Additionally, to tackle the semantic/ color discontinuities between clips, we design an Inter-clip Affinity Learning Module (ICA) to model temporal relationships across clips. Our method achieves precise facial motion control (i.e., expressions and gaze), faithful identity preservation, and generates animation videos that maintain both intra/inter-clip temporal consistency. Moreover, it easily adapts to various modalities of driving sources. Extensive experiments demonstrate the superiority of our method.<|reference_end|>
|
arxiv
|
@article{han2024mimaface:,
title={MIMAFace: Face Animation via Motion-Identity Modulated Appearance
Feature Learning},
author={Yue Han, Junwei Zhu, Yuxiang Feng, Xiaozhong Ji, Keke He, Xiangtai Li,
zhucun xue, Yong Liu},
journal={arXiv preprint arXiv:2409.15179},
year={2024},
archivePrefix={arXiv},
eprint={2409.15179},
primaryClass={cs.CV}
}
|
han2024mimaface:
|
arxiv-660872
|
2409.15180
|
A Comprehensive Survey with Critical Analysis for Deepfake Speech Detection
|
<|reference_start|>A Comprehensive Survey with Critical Analysis for Deepfake Speech Detection: Thanks to advancements in deep learning, speech generation systems now power a variety of real-world applications, such as text-to-speech for individuals with speech disorders, voice chatbots in call centers, cross-linguistic speech translation, etc. While these systems can autonomously generate human-like speech and replicate specific voices, they also pose risks when misused for malicious purposes. This motivates the research community to develop models for detecting synthesized speech (e.g., fake speech) generated by deep-learning-based models, referred to as the Deepfake Speech Detection task. As the Deepfake Speech Detection task has emerged in recent years, there are not many survey papers proposed for this task. Additionally, existing surveys for the Deepfake Speech Detection task tend to summarize techniques used to construct a Deepfake Speech Detection system rather than providing a thorough analysis. This gap motivated us to conduct a comprehensive survey, providing a critical analysis of the challenges and developments in Deepfake Speech Detection. Our survey is innovatively structured, offering an in-depth analysis of current challenge competitions, public datasets, and the deep-learning techniques that provide enhanced solutions to address existing challenges in the field. From our analysis, we propose hypotheses on leveraging and combining specific deep learning techniques to improve the effectiveness of Deepfake Speech Detection systems. Beyond conducting a survey, we perform extensive experiments to validate these hypotheses and propose a highly competitive model for the task of Deepfake Speech Detection. Given the analysis and the experimental results, we finally indicate potential and promising research directions for the Deepfake Speech Detection task.<|reference_end|>
|
arxiv
|
@article{pham2024a,
title={A Comprehensive Survey with Critical Analysis for Deepfake Speech
Detection},
author={Lam Pham, Phat Lam, Tin Nguyen, Hieu Tang, Dat Tran, Alexander
Schindler, Taron Zakaryan, Alexander Polonsky, Canh Vu},
journal={arXiv preprint arXiv:2409.15180},
year={2024},
archivePrefix={arXiv},
eprint={2409.15180},
primaryClass={cs.SD eess.AS}
}
|
pham2024a
|
arxiv-660873
|
2409.15181
|
Fast Virtual Gate Extraction For Silicon Quantum Dot Devices
|
<|reference_start|>Fast Virtual Gate Extraction For Silicon Quantum Dot Devices: Silicon quantum dot devices stand as promising candidates for large-scale quantum computing due to their extended coherence times, compact size, and recent experimental demonstrations of sizable qubit arrays. Despite the great potential, controlling these arrays remains a significant challenge. This paper introduces a new virtual gate extraction method to quickly establish orthogonal control on the potentials for individual quantum dots. Leveraging insights from the device physics, the proposed approach significantly reduces the experimental overhead by focusing on crucial regions around charge state transition. Furthermore, by employing an efficient voltage sweeping method, we can efficiently pinpoint these charge state transition lines and filter out erroneous points. Experimental evaluation using real quantum dot chip datasets demonstrates a substantial 5.84x to 19.34x speedup over conventional methods, thereby showcasing promising prospects for accelerating the scaling of silicon spin qubit devices.<|reference_end|>
|
arxiv
|
@article{che2024fast,
title={Fast Virtual Gate Extraction For Silicon Quantum Dot Devices},
author={Shize Che, Seong W Oh, Haoyun Qin, Yuhao Liu, Anthony Sigillito, Gushu
Li},
journal={arXiv preprint arXiv:2409.15181},
year={2024},
archivePrefix={arXiv},
eprint={2409.15181},
primaryClass={cond-mat.mes-hall cs.AR}
}
|
che2024fast
|
arxiv-660874
|
2409.15182
|
Goal-based Neural Physics Vehicle Trajectory Prediction Model
|
<|reference_start|>Goal-based Neural Physics Vehicle Trajectory Prediction Model: Vehicle trajectory prediction plays a vital role in intelligent transportation systems and autonomous driving, as it significantly affects vehicle behavior planning and control, thereby influencing traffic safety and efficiency. Numerous studies have been conducted to predict short-term vehicle trajectories in the immediate future. However, long-term trajectory prediction remains a major challenge due to accumulated errors and uncertainties. Additionally, balancing accuracy with interpretability in the prediction is another challenging issue in predicting vehicle trajectory. To address these challenges, this paper proposes a Goal-based Neural Physics Vehicle Trajectory Prediction Model (GNP). The GNP model simplifies vehicle trajectory prediction into a two-stage process: determining the vehicle's goal and then choosing the appropriate trajectory to reach this goal. The GNP model contains two sub-modules to achieve this process. The first sub-module employs a multi-head attention mechanism to accurately predict goals. The second sub-module integrates a deep learning model with a physics-based social force model to progressively predict the complete trajectory using the generated goals. The GNP demonstrates state-of-the-art long-term prediction accuracy compared to four baseline models. We provide interpretable visualization results to highlight the multi-modality and inherent nature of our neural physics framework. Additionally, ablation studies are performed to validate the effectiveness of our key designs.<|reference_end|>
|
arxiv
|
@article{gan2024goal-based,
title={Goal-based Neural Physics Vehicle Trajectory Prediction Model},
author={Rui Gan, Haotian Shi, Pei Li, Keshu Wu, Bocheng An, Linheng Li, Junyi
Ma, Chengyuan Ma, Bin Ran},
journal={arXiv preprint arXiv:2409.15182},
year={2024},
archivePrefix={arXiv},
eprint={2409.15182},
primaryClass={cs.AI}
}
|
gan2024goal-based
|
arxiv-660875
|
2409.15183
|
Chattronics: using GPTs to assist in the design of data acquisition systems
|
<|reference_start|>Chattronics: using GPTs to assist in the design of data acquisition systems: The usefulness of Large Language Models (LLM) is being continuously tested in various fields. However, their intrinsic linguistic characteristic is still one of the limiting factors when applying these models to exact sciences. In this article, a novel approach to use General Pre-Trained Transformers to assist in the design phase of data acquisition systems will be presented. The solution is packaged in the form of an application that retains the conversational aspects of LLMs, in such a manner that the user must provide details on the desired project in order for the model to draft both a system-level architectural diagram and the block-level specifications, following a Top-Down methodology based on restrictions. To test this tool, two distinct user emulations were used, one of which uses an additional GPT model. In total, 4 different data acquisition projects were used in the testing phase, each with its own measurement requirements: angular position, temperature, acceleration and a fourth project with both pressure and superficial temperature measurements. After 160 test iterations, the study concludes that there is potential for these models to serve adequately as synthesis/assistant tools for data acquisition systems, but there are still technological limitations. The results show coherent architectures and topologies, but that GPTs have difficulties in simultaneously considering all requirements and many times commits theoretical mistakes.<|reference_end|>
|
arxiv
|
@article{brown2024chattronics:,
title={Chattronics: using GPTs to assist in the design of data acquisition
systems},
author={Jonathan Paul Driemeyer Brown, Tiago Oliveira Weber},
journal={arXiv preprint arXiv:2409.15183},
year={2024},
archivePrefix={arXiv},
eprint={2409.15183},
primaryClass={cs.AI cs.AR eess.SP}
}
|
brown2024chattronics:
|
arxiv-660876
|
2409.15186
|
Location is Key: Leveraging Large Language Model for Functional Bug Localization in Verilog
|
<|reference_start|>Location is Key: Leveraging Large Language Model for Functional Bug Localization in Verilog: Bug localization in Verilog code is a crucial and time-consuming task during the verification of hardware design. Since introduction, Large Language Models (LLMs) have showed their strong programming capabilities. However, no work has yet considered using LLMs for bug localization in Verilog code. This paper presents Location-is-Key, an opensource LLM solution to locate functional errors in Verilog snippets. LiK achieves high localization accuracy, with a pass@1 localization accuracy of 93.3% on our test dataset based on RTLLM, surpassing GPT-4's 77.9% and comparable to Claude-3.5's 90.8%. Additionally, the bug location obtained by LiK significantly improves GPT-3.5's bug repair efficiency (Functional pass@1 increased from 40.39% to 58.92%), highlighting the importance of bug localization in LLM-based Verilog debugging. Compared to existing methods, LiK only requires the design specification and the erroneous code snippet, without the need for testbenches, assertions, or any other EDA tools. This research demonstrates the feasibility of using LLMs for Verilog error localization, thus providing a new direction for automatic Verilog code debugging.<|reference_end|>
|
arxiv
|
@article{yao2024location,
title={Location is Key: Leveraging Large Language Model for Functional Bug
Localization in Verilog},
author={Bingkun Yao, Ning Wang, Jie Zhou, Xi Wang, Hong Gao, Zhe Jiang, Nan
Guan},
journal={arXiv preprint arXiv:2409.15186},
year={2024},
archivePrefix={arXiv},
eprint={2409.15186},
primaryClass={cs.AR cs.AI}
}
|
yao2024location
|
arxiv-660877
|
2409.15187
|
Loopy Movements: Emergence of Rotation in a Multicellular Robot
|
<|reference_start|>Loopy Movements: Emergence of Rotation in a Multicellular Robot: Unlike most human-engineered systems, many biological systems rely on emergent behaviors from low-level interactions, enabling greater diversity and superior adaptation to complex, dynamic environments. This study explores emergent decentralized rotation in the Loopy multicellular robot, composed of homogeneous, physically linked, 1-degree-of-freedom cells. Inspired by biological systems like sunflowers, Loopy uses simple local interactions-diffusion, reaction, and active transport of simulated chemicals, called morphogens-without centralized control or knowledge of its global morphology. Through these interactions, the robot self-organizes to achieve coordinated rotational motion and forms lobes-local protrusions created by clusters of motor cells. This study investigates how these interactions drive Loopy's rotation, the impact of its morphology, and its resilience to actuator failures. Our findings reveal two distinct behaviors: 1) inner valleys between lobes rotate faster than the outer peaks, contrasting with rigid body dynamics, and 2) cells rotate in the opposite direction of the overall morphology. The experiments show that while Loopy's morphology does not affect its angular velocity relative to its cells, larger lobes increase cellular rotation and decrease morphology rotation relative to the environment. Even with up to one-third of its actuators disabled and significant morphological changes, Loopy maintains its rotational abilities, highlighting the potential of decentralized, bio-inspired strategies for resilient and adaptable robotic systems.<|reference_end|>
|
arxiv
|
@article{smith2024loopy,
title={Loopy Movements: Emergence of Rotation in a Multicellular Robot},
author={Trevor Smith, Yu Gu},
journal={arXiv preprint arXiv:2409.15187},
year={2024},
archivePrefix={arXiv},
eprint={2409.15187},
primaryClass={cs.RO}
}
|
smith2024loopy
|
arxiv-660878
|
2409.15188
|
PALLM: Evaluating and Enhancing PALLiative Care Conversations with Large Language Models
|
<|reference_start|>PALLM: Evaluating and Enhancing PALLiative Care Conversations with Large Language Models: Effective patient-provider communication is crucial in clinical care, directly impacting patient outcomes and quality of life. Traditional evaluation methods, such as human ratings, patient feedback, and provider self-assessments, are often limited by high costs and scalability issues. Although existing natural language processing (NLP) techniques show promise, they struggle with the nuances of clinical communication and require sensitive clinical data for training, reducing their effectiveness in real-world applications. Emerging large language models (LLMs) offer a new approach to assessing complex communication metrics, with the potential to advance the field through integration into passive sensing and just-in-time intervention systems. This study explores LLMs as evaluators of palliative care communication quality, leveraging their linguistic, in-context learning, and reasoning capabilities. Specifically, using simulated scripts crafted and labeled by healthcare professionals, we test proprietary models (e.g., GPT-4) and fine-tune open-source LLMs (e.g., LLaMA2) with a synthetic dataset generated by GPT-4 to evaluate clinical conversations, to identify key metrics such as `understanding' and `empathy'. Our findings demonstrated LLMs' superior performance in evaluating clinical communication, providing actionable feedback with reasoning, and demonstrating the feasibility and practical viability of developing in-house LLMs. This research highlights LLMs' potential to enhance patient-provider interactions and lays the groundwork for downstream steps in developing LLM-empowered clinical health systems.<|reference_end|>
|
arxiv
|
@article{wang2024pallm:,
title={PALLM: Evaluating and Enhancing PALLiative Care Conversations with Large
Language Models},
author={Zhiyuan Wang, Fangxu Yuan, Virginia LeBaron, Tabor Flickinger, Laura
E. Barnes},
journal={arXiv preprint arXiv:2409.15188},
year={2024},
archivePrefix={arXiv},
eprint={2409.15188},
primaryClass={cs.CL cs.HC}
}
|
wang2024pallm:
|
arxiv-660879
|
2409.15190
|
Interpretability-Guided Test-Time Adversarial Defense
|
<|reference_start|>Interpretability-Guided Test-Time Adversarial Defense: We propose a novel and low-cost test-time adversarial defense by devising interpretability-guided neuron importance ranking methods to identify neurons important to the output classes. Our method is a training-free approach that can significantly improve the robustness-accuracy tradeoff while incurring minimal computational overhead. While being among the most efficient test-time defenses (4x faster), our method is also robust to a wide range of black-box, white-box, and adaptive attacks that break previous test-time defenses. We demonstrate the efficacy of our method for CIFAR10, CIFAR100, and ImageNet-1k on the standard RobustBench benchmark (with average gains of 2.6%, 4.9%, and 2.8% respectively). We also show improvements (average 1.5%) over the state-of-the-art test-time defenses even under strong adaptive attacks.<|reference_end|>
|
arxiv
|
@article{kulkarni2024interpretability-guided,
title={Interpretability-Guided Test-Time Adversarial Defense},
author={Akshay Kulkarni and Tsui-Wei Weng},
journal={arXiv preprint arXiv:2409.15190},
year={2024},
archivePrefix={arXiv},
eprint={2409.15190},
primaryClass={cs.CV cs.CR cs.LG}
}
|
kulkarni2024interpretability-guided
|
arxiv-660880
|
2409.15192
|
The Complexity of Counting Turns in the Line-Based Dial-a-Ride Problem
|
<|reference_start|>The Complexity of Counting Turns in the Line-Based Dial-a-Ride Problem: Dial-a-Ride problems have been proposed to model the challenge to consolidate passenger transportation requests with a fleet of shared vehicles. The line-based Dial-a-Ride problem (LiDARP) is a variant where the passengers are transported along a fixed sequence of stops, with the option of taking shortcuts. In this paper we consider the LiDARP with the objective function to maximize the number of transported requests. We investigate the complexity of two optimization problems: the LiDARP, and the problem to determine the minimum number of turns needed in an optimal LiDARP solution, called the MinTurn problem. Based on a number of instance parameters and characteristics, we are able to state the boundary between polynomially solvable and NP-hard instances for both problems. Furthermore, we provide parameterized algorithms that are able to solve both the LiDARP and MinTurn problem.<|reference_end|>
|
arxiv
|
@article{lauerbach2024the,
title={The Complexity of Counting Turns in the Line-Based Dial-a-Ride Problem},
author={Antonio Lauerbach, Kendra Reiter, Marie Schmidt},
journal={arXiv preprint arXiv:2409.15192},
year={2024},
archivePrefix={arXiv},
eprint={2409.15192},
primaryClass={cs.CC}
}
|
lauerbach2024the
|
arxiv-660881
|
2409.15196
|
HOTVCOM: Generating Buzzworthy Comments for Videos
|
<|reference_start|>HOTVCOM: Generating Buzzworthy Comments for Videos: In the era of social media video platforms, popular ``hot-comments'' play a crucial role in attracting user impressions of short-form videos, making them vital for marketing and branding purpose. However, existing research predominantly focuses on generating descriptive comments or ``danmaku'' in English, offering immediate reactions to specific video moments. Addressing this gap, our study introduces \textsc{HotVCom}, the largest Chinese video hot-comment dataset, comprising 94k diverse videos and 137 million comments. We also present the \texttt{ComHeat} framework, which synergistically integrates visual, auditory, and textual data to generate influential hot-comments on the Chinese video dataset. Empirical evaluations highlight the effectiveness of our framework, demonstrating its excellence on both the newly constructed and existing datasets.<|reference_end|>
|
arxiv
|
@article{chen2024hotvcom:,
title={HOTVCOM: Generating Buzzworthy Comments for Videos},
author={Yuyan Chen, Yiwen Qian, Songzhou Yan, Jiyuan Jia, Zhixu Li, Yanghua
Xiao, Xiaobo Li, Ming Yang, Qingpei Guo},
journal={arXiv preprint arXiv:2409.15196},
year={2024},
archivePrefix={arXiv},
eprint={2409.15196},
primaryClass={cs.CV cs.AI}
}
|
chen2024hotvcom:
|
arxiv-660882
|
2409.15199
|
Learning from Contrastive Prompts: Automated Optimization and Adaptation
|
<|reference_start|>Learning from Contrastive Prompts: Automated Optimization and Adaptation: As LLMs evolve, significant effort is spent on manually crafting prompts. While existing prompt optimization methods automate this process, they rely solely on learning from incorrect samples, leading to a sub-optimal performance. Additionally, an unexplored challenge in the literature is prompts effective for prior models may not perform well on newer versions or different languages. We propose the Learning from Contrastive Prompts (LCP) framework to address these gaps, enhancing both prompt optimization and adaptation. LCP employs contrastive learning to generate effective prompts by analyzing patterns in good and bad prompt examples. Our evaluation on the Big-Bench Hard dataset shows that LCP has a win rate of over 76% over existing methods in prompt optimization and demonstrates strong adaptability across different model versions, families, and languages. LCP offers a systematic approach to prompt engineering, reducing manual effort in deploying LLMs across varied contexts.<|reference_end|>
|
arxiv
|
@article{li2024learning,
title={Learning from Contrastive Prompts: Automated Optimization and Adaptation},
author={Mingqi Li, Karan Aggarwal, Yong Xie, Aitzaz Ahmad, Stephen Lau},
journal={arXiv preprint arXiv:2409.15199},
year={2024},
archivePrefix={arXiv},
eprint={2409.15199},
primaryClass={cs.CL cs.AI}
}
|
li2024learning
|
arxiv-660883
|
2409.15200
|
Enabling Tensor Decomposition for Time-Series Classification via A Simple Pseudo-Laplacian Contrast
|
<|reference_start|>Enabling Tensor Decomposition for Time-Series Classification via A Simple Pseudo-Laplacian Contrast: Tensor decomposition has emerged as a prominent technique to learn low-dimensional representation under the supervision of reconstruction error, primarily benefiting data inference tasks like completion and imputation, but not classification task. We argue that the non-uniqueness and rotation invariance of tensor decomposition allow us to identify the directions with largest class-variability and simple graph Laplacian can effectively achieve this objective. Therefore we propose a novel Pseudo Laplacian Contrast (PLC) tensor decomposition framework, which integrates the data augmentation and cross-view Laplacian to enable the extraction of class-aware representations while effectively capturing the intrinsic low-rank structure within reconstruction constraint. An unsupervised alternative optimization algorithm is further developed to iteratively estimate the pseudo graph and minimize the loss using Alternating Least Square (ALS). Extensive experimental results on various datasets demonstrate the effectiveness of our approach.<|reference_end|>
|
arxiv
|
@article{li2024enabling,
title={Enabling Tensor Decomposition for Time-Series Classification via A
Simple Pseudo-Laplacian Contrast},
author={Man Li, Ziyue Li, Lijun Sun, Fugee Tsung},
journal={arXiv preprint arXiv:2409.15200},
year={2024},
archivePrefix={arXiv},
eprint={2409.15200},
primaryClass={cs.LG}
}
|
li2024enabling
|
arxiv-660884
|
2409.15202
|
ASTE Transformer Modelling Dependencies in Aspect-Sentiment Triplet Extraction
|
<|reference_start|>ASTE Transformer Modelling Dependencies in Aspect-Sentiment Triplet Extraction: Aspect-Sentiment Triplet Extraction (ASTE) is a recently proposed task of aspect-based sentiment analysis that consists in extracting (aspect phrase, opinion phrase, sentiment polarity) triples from a given sentence. Recent state-of-the-art methods approach this task by first extracting all possible text spans from a given text, then filtering the potential aspect and opinion phrases with a classifier, and finally considering all their pairs with another classifier that additionally assigns sentiment polarity to them. Although several variations of the above scheme have been proposed, the common feature is that the final result is constructed by a sequence of independent classifier decisions. This hinders the exploitation of dependencies between extracted phrases and prevents the use of knowledge about the interrelationships between classifier predictions to improve performance. In this paper, we propose a new ASTE approach consisting of three transformer-inspired layers, which enables the modelling of dependencies both between phrases and between the final classifier decisions. Experimental results show that the method achieves higher performance in terms of F1 measure than other methods studied on popular benchmarks. In addition, we show that a simple pre-training technique further improves the performance of the model.<|reference_end|>
|
arxiv
|
@article{naglik2024aste,
title={ASTE Transformer Modelling Dependencies in Aspect-Sentiment Triplet
Extraction},
author={Iwo Naglik and Mateusz Lango},
journal={arXiv preprint arXiv:2409.15202},
year={2024},
archivePrefix={arXiv},
eprint={2409.15202},
primaryClass={cs.CL cs.AI cs.LG}
}
|
naglik2024aste
|
arxiv-660885
|
2409.15203
|
Locality vs Quantum Codes
|
<|reference_start|>Locality vs Quantum Codes: This paper proves optimal tradeoffs between the locality and parameters of quantum error-correcting codes. Quantum codes give a promising avenue towards quantum fault tolerance, but the practical constraint of locality limits their quality. The seminal Bravyi-Poulin-Terhal (BPT) bound says that a $[[n,k,d]]$ quantum stabilizer code with 2D-locality must satisfy $kd^2\le O(n)$. We answer the natural question: for better code parameters, how much "non-locality" is needed? In particular, (i) how long must the long-range interactions be, and (ii) how many long-range interactions must there be? We give a complete answer to both questions for all $n,k,d$: above the BPT bound, any 2D-embedding must have at least $\Omega(\#^*)$ interactions of length $\Omega(\ell^*)$, where $\#^*= \max(k,d)$ and $\ell^*=\max\big(\frac{d}{\sqrt{n}}, \big( \frac{kd^2}{n} \big)^{1/4} \big)$. Conversely, we exhibit quantum codes that show, in strong ways, that our interaction length $\ell^*$ and interaction count $\#^*$ are asymptotically optimal for all $n,k,d$. Our results generalize or improve all prior works on this question, including the BPT bound and the results of Baspin and Krishna. One takeaway of our work is that, for any desired distance $d$ and dimension $k$, the number of long-range interactions is asymptotically minimized by a good qLDPC code of length $\Theta(\max(k,d))$. Following Baspin and Krishna, we also apply our results to the codes implemented in the stacked architecture and obtain better bounds. In particular, we rule out any implementation of hypergraph product codes in the stacked architecture.<|reference_end|>
|
arxiv
|
@article{dai2024locality,
title={Locality vs Quantum Codes},
author={Samuel Dai, Ray Li},
journal={arXiv preprint arXiv:2409.15203},
year={2024},
archivePrefix={arXiv},
eprint={2409.15203},
primaryClass={quant-ph cs.IT math.IT}
}
|
dai2024locality
|
arxiv-660886
|
2409.15204
|
RAMBO: Enhancing RAG-based Repository-Level Method Body Completion
|
<|reference_start|>RAMBO: Enhancing RAG-based Repository-Level Method Body Completion: Code completion is essential in software development, helping developers by predicting code snippets based on context. Among completion tasks, Method Body Completion (MBC) is particularly challenging as it involves generating complete method bodies based on their signatures and context. This task becomes significantly harder in large repositories, where method bodies must integrate repositoryspecific elements such as custom APIs, inter-module dependencies, and project-specific conventions. In this paper, we introduce RAMBO, a novel RAG-based approach for repository-level MBC. Instead of retrieving similar method bodies, RAMBO identifies essential repository-specific elements, such as classes, methods, and variables/fields, and their relevant usages. By incorporating these elements and their relevant usages into the code generation process, RAMBO ensures more accurate and contextually relevant method bodies. Our experimental results with leading code LLMs across 40 Java projects show that RAMBO significantly outperformed the state-of-the-art repository-level MBC approaches, with the improvements of up to 46% in BLEU, 57% in CodeBLEU, 36% in Compilation Rate, and up to 3X in Exact Match. Notably, RAMBO surpassed RepoCoder Oracle method by up to 12% in Exact Match, setting a new benchmark for repository-level MBC.<|reference_end|>
|
arxiv
|
@article{bui2024rambo:,
title={RAMBO: Enhancing RAG-based Repository-Level Method Body Completion},
author={Tuan-Dung Bui, Duc-Thieu Luu-Van, Thanh-Phat Nguyen, Thu-Trang Nguyen,
Son Nguyen, and Hieu Dinh Vo},
journal={arXiv preprint arXiv:2409.15204},
year={2024},
archivePrefix={arXiv},
eprint={2409.15204},
primaryClass={cs.SE cs.LG}
}
|
bui2024rambo:
|
arxiv-660887
|
2409.15205
|
Fast and Accurate Triangle Counting in Graph Streams Using Predictions
|
<|reference_start|>Fast and Accurate Triangle Counting in Graph Streams Using Predictions: In this work, we present the first efficient and practical algorithm for estimating the number of triangles in a graph stream using predictions. Our algorithm combines waiting room sampling and reservoir sampling with a predictor for the heaviness of edges, that is, the number of triangles in which an edge is involved. As a result, our algorithm is fast, provides guarantees on the amount of memory used, and exploits the additional information provided by the predictor to produce highly accurate estimates. We also propose a simple and domain-independent predictor, based on the degree of nodes, that can be easily computed with one pass on a stream of edges when the stream is available beforehand. Our analytical results show that, when the predictor provides useful information on the heaviness of edges, it leads to estimates with reduced variance compared to the state-of-the-art, even when the predictions are far from perfect. Our experimental results show that, when analyzing a single graph stream, our algorithm is faster than the state-of-the-art for a given memory budget, while providing significantly more accurate estimates. Even more interestingly, when sequences of hundreds of graph streams are analyzed, our algorithm significantly outperforms the state-of-the-art using our simple degree-based predictor built by analyzing only the first graph of the sequence.<|reference_end|>
|
arxiv
|
@article{boldrin2024fast,
title={Fast and Accurate Triangle Counting in Graph Streams Using Predictions},
author={Cristian Boldrin and Fabio Vandin},
journal={arXiv preprint arXiv:2409.15205},
year={2024},
archivePrefix={arXiv},
eprint={2409.15205},
primaryClass={cs.DS cs.LG}
}
|
boldrin2024fast
|
arxiv-660888
|
2409.15213
|
HydroVision: LiDAR-Guided Hydrometric Prediction with Vision Transformers and Hybrid Graph Learning
|
<|reference_start|>HydroVision: LiDAR-Guided Hydrometric Prediction with Vision Transformers and Hybrid Graph Learning: Hydrometric forecasting is crucial for managing water resources, flood prediction, and environmental protection. Water stations are interconnected, and this connectivity influences the measurements at other stations. However, the dynamic and implicit nature of water flow paths makes it challenging to extract a priori knowledge of the connectivity structure. We hypothesize that terrain elevation significantly affects flow and connectivity. To incorporate this, we use LiDAR terrain elevation data encoded through a Vision Transformer (ViT). The ViT, which has demonstrated excellent performance in image classification by directly applying transformers to sequences of image patches, efficiently captures spatial features of terrain elevation. To account for both spatial and temporal features, we employ GRU blocks enhanced with graph convolution, a method widely used in the literature. We propose a hybrid graph learning structure that combines static and dynamic graph learning. A static graph, derived from transformer-encoded LiDAR data, captures terrain elevation relationships, while a dynamic graph adapts to temporal changes, improving the overall graph representation. We apply graph convolution in two layers through these static and dynamic graphs. Our method makes daily predictions up to 12 days ahead. Empirical results from multiple water stations in Quebec demonstrate that our method significantly reduces prediction error by an average of 10\% across all days, with greater improvements for longer forecasting horizons.<|reference_end|>
|
arxiv
|
@article{roudbari2024hydrovision:,
title={HydroVision: LiDAR-Guided Hydrometric Prediction with Vision
Transformers and Hybrid Graph Learning},
author={Naghmeh Shafiee Roudbari, Ursula Eicker, Charalambos Poullis, Zachary
Patterson},
journal={arXiv preprint arXiv:2409.15213},
year={2024},
archivePrefix={arXiv},
eprint={2409.15213},
primaryClass={cs.CV cs.LG}
}
|
roudbari2024hydrovision:
|
arxiv-660889
|
2409.15216
|
FLeNS: Federated Learning with Enhanced Nesterov-Newton Sketch
|
<|reference_start|>FLeNS: Federated Learning with Enhanced Nesterov-Newton Sketch: Federated learning faces a critical challenge in balancing communication efficiency with rapid convergence, especially for second-order methods. While Newton-type algorithms achieve linear convergence in communication rounds, transmitting full Hessian matrices is often impractical due to quadratic complexity. We introduce Federated Learning with Enhanced Nesterov-Newton Sketch (FLeNS), a novel method that harnesses both the acceleration capabilities of Nesterov's method and the dimensionality reduction benefits of Hessian sketching. FLeNS approximates the centralized Newton's method without relying on the exact Hessian, significantly reducing communication overhead. By combining Nesterov's acceleration with adaptive Hessian sketching, FLeNS preserves crucial second-order information while preserving the rapid convergence characteristics. Our theoretical analysis, grounded in statistical learning, demonstrates that FLeNS achieves super-linear convergence rates in communication rounds - a notable advancement in federated optimization. We provide rigorous convergence guarantees and characterize tradeoffs between acceleration, sketch size, and convergence speed. Extensive empirical evaluation validates our theoretical findings, showcasing FLeNS's state-of-the-art performance with reduced communication requirements, particularly in privacy-sensitive and edge-computing scenarios. The code is available at https://github.com/sunnyinAI/FLeNS<|reference_end|>
|
arxiv
|
@article{gupta2024flens:,
title={FLeNS: Federated Learning with Enhanced Nesterov-Newton Sketch},
author={Sunny Gupta, Mohit Jindal, Pankhi Kashyap, Pranav Jeevan, Amit Sethi},
journal={arXiv preprint arXiv:2409.15216},
year={2024},
archivePrefix={arXiv},
eprint={2409.15216},
primaryClass={cs.LG cs.CV cs.DC math.OC}
}
|
gupta2024flens:
|
arxiv-660890
|
2409.15219
|
MotifDisco: Motif Causal Discovery For Time Series Motifs
|
<|reference_start|>MotifDisco: Motif Causal Discovery For Time Series Motifs: Many time series, particularly health data streams, can be best understood as a sequence of phenomenon or events, which we call motifs. A time series motif is a short trace segment which may implicitly capture an underlying phenomenon within the time series. Specifically, we focus on glucose traces collected from continuous glucose monitors (CGMs), which inherently contain motifs representing underlying human behaviors such as eating and exercise. The ability to identify and quantify causal relationships amongst motifs can provide a mechanism to better understand and represent these patterns, useful for improving deep learning and generative models and for advanced technology development (e.g., personalized coaching and artificial insulin delivery systems). However, no previous work has developed causal discovery methods for time series motifs. Therefore, in this paper we develop MotifDisco (motif disco-very of causality), a novel causal discovery framework to learn causal relations amongst motifs from time series traces. We formalize a notion of Motif Causality (MC), inspired from Granger Causality and Transfer Entropy, and develop a Graph Neural Network-based framework that learns causality between motifs by solving an unsupervised link prediction problem. We also integrate MC with three model use cases of forecasting, anomaly detection and clustering, to showcase the use of MC as a building block for other downstream tasks. Finally, we evaluate our framework and find that Motif Causality provides a significant performance improvement in all use cases.<|reference_end|>
|
arxiv
|
@article{lamp2024motifdisco:,
title={MotifDisco: Motif Causal Discovery For Time Series Motifs},
author={Josephine Lamp, Mark Derdzinski, Christopher Hannemann, Sam Hatfield,
Joost van der Linden},
journal={arXiv preprint arXiv:2409.15219},
year={2024},
archivePrefix={arXiv},
eprint={2409.15219},
primaryClass={cs.LG}
}
|
lamp2024motifdisco:
|
arxiv-660891
|
2409.15220
|
Geometric Design and Gait Co-Optimization for Soft Continuum Robots Swimming at Low and High Reynolds Numbers
|
<|reference_start|>Geometric Design and Gait Co-Optimization for Soft Continuum Robots Swimming at Low and High Reynolds Numbers: Recent advancements in soft actuators have enabled soft continuum swimming robots to achieve higher efficiency and more closely mimic the behaviors of real marine animals. However, optimizing the design and control of these soft continuum robots remains a significant challenge. In this paper, we present a practical framework for the co-optimization of the design and control of soft continuum robots, approached from a geometric locomotion analysis perspective. This framework is based on the principles of geometric mechanics, accounting for swimming at both low and high Reynolds numbers. By generalizing geometric principles to continuum bodies, we achieve efficient geometric variational co-optimization of designs and gaits across different power consumption metrics and swimming environments. The resulting optimal designs and gaits exhibit greater efficiencies at both low and high Reynolds numbers compared to three-link or serpenoid swimmers with the same degrees of freedom, approaching or even surpassing the efficiencies of infinitely flexible swimmers and those with higher degrees of freedom.<|reference_end|>
|
arxiv
|
@article{yang2024geometric,
title={Geometric Design and Gait Co-Optimization for Soft Continuum Robots
Swimming at Low and High Reynolds Numbers},
author={Yanhao Yang, Ross L. Hatton},
journal={arXiv preprint arXiv:2409.15220},
year={2024},
archivePrefix={arXiv},
eprint={2409.15220},
primaryClass={cs.RO}
}
|
yang2024geometric
|
arxiv-660892
|
2409.15224
|
Enhancing Pedestrian Trajectory Prediction with Crowd Trip Information
|
<|reference_start|>Enhancing Pedestrian Trajectory Prediction with Crowd Trip Information: Pedestrian trajectory prediction is essential for various applications in active traffic management, urban planning, traffic control, crowd management, and autonomous driving, aiming to enhance traffic safety and efficiency. Accurately predicting pedestrian trajectories requires a deep understanding of individual behaviors, social interactions, and road environments. Existing studies have developed various models to capture the influence of social interactions and road conditions on pedestrian trajectories. However, these approaches are limited by the lack of a comprehensive view of social interactions and road environments. To address these limitations and enhance the accuracy of pedestrian trajectory prediction, we propose a novel approach incorporating trip information as a new modality into pedestrian trajectory models. We propose RNTransformer, a generic model that utilizes crowd trip information to capture global information on social interactions. We incorporated RNTransformer with various socially aware local pedestrian trajectory prediction models to demonstrate its performance. Specifically, by leveraging a pre-trained RNTransformer when training different pedestrian trajectory prediction models, we observed improvements in performance metrics: a 1.3/2.2% enhancement in ADE/FDE on Social-LSTM, a 6.5/28.4% improvement on Social-STGCNN, and an 8.6/4.3% improvement on S-Implicit. Evaluation results demonstrate that RNTransformer significantly enhances the accuracy of various pedestrian trajectory prediction models across multiple datasets. Further investigation reveals that the RNTransformer effectively guides local models to more accurate directions due to the consideration of global information. By exploring crowd behavior within the road network, our approach shows great promise in improving pedestrian safety through accurate trajectory predictions.<|reference_end|>
|
arxiv
|
@article{tamaru2024enhancing,
title={Enhancing Pedestrian Trajectory Prediction with Crowd Trip Information},
author={Rei Tamaru, Pei Li, Bin Ran},
journal={arXiv preprint arXiv:2409.15224},
year={2024},
archivePrefix={arXiv},
eprint={2409.15224},
primaryClass={cs.CV cs.AI cs.LG}
}
|
tamaru2024enhancing
|
arxiv-660893
|
2409.15226
|
Intelligent Routing Algorithm over SDN: Reusable Reinforcement Learning Approach
|
<|reference_start|>Intelligent Routing Algorithm over SDN: Reusable Reinforcement Learning Approach: Traffic routing is vital for the proper functioning of the Internet. As users and network traffic increase, researchers try to develop adaptive and intelligent routing algorithms that can fulfill various QoS requirements. Reinforcement Learning (RL) based routing algorithms have shown better performance than traditional approaches. We developed a QoS-aware, reusable RL routing algorithm, RLSR-Routing over SDN. During the learning process, our algorithm ensures loop-free path exploration. While finding the path for one traffic demand (a source destination pair with certain amount of traffic), RLSR-Routing learns the overall network QoS status, which can be used to speed up algorithm convergence when finding the path for other traffic demands. By adapting Segment Routing, our algorithm can achieve flow-based, source packet routing, and reduce communications required between SDN controller and network plane. Our algorithm shows better performance in terms of load balancing than the traditional approaches. It also has faster convergence than the non-reusable RL approach when finding paths for multiple traffic demands.<|reference_end|>
|
arxiv
|
@article{wumian2024intelligent,
title={Intelligent Routing Algorithm over SDN: Reusable Reinforcement Learning
Approach},
author={Wang Wumian, Sajal Saha, Anwar Haque, and Greg Sidebottom},
journal={arXiv preprint arXiv:2409.15226},
year={2024},
archivePrefix={arXiv},
eprint={2409.15226},
primaryClass={cs.NI cs.LG}
}
|
wumian2024intelligent
|
arxiv-660894
|
2409.15228
|
A Comprehensive Framework for Evaluating API-oriented Code Generation in Large Language Models
|
<|reference_start|>A Comprehensive Framework for Evaluating API-oriented Code Generation in Large Language Models: Large language models (LLMs) like GitHub Copilot and ChatGPT have emerged as powerful tools for code generation, significantly enhancing productivity and accelerating software development. However, existing benchmarks primarily focus on general code generation without considering API-oriented code generation, i.e., generating code that invokes APIs from specific libraries. Given the growing demand for API-oriented code generation, there is a pressing need for a systematic and automated approach to evaluate LLM on API-oriented code generation. To address this gap, we propose AutoAPIEval, a lightweight and automated framework designed to evaluate the capabilities of LLMs in API-oriented code generation. Our framework works with any library that provides API documentation and focuses on two unit tasks: API recommendation and code example generation, along with four metrics to evaluate the generated APIs and code examples, such as the proportion of incorrect API recommendations for Task 1, and the proportion of code examples where no specific API is invoked and uncompilable/unexecutable code examples for Task 2. In addition, we conducted a case study on three LLMs (ChatGPT, MagiCoder, and DeepSeek Coder) and Java Runtime Environment 8 to demonstrate the framework's effectiveness. Our findings reveal substantial variability in LLM performance across tasks, with ChatGPT adhering better to instructions, while sharing similar effectiveness in code example generation with its counterparts (i.e., MagiCoder and DeekSeek Coder). We also identify key factors associated with code quality, such as API popularity and model confidence, and build classifiers that achieve high accuracy in detecting incorrect API recommendations and erroneous code examples. Retrieval-augmented generation enhances the quality of code generated by LLMs, though its effectiveness varies across different LLMs.<|reference_end|>
|
arxiv
|
@article{wu2024a,
title={A Comprehensive Framework for Evaluating API-oriented Code Generation in
Large Language Models},
author={Yixi Wu, Pengfei He, Zehao Wang, Shaowei Wang, Yuan Tian and Tse-Hsun
Chen},
journal={arXiv preprint arXiv:2409.15228},
year={2024},
archivePrefix={arXiv},
eprint={2409.15228},
primaryClass={cs.SE cs.AI cs.LG}
}
|
wu2024a
|
arxiv-660895
|
2409.15234
|
CA-MHFA: A Context-Aware Multi-Head Factorized Attentive Pooling for SSL-Based Speaker Verification
|
<|reference_start|>CA-MHFA: A Context-Aware Multi-Head Factorized Attentive Pooling for SSL-Based Speaker Verification: Self-supervised learning (SSL) models for speaker verification (SV) have gained significant attention in recent years. However, existing SSL-based SV systems often struggle to capture local temporal dependencies and generalize across different tasks. In this paper, we propose context-aware multi-head factorized attentive pooling (CA-MHFA), a lightweight framework that incorporates contextual information from surrounding frames. CA-MHFA leverages grouped, learnable queries to effectively model contextual dependencies while maintaining efficiency by sharing keys and values across groups. Experimental results on the VoxCeleb dataset show that CA-MHFA achieves EERs of 0.42\%, 0.48\%, and 0.96\% on Vox1-O, Vox1-E, and Vox1-H, respectively, outperforming complex models like WavLM-TDNN with fewer parameters and faster convergence. Additionally, CA-MHFA demonstrates strong generalization across multiple SSL models and tasks, including emotion recognition and anti-spoofing, highlighting its robustness and versatility.<|reference_end|>
|
arxiv
|
@article{peng2024ca-mhfa:,
title={CA-MHFA: A Context-Aware Multi-Head Factorized Attentive Pooling for
SSL-Based Speaker Verification},
author={Junyi Peng, Ladislav Mov{s}ner, Lin Zhang, Oldv{r}ich Plchot, Themos
Stafylakis, Luk'av{s} Burget, Jan v{C}ernock'y},
journal={arXiv preprint arXiv:2409.15234},
year={2024},
archivePrefix={arXiv},
eprint={2409.15234},
primaryClass={eess.AS cs.SD}
}
|
peng2024ca-mhfa:
|
arxiv-660896
|
2409.15239
|
TacPalm: A Soft Gripper with a Biomimetic Optical Tactile Palm for Stable Precise Grasping
|
<|reference_start|>TacPalm: A Soft Gripper with a Biomimetic Optical Tactile Palm for Stable Precise Grasping: Manipulating fragile objects in environments such as homes and factories requires stable and gentle grasping along with precise and safe placement. Compared to traditional rigid grippers, the use of soft grippers reduces the control complexity and the risk of damaging objects. However, it is challenging to integrate camera-based optical tactile sensing into a soft gripper without compromising the flexibility and adaptability of the fingers, while also ensuring that the precision of tactile perception remains unaffected by passive deformations of the soft structure during object contact. In this paper, we demonstrate a modular soft two-fingered gripper with a 3D-printed optical tactile sensor (the TacTip) integrated in the palm. We propose a soft-grasping strategy that includes three functions: light contact detection, grasp pose adjustment and loss-of-contact detection, so that objects of different shapes and sizes can be grasped stably and placed precisely, which we test with both artificial and household objects. By sequentially implementing these three functions, the grasp success rate progressively improves from 45% without any functions, to 59% with light contact detection, 90% with grasp pose adjustment, and 97% with loss-of-contact detection, achieving a sub-millimeter placement precision. Overall, this work demonstrates the feasibility and utility of integrating optical tactile sensors into the palm of a soft gripper, of applicability to various types of soft manipulators. The proposed grasping strategy has potential applications in areas such as fragile product processing and home assistance.<|reference_end|>
|
arxiv
|
@article{zhang2024tacpalm:,
title={TacPalm: A Soft Gripper with a Biomimetic Optical Tactile Palm for
Stable Precise Grasping},
author={Xuyang Zhang, Tianqi Yang, Dandan Zhang, Nathan F. Lepora},
journal={arXiv preprint arXiv:2409.15239},
year={2024},
doi={10.1109/JSEN.2024.3471812},
archivePrefix={arXiv},
eprint={2409.15239},
primaryClass={cs.RO}
}
|
zhang2024tacpalm:
|
arxiv-660897
|
2409.15240
|
MemBench: Towards Real-world Evaluation of Memory-Augmented Dialogue Systems
|
<|reference_start|>MemBench: Towards Real-world Evaluation of Memory-Augmented Dialogue Systems: Long-term memory is so important for chatbots and dialogue systems (DS) that researchers have developed numerous memory-augmented DS. However, their evaluation methods are different from the real situation in human conversation. They only measured the accuracy of factual information or the perplexity of generated responses given a query, which hardly reflected their performance. Moreover, they only consider passive memory retrieval based on similarity, neglecting diverse memory-recalling paradigms in humans, e.g. emotions and surroundings. To bridge the gap, we construct a novel benchmark covering various memory recalling paradigms based on cognitive science and psychology theory. The Memory Benchmark (MemBench) contains two tasks according to the two-phrase theory in cognitive science: memory retrieval, memory recognition and injection. The benchmark considers both passive and proactive memory recalling based on meta information for the first time. In addition, novel scoring aspects are proposed to comprehensively measure the generated responses. Results from the strongest embedding models and LLMs on MemBench show that there is plenty of room for improvement in existing dialogue systems. Extensive experiments also reveal the correlation between memory injection and emotion supporting (ES) skillfulness, and intimacy. Our code and dataset will be released.<|reference_end|>
|
arxiv
|
@article{he2024madial-bench:,
title={MADial-Bench: Towards Real-world Evaluation of Memory-Augmented Dialogue
Generation},
author={Junqing He, Liang Zhu, Rui Wang, Xi Wang, Reza Haffari, Jiaxing Zhang},
journal={arXiv preprint arXiv:2409.15240},
year={2024},
archivePrefix={arXiv},
eprint={2409.15240},
primaryClass={cs.CL cs.AI}
}
|
he2024madial-bench:
|
arxiv-660898
|
2409.15241
|
Domino: Eliminating Communication in LLM Training via Generic Tensor Slicing and Overlapping
|
<|reference_start|>Domino: Eliminating Communication in LLM Training via Generic Tensor Slicing and Overlapping: Given the popularity of generative AI, Large Language Models (LLMs) often consume hundreds or thousands of GPUs for parallelizing and accelerating the training process. Communication overhead becomes more pronounced when training LLMs at scale. To eliminate communication overhead in distributed LLM training, we propose Domino, which provides a generic scheme to hide communication behind computation. By breaking data dependency of a single batch training into smaller independent pieces, Domino pipelines these independent pieces training and provides generic strategy of fine-grained communication and computation overlapping. Extensive results show that, comparing with Megatron-LM, Domino achieves up to 1.3x speedup for LLM training on Nvidia DGX-H100 GPUs.<|reference_end|>
|
arxiv
|
@article{wang2024domino:,
title={Domino: Eliminating Communication in LLM Training via Generic Tensor
Slicing and Overlapping},
author={Guanhua Wang, Chengming Zhang, Zheyu Shen, Ang Li, Olatunji Ruwase},
journal={arXiv preprint arXiv:2409.15241},
year={2024},
archivePrefix={arXiv},
eprint={2409.15241},
primaryClass={cs.DC cs.AI cs.LG}
}
|
wang2024domino:
|
arxiv-660899
|
2409.15242
|
Skeletal Data Matching and Merging from Multiple RGB-D Sensors for Room-Scale Human Behaviour Tracking
|
<|reference_start|>Skeletal Data Matching and Merging from Multiple RGB-D Sensors for Room-Scale Human Behaviour Tracking: A popular and affordable option to provide room-scale human behaviour tracking is to rely on commodity RGB-D sensors %todo: such as the Kinect family of devices? as such devices offer body tracking capabilities at a reasonable price point. While their capabilities may be sufficient for applications such as entertainment systems where a person plays in front of a television, RGB-D sensors are sensitive to occlusions from objects or other persons that might be in the way in more complex room-scale setups. To alleviate the occlusion issue but also in order to extend the tracking range and strengthen its accuracy, it is possible to rely on multiple RGB-D sensors and perform data fusion. Unfortunately, fusing the data in a meaningful manner raises additional challenges related to the calibration of the sensors relative to each other to provide a common frame of reference, but also regarding skeleton matching and merging when actually combining the data. In this paper, we discuss our approach to tackle these challenges and present the results we achieved, through aligned point clouds and combined skeleton lists. These results successfully enable unobtrusive and occlusion-resilient human behaviour tracking at room scale, that may be used as input for interactive applications as well as (possibly remote) collaborative systems.<|reference_end|>
|
arxiv
|
@article{coppens2024skeletal,
title={Skeletal Data Matching and Merging from Multiple RGB-D Sensors for
Room-Scale Human Behaviour Tracking},
author={Adrien Coppens and Val'erie Maquil},
journal={arXiv preprint arXiv:2409.15242},
year={2024},
doi={10.1007/978-3-031-71315-6_30},
archivePrefix={arXiv},
eprint={2409.15242},
primaryClass={cs.HC}
}
|
coppens2024skeletal
|
arxiv-660900
|
2409.15243
|
MACeIP: A Multimodal Ambient Context-enriched Intelligence Platform in Smart Cities
|
<|reference_start|>MACeIP: A Multimodal Ambient Context-enriched Intelligence Platform in Smart Cities: This paper presents a Multimodal Ambient Context-enriched Intelligence Platform (MACeIP) for Smart Cities, a comprehensive system designed to enhance urban management and citizen engagement. Our platform integrates advanced technologies, including Internet of Things (IoT) sensors, edge and cloud computing, and Multimodal AI, to create a responsive and intelligent urban ecosystem. Key components include Interactive Hubs for citizen interaction, an extensive IoT sensor network, intelligent public asset management, a pedestrian monitoring system, a City Planning Portal, and a Cloud Computing System. We demonstrate the prototype of MACeIP in several cities, focusing on Fredericton, New Brunswick. This work contributes to innovative city development by offering a scalable, efficient, and user-centric approach to urban intelligence and management.<|reference_end|>
|
arxiv
|
@article{nguyen2024maceip:,
title={MACeIP: A Multimodal Ambient Context-enriched Intelligence Platform in
Smart Cities},
author={Truong Thanh Hung Nguyen, Phuc Truong Loc Nguyen, Monica Wachowicz,
Hung Cao},
journal={arXiv preprint arXiv:2409.15243},
year={2024},
archivePrefix={arXiv},
eprint={2409.15243},
primaryClass={cs.AI cs.ET cs.HC}
}
|
nguyen2024maceip:
|
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