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arxiv-663401
|
2409.19892
|
VAP: The Vulnerability-Adaptive Protection Paradigm Toward Reliable Autonomous Machines
|
<|reference_start|>VAP: The Vulnerability-Adaptive Protection Paradigm Toward Reliable Autonomous Machines: The next ubiquitous computing platform, following personal computers and smartphones, is poised to be inherently autonomous, encompassing technologies like drones, robots, and self-driving cars. Ensuring reliability for these autonomous machines is critical. However, current resiliency solutions make fundamental trade-offs between reliability and cost, resulting in significant overhead in performance, energy consumption, and chip area. This is due to the "one-size-fits-all" approach commonly used, where the same protection scheme is applied throughout the entire software computing stack. This paper presents the key insight that to achieve high protection coverage with minimal cost, we must leverage the inherent variations in robustness across different layers of the autonomous machine software stack. Specifically, we demonstrate that various nodes in this complex stack exhibit different levels of robustness against hardware faults. Our findings reveal that the front-end of an autonomous machine's software stack tends to be more robust, whereas the back-end is generally more vulnerable. Building on these inherent robustness differences, we propose a Vulnerability-Adaptive Protection (VAP) design paradigm. In this paradigm, the allocation of protection resources - whether spatially (e.g., through modular redundancy) or temporally (e.g., via re-execution) - is made inversely proportional to the inherent robustness of tasks or algorithms within the autonomous machine system. Experimental results show that VAP provides high protection coverage while maintaining low overhead in both autonomous vehicle and drone systems.<|reference_end|>
|
arxiv
|
@article{wan2024vap:,
title={VAP: The Vulnerability-Adaptive Protection Paradigm Toward Reliable
Autonomous Machines},
author={Zishen Wan, Yiming Gan, Bo Yu, Shaoshan Liu, Arijit Raychowdhury,
Yuhao Zhu},
journal={arXiv preprint arXiv:2409.19892},
year={2024},
archivePrefix={arXiv},
eprint={2409.19892},
primaryClass={cs.RO}
}
|
wan2024vap:
|
arxiv-663402
|
2409.19894
|
TRANSAGENT: An LLM-Based Multi-Agent System for Code Translation
|
<|reference_start|>TRANSAGENT: An LLM-Based Multi-Agent System for Code Translation: Code translation converts code from one programming language to another while maintaining its original functionality, which is crucial for software migration, system refactoring, and cross-platform development. Traditional rule-based methods rely on manually-written rules, which can be time-consuming and often result in less readable code. To overcome this, learning-based methods have been developed, leveraging parallel data to train models for automated code translation. More recently, the advance of Large Language Models (LLMs) further boosts learning-based code translation. Although promising, LLM-translated program still suffers from diverse quality issues (e.g., syntax errors and semantic errors). In particular, it can be challenging for LLMs to self-debug these errors when simply provided with the corresponding error messages. In this work, we propose a novel LLM-based multi-agent system TRANSAGENT, which enhances LLM-based code translation by fixing the syntax errors and semantic errors with the synergy between four LLM-based agents, including Initial Code Translator, Syntax Error Fixer, Code Aligner, and Semantic Error Fixer. The main insight of TRANSAGENT is to first localize the error code block in the target program based on the execution alignment between the target and source program, which can narrow down the fixing space and thus lower down the fixing difficulties. To evaluate TRANSAGENT, we first construct a new benchmark from recent programming tasks to mitigate the potential data leakage issue. On our benchmark, TRANSAGENT outperforms the latest LLM-based code translation technique UniTrans in both translation effectiveness and efficiency; additionally, our evaluation on different LLMs show the generalization of TRANSAGENT and our ablation study shows the contribution of each agent.<|reference_end|>
|
arxiv
|
@article{yuan2024transagent:,
title={TRANSAGENT: An LLM-Based Multi-Agent System for Code Translation},
author={Zhiqiang Yuan, Weitong Chen, Hanlin Wang, Kai Yu, Xin Peng, Yiling Lou},
journal={arXiv preprint arXiv:2409.19894},
year={2024},
archivePrefix={arXiv},
eprint={2409.19894},
primaryClass={cs.SE cs.AI}
}
|
yuan2024transagent:
|
arxiv-663403
|
2409.19898
|
UniSumEval: Towards Unified, Fine-Grained, Multi-Dimensional Summarization Evaluation for LLMs
|
<|reference_start|>UniSumEval: Towards Unified, Fine-Grained, Multi-Dimensional Summarization Evaluation for LLMs: Existing benchmarks for summarization quality evaluation often lack diverse input scenarios, focus on narrowly defined dimensions (e.g., faithfulness), and struggle with subjective and coarse-grained annotation schemes. To address these shortcomings, we create UniSumEval benchmark, which extends the range of input context (e.g., domain, length) and provides fine-grained, multi-dimensional annotations. We use AI assistance in data creation, identifying potentially hallucinogenic input texts, and also helping human annotators reduce the difficulty of fine-grained annotation tasks. With UniSumEval, we benchmark nine latest language models as summarizers, offering insights into their performance across varying input contexts and evaluation dimensions. Furthermore, we conduct a thorough comparison of SOTA automated summary evaluators. Our benchmark data will be available at https://github.com/DISL-Lab/UniSumEval-v1.0.<|reference_end|>
|
arxiv
|
@article{lee2024unisumeval:,
title={UniSumEval: Towards Unified, Fine-Grained, Multi-Dimensional
Summarization Evaluation for LLMs},
author={Yuho Lee and Taewon Yun and Jason Cai and Hang Su and Hwanjun Song},
journal={arXiv preprint arXiv:2409.19898},
year={2024},
archivePrefix={arXiv},
eprint={2409.19898},
primaryClass={cs.CL cs.AI}
}
|
lee2024unisumeval:
|
arxiv-663404
|
2409.19899
|
OpenKD: Opening Prompt Diversity for Zero- and Few-shot Keypoint Detection
|
<|reference_start|>OpenKD: Opening Prompt Diversity for Zero- and Few-shot Keypoint Detection: Exploiting the foundation models (e.g., CLIP) to build a versatile keypoint detector has gained increasing attention. Most existing models accept either the text prompt (e.g., ``the nose of a cat''), or the visual prompt (e.g., support image with keypoint annotations), to detect the corresponding keypoints in query image, thereby, exhibiting either zero-shot or few-shot detection ability. However, the research on taking multimodal prompt is still underexplored, and the prompt diversity in semantics and language is far from opened. For example, how to handle unseen text prompts for novel keypoint detection and the diverse text prompts like ``Can you detect the nose and ears of a cat?'' In this work, we open the prompt diversity from three aspects: modality, semantics (seen v.s. unseen), and language, to enable a more generalized zero- and few-shot keypoint detection (Z-FSKD). We propose a novel OpenKD model which leverages multimodal prototype set to support both visual and textual prompting. Further, to infer the keypoint location of unseen texts, we add the auxiliary keypoints and texts interpolated from visual and textual domains into training, which improves the spatial reasoning of our model and significantly enhances zero-shot novel keypoint detection. We also found large language model (LLM) is a good parser, which achieves over 96% accuracy to parse keypoints from texts. With LLM, OpenKD can handle diverse text prompts. Experimental results show that our method achieves state-of-the-art performance on Z-FSKD and initiates new ways to deal with unseen text and diverse texts. The source code and data are available at https://github.com/AlanLuSun/OpenKD.<|reference_end|>
|
arxiv
|
@article{lu2024openkd:,
title={OpenKD: Opening Prompt Diversity for Zero- and Few-shot Keypoint
Detection},
author={Changsheng Lu, Zheyuan Liu, Piotr Koniusz},
journal={arXiv preprint arXiv:2409.19899},
year={2024},
archivePrefix={arXiv},
eprint={2409.19899},
primaryClass={cs.CV}
}
|
lu2024openkd:
|
arxiv-663405
|
2409.19901
|
SurvCORN: Survival Analysis with Conditional Ordinal Ranking Neural Network
|
<|reference_start|>SurvCORN: Survival Analysis with Conditional Ordinal Ranking Neural Network: Survival analysis plays a crucial role in estimating the likelihood of future events for patients by modeling time-to-event data, particularly in healthcare settings where predictions about outcomes such as death and disease recurrence are essential. However, this analysis poses challenges due to the presence of censored data, where time-to-event information is missing for certain data points. Yet, censored data can offer valuable insights, provided we appropriately incorporate the censoring time during modeling. In this paper, we propose SurvCORN, a novel method utilizing conditional ordinal ranking networks to predict survival curves directly. Additionally, we introduce SurvMAE, a metric designed to evaluate the accuracy of model predictions in estimating time-to-event outcomes. Through empirical evaluation on two real-world cancer datasets, we demonstrate SurvCORN's ability to maintain accurate ordering between patient outcomes while improving individual time-to-event predictions. Our contributions extend recent advancements in ordinal regression to survival analysis, offering valuable insights into accurate prognosis in healthcare settings.<|reference_end|>
|
arxiv
|
@article{ridzuan2024survcorn:,
title={SurvCORN: Survival Analysis with Conditional Ordinal Ranking Neural
Network},
author={Muhammad Ridzuan, Numan Saeed, Fadillah Adamsyah Maani, Karthik
Nandakumar, Mohammad Yaqub},
journal={arXiv preprint arXiv:2409.19901},
year={2024},
archivePrefix={arXiv},
eprint={2409.19901},
primaryClass={cs.LG stat.ML}
}
|
ridzuan2024survcorn:
|
arxiv-663406
|
2409.19904
|
WildFusion: Multimodal Implicit 3D Reconstructions in the Wild
|
<|reference_start|>WildFusion: Multimodal Implicit 3D Reconstructions in the Wild: We propose WildFusion, a novel approach for 3D scene reconstruction in unstructured, in-the-wild environments using multimodal implicit neural representations. WildFusion integrates signals from LiDAR, RGB camera, contact microphones, tactile sensors, and IMU. This multimodal fusion generates comprehensive, continuous environmental representations, including pixel-level geometry, color, semantics, and traversability. Through real-world experiments on legged robot navigation in challenging forest environments, WildFusion demonstrates improved route selection by accurately predicting traversability. Our results highlight its potential to advance robotic navigation and 3D mapping in complex outdoor terrains.<|reference_end|>
|
arxiv
|
@article{liu2024wildfusion:,
title={WildFusion: Multimodal Implicit 3D Reconstructions in the Wild},
author={Yanbaihui Liu, Boyuan Chen},
journal={arXiv preprint arXiv:2409.19904},
year={2024},
archivePrefix={arXiv},
eprint={2409.19904},
primaryClass={cs.RO cs.MM eess.SP}
}
|
liu2024wildfusion:
|
arxiv-663407
|
2409.19911
|
Replace Anyone in Videos
|
<|reference_start|>Replace Anyone in Videos: Recent advancements in controllable human-centric video generation, particularly with the rise of diffusion models, have demonstrated considerable progress. However, achieving precise and localized control over human motion, e.g., replacing or inserting individuals into videos while exhibiting desired motion patterns, still remains challenging. In this work, we propose the ReplaceAnyone framework, which focuses on localizing and manipulating human motion in videos with diverse and intricate backgrounds. Specifically, we formulate this task as an image-conditioned pose-driven video inpainting paradigm, employing a unified video diffusion architecture that facilitates image-conditioned pose-driven video generation and inpainting within masked video regions. Moreover, we introduce diverse mask forms involving regular and irregular shapes to avoid shape leakage and allow granular local control. Additionally, we implement a two-stage training methodology, initially training an image-conditioned pose driven video generation model, followed by joint training of the video inpainting within masked areas. In this way, our approach enables seamless replacement or insertion of characters while maintaining the desired pose motion and reference appearance within a single framework. Experimental results demonstrate the effectiveness of our method in generating realistic and coherent video content.<|reference_end|>
|
arxiv
|
@article{wang2024replace,
title={Replace Anyone in Videos},
author={Xiang Wang, Changxin Gao, Yuehuan Wang, Nong Sang},
journal={arXiv preprint arXiv:2409.19911},
year={2024},
archivePrefix={arXiv},
eprint={2409.19911},
primaryClass={cs.CV}
}
|
wang2024replace
|
arxiv-663408
|
2409.19912
|
HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning
|
<|reference_start|>HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning: Data heterogeneity among Federated Learning (FL) users poses a significant challenge, resulting in reduced global model performance. The community has designed various techniques to tackle this issue, among which Knowledge Distillation (KD)-based techniques are common. While these techniques effectively improve performance under high heterogeneity, they inadvertently cause higher accuracy degradation under model poisoning attacks (known as attack amplification). This paper presents a case study to reveal this critical vulnerability in KD-based FL systems. We show why KD causes this issue through empirical evidence and use it as motivation to design a hybrid distillation technique. We introduce a novel algorithm, Hybrid Knowledge Distillation for Robust and Accurate FL (HYDRA-FL), which reduces the impact of attacks in attack scenarios by offloading some of the KD loss to a shallow layer via an auxiliary classifier. We model HYDRA-FL as a generic framework and adapt it to two KD-based FL algorithms, FedNTD and MOON. Using these two as case studies, we demonstrate that our technique outperforms baselines in attack settings while maintaining comparable performance in benign settings.<|reference_end|>
|
arxiv
|
@article{khan2024hydra-fl:,
title={HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate
Federated Learning},
author={Momin Ahmad Khan, Yasra Chandio, Fatima Muhammad Anwar},
journal={arXiv preprint arXiv:2409.19912},
year={2024},
archivePrefix={arXiv},
eprint={2409.19912},
primaryClass={cs.LG cs.CR}
}
|
khan2024hydra-fl:
|
arxiv-663409
|
2409.19913
|
Scaling Optimal LR Across Token Horizons
|
<|reference_start|>Scaling Optimal LR Across Token Horizons: State-of-the-art LLMs are powered by scaling -- scaling model size, dataset size and cluster size. It is economically infeasible to extensively tune hyperparameter for the largest runs. Instead, approximately optimal hyperparameters must be inferred or \textit{transferred} from smaller experiments. Hyperparameter transfer across model sizes has been studied in Yang et al. However, hyperparameter transfer across dataset size -- or token horizon -- has not been studied yet. To remedy this we conduct a large scale empirical study on how optimal learning rate (LR) depends on token horizon in LLM training. We first demonstrate that the optimal LR changes significantly with token horizon -- longer training necessitates smaller LR. Secondly we demonstrate the the optimal LR follows a scaling law, and that the optimal LR for longer horizons can be accurately estimated from shorter horizons via such scaling laws. We also provide a rule-of-thumb for transferring LR across token horizons with zero overhead over current practices. Lastly we provide evidence that LLama-1 used too high LR, and estimate the performance hit from this. We thus argue that hyperparameter transfer across data size is an important and overlooked component of LLM training.<|reference_end|>
|
arxiv
|
@article{bjorck2024scaling,
title={Scaling Optimal LR Across Token Horizons},
author={Johan Bjorck, Alon Benhaim, Vishrav Chaudhary, Furu Wei, Xia Song},
journal={arXiv preprint arXiv:2409.19913},
year={2024},
archivePrefix={arXiv},
eprint={2409.19913},
primaryClass={cs.LG cs.AI cs.CL}
}
|
bjorck2024scaling
|
arxiv-663410
|
2409.19916
|
Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Object-Oriented Programming
|
<|reference_start|>Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Object-Oriented Programming: Object-Oriented Programming (OOP) has become a crucial paradigm for managing the growing complexity of modern software systems, particularly in fields like machine learning, deep learning, large language models (LLM), and data analytics. This work provides a comprehensive introduction to the integration of OOP techniques within these domains, with a focus on improving code modularity, maintainability, and scalability. We begin by outlining the evolution of computing and the rise of OOP, followed by an in-depth discussion of key OOP principles such as encapsulation, inheritance, polymorphism, and abstraction. The practical application of these principles is demonstrated using Python, a widely adopted language in AI and data science. Furthermore, we examine how design patterns and modular programming can be employed to enhance the structure and efficiency of machine learning systems. In subsequent sections, we apply these OOP concepts to real-world AI tasks, including the encapsulation of preprocessing workflows, machine learning model training, and evaluation. Detailed examples illustrate how OOP can be used to build reusable, scalable machine learning systems while maintaining code clarity and reducing redundancy.This work is intended to serve as a bridge for both beginners and experienced developers, equipping them with the necessary knowledge to apply OOP methodologies in AI-driven projects, ultimately fostering the development of more robust and maintainable systems.<|reference_end|>
|
arxiv
|
@article{wang2024deep,
title={Deep Learning and Machine Learning, Advancing Big Data Analytics and
Management: Object-Oriented Programming},
author={Tianyang Wang, Ziqian Bi, Keyu Chen, Jiawei Xu, Qian Niu, Junyu Liu,
Benji Peng, Ming Li, Sen Zhang, Xuanhe Pan, Jinlang Wang, Pohsun Feng,
Caitlyn Heqi Yin, Yizhu Wen, Ming Liu},
journal={arXiv preprint arXiv:2409.19916},
year={2024},
archivePrefix={arXiv},
eprint={2409.19916},
primaryClass={cs.CL cs.SE}
}
|
wang2024deep
|
arxiv-663411
|
2409.19917
|
Towards Effective Utilization of Mixed-Quality Demonstrations in Robotic Manipulation via Segment-Level Selection and Optimization
|
<|reference_start|>Towards Effective Utilization of Mixed-Quality Demonstrations in Robotic Manipulation via Segment-Level Selection and Optimization: Data is crucial for robotic manipulation, as it underpins the development of robotic systems for complex tasks. While high-quality, diverse datasets enhance the performance and adaptability of robotic manipulation policies, collecting extensive expert-level data is resource-intensive. Consequently, many current datasets suffer from quality inconsistencies due to operator variability, highlighting the need for methods to utilize mixed-quality data effectively. To mitigate these issues, we propose "Select Segments to Imitate" (S2I), a framework that selects and optimizes mixed-quality demonstration data at the segment level, while ensuring plug-and-play compatibility with existing robotic manipulation policies. The framework has three components: demonstration segmentation dividing origin data into meaningful segments, segment selection using contrastive learning to find high-quality segments, and trajectory optimization to refine suboptimal segments for better policy learning. We evaluate S2I through comprehensive experiments in simulation and real-world environments across six tasks, demonstrating that with only 3 expert demonstrations for reference, S2I can improve the performance of various downstream policies when trained with mixed-quality demonstrations. Project website: https://tonyfang.net/s2i/.<|reference_end|>
|
arxiv
|
@article{chen2024towards,
title={Towards Effective Utilization of Mixed-Quality Demonstrations in Robotic
Manipulation via Segment-Level Selection and Optimization},
author={Jingjing Chen, Hongjie Fang, Hao-Shu Fang and Cewu Lu},
journal={arXiv preprint arXiv:2409.19917},
year={2024},
archivePrefix={arXiv},
eprint={2409.19917},
primaryClass={cs.RO}
}
|
chen2024towards
|
arxiv-663412
|
2409.19918
|
A Robotic System for Precision Pollination in Apples: Design, Development and Field Evaluation
|
<|reference_start|>A Robotic System for Precision Pollination in Apples: Design, Development and Field Evaluation: Global food production depends upon successful pollination, a process that relies on natural and managed pollinators. However, natural pollinators are declining due to different factors, including climate change, habitat loss, and pesticide use. Thus, developing alternative pollination methods is essential for sustainable crop production. This paper introduces a robotic system for precision pollination in apples, which are not self-pollinating and require precise delivery of pollen to the stigmatic surfaces of the flowers. The proposed robotic system consists of a machine vision system to identify target flowers and a mechatronic system with a 6-DOF UR5e robotic manipulator and an electrostatic sprayer. Field trials of this system in 'Honeycrisp' and 'Fuji' apple orchards have shown promising results, with the ability to pollinate flower clusters at an average spray cycle time of 6.5 seconds. The robotic pollination system has achieved encouraging fruit set and quality, comparable to naturally pollinated fruits in terms of color, weight, diameter, firmness, soluble solids, and starch content. However, the results for fruit set and quality varied between different apple cultivars and pollen concentrations. This study demonstrates the potential for a robotic artificial pollination system to be an efficient and sustainable method for commercial apple production. Further research is needed to refine the system and assess its suitability across diverse orchard environments and apple cultivars.<|reference_end|>
|
arxiv
|
@article{bhattarai2024a,
title={A Robotic System for Precision Pollination in Apples: Design,
Development and Field Evaluation},
author={Uddhav Bhattarai, Ranjan Sapkota, Safal Kshetri, Changki Mo, Matthew
D. Whiting, Qin Zhang, Manoj Karkee},
journal={arXiv preprint arXiv:2409.19918},
year={2024},
archivePrefix={arXiv},
eprint={2409.19918},
primaryClass={cs.RO}
}
|
bhattarai2024a
|
arxiv-663413
|
2409.19919
|
Understanding Higher-Order Correlations Among Semantic Components in Embeddings
|
<|reference_start|>Understanding Higher-Order Correlations Among Semantic Components in Embeddings: Independent Component Analysis (ICA) offers interpretable semantic components of embeddings. While ICA theory assumes that embeddings can be linearly decomposed into independent components, real-world data often do not satisfy this assumption. Consequently, non-independencies remain between the estimated components, which ICA cannot eliminate. We quantified these non-independencies using higher-order correlations and demonstrated that when the higher-order correlation between two components is large, it indicates a strong semantic association between them, along with many words sharing common meanings with both components. The entire structure of non-independencies was visualized using a maximum spanning tree of semantic components. These findings provide deeper insights into embeddings through ICA.<|reference_end|>
|
arxiv
|
@article{oyama2024understanding,
title={Understanding Higher-Order Correlations Among Semantic Components in
Embeddings},
author={Momose Oyama, Hiroaki Yamagiwa, Hidetoshi Shimodaira},
journal={arXiv preprint arXiv:2409.19919},
year={2024},
archivePrefix={arXiv},
eprint={2409.19919},
primaryClass={cs.CL}
}
|
oyama2024understanding
|
arxiv-663414
|
2409.19920
|
Playful DoggyBot: Learning Agile and Precise Quadrupedal Locomotion
|
<|reference_start|>Playful DoggyBot: Learning Agile and Precise Quadrupedal Locomotion: Quadrupedal animals have the ability to perform agile while accurate tasks: a trained dog can chase and catch a flying frisbee before it touches the ground; a cat alone at home can jump and grab the door handle accurately. However, agility and precision are usually a trade-off in robotics problems. Recent works in quadruped robots either focus on agile but not-so-accurate tasks, such as locomotion in challenging terrain, or accurate but not-so-fast tasks, such as using an additional manipulator to interact with objects. In this work, we aim at an accurate and agile task, catching a small object hanging above the robot. We mount a passive gripper in front of the robot chassis, so that the robot has to jump and catch the object with extreme precision. Our experiment shows that our system is able to jump and successfully catch the ball at 1.05m high in simulation and 0.8m high in the real world, while the robot is 0.3m high when standing.<|reference_end|>
|
arxiv
|
@article{duan2024playful,
title={Playful DoggyBot: Learning Agile and Precise Quadrupedal Locomotion},
author={Xin Duan, Ziwen Zhuang, Hang Zhao, Soeren Schwertfeger},
journal={arXiv preprint arXiv:2409.19920},
year={2024},
archivePrefix={arXiv},
eprint={2409.19920},
primaryClass={cs.RO}
}
|
duan2024playful
|
arxiv-663415
|
2409.19922
|
Benchmarking ChatGPT, Codeium, and GitHub Copilot: A Comparative Study of AI-Driven Programming and Debugging Assistants
|
<|reference_start|>Benchmarking ChatGPT, Codeium, and GitHub Copilot: A Comparative Study of AI-Driven Programming and Debugging Assistants: With the increasing adoption of AI-driven tools in software development, large language models (LLMs) have become essential for tasks like code generation, bug fixing, and optimization. Tools like ChatGPT, GitHub Copilot, and Codeium provide valuable assistance in solving programming challenges, yet their effectiveness remains underexplored. This paper presents a comparative study of ChatGPT, Codeium, and GitHub Copilot, evaluating their performance on LeetCode problems across varying difficulty levels and categories. Key metrics such as success rates, runtime efficiency, memory usage, and error-handling capabilities are assessed. GitHub Copilot showed superior performance on easier and medium tasks, while ChatGPT excelled in memory efficiency and debugging. Codeium, though promising, struggled with more complex problems. Despite their strengths, all tools faced challenges in handling harder problems. These insights provide a deeper understanding of each tool's capabilities and limitations, offering guidance for developers and researchers seeking to optimize AI integration in coding workflows.<|reference_end|>
|
arxiv
|
@article{ovi2024benchmarking,
title={Benchmarking ChatGPT, Codeium, and GitHub Copilot: A Comparative Study
of AI-Driven Programming and Debugging Assistants},
author={Md Sultanul Islam Ovi, Nafisa Anjum, Tasmina Haque Bithe, Md.
Mahabubur Rahman, and Mst. Shahnaj Akter Smrity},
journal={arXiv preprint arXiv:2409.19922},
year={2024},
archivePrefix={arXiv},
eprint={2409.19922},
primaryClass={cs.SE}
}
|
ovi2024benchmarking
|
arxiv-663416
|
2409.19924
|
On The Planning Abilities of OpenAI's o1 Models: Feasibility, Optimality, and Generalizability
|
<|reference_start|>On The Planning Abilities of OpenAI's o1 Models: Feasibility, Optimality, and Generalizability: Recent advancements in Large Language Models (LLMs) have showcased their ability to perform complex reasoning tasks, but their effectiveness in planning remains underexplored. In this study, we evaluate the planning capabilities of OpenAI's o1 models across a variety of benchmark tasks, focusing on three key aspects: feasibility, optimality, and generalizability. Through empirical evaluations on constraint-heavy tasks (e.g., $\textit{Barman}$, $\textit{Tyreworld}$) and spatially complex environments (e.g., $\textit{Termes}$, $\textit{Floortile}$), we highlight o1-preview's strengths in self-evaluation and constraint-following, while also identifying bottlenecks in decision-making and memory management, particularly in tasks requiring robust spatial reasoning. Our results reveal that o1-preview outperforms GPT-4 in adhering to task constraints and managing state transitions in structured environments. However, the model often generates suboptimal solutions with redundant actions and struggles to generalize effectively in spatially complex tasks. This pilot study provides foundational insights into the planning limitations of LLMs, offering key directions for future research on improving memory management, decision-making, and generalization in LLM-based planning.<|reference_end|>
|
arxiv
|
@article{wang2024on,
title={On The Planning Abilities of OpenAI's o1 Models: Feasibility,
Optimality, and Generalizability},
author={Kevin Wang, Junbo Li, Neel P. Bhatt, Yihan Xi, Qiang Liu, Ufuk Topcu,
and Zhangyang Wang},
journal={arXiv preprint arXiv:2409.19924},
year={2024},
archivePrefix={arXiv},
eprint={2409.19924},
primaryClass={cs.AI cs.LG cs.RO}
}
|
wang2024on
|
arxiv-663417
|
2409.19925
|
Large Language Model Empowered Embedding Generator for Sequential Recommendation
|
<|reference_start|>Large Language Model Empowered Embedding Generator for Sequential Recommendation: Sequential Recommender Systems (SRS) are extensively applied across various domains to predict users' next interaction by modeling their interaction sequences. However, these systems typically grapple with the long-tail problem, where they struggle to recommend items that are less popular. This challenge results in a decline in user discovery and reduced earnings for vendors, negatively impacting the system as a whole. Large Language Model (LLM) has the potential to understand the semantic connections between items, regardless of their popularity, positioning them as a viable solution to this dilemma. In our paper, we present LLMEmb, an innovative technique that harnesses LLM to create item embeddings that bolster the performance of SRS. To align the capabilities of general-purpose LLM with the needs of the recommendation domain, we introduce a method called Supervised Contrastive Fine-Tuning (SCFT). This method involves attribute-level data augmentation and a custom contrastive loss designed to tailor LLM for enhanced recommendation performance. Moreover, we highlight the necessity of incorporating collaborative filtering signals into LLM-generated embeddings and propose Recommendation Adaptation Training (RAT) for this purpose. RAT refines the embeddings to be optimally suited for SRS. The embeddings derived from LLMEmb can be easily integrated with any SRS model, showcasing its practical utility. Extensive experimentation on three real-world datasets has shown that LLMEmb significantly improves upon current methods when applied across different SRS models.<|reference_end|>
|
arxiv
|
@article{liu2024large,
title={Large Language Model Empowered Embedding Generator for Sequential
Recommendation},
author={Qidong Liu, Xian Wu, Wanyu Wang, Yejing Wang, Yuanshao Zhu, Xiangyu
Zhao, Feng Tian, Yefeng Zheng},
journal={arXiv preprint arXiv:2409.19925},
year={2024},
archivePrefix={arXiv},
eprint={2409.19925},
primaryClass={cs.IR cs.CL}
}
|
liu2024large
|
arxiv-663418
|
2409.19926
|
Data-driven decision-making under uncertainty with entropic risk measure
|
<|reference_start|>Data-driven decision-making under uncertainty with entropic risk measure: The entropic risk measure is widely used in high-stakes decision making to account for tail risks associated with an uncertain loss. With limited data, the empirical entropic risk estimator, i.e. replacing the expectation in the entropic risk measure with a sample average, underestimates the true risk. To debias the empirical entropic risk estimator, we propose a strongly asymptotically consistent bootstrapping procedure. The first step of the procedure involves fitting a distribution to the data, whereas the second step estimates the bias of the empirical entropic risk estimator using bootstrapping, and corrects for it. We show that naively fitting a Gaussian Mixture Model to the data using the maximum likelihood criterion typically leads to an underestimation of the risk. To mitigate this issue, we consider two alternative methods: a more computationally demanding one that fits the distribution of empirical entropic risk, and a simpler one that fits the extreme value distribution. As an application of the approach, we study a distributionally robust entropic risk minimization problem with type-$\infty$ Wasserstein ambiguity set, where debiasing the validation performance using our techniques significantly improves the calibration of the size of the ambiguity set. Furthermore, we propose a distributionally robust optimization model for a well-studied insurance contract design problem. The model considers multiple (potential) policyholders that have dependent risks and the insurer and policyholders use entropic risk measure. We show that cross validation methods can result in significantly higher out-of-sample risk for the insurer if the bias in validation performance is not corrected for. This improvement can be explained from the observation that our methods suggest a higher (and more accurate) premium to homeowners.<|reference_end|>
|
arxiv
|
@article{sadana2024data-driven,
title={Data-driven decision-making under uncertainty with entropic risk measure},
author={Utsav Sadana, Erick Delage, Angelos Georghiou},
journal={arXiv preprint arXiv:2409.19926},
year={2024},
archivePrefix={arXiv},
eprint={2409.19926},
primaryClass={math.OC cs.LG stat.ML}
}
|
sadana2024data-driven
|
arxiv-663419
|
2409.19928
|
DynORecon: Dynamic Object Reconstruction for Navigation
|
<|reference_start|>DynORecon: Dynamic Object Reconstruction for Navigation: This paper presents DynORecon, a Dynamic Object Reconstruction system that leverages the information provided by Dynamic SLAM to simultaneously generate a volumetric map of observed moving entities while estimating free space to support navigation. By capitalising on the motion estimations provided by Dynamic SLAM, DynORecon continuously refines the representation of dynamic objects to eliminate residual artefacts from past observations and incrementally reconstructs each object, seamlessly integrating new observations to capture previously unseen structures. Our system is highly efficient (~20 FPS) and produces accurate (~10 cm) reconstructions of dynamic objects using simulated and real-world outdoor datasets.<|reference_end|>
|
arxiv
|
@article{wang2024dynorecon:,
title={DynORecon: Dynamic Object Reconstruction for Navigation},
author={Yiduo Wang, Jesse Morris, Lan Wu, Teresa Vidal-Calleja and Viorela Ila},
journal={arXiv preprint arXiv:2409.19928},
year={2024},
archivePrefix={arXiv},
eprint={2409.19928},
primaryClass={cs.RO}
}
|
wang2024dynorecon:
|
arxiv-663420
|
2409.19930
|
EndoDepth: A Benchmark for Assessing Robustness in Endoscopic Depth Prediction
|
<|reference_start|>EndoDepth: A Benchmark for Assessing Robustness in Endoscopic Depth Prediction: Accurate depth estimation in endoscopy is vital for successfully implementing computer vision pipelines for various medical procedures and CAD tools. In this paper, we present the EndoDepth benchmark, an evaluation framework designed to assess the robustness of monocular depth prediction models in endoscopic scenarios. Unlike traditional datasets, the EndoDepth benchmark incorporates common challenges encountered during endoscopic procedures. We present an evaluation approach that is consistent and specifically designed to evaluate the robustness performance of the model in endoscopic scenarios. Among these is a novel composite metric called the mean Depth Estimation Robustness Score (mDERS), which offers an in-depth evaluation of a model's accuracy against errors brought on by endoscopic image corruptions. Moreover, we present SCARED-C, a new dataset designed specifically to assess endoscopy robustness. Through extensive experimentation, we evaluate state-of-the-art depth prediction architectures on the EndoDepth benchmark, revealing their strengths and weaknesses in handling endoscopic challenging imaging artifacts. Our results demonstrate the importance of specialized techniques for accurate depth estimation in endoscopy and provide valuable insights for future research directions.<|reference_end|>
|
arxiv
|
@article{reyes-amezcua2024endodepth:,
title={EndoDepth: A Benchmark for Assessing Robustness in Endoscopic Depth
Prediction},
author={Ivan Reyes-Amezcua, Ricardo Espinosa, Christian Daul, Gilberto
Ochoa-Ruiz, Andres Mendez-Vazquez},
journal={arXiv preprint arXiv:2409.19930},
year={2024},
archivePrefix={arXiv},
eprint={2409.19930},
primaryClass={cs.CV}
}
|
reyes-amezcua2024endodepth:
|
arxiv-663421
|
2409.19933
|
CCDepth: A Lightweight Self-supervised Depth Estimation Network with Enhanced Interpretability
|
<|reference_start|>CCDepth: A Lightweight Self-supervised Depth Estimation Network with Enhanced Interpretability: Self-supervised depth estimation, which solely requires monocular image sequence as input, has become increasingly popular and promising in recent years. Current research primarily focuses on enhancing the prediction accuracy of the models. However, the excessive number of parameters impedes the universal deployment of the model on edge devices. Moreover, the emerging neural networks, being black-box models, are difficult to analyze, leading to challenges in understanding the rationales for performance improvements. To mitigate these issues, this study proposes a novel hybrid self-supervised depth estimation network, CCDepth, comprising convolutional neural networks (CNNs) and the white-box CRATE (Coding RAte reduction TransformEr) network. This novel network uses CNNs and the CRATE modules to extract local and global information in images, respectively, thereby boosting learning efficiency and reducing model size. Furthermore, incorporating the CRATE modules into the network enables a mathematically interpretable process in capturing global features. Extensive experiments on the KITTI dataset indicate that the proposed CCDepth network can achieve performance comparable with those state-of-the-art methods, while the model size has been significantly reduced. In addition, a series of quantitative and qualitative analyses on the inner features in the CCDepth network further confirm the effectiveness of the proposed method.<|reference_end|>
|
arxiv
|
@article{zhang2024ccdepth:,
title={CCDepth: A Lightweight Self-supervised Depth Estimation Network with
Enhanced Interpretability},
author={Xi Zhang, Yaru Xue, Shaocheng Jia and Xin Pei},
journal={arXiv preprint arXiv:2409.19933},
year={2024},
archivePrefix={arXiv},
eprint={2409.19933},
primaryClass={cs.CV}
}
|
zhang2024ccdepth:
|
arxiv-663422
|
2409.19934
|
Leveraging Pre-trained Models for Robust Federated Learning for Kidney Stone Type Recognition
|
<|reference_start|>Leveraging Pre-trained Models for Robust Federated Learning for Kidney Stone Type Recognition: Deep learning developments have improved medical imaging diagnoses dramatically, increasing accuracy in several domains. Nonetheless, obstacles continue to exist because of the requirement for huge datasets and legal limitations on data exchange. A solution is provided by Federated Learning (FL), which permits decentralized model training while maintaining data privacy. However, FL models are susceptible to data corruption, which may result in performance degradation. Using pre-trained models, this research suggests a strong FL framework to improve kidney stone diagnosis. Two different kidney stone datasets, each with six different categories of images, are used in our experimental setting. Our method involves two stages: Learning Parameter Optimization (LPO) and Federated Robustness Validation (FRV). We achieved a peak accuracy of 84.1% with seven epochs and 10 rounds during LPO stage, and 77.2% during FRV stage, showing enhanced diagnostic accuracy and robustness against image corruption. This highlights the potential of merging pre-trained models with FL to address privacy and performance concerns in medical diagnostics, and guarantees improved patient care and enhanced trust in FL-based medical systems.<|reference_end|>
|
arxiv
|
@article{reyes-amezcua2024leveraging,
title={Leveraging Pre-trained Models for Robust Federated Learning for Kidney
Stone Type Recognition},
author={Ivan Reyes-Amezcua, Michael Rojas-Ruiz, Gilberto Ochoa-Ruiz, Andres
Mendez-Vazquez, Christian Daul},
journal={arXiv preprint arXiv:2409.19934},
year={2024},
archivePrefix={arXiv},
eprint={2409.19934},
primaryClass={cs.CV}
}
|
reyes-amezcua2024leveraging
|
arxiv-663423
|
2409.19935
|
Understanding Challenges and Opportunities in Body Movement Education of People who are Blind or have Low Vision
|
<|reference_start|>Understanding Challenges and Opportunities in Body Movement Education of People who are Blind or have Low Vision: Actively participating in body movement such as dance, sports, and fitness activities is challenging for people who are blind or have low vision (BLV). Teachers primarily rely on verbal instructions and physical demonstrations with limited accessibility. Recent work shows that technology can support body movement education for BLV people. However, there is limited involvement with the BLV community and their teachers to understand their needs. By conducting a series of two surveys, 23 interviews and four focus groups, we gather the voices and perspectives of BLV people and their teachers. This provides a rich understanding of the challenges of body movement education. We identify ten major themes, four key design challenges, and propose potential solutions. We encourage the assistive technologies community to co-design potential solutions to these identified design challenges promoting the quality of life of BLV people and supporting the teachers in the provision of inclusive education.<|reference_end|>
|
arxiv
|
@article{de silva2024understanding,
title={Understanding Challenges and Opportunities in Body Movement Education of
People who are Blind or have Low Vision},
author={Madhuka Thisuri De Silva, Sarah Goodwin, Leona M Holloway, Matthew
Butler},
journal={arXiv preprint arXiv:2409.19935},
year={2024},
doi={10.1145/3597638.3608409},
archivePrefix={arXiv},
eprint={2409.19935},
primaryClass={cs.HC}
}
|
de silva2024understanding
|
arxiv-663424
|
2409.19936
|
Spacecraft Attitude Control Under Reaction Wheel Constraints Using Control Lyapunov and Control Barrier Functions
|
<|reference_start|>Spacecraft Attitude Control Under Reaction Wheel Constraints Using Control Lyapunov and Control Barrier Functions: This paper introduces a novel control strategy for agile spacecraft attitude control, addressing reaction wheel-related input and state constraints. An optimal-decay control Lyapunov function quadratic program stabilizes the system and mitigates chattering at low sampling frequencies, while control barrier functions enforce hard state constraints. Numerical simulations validate the method's practicality and efficiency for real-time agile spacecraft attitude control.<|reference_end|>
|
arxiv
|
@article{shahraki2024spacecraft,
title={Spacecraft Attitude Control Under Reaction Wheel Constraints Using
Control Lyapunov and Control Barrier Functions},
author={Milad Alipour Shahraki and Laurent Lessard},
journal={arXiv preprint arXiv:2409.19936},
year={2024},
archivePrefix={arXiv},
eprint={2409.19936},
primaryClass={eess.SY cs.SY}
}
|
shahraki2024spacecraft
|
arxiv-663425
|
2409.19937
|
MaskMamba: A Hybrid Mamba-Transformer Model for Masked Image Generation
|
<|reference_start|>MaskMamba: A Hybrid Mamba-Transformer Model for Masked Image Generation: Image generation models have encountered challenges related to scalability and quadratic complexity, primarily due to the reliance on Transformer-based backbones. In this study, we introduce MaskMamba, a novel hybrid model that combines Mamba and Transformer architectures, utilizing Masked Image Modeling for non-autoregressive image synthesis. We meticulously redesign the bidirectional Mamba architecture by implementing two key modifications: (1) replacing causal convolutions with standard convolutions to better capture global context, and (2) utilizing concatenation instead of multiplication, which significantly boosts performance while accelerating inference speed. Additionally, we explore various hybrid schemes of MaskMamba, including both serial and grouped parallel arrangements. Furthermore, we incorporate an in-context condition that allows our model to perform both class-to-image and text-to-image generation tasks. Our MaskMamba outperforms Mamba-based and Transformer-based models in generation quality. Notably, it achieves a remarkable $54.44\%$ improvement in inference speed at a resolution of $2048\times 2048$ over Transformer.<|reference_end|>
|
arxiv
|
@article{chen2024maskmamba:,
title={MaskMamba: A Hybrid Mamba-Transformer Model for Masked Image Generation},
author={Wenchao Chen, Liqiang Niu, Ziyao Lu, Fandong Meng, Jie Zhou},
journal={arXiv preprint arXiv:2409.19937},
year={2024},
archivePrefix={arXiv},
eprint={2409.19937},
primaryClass={cs.CV}
}
|
chen2024maskmamba:
|
arxiv-663426
|
2409.19940
|
Positive-Sum Fairness: Leveraging Demographic Attributes to Achieve Fair AI Outcomes Without Sacrificing Group Gains
|
<|reference_start|>Positive-Sum Fairness: Leveraging Demographic Attributes to Achieve Fair AI Outcomes Without Sacrificing Group Gains: Fairness in medical AI is increasingly recognized as a crucial aspect of healthcare delivery. While most of the prior work done on fairness emphasizes the importance of equal performance, we argue that decreases in fairness can be either harmful or non-harmful, depending on the type of change and how sensitive attributes are used. To this end, we introduce the notion of positive-sum fairness, which states that an increase in performance that results in a larger group disparity is acceptable as long as it does not come at the cost of individual subgroup performance. This allows sensitive attributes correlated with the disease to be used to increase performance without compromising on fairness. We illustrate this idea by comparing four CNN models that make different use of the race attribute in the training phase. The results show that removing all demographic encodings from the images helps close the gap in performance between the different subgroups, whereas leveraging the race attribute as a model's input increases the overall performance while widening the disparities between subgroups. These larger gaps are then put in perspective of the collective benefit through our notion of positive-sum fairness to distinguish harmful from non harmful disparities.<|reference_end|>
|
arxiv
|
@article{belhadj2024positive-sum,
title={Positive-Sum Fairness: Leveraging Demographic Attributes to Achieve Fair
AI Outcomes Without Sacrificing Group Gains},
author={Samia Belhadj, Sanguk Park, Ambika Seth, Hesham Dar, Thijs Kooi},
journal={arXiv preprint arXiv:2409.19940},
year={2024},
archivePrefix={arXiv},
eprint={2409.19940},
primaryClass={cs.LG cs.AI cs.CV cs.CY}
}
|
belhadj2024positive-sum
|
arxiv-663427
|
2409.19942
|
CycleCrash: A Dataset of Bicycle Collision Videos for Collision Prediction and Analysis
|
<|reference_start|>CycleCrash: A Dataset of Bicycle Collision Videos for Collision Prediction and Analysis: Self-driving research often underrepresents cyclist collisions and safety. To address this, we present CycleCrash, a novel dataset consisting of 3,000 dashcam videos with 436,347 frames that capture cyclists in a range of critical situations, from collisions to safe interactions. This dataset enables 9 different cyclist collision prediction and classification tasks focusing on potentially hazardous conditions for cyclists and is annotated with collision-related, cyclist-related, and scene-related labels. Next, we propose VidNeXt, a novel method that leverages a ConvNeXt spatial encoder and a non-stationary transformer to capture the temporal dynamics of videos for the tasks defined in our dataset. To demonstrate the effectiveness of our method and create additional baselines on CycleCrash, we apply and compare 7 models along with a detailed ablation. We release the dataset and code at https://github.com/DeSinister/CycleCrash/ .<|reference_end|>
|
arxiv
|
@article{desai2024cyclecrash:,
title={CycleCrash: A Dataset of Bicycle Collision Videos for Collision
Prediction and Analysis},
author={Nishq Poorav Desai, Ali Etemad and Michael Greenspan},
journal={arXiv preprint arXiv:2409.19942},
year={2024},
archivePrefix={arXiv},
eprint={2409.19942},
primaryClass={cs.CV}
}
|
desai2024cyclecrash:
|
arxiv-663428
|
2409.19945
|
One Shot GANs for Long Tail Problem in Skin Lesion Dataset using novel content space assessment metric
|
<|reference_start|>One Shot GANs for Long Tail Problem in Skin Lesion Dataset using novel content space assessment metric: Long tail problems frequently arise in the medical field, particularly due to the scarcity of medical data for rare conditions. This scarcity often leads to models overfitting on such limited samples. Consequently, when training models on datasets with heavily skewed classes, where the number of samples varies significantly, a problem emerges. Training on such imbalanced datasets can result in selective detection, where a model accurately identifies images belonging to the majority classes but disregards those from minority classes. This causes the model to lack generalizability, preventing its use on newer data. This poses a significant challenge in developing image detection and diagnosis models for medical image datasets. To address this challenge, the One Shot GANs model was employed to augment the tail class of HAM10000 dataset by generating additional samples. Furthermore, to enhance accuracy, a novel metric tailored to suit One Shot GANs was utilized.<|reference_end|>
|
arxiv
|
@article{deo2024one,
title={One Shot GANs for Long Tail Problem in Skin Lesion Dataset using novel
content space assessment metric},
author={Kunal Deo, Deval Mehta and Kshitij Jadhav},
journal={arXiv preprint arXiv:2409.19945},
year={2024},
archivePrefix={arXiv},
eprint={2409.19945},
primaryClass={eess.IV cs.CV cs.LG}
}
|
deo2024one
|
arxiv-663429
|
2409.19946
|
Illustrious: an Open Advanced Illustration Model
|
<|reference_start|>Illustrious: an Open Advanced Illustration Model: In this work, we share the insights for achieving state-of-the-art quality in our text-to-image anime image generative model, called Illustrious. To achieve high resolution, dynamic color range images, and high restoration ability, we focus on three critical approaches for model improvement. First, we delve into the significance of the batch size and dropout control, which enables faster learning of controllable token based concept activations. Second, we increase the training resolution of images, affecting the accurate depiction of character anatomy in much higher resolution, extending its generation capability over 20MP with proper methods. Finally, we propose the refined multi-level captions, covering all tags and various natural language captions as a critical factor for model development. Through extensive analysis and experiments, Illustrious demonstrates state-of-the-art performance in terms of animation style, outperforming widely-used models in illustration domains, propelling easier customization and personalization with nature of open source. We plan to publicly release updated Illustrious model series sequentially as well as sustainable plans for improvements.<|reference_end|>
|
arxiv
|
@article{park2024illustrious:,
title={Illustrious: an Open Advanced Illustration Model},
author={Sang Hyun Park, Jun Young Koh, Junha Lee, Joy Song, Dongha Kim, Hoyeon
Moon, Hyunju Lee, Min Song},
journal={arXiv preprint arXiv:2409.19946},
year={2024},
archivePrefix={arXiv},
eprint={2409.19946},
primaryClass={cs.CV}
}
|
park2024illustrious:
|
arxiv-663430
|
2409.19947
|
Classification with a Network of Partially Informative Agents: Enabling Wise Crowds from Individually Myopic Classifiers
|
<|reference_start|>Classification with a Network of Partially Informative Agents: Enabling Wise Crowds from Individually Myopic Classifiers: We consider the problem of classification with a (peer-to-peer) network of heterogeneous and partially informative agents, each receiving local data generated by an underlying true class, and equipped with a classifier that can only distinguish between a subset of the entire set of classes. We propose an iterative algorithm that uses the posterior probabilities of the local classifier and recursively updates each agent's local belief on all the possible classes, based on its local signals and belief information from its neighbors. We then adopt a novel distributed min-rule to update each agent's global belief and enable learning of the true class for all agents. We show that under certain assumptions, the beliefs on the true class converge to one asymptotically almost surely. We provide the asymptotic convergence rate, and demonstrate the performance of our algorithm through simulation with image data and experimented with random forest classifiers and MobileNet.<|reference_end|>
|
arxiv
|
@article{yao2024classification,
title={Classification with a Network of Partially Informative Agents: Enabling
Wise Crowds from Individually Myopic Classifiers},
author={Tong Yao, Shreyas Sundaram},
journal={arXiv preprint arXiv:2409.19947},
year={2024},
archivePrefix={arXiv},
eprint={2409.19947},
primaryClass={cs.LG cs.MA}
}
|
yao2024classification
|
arxiv-663431
|
2409.19948
|
JaPOC: Japanese Post-OCR Correction Benchmark using Vouchers
|
<|reference_start|>JaPOC: Japanese Post-OCR Correction Benchmark using Vouchers: In this paper, we create benchmarks and assess the effectiveness of error correction methods for Japanese vouchers in OCR (Optical Character Recognition) systems. It is essential for automation processing to correctly recognize scanned voucher text, such as the company name on invoices. However, perfect recognition is complex due to the noise, such as stamps. Therefore, it is crucial to correctly rectify erroneous OCR results. However, no publicly available OCR error correction benchmarks for Japanese exist, and methods have not been adequately researched. In this study, we measured text recognition accuracy by existing services on Japanese vouchers and developed a post-OCR correction benchmark. Then, we proposed simple baselines for error correction using language models and verified whether the proposed method could effectively correct these errors. In the experiments, the proposed error correction algorithm significantly improved overall recognition accuracy.<|reference_end|>
|
arxiv
|
@article{fujitake2024japoc:,
title={JaPOC: Japanese Post-OCR Correction Benchmark using Vouchers},
author={Masato Fujitake},
journal={arXiv preprint arXiv:2409.19948},
year={2024},
archivePrefix={arXiv},
eprint={2409.19948},
primaryClass={cs.CL cs.AI cs.CV cs.LG}
}
|
fujitake2024japoc:
|
arxiv-663432
|
2409.19949
|
Task-agnostic Pre-training and Task-guided Fine-tuning for Versatile Diffusion Planner
|
<|reference_start|>Task-agnostic Pre-training and Task-guided Fine-tuning for Versatile Diffusion Planner: Diffusion models have demonstrated their capabilities in modeling trajectories of multi-tasks. However, existing multi-task planners or policies typically rely on task-specific demonstrations via multi-task imitation, or require task-specific reward labels to facilitate policy optimization via Reinforcement Learning (RL). To address these challenges, we aim to develop a versatile diffusion planner that can leverage large-scale inferior data that contains task-agnostic sub-optimal trajectories, with the ability to fast adapt to specific tasks. In this paper, we propose \textbf{SODP}, a two-stage framework that leverages \textbf{S}ub-\textbf{O}ptimal data to learn a \textbf{D}iffusion \textbf{P}lanner, which is generalizable for various downstream tasks. Specifically, in the pre-training stage, we train a foundation diffusion planner that extracts general planning capabilities by modeling the versatile distribution of multi-task trajectories, which can be sub-optimal and has wide data coverage. Then for downstream tasks, we adopt RL-based fine-tuning with task-specific rewards to fast refine the diffusion planner, which aims to generate action sequences with higher task-specific returns. Experimental results from multi-task domains including Meta-World and Adroit demonstrate that SODP outperforms state-of-the-art methods with only a small amount of data for reward-guided fine-tuning.<|reference_end|>
|
arxiv
|
@article{fan2024task-agnostic,
title={Task-agnostic Pre-training and Task-guided Fine-tuning for Versatile
Diffusion Planner},
author={Chenyou Fan, Chenjia Bai, Zhao Shan, Haoran He, Yang Zhang, Zhen Wang},
journal={arXiv preprint arXiv:2409.19949},
year={2024},
archivePrefix={arXiv},
eprint={2409.19949},
primaryClass={cs.LG cs.AI}
}
|
fan2024task-agnostic
|
arxiv-663433
|
2409.19951
|
Law of the Weakest Link: Cross Capabilities of Large Language Models
|
<|reference_start|>Law of the Weakest Link: Cross Capabilities of Large Language Models: The development and evaluation of Large Language Models (LLMs) have largely focused on individual capabilities. However, this overlooks the intersection of multiple abilities across different types of expertise that are often required for real-world tasks, which we term cross capabilities. To systematically explore this concept, we first define seven core individual capabilities and then pair them to form seven common cross capabilities, each supported by a manually constructed taxonomy. Building on these definitions, we introduce CrossEval, a benchmark comprising 1,400 human-annotated prompts, with 100 prompts for each individual and cross capability. To ensure reliable evaluation, we involve expert annotators to assess 4,200 model responses, gathering 8,400 human ratings with detailed explanations to serve as reference examples. Our findings reveal that, in both static evaluations and attempts to enhance specific abilities, current LLMs consistently exhibit the "Law of the Weakest Link," where cross-capability performance is significantly constrained by the weakest component. Specifically, across 58 cross-capability scores from 17 models, 38 scores are lower than all individual capabilities, while 20 fall between strong and weak, but closer to the weaker ability. These results highlight the under-performance of LLMs in cross-capability tasks, making the identification and improvement of the weakest capabilities a critical priority for future research to optimize performance in complex, multi-dimensional scenarios.<|reference_end|>
|
arxiv
|
@article{zhong2024law,
title={Law of the Weakest Link: Cross Capabilities of Large Language Models},
author={Ming Zhong, Aston Zhang, Xuewei Wang, Rui Hou, Wenhan Xiong, Chenguang
Zhu, Zhengxing Chen, Liang Tan, Chloe Bi, Mike Lewis, Sravya Popuri, Sharan
Narang, Melanie Kambadur, Dhruv Mahajan, Sergey Edunov, Jiawei Han, Laurens
van der Maaten},
journal={arXiv preprint arXiv:2409.19951},
year={2024},
archivePrefix={arXiv},
eprint={2409.19951},
primaryClass={cs.AI cs.CL cs.CV}
}
|
zhong2024law
|
arxiv-663434
|
2409.19952
|
Image Copy Detection for Diffusion Models
|
<|reference_start|>Image Copy Detection for Diffusion Models: Images produced by diffusion models are increasingly popular in digital artwork and visual marketing. However, such generated images might replicate content from existing ones and pose the challenge of content originality. Existing Image Copy Detection (ICD) models, though accurate in detecting hand-crafted replicas, overlook the challenge from diffusion models. This motivates us to introduce ICDiff, the first ICD specialized for diffusion models. To this end, we construct a Diffusion-Replication (D-Rep) dataset and correspondingly propose a novel deep embedding method. D-Rep uses a state-of-the-art diffusion model (Stable Diffusion V1.5) to generate 40, 000 image-replica pairs, which are manually annotated into 6 replication levels ranging from 0 (no replication) to 5 (total replication). Our method, PDF-Embedding, transforms the replication level of each image-replica pair into a probability density function (PDF) as the supervision signal. The intuition is that the probability of neighboring replication levels should be continuous and smooth. Experimental results show that PDF-Embedding surpasses protocol-driven methods and non-PDF choices on the D-Rep test set. Moreover, by utilizing PDF-Embedding, we find that the replication ratios of well-known diffusion models against an open-source gallery range from 10% to 20%. The project is publicly available at https://icdiff.github.io/.<|reference_end|>
|
arxiv
|
@article{wang2024image,
title={Image Copy Detection for Diffusion Models},
author={Wenhao Wang, Yifan Sun, Zhentao Tan, Yi Yang},
journal={arXiv preprint arXiv:2409.19952},
year={2024},
archivePrefix={arXiv},
eprint={2409.19952},
primaryClass={cs.CV}
}
|
wang2024image
|
arxiv-663435
|
2409.19954
|
Attribute-Text Guided Forgetting Compensation for Lifelong Person Re-Identification
|
<|reference_start|>Attribute-Text Guided Forgetting Compensation for Lifelong Person Re-Identification: Lifelong person re-identification (LReID) aims to continuously learn from non-stationary data to match individuals in different environments. Each task is affected by variations in illumination and person-related information (such as pose and clothing), leading to task-wise domain gaps. Current LReID methods focus on task-specific knowledge and ignore intrinsic task-shared representations within domain gaps, limiting model performance. Bridging task-wise domain gaps is crucial for improving anti-forgetting and generalization capabilities, especially when accessing limited old classes during training. To address these issues, we propose a novel attribute-text guided forgetting compensation (ATFC) model, which explores text-driven global representations of identity-related information and attribute-related local representations of identity-free information for LReID. Due to the lack of paired text-image data, we design an attribute-text generator (ATG) to dynamically generate a text descriptor for each instance. We then introduce a text-guided aggregation network (TGA) to explore robust text-driven global representations for each identity and knowledge transfer. Furthermore, we propose an attribute compensation network (ACN) to investigate attribute-related local representations, which distinguish similar identities and bridge domain gaps. Finally, we develop an attribute anti-forgetting (AF) loss and knowledge transfer (KT) loss to minimize domain gaps and achieve knowledge transfer, improving model performance. Extensive experiments demonstrate that our ATFC method achieves superior performance, outperforming existing LReID methods by over 9.0$\%$/7.4$\%$ in average mAP/R-1 on the seen dataset.<|reference_end|>
|
arxiv
|
@article{liu2024attribute-text,
title={Attribute-Text Guided Forgetting Compensation for Lifelong Person
Re-Identification},
author={Shiben Liu, Huijie Fan, Qiang Wang, Weihong Ren, Yandong Tang},
journal={arXiv preprint arXiv:2409.19954},
year={2024},
archivePrefix={arXiv},
eprint={2409.19954},
primaryClass={cs.CV cs.AI}
}
|
liu2024attribute-text
|
arxiv-663436
|
2409.19959
|
Early review of Gender Bias of OpenAI o1-mini: Higher Intelligence of LLM does not necessarily solve Gender Bias and Stereotyping issues
|
<|reference_start|>Early review of Gender Bias of OpenAI o1-mini: Higher Intelligence of LLM does not necessarily solve Gender Bias and Stereotyping issues: In this paper, we present an early evaluation of the OpenAI o1-mini model, analyzing its performance in gender inclusivity and bias. Our research, conducted on 700 personas 350 from GPT-4o mini and 350 from o1-mini, reveals that despite improvements in inclusivity regarding personality traits and preferences, significant gender biases remain. For instance, o1-mini rated male personas higher in competency, with a score of 8.06, compared to female personas at 7.88 and non-binary personas at 7.80. Additionally, o1-mini assigned PhD roles to 28% of male personas but only 22.4% of females and 0% of non-binary personas. Male personas were also more likely to be perceived as successful founders, at 69.4%, and CEOs, at 62.17%, compared to female personas at 67.97% and 61.11%, and non-binary personas at 65.7% and 58.37%. The analysis reveals persistent gender biases across fields like Engineering, Data, and Technology, where males dominate, reflecting traditional stereotypes. Conversely, fields like Design, Art, and Marketing show a stronger presence of females, reinforcing societal notions that associate creativity and communication with females. These findings highlight ongoing challenges in mitigating gender bias, reinforcing the need for further interventions to ensure equitable representation across all genders in AI models.<|reference_end|>
|
arxiv
|
@article{ranjan2024early,
title={Early review of Gender Bias of OpenAI o1-mini: Higher Intelligence of
LLM does not necessarily solve Gender Bias and Stereotyping issues},
author={Rajesh Ranjan, Shailja Gupta, Surya Naranyan Singh},
journal={arXiv preprint arXiv:2409.19959},
year={2024},
archivePrefix={arXiv},
eprint={2409.19959},
primaryClass={cs.CY}
}
|
ranjan2024early
|
arxiv-663437
|
2409.19960
|
TROPE: TRaining-Free Object-Part Enhancement for Seamlessly Improving Fine-Grained Zero-Shot Image Captioning
|
<|reference_start|>TROPE: TRaining-Free Object-Part Enhancement for Seamlessly Improving Fine-Grained Zero-Shot Image Captioning: Zero-shot inference, where pre-trained models perform tasks without specific training data, is an exciting emergent ability of large models like CLIP. Although there has been considerable exploration into enhancing zero-shot abilities in image captioning (IC) for popular datasets such as MSCOCO and Flickr8k, these approaches fall short with fine-grained datasets like CUB, FLO, UCM-Captions, and Sydney-Captions. These datasets require captions to discern between visually and semantically similar classes, focusing on detailed object parts and their attributes. To overcome this challenge, we introduce TRaining-Free Object-Part Enhancement (TROPE). TROPE enriches a base caption with additional object-part details using object detector proposals and Natural Language Processing techniques. It complements rather than alters the base caption, allowing seamless integration with other captioning methods and offering users enhanced flexibility. Our evaluations show that TROPE consistently boosts performance across all tested zero-shot IC approaches and achieves state-of-the-art results on fine-grained IC datasets.<|reference_end|>
|
arxiv
|
@article{feinglass2024trope:,
title={TROPE: TRaining-Free Object-Part Enhancement for Seamlessly Improving
Fine-Grained Zero-Shot Image Captioning},
author={Joshua Feinglass and Yezhou Yang},
journal={arXiv preprint arXiv:2409.19960},
year={2024},
archivePrefix={arXiv},
eprint={2409.19960},
primaryClass={cs.CV cs.CL}
}
|
feinglass2024trope:
|
arxiv-663438
|
2409.19961
|
Multimodal LLM Enhanced Cross-lingual Cross-modal Retrieval
|
<|reference_start|>Multimodal LLM Enhanced Cross-lingual Cross-modal Retrieval: Cross-lingual cross-modal retrieval (CCR) aims to retrieve visually relevant content based on non-English queries, without relying on human-labeled cross-modal data pairs during training. One popular approach involves utilizing machine translation (MT) to create pseudo-parallel data pairs, establishing correspondence between visual and non-English textual data. However, aligning their representations poses challenges due to the significant semantic gap between vision and text, as well as the lower quality of non-English representations caused by pre-trained encoders and data noise. To overcome these challenges, we propose LECCR, a novel solution that incorporates the multi-modal large language model (MLLM) to improve the alignment between visual and non-English representations. Specifically, we first employ MLLM to generate detailed visual content descriptions and aggregate them into multi-view semantic slots that encapsulate different semantics. Then, we take these semantic slots as internal features and leverage them to interact with the visual features. By doing so, we enhance the semantic information within the visual features, narrowing the semantic gap between modalities and generating local visual semantics for subsequent multi-level matching. Additionally, to further enhance the alignment between visual and non-English features, we introduce softened matching under English guidance. This approach provides more comprehensive and reliable inter-modal correspondences between visual and non-English features. Extensive experiments on four CCR benchmarks, \ie Multi30K, MSCOCO, VATEX, and MSR-VTT-CN, demonstrate the effectiveness of our proposed method. Code: \url{https://github.com/LiJiaBei-7/leccr}.<|reference_end|>
|
arxiv
|
@article{wang2024multimodal,
title={Multimodal LLM Enhanced Cross-lingual Cross-modal Retrieval},
author={Yabing Wang, Le Wang, Qiang Zhou, Zhibin Wang, Hao Li, Gang Hua, Wei
Tang},
journal={arXiv preprint arXiv:2409.19961},
year={2024},
archivePrefix={arXiv},
eprint={2409.19961},
primaryClass={cs.CV cs.CL}
}
|
wang2024multimodal
|
arxiv-663439
|
2409.19962
|
Two-Stage Optimization for Efficient V2G Coordination in Distribution Power System
|
<|reference_start|>Two-Stage Optimization for Efficient V2G Coordination in Distribution Power System: With the growing popularity of electric vehicles (EVs), maintaining power grid stability has become a significant challenge. To address this issue, EV scheduling control strategies have been developed to manage vehicle-to-grid (V2G) in coordination with the optimal power flow. In existing studies, such coordination optimization is formulated as a mixed-integer nonlinear programming (MINP), which is computationally challenging due to the binary EV charging and discharging variables. To address this challenge, we develop an efficient two-stage optimization method for this mixed-integer nonlinear coordination problem. This method first employs an efficient technique called the difference of convex (DC) to relax the integrality and reformulate MINP into a series of path-following continuous programming. Although the DC approach shows promising efficiency for solving MINP, it cannot guarantee the feasibility of the solutions. Consequently, we propose a trust region optimization method in stage two that constructs a trust region around DC's solution and then searches for the best feasible solution within this region. Our simulation results demonstrate that, compared to the open-source optimization solver SCIP, our proposed method significantly enhances computational efficiency while achieving near optimality.<|reference_end|>
|
arxiv
|
@article{tian2024two-stage,
title={Two-Stage Optimization for Efficient V2G Coordination in Distribution
Power System},
author={Pengchao Tian, Siqi Yan, Bikang Pan, Ye Shi},
journal={arXiv preprint arXiv:2409.19962},
year={2024},
archivePrefix={arXiv},
eprint={2409.19962},
primaryClass={cs.CE}
}
|
tian2024two-stage
|
arxiv-663440
|
2409.19963
|
A Self-attention Residual Convolutional Neural Network for Health Condition Classification of Cow Teat Images
|
<|reference_start|>A Self-attention Residual Convolutional Neural Network for Health Condition Classification of Cow Teat Images: Milk is a highly important consumer for Americans and the health of the cows' teats directly affects the quality of the milk. Traditionally, veterinarians manually assessed teat health by visually inspecting teat-end hyperkeratosis during the milking process which is limited in time, usually only tens of seconds, and weakens the accuracy of the health assessment of cows' teats. Convolutional neural networks (CNNs) have been used for cows' teat-end health assessment. However, there are challenges in using CNNs for cows' teat-end health assessment, such as complex environments, changing positions and postures of cows' teats, and difficulty in identifying cows' teats from images. To address these challenges, this paper proposes a cows' teats self-attention residual convolutional neural network (CTSAR-CNN) model that combines residual connectivity and self-attention mechanisms to assist commercial farms in the health assessment of cows' teats by classifying the magnitude of teat-end hyperkeratosis using digital images. The results showed that upon integrating residual connectivity and self-attention mechanisms, the accuracy of CTSAR-CNN has been improved. This research illustrates that CTSAR-CNN can be more adaptable and speedy to assist veterinarians in assessing the health of cows' teats and ultimately benefit the dairy industry.<|reference_end|>
|
arxiv
|
@article{wang2024a,
title={A Self-attention Residual Convolutional Neural Network for Health
Condition Classification of Cow Teat Images},
author={Minghao Wang},
journal={arXiv preprint arXiv:2409.19963},
year={2024},
archivePrefix={arXiv},
eprint={2409.19963},
primaryClass={eess.IV cs.CV cs.LG}
}
|
wang2024a
|
arxiv-663441
|
2409.19964
|
Comments on "Privacy-Enhanced Federated Learning Against Poisoning Adversaries"
|
<|reference_start|>Comments on "Privacy-Enhanced Federated Learning Against Poisoning Adversaries": In August 2021, Liu et al. (IEEE TIFS'21) proposed a privacy-enhanced framework named PEFL to efficiently detect poisoning behaviours in Federated Learning (FL) using homomorphic encryption. In this article, we show that PEFL does not preserve privacy. In particular, we illustrate that PEFL reveals the entire gradient vector of all users in clear to one of the participating entities, thereby violating privacy. Furthermore, we clearly show that an immediate fix for this issue is still insufficient to achieve privacy by pointing out multiple flaws in the proposed system. Note: Although our privacy issues mentioned in Section II have been published in January 2023 (Schneider et. al., IEEE TIFS'23), several subsequent papers continued to reference Liu et al. (IEEE TIFS'21) as a potential solution for private federated learning. While a few works have acknowledged the privacy concerns we raised, several of subsequent works either propagate these errors or adopt the constructions from Liu et al. (IEEE TIFS'21), thereby unintentionally inheriting the same privacy vulnerabilities. We believe this oversight is partly due to the limited visibility of our comments paper at TIFS'23 (Schneider et. al., IEEE TIFS'23). Consequently, to prevent the continued propagation of the flawed algorithms in Liu et al. (IEEE TIFS'21) into future research, we also put this article to an ePrint.<|reference_end|>
|
arxiv
|
@article{schneider2024comments,
title={Comments on "Privacy-Enhanced Federated Learning Against Poisoning
Adversaries"},
author={Thomas Schneider and Ajith Suresh and Hossein Yalame},
journal={arXiv preprint arXiv:2409.19964},
year={2024},
doi={10.1109/TIFS.2023.3238544},
archivePrefix={arXiv},
eprint={2409.19964},
primaryClass={cs.CR cs.LG}
}
|
schneider2024comments
|
arxiv-663442
|
2409.19965
|
Variational Auto-encoder Based Solutions to Interactive Dynamic Influence Diagrams
|
<|reference_start|>Variational Auto-encoder Based Solutions to Interactive Dynamic Influence Diagrams: Addressing multiagent decision problems in AI, especially those involving collaborative or competitive agents acting concurrently in a partially observable and stochastic environment, remains a formidable challenge. While Interactive Dynamic Influence Diagrams~(I-DIDs) have offered a promising decision framework for such problems, they encounter limitations when the subject agent encounters unknown behaviors exhibited by other agents that are not explicitly modeled within the I-DID. This can lead to sub-optimal responses from the subject agent. In this paper, we propose a novel data-driven approach that utilizes an encoder-decoder architecture, particularly a variational autoencoder, to enhance I-DID solutions. By integrating a perplexity-based tree loss function into the optimization algorithm of the variational autoencoder, coupled with the advantages of Zig-Zag One-Hot encoding and decoding, we generate potential behaviors of other agents within the I-DID that are more likely to contain their true behaviors, even from limited interactions. This new approach enables the subject agent to respond more appropriately to unknown behaviors, thus improving its decision quality. We empirically demonstrate the effectiveness of the proposed approach in two well-established problem domains, highlighting its potential for handling multi-agent decision problems with unknown behaviors. This work is the first time of using neural networks based approaches to deal with the I-DID challenge in agent planning and learning problems.<|reference_end|>
|
arxiv
|
@article{pan2024variational,
title={Variational Auto-encoder Based Solutions to Interactive Dynamic
Influence Diagrams},
author={Yinghui Pan, Biyang Ma, Hanyi Zhang, Yifeng Zeng},
journal={arXiv preprint arXiv:2409.19965},
year={2024},
archivePrefix={arXiv},
eprint={2409.19965},
primaryClass={cs.MA}
}
|
pan2024variational
|
arxiv-663443
|
2409.19967
|
Magnet: We Never Know How Text-to-Image Diffusion Models Work, Until We Learn How Vision-Language Models Function
|
<|reference_start|>Magnet: We Never Know How Text-to-Image Diffusion Models Work, Until We Learn How Vision-Language Models Function: Text-to-image diffusion models particularly Stable Diffusion, have revolutionized the field of computer vision. However, the synthesis quality often deteriorates when asked to generate images that faithfully represent complex prompts involving multiple attributes and objects. While previous studies suggest that blended text embeddings lead to improper attribute binding, few have explored this in depth. In this work, we critically examine the limitations of the CLIP text encoder in understanding attributes and investigate how this affects diffusion models. We discern a phenomenon of attribute bias in the text space and highlight a contextual issue in padding embeddings that entangle different concepts. We propose \textbf{Magnet}, a novel training-free approach to tackle the attribute binding problem. We introduce positive and negative binding vectors to enhance disentanglement, further with a neighbor strategy to increase accuracy. Extensive experiments show that Magnet significantly improves synthesis quality and binding accuracy with negligible computational cost, enabling the generation of unconventional and unnatural concepts.<|reference_end|>
|
arxiv
|
@article{zhuang2024magnet:,
title={Magnet: We Never Know How Text-to-Image Diffusion Models Work, Until We
Learn How Vision-Language Models Function},
author={Chenyi Zhuang, Ying Hu, Pan Gao},
journal={arXiv preprint arXiv:2409.19967},
year={2024},
archivePrefix={arXiv},
eprint={2409.19967},
primaryClass={cs.CV}
}
|
zhuang2024magnet:
|
arxiv-663444
|
2409.19970
|
A Hybrid Model and Learning-Based Force Estimation Framework for Surgical Robots
|
<|reference_start|>A Hybrid Model and Learning-Based Force Estimation Framework for Surgical Robots: Haptic feedback to the surgeon during robotic surgery would enable safer and more immersive surgeries but estimating tissue interaction forces at the tips of robotically controlled surgical instruments has proven challenging. Few existing surgical robots can measure interaction forces directly and the additional sensor may limit the life of instruments. We present a hybrid model and learning-based framework for force estimation for the Patient Side Manipulators (PSM) of a da Vinci Research Kit (dVRK). The model-based component identifies the dynamic parameters of the robot and estimates free-space joint torque, while the learning-based component compensates for environmental factors, such as the additional torque caused by trocar interaction between the PSM instrument and the patient's body wall. We evaluate our method in an abdominal phantom and achieve an error in force estimation of under 10% normalized root-mean-squared error. We show that by using a model-based method to perform dynamics identification, we reduce reliance on the training data covering the entire workspace. Although originally developed for the dVRK, the proposed method is a generalizable framework for other compliant surgical robots. The code is available at https://github.com/vu-maple-lab/dvrk_force_estimation.<|reference_end|>
|
arxiv
|
@article{yang2024a,
title={A Hybrid Model and Learning-Based Force Estimation Framework for
Surgical Robots},
author={Hao Yang, Haoying Zhou, Gregory S. Fischer and Jie Ying Wu},
journal={arXiv preprint arXiv:2409.19970},
year={2024},
archivePrefix={arXiv},
eprint={2409.19970},
primaryClass={cs.RO}
}
|
yang2024a
|
arxiv-663445
|
2409.19972
|
DAOcc: 3D Object Detection Assisted Multi-Sensor Fusion for 3D Occupancy Prediction
|
<|reference_start|>DAOcc: 3D Object Detection Assisted Multi-Sensor Fusion for 3D Occupancy Prediction: Multi-sensor fusion significantly enhances the accuracy and robustness of 3D semantic occupancy prediction, which is crucial for autonomous driving and robotics. However, existing approaches depend on large image resolutions and complex networks to achieve top performance, hindering their application in practical scenarios. Additionally, most multi-sensor fusion approaches focus on improving fusion features while overlooking the exploration of supervision strategies for these features. To this end, we propose DAOcc, a novel multi-sensor fusion occupancy network that leverages 3D object detection supervision to assist in achieving superior performance, while using a deployment-friendly image feature extraction network and practical input image resolution. Furthermore, we introduce a BEV View Range Extension strategy to mitigate the adverse effects of reduced image resolution. As a result, our approach achieves new state-of-the-art results on the Occ3D-nuScenes and SurroundOcc datasets, using ResNet50 and a 256x704 input image resolution. Code will be made available at https://github.com/AlphaPlusTT/DAOcc.<|reference_end|>
|
arxiv
|
@article{yang2024daocc:,
title={DAOcc: 3D Object Detection Assisted Multi-Sensor Fusion for 3D Occupancy
Prediction},
author={Zhen Yang, Yanpeng Dong, Heng Wang},
journal={arXiv preprint arXiv:2409.19972},
year={2024},
archivePrefix={arXiv},
eprint={2409.19972},
primaryClass={cs.CV}
}
|
yang2024daocc:
|
arxiv-663446
|
2409.19974
|
Adaptive Mesh Refinement for Two-Phase Viscoelastic Fluid Mixture Models
|
<|reference_start|>Adaptive Mesh Refinement for Two-Phase Viscoelastic Fluid Mixture Models: Multiphase flows are an important class of fluid flow and their study facilitates the development of diverse applications in industrial, natural and biomedical systems. Simulating such flows requires significant computational resources, making it prudent to devise an adaptive mesh refinement (AMR) method to mitigate this burden. We use a mathematical model that takes a continuum mechanical approach to describe multiphase mixture flows. The resulting system of equations poses numerical challenges due to the presence of multiple non-linear terms and a co-incompressibility condition, while the resulting fluid dynamics necessitate the development of an adaptive mesh refinement technique to accurately capture regions of interest while keeping computational costs low. We present an accurate, robust, and efficient computational method for simulating multiphase mixtures on adaptive grids, and utilize a multigrid solver to precondition the saddle-point system. We demonstrate that the AMR solver asymptotically approaches second order accuracy in $L^1$, $L^2$ and $L^\infty$ norms for all solution variables of the Newtonian and non-Newtonian models. All experiments demonstrate the solver is stable provided the time step size satisfies the imposed CFL condition. The solver can accurately resolve sharp gradients in the solution and, with the multigrid preconditioner, the solver behavior is independent of grid spacing. Our AMR solver offers a major cost savings benefit, providing up to 10x speedup in the numerical experiments presented here, with greater speedup possible depending on the problem set-up.<|reference_end|>
|
arxiv
|
@article{nagda2024adaptive,
title={Adaptive Mesh Refinement for Two-Phase Viscoelastic Fluid Mixture Models},
author={Bindi M. Nagda, Aaron Barrett, Boyce E. Griffith, Aaron L. Fogelson,
Jian Du},
journal={arXiv preprint arXiv:2409.19974},
year={2024},
archivePrefix={arXiv},
eprint={2409.19974},
primaryClass={physics.flu-dyn cs.NA math.NA}
}
|
nagda2024adaptive
|
arxiv-663447
|
2409.19975
|
Exploiting Adjacent Similarity in Multi-Armed Bandit Tasks via Transfer of Reward Samples
|
<|reference_start|>Exploiting Adjacent Similarity in Multi-Armed Bandit Tasks via Transfer of Reward Samples: We consider a sequential multi-task problem, where each task is modeled as the stochastic multi-armed bandit with K arms. We assume the bandit tasks are adjacently similar in the sense that the difference between the mean rewards of the arms for any two consecutive tasks is bounded by a parameter. We propose two algorithms (one assumes the parameter is known while the other does not) based on UCB to transfer reward samples from preceding tasks to improve the overall regret across all tasks. Our analysis shows that transferring samples reduces the regret as compared to the case of no transfer. We provide empirical results for our algorithms, which show performance improvement over the standard UCB algorithm without transfer and a naive transfer algorithm.<|reference_end|>
|
arxiv
|
@article{rahul2024exploiting,
title={Exploiting Adjacent Similarity in Multi-Armed Bandit Tasks via Transfer
of Reward Samples},
author={NR Rahul, Vaibhav Katewa},
journal={arXiv preprint arXiv:2409.19975},
year={2024},
archivePrefix={arXiv},
eprint={2409.19975},
primaryClass={cs.LG stat.ML}
}
|
rahul2024exploiting
|
arxiv-663448
|
2409.19976
|
Learning Partial Differential Equations with Deep Parallel Neural Operators
|
<|reference_start|>Learning Partial Differential Equations with Deep Parallel Neural Operators: In recent years, Solving partial differential equations has shifted the focus of traditional neural network studies from finite-dimensional Euclidean spaces to generalized functional spaces in research. A novel methodology is to learn an operator as a means of approximating the mapping between outputs. Currently, researchers have proposed a variety of operator architectures. Nevertheless, the majority of these architectures adopt an iterative update architecture, whereby a single operator is learned from the same function space. In practical physical science problems, the numerical solutions of partial differential equations are complex, and a serial single operator is unable to accurately approximate the intricate mapping between input and output. So, We propose a deep parallel operator model (DPNO) for efficiently and accurately solving partial differential equations. DPNO employs convolutional neural networks to extract local features and map data into distinct latent spaces. Designing a parallel block of double Fourier neural operators to solve the iterative error problem. DPNO approximates complex mappings between inputs and outputs by learning multiple operators in different potential spaces in parallel blocks. DPNO achieved the best performance on five of them, with an average improvement of 10.5\%, and ranked second on one dataset.<|reference_end|>
|
arxiv
|
@article{ma2024learning,
title={Learning Partial Differential Equations with Deep Parallel Neural
Operator},
author={Qinglong Ma, Peizhi Zhao, Sen Wang, Tao Song},
journal={arXiv preprint arXiv:2409.19976},
year={2024},
archivePrefix={arXiv},
eprint={2409.19976},
primaryClass={cs.LG cs.NA math.NA}
}
|
ma2024learning
|
arxiv-663449
|
2409.19977
|
Knowledge Graph Embedding by Normalizing Flows
|
<|reference_start|>Knowledge Graph Embedding by Normalizing Flows: A key to knowledge graph embedding (KGE) is to choose a proper representation space, e.g., point-wise Euclidean space and complex vector space. In this paper, we propose a unified perspective of embedding and introduce uncertainty into KGE from the view of group theory. Our model can incorporate existing models (i.e., generality), ensure the computation is tractable (i.e., efficiency) and enjoy the expressive power of complex random variables (i.e., expressiveness). The core idea is that we embed entities/relations as elements of a symmetric group, i.e., permutations of a set. Permutations of different sets can reflect different properties of embedding. And the group operation of symmetric groups is easy to compute. In specific, we show that the embedding of many existing models, point vectors, can be seen as elements of a symmetric group. To reflect uncertainty, we first embed entities/relations as permutations of a set of random variables. A permutation can transform a simple random variable into a complex random variable for greater expressiveness, called a normalizing flow. We then define scoring functions by measuring the similarity of two normalizing flows, namely NFE. We construct several instantiating models and prove that they are able to learn logical rules. Experimental results demonstrate the effectiveness of introducing uncertainty and our model. The code is available at https://github.com/changyi7231/NFE.<|reference_end|>
|
arxiv
|
@article{xiao2024knowledge,
title={Knowledge Graph Embedding by Normalizing Flows},
author={Changyi Xiao, Xiangnan He, Yixin Cao},
journal={arXiv preprint arXiv:2409.19977},
year={2024},
archivePrefix={arXiv},
eprint={2409.19977},
primaryClass={cs.LG cs.AI}
}
|
xiao2024knowledge
|
arxiv-663450
|
2409.19978
|
Violina: Various-of-trajectories Identification of Linear Time-invariant Non-Markovian Dynamics
|
<|reference_start|>Violina: Various-of-trajectories Identification of Linear Time-invariant Non-Markovian Dynamics: We propose a new system identification method Violina (various-of-trajectories identification of linear time-invariant non-Markovian dynamics). In the Violina framework, we optimize the coefficient matrices of state-space model and memory kernel in the given space using a projected gradient descent method so that its model prediction matches the set of multiple observed data. Using Violina we can identify a linear non-Markovian dynamical system with constraints corresponding to a priori knowledge on the model parameters and memory effects. Using synthetic data, we numerically demonstrate that the Markovian and non-Markovian state-space models identified by the proposed method have considerably better generalization performances compared to the models identified by an existing dynamic decomposition-based method.<|reference_end|>
|
arxiv
|
@article{anzaki2024violina:,
title={Violina: Various-of-trajectories Identification of Linear Time-invariant
Non-Markovian Dynamics},
author={Ryoji Anzaki and Kazuhiro Sato},
journal={arXiv preprint arXiv:2409.19978},
year={2024},
archivePrefix={arXiv},
eprint={2409.19978},
primaryClass={math.OC cs.LG}
}
|
anzaki2024violina:
|
arxiv-663451
|
2409.19979
|
Enhancing High-order Interaction Awareness in LLM-based Recommender Model
|
<|reference_start|>Enhancing High-order Interaction Awareness in LLM-based Recommender Model: Large language models (LLMs) have demonstrated prominent reasoning capabilities in recommendation tasks by transforming them into text-generation tasks. However, existing approaches either disregard or ineffectively model the user-item high-order interactions. To this end, this paper presents an enhanced LLM-based recommender (ELMRec). We enhance whole-word embeddings to substantially enhance LLMs' interpretation of graph-constructed interactions for recommendations, without requiring graph pre-training. This finding may inspire endeavors to incorporate rich knowledge graphs into LLM-based recommenders via whole-word embedding. We also found that LLMs often recommend items based on users' earlier interactions rather than recent ones, and present a reranking solution. Our ELMRec outperforms state-of-the-art (SOTA) methods in both direct and sequential recommendations.<|reference_end|>
|
arxiv
|
@article{wang2024enhancing,
title={Enhancing High-order Interaction Awareness in LLM-based Recommender
Model},
author={Xinfeng Wang, Jin Cui, Fumiyo Fukumoto and Yoshimi Suzuki},
journal={arXiv preprint arXiv:2409.19979},
year={2024},
archivePrefix={arXiv},
eprint={2409.19979},
primaryClass={cs.IR cs.CL}
}
|
wang2024enhancing
|
arxiv-663452
|
2409.19982
|
Understanding How Psychological Distance Influences User Preferences in Conversational Versus Web Search
|
<|reference_start|>Understanding How Psychological Distance Influences User Preferences in Conversational Versus Web Search: Conversational search offers an easier and faster alternative to conventional web search, while having downsides like lack of source verification. Research has examined performance disparities between these two systems in different settings. However, little work has considered the effects of variations within a given search task. We hypothesize that psychological distance - one''s perceived closeness to a target event - affects information needs in search tasks, and investigate the corresponding effects on user preferences between web and conversational search systems. We find that with greater psychological distances, users perceive conversational search as more credible, useful, enjoyable, and easy to use, and demonstrate increased preference for this system. We reveal qualitative reasons for these differences and provide design implications for search system designers.<|reference_end|>
|
arxiv
|
@article{yang2024understanding,
title={Understanding How Psychological Distance Influences User Preferences in
Conversational Versus Web Search},
author={Yitian Yang, Yugin Tan, Yang Chen Lin, Jung-Tai King, Zihan Liu,
Yi-Chieh Lee},
journal={arXiv preprint arXiv:2409.19982},
year={2024},
archivePrefix={arXiv},
eprint={2409.19982},
primaryClass={cs.HC}
}
|
yang2024understanding
|
arxiv-663453
|
2409.19983
|
TSdetector: Temporal-Spatial Self-correction Collaborative Learning for Colonoscopy Video Detection
|
<|reference_start|>TSdetector: Temporal-Spatial Self-correction Collaborative Learning for Colonoscopy Video Detection: CNN-based object detection models that strike a balance between performance and speed have been gradually used in polyp detection tasks. Nevertheless, accurately locating polyps within complex colonoscopy video scenes remains challenging since existing methods ignore two key issues: intra-sequence distribution heterogeneity and precision-confidence discrepancy. To address these challenges, we propose a novel Temporal-Spatial self-correction detector (TSdetector), which first integrates temporal-level consistency learning and spatial-level reliability learning to detect objects continuously. Technically, we first propose a global temporal-aware convolution, assembling the preceding information to dynamically guide the current convolution kernel to focus on global features between sequences. In addition, we designed a hierarchical queue integration mechanism to combine multi-temporal features through a progressive accumulation manner, fully leveraging contextual consistency information together with retaining long-sequence-dependency features. Meanwhile, at the spatial level, we advance a position-aware clustering to explore the spatial relationships among candidate boxes for recalibrating prediction confidence adaptively, thus eliminating redundant bounding boxes efficiently. The experimental results on three publicly available polyp video dataset show that TSdetector achieves the highest polyp detection rate and outperforms other state-of-the-art methods. The code can be available at https://github.com/soleilssss/TSdetector.<|reference_end|>
|
arxiv
|
@article{wang2024tsdetector:,
title={TSdetector: Temporal-Spatial Self-correction Collaborative Learning for
Colonoscopy Video Detection},
author={Kaini Wang, Haolin Wang, Guang-Quan Zhou, Yangang Wang, Ling Yang,
Yang Chen, Shuo Li},
journal={arXiv preprint arXiv:2409.19983},
year={2024},
archivePrefix={arXiv},
eprint={2409.19983},
primaryClass={cs.CV}
}
|
wang2024tsdetector:
|
arxiv-663454
|
2409.19984
|
CONTESTS: a Framework for Consistency Testing of Span Probabilities in Language Models
|
<|reference_start|>CONTESTS: a Framework for Consistency Testing of Span Probabilities in Language Models: Although language model scores are often treated as probabilities, their reliability as probability estimators has mainly been studied through calibration, overlooking other aspects. In particular, it is unclear whether language models produce the same value for different ways of assigning joint probabilities to word spans. Our work introduces a novel framework, ConTestS (Consistency Testing over Spans), involving statistical tests to assess score consistency across interchangeable completion and conditioning orders. We conduct experiments on post-release real and synthetic data to eliminate training effects. Our findings reveal that both Masked Language Models (MLMs) and autoregressive models exhibit inconsistent predictions, with autoregressive models showing larger discrepancies. Larger MLMs tend to produce more consistent predictions, while autoregressive models show the opposite trend. Moreover, for both model types, prediction entropies offer insights into the true word span likelihood and therefore can aid in selecting optimal decoding strategies. The inconsistencies revealed by our analysis, as well their connection to prediction entropies and differences between model types, can serve as useful guides for future research on addressing these limitations.<|reference_end|>
|
arxiv
|
@article{wagner2024contests:,
title={CONTESTS: a Framework for Consistency Testing of Span Probabilities in
Language Models},
author={Eitan Wagner, Yuli Slavutsky and Omri Abend},
journal={arXiv preprint arXiv:2409.19984},
year={2024},
archivePrefix={arXiv},
eprint={2409.19984},
primaryClass={cs.CL cs.AI}
}
|
wagner2024contests:
|
arxiv-663455
|
2409.19986
|
GearTrack: Automating 6D Pose Estimation
|
<|reference_start|>GearTrack: Automating 6D Pose Estimation: We developed a robust solution for real-time 6D object detection in industrial applications by integrating FoundationPose, SAM2, and LightGlue, eliminating the need for retraining. Our approach addresses two key challenges: the requirement for an initial object mask in the first frame in FoundationPose and issues with tracking loss and automatic rotation for symmetric objects. The algorithm requires only a CAD model of the target object, with the user clicking on its location in the live feed during the initial setup. Once set, the algorithm automatically saves a reference image of the object and, in subsequent runs, employs LightGlue for feature matching between the object and the real-time scene, providing an initial prompt for detection. Tested on the YCB dataset and industrial components such as bleach cleanser and gears, the algorithm demonstrated reliable 6D detection and tracking. By integrating SAM2 and FoundationPose, we effectively mitigated common limitations such as the problem of tracking loss, ensuring continuous and accurate tracking under challenging conditions like occlusion or rapid movement.<|reference_end|>
|
arxiv
|
@article{deng2024superpose:,
title={SuperPose: Improved 6D Pose Estimation with Robust Tracking and
Mask-Free Initialization},
author={Yu Deng, Jiahong Xue, Teng Cao, Yingxing Zhang, Lanxi Wen and Yiyang
Chen},
journal={arXiv preprint arXiv:2409.19986},
year={2024},
archivePrefix={arXiv},
eprint={2409.19986},
primaryClass={cs.CV}
}
|
deng2024superpose:
|
arxiv-663456
|
2409.19987
|
OccRWKV: Rethinking Efficient 3D Semantic Occupancy Prediction with Linear Complexity
|
<|reference_start|>OccRWKV: Rethinking Efficient 3D Semantic Occupancy Prediction with Linear Complexity: 3D semantic occupancy prediction networks have demonstrated remarkable capabilities in reconstructing the geometric and semantic structure of 3D scenes, providing crucial information for robot navigation and autonomous driving systems. However, due to their large overhead from dense network structure designs, existing networks face challenges balancing accuracy and latency. In this paper, we introduce OccRWKV, an efficient semantic occupancy network inspired by Receptance Weighted Key Value (RWKV). OccRWKV separates semantics, occupancy prediction, and feature fusion into distinct branches, each incorporating Sem-RWKV and Geo-RWKV blocks. These blocks are designed to capture long-range dependencies, enabling the network to learn domain-specific representation (i.e., semantics and geometry), which enhances prediction accuracy. Leveraging the sparse nature of real-world 3D occupancy, we reduce computational overhead by projecting features into the bird's-eye view (BEV) space and propose a BEV-RWKV block for efficient feature enhancement and fusion. This enables real-time inference at 22.2 FPS without compromising performance. Experiments demonstrate that OccRWKV outperforms the state-of-the-art methods on the SemanticKITTI dataset, achieving a mIoU of 25.1 while being 20 times faster than the best baseline, Co-Occ, making it suitable for real-time deployment on robots to enhance autonomous navigation efficiency. Code and video are available on our project page: https://jmwang0117.github.io/OccRWKV/.<|reference_end|>
|
arxiv
|
@article{wang2024occrwkv:,
title={OccRWKV: Rethinking Efficient 3D Semantic Occupancy Prediction with
Linear Complexity},
author={Junming Wang and Wei Yin and Xiaoxiao Long and Xingyu Zhang and Zebin
Xing and Xiaoyang Guo and Qian Zhang},
journal={arXiv preprint arXiv:2409.19987},
year={2024},
archivePrefix={arXiv},
eprint={2409.19987},
primaryClass={cs.CV cs.RO}
}
|
wang2024occrwkv:
|
arxiv-663457
|
2409.19988
|
Enhancing Security Using Random Binary Weights in Privacy-Preserving Federated Learning
|
<|reference_start|>Enhancing Security Using Random Binary Weights in Privacy-Preserving Federated Learning: In this paper, we propose a novel method for enhancing security in privacy-preserving federated learning using the Vision Transformer. In federated learning, learning is performed by collecting updated information without collecting raw data from each client. However, the problem is that this raw data may be inferred from the updated information. Conventional data-guessing countermeasures (security enhancement methods) for addressing this issue have a trade-off relationship between privacy protection strength and learning efficiency, and they generally degrade model performance. In this paper, we propose a novel method of federated learning that does not degrade model performance and that is robust against data-guessing attacks on updated information. In the proposed method, each client independently prepares a sequence of binary (0 or 1) random numbers, multiplies it by the updated information, and sends it to a server for model learning. In experiments, the effectiveness of the proposed method is confirmed in terms of model performance and resistance to the APRIL (Attention PRIvacy Leakage) restoration attack.<|reference_end|>
|
arxiv
|
@article{sawada2024enhancing,
title={Enhancing Security Using Random Binary Weights in Privacy-Preserving
Federated Learning},
author={Hiroto Sawada, Shoko Imaizumi, and Hitoshi Kiya},
journal={arXiv preprint arXiv:2409.19988},
year={2024},
archivePrefix={arXiv},
eprint={2409.19988},
primaryClass={cs.CR}
}
|
sawada2024enhancing
|
arxiv-663458
|
2409.19989
|
RoCoTex: A Robust Method for Consistent Texture Synthesis with Diffusion Models
|
<|reference_start|>RoCoTex: A Robust Method for Consistent Texture Synthesis with Diffusion Models: Text-to-texture generation has recently attracted increasing attention, but existing methods often suffer from the problems of view inconsistencies, apparent seams, and misalignment between textures and the underlying mesh. In this paper, we propose a robust text-to-texture method for generating consistent and seamless textures that are well aligned with the mesh. Our method leverages state-of-the-art 2D diffusion models, including SDXL and multiple ControlNets, to capture structural features and intricate details in the generated textures. The method also employs a symmetrical view synthesis strategy combined with regional prompts for enhancing view consistency. Additionally, it introduces novel texture blending and soft-inpainting techniques, which significantly reduce the seam regions. Extensive experiments demonstrate that our method outperforms existing state-of-the-art methods.<|reference_end|>
|
arxiv
|
@article{kim2024rocotex:,
title={RoCoTex: A Robust Method for Consistent Texture Synthesis with Diffusion
Models},
author={Jangyeong Kim, Donggoo Kang, Junyoung Choi, Jeonga Wi, Junho Gwon,
Jiun Bae, Dumim Yoon, Junghyun Han},
journal={arXiv preprint arXiv:2409.19989},
year={2024},
archivePrefix={arXiv},
eprint={2409.19989},
primaryClass={cs.CV cs.GR}
}
|
kim2024rocotex:
|
arxiv-663459
|
2409.19990
|
Predictive Speech Recognition and End-of-Utterance Detection Towards Spoken Dialog Systems
|
<|reference_start|>Predictive Speech Recognition and End-of-Utterance Detection Towards Spoken Dialog Systems: Effective spoken dialog systems should facilitate natural interactions with quick and rhythmic timing, mirroring human communication patterns. To reduce response times, previous efforts have focused on minimizing the latency in automatic speech recognition (ASR) to optimize system efficiency. However, this approach requires waiting for ASR to complete processing until a speaker has finished speaking, which limits the time available for natural language processing (NLP) to formulate accurate responses. As humans, we continuously anticipate and prepare responses even while the other party is still speaking. This allows us to respond appropriately without missing the optimal time to speak. In this work, as a pioneering study toward a conversational system that simulates such human anticipatory behavior, we aim to realize a function that can predict the forthcoming words and estimate the time remaining until the end of an utterance (EOU), using the middle portion of an utterance. To achieve this, we propose a training strategy for an encoder-decoder-based ASR system, which involves masking future segments of an utterance and prompting the decoder to predict the words in the masked audio. Additionally, we develop a cross-attention-based algorithm that incorporates both acoustic and linguistic information to accurately detect the EOU. The experimental results demonstrate the proposed model's ability to predict upcoming words and estimate future EOU events up to 300ms prior to the actual EOU. Moreover, the proposed training strategy exhibits general improvements in ASR performance.<|reference_end|>
|
arxiv
|
@article{zink2024predictive,
title={Predictive Speech Recognition and End-of-Utterance Detection Towards
Spoken Dialog Systems},
author={Oswald Zink, Yosuke Higuchi, Carlos Mullov, Alexander Waibel,
Tetsunori Kobayashi},
journal={arXiv preprint arXiv:2409.19990},
year={2024},
archivePrefix={arXiv},
eprint={2409.19990},
primaryClass={eess.AS cs.CL cs.SD}
}
|
zink2024predictive
|
arxiv-663460
|
2409.19991
|
Robust Multi-view Co-expression Network Inference
|
<|reference_start|>Robust Multi-view Co-expression Network Inference: Unraveling the co-expression of genes across studies enhances the understanding of cellular processes. Inferring gene co-expression networks from transcriptome data presents many challenges, including spurious gene correlations, sample correlations, and batch effects. To address these complexities, we introduce a robust method for high-dimensional graph inference from multiple independent studies. We base our approach on the premise that each dataset is essentially a noisy linear mixture of gene loadings that follow a multivariate $t$-distribution with a sparse precision matrix, which is shared across studies. This allows us to show that we can identify the co-expression matrix up to a scaling factor among other model parameters. Our method employs an Expectation-Maximization procedure for parameter estimation. Empirical evaluation on synthetic and gene expression data demonstrates our method's improved ability to learn the underlying graph structure compared to baseline methods.<|reference_end|>
|
arxiv
|
@article{pandeva2024robust,
title={Robust Multi-view Co-expression Network Inference},
author={Teodora Pandeva and Martijs Jonker and Leendert Hamoen and Joris Mooij
and Patrick Forr'e},
journal={arXiv preprint arXiv:2409.19991},
year={2024},
archivePrefix={arXiv},
eprint={2409.19991},
primaryClass={stat.ML cs.LG q-bio.QM stat.AP}
}
|
pandeva2024robust
|
arxiv-663461
|
2409.19992
|
A large-scale operational study of fingerprint quality and demographics
|
<|reference_start|>A large-scale operational study of fingerprint quality and demographics: Even though a few initial works have shown on small sets of data some level of bias in the performance of fingerprint recognition technology with respect to certain demographic groups, there is still not sufficient evidence to understand the impact that certain factors such as gender, age or finger-type may have on fingerprint quality and, in turn, also on fingerprint matching accuracy. The present work addresses this still under researched topic, on a large-scale database of operational data containing 10-print impressions of almost 16,000 subjects. The results reached provide further insight into the dependency of fingerprint quality and demographics, and show that there in fact exists a certain degree of performance variability in fingerprint-based recognition systems for different segments of the population. Based on the experimental evaluation, the work points out new observations based on data-driven evidence, provides plausible hypotheses to explain such observations, and concludes with potential follow-up actions that can help to reduce the observed fingerprint quality differences. This way, the current paper can be considered as a contribution to further increase the algorithmic fairness and equality of biometric technology.<|reference_end|>
|
arxiv
|
@article{galbally2024a,
title={A large-scale operational study of fingerprint quality and demographics},
author={Javier Galbally, Aleksandrs Cepilovs, Ramon Blanco-Gonzalo, Gillian
Ormiston, Oscar Miguel-Hurtado, and Istvan Sz. Racz},
journal={arXiv preprint arXiv:2409.19992},
year={2024},
archivePrefix={arXiv},
eprint={2409.19992},
primaryClass={cs.CV cs.AI cs.LG}
}
|
galbally2024a
|
arxiv-663462
|
2409.19993
|
Mitigating Backdoor Threats to Large Language Models: Advancement and Challenges
|
<|reference_start|>Mitigating Backdoor Threats to Large Language Models: Advancement and Challenges: The advancement of Large Language Models (LLMs) has significantly impacted various domains, including Web search, healthcare, and software development. However, as these models scale, they become more vulnerable to cybersecurity risks, particularly backdoor attacks. By exploiting the potent memorization capacity of LLMs, adversaries can easily inject backdoors into LLMs by manipulating a small portion of training data, leading to malicious behaviors in downstream applications whenever the hidden backdoor is activated by the pre-defined triggers. Moreover, emerging learning paradigms like instruction tuning and reinforcement learning from human feedback (RLHF) exacerbate these risks as they rely heavily on crowdsourced data and human feedback, which are not fully controlled. In this paper, we present a comprehensive survey of emerging backdoor threats to LLMs that appear during LLM development or inference, and cover recent advancement in both defense and detection strategies for mitigating backdoor threats to LLMs. We also outline key challenges in addressing these threats, highlighting areas for future research.<|reference_end|>
|
arxiv
|
@article{liu2024mitigating,
title={Mitigating Backdoor Threats to Large Language Models: Advancement and
Challenges},
author={Qin Liu, Wenjie Mo, Terry Tong, Jiashu Xu, Fei Wang, Chaowei Xiao,
Muhao Chen},
journal={arXiv preprint arXiv:2409.19993},
year={2024},
archivePrefix={arXiv},
eprint={2409.19993},
primaryClass={cs.CR cs.AI cs.CL cs.LG cs.SY eess.SY}
}
|
liu2024mitigating
|
arxiv-663463
|
2409.19995
|
A Screening Method for Power System Inertia Zones Identification
|
<|reference_start|>A Screening Method for Power System Inertia Zones Identification: The heterogeneous distribution of frequency support from dispersed renewable generation sources results in varying inertia within the system. The effects of disturbances exhibit non-uniform variations contingent upon the disturbance's location and the affected region's topology and inertia. A screening method for inertia-zone identification is proposed considering the combination of network structure and generator inertia distribution that will aid in comprehending the response of nodes to disturbances. The nodes' dynamic nodal weight (DNW) is defined using maximal entropy random walk that defines each node's spreading power dynamics. Further, a modified weighted kmeans++ clustering technique is proposed using DNW to obtain the equivalent spatial points of each zone and the system to parameterize the inertia status of each zone. The impact of the proposed scheme is justified by simulating a modified IEEE 39 bus system with doubly-fed induction generator (DFIG) integration in the real-time digital simulator.<|reference_end|>
|
arxiv
|
@article{prasad2024a,
title={A Screening Method for Power System Inertia Zones Identification},
author={{Rashmi Prasad, Pedro P. Vergara, Narayana Prasad Padhy, Robert
Dimitrovski, and Aleksandra Leki'c},
journal={PESGM 2024},
year={2024},
archivePrefix={arXiv},
eprint={2409.19995},
primaryClass={eess.SY cs.SY}
}
|
prasad2024a
|
arxiv-663464
|
2409.19996
|
Analysis and Modeling of the Hybrid Vessel's Electrical Power System
|
<|reference_start|>Analysis and Modeling of the Hybrid Vessel's Electrical Power System: With the maritime industry poised on the cusp of a hybrid revolution, the design and analysis of advanced vessel systems have become paramount for engineers. This paper presents AC and DC electrical hybrid power system models in ETAP, the simulation software that can be adapted to engineer future hybrid vessels. These models are also a step towards a digital twin model that can help in troubleshooting and preventing issues, reducing risk and engineering time. The testing of the models is focused on time domain analysis, short-circuit currents, and protection \& coordination. The models are based on actual vessels and manufacturer parameters are used where available.<|reference_end|>
|
arxiv
|
@article{mosselaar2024analysis,
title={Analysis and Modeling of the Hybrid Vessel's Electrical Power System},
author={Matthijs Mosselaar, Zoran Malbav{s}i'c, Aihui Fu and Aleksandra
Leki'c},
journal={PESGM 2024},
year={2024},
archivePrefix={arXiv},
eprint={2409.19996},
primaryClass={eess.SY cs.SY}
}
|
mosselaar2024analysis
|
arxiv-663465
|
2409.19998
|
Do Influence Functions Work on Large Language Models?
|
<|reference_start|>Do Influence Functions Work on Large Language Models?: Influence functions aim to quantify the impact of individual training data points on a model's predictions. While extensive research has been conducted on influence functions in traditional machine learning models, their application to large language models (LLMs) has been limited. In this work, we conduct a systematic study to address a key question: do influence functions work on LLMs? Specifically, we evaluate influence functions across multiple tasks and find that they consistently perform poorly in most settings. Our further investigation reveals that their poor performance can be attributed to: (1) inevitable approximation errors when estimating the iHVP component due to the scale of LLMs, (2) uncertain convergence during fine-tuning, and, more fundamentally, (3) the definition itself, as changes in model parameters do not necessarily correlate with changes in LLM behavior. Our study thus suggests the need for alternative approaches for identifying influential samples. To support future work, our code is made available at https://github.com/plumprc/Failures-of-Influence-Functions-in-LLMs.<|reference_end|>
|
arxiv
|
@article{li2024do,
title={Do Influence Functions Work on Large Language Models?},
author={Zhe Li, Wei Zhao, Yige Li, Jun Sun},
journal={arXiv preprint arXiv:2409.19998},
year={2024},
archivePrefix={arXiv},
eprint={2409.19998},
primaryClass={cs.CL cs.AI}
}
|
li2024do
|
arxiv-663466
|
2409.20002
|
The Early Bird Catches the Leak: Unveiling Timing Side Channels in LLM Serving Systems
|
<|reference_start|>The Early Bird Catches the Leak: Unveiling Timing Side Channels in LLM Serving Systems: The wide deployment of Large Language Models (LLMs) has given rise to strong demands for optimizing their inference performance. Today's techniques serving this purpose primarily focus on reducing latency and improving throughput through algorithmic and hardware enhancements, while largely overlooking their privacy side effects, particularly in a multi-user environment. In our research, for the first time, we discovered a set of new timing side channels in LLM systems, arising from shared caches and GPU memory allocations, which can be exploited to infer both confidential system prompts and those issued by other users. These vulnerabilities echo security challenges observed in traditional computing systems, highlighting an urgent need to address potential information leakage in LLM serving infrastructures. In this paper, we report novel attack strategies designed to exploit such timing side channels inherent in LLM deployments, specifically targeting the Key-Value (KV) cache and semantic cache widely used to enhance LLM inference performance. Our approach leverages timing measurements and classification models to detect cache hits, allowing an adversary to infer private prompts with high accuracy. We also propose a token-by-token search algorithm to efficiently recover shared prompt prefixes in the caches, showing the feasibility of stealing system prompts and those produced by peer users. Our experimental studies on black-box testing of popular online LLM services demonstrate that such privacy risks are completely realistic, with significant consequences. Our findings underscore the need for robust mitigation to protect LLM systems against such emerging threats.<|reference_end|>
|
arxiv
|
@article{song2024the,
title={The Early Bird Catches the Leak: Unveiling Timing Side Channels in LLM
Serving Systems},
author={Linke Song, Zixuan Pang, Wenhao Wang, Zihao Wang, XiaoFeng Wang,
Hongbo Chen, Wei Song, Yier Jin, Dan Meng, Rui Hou},
journal={arXiv preprint arXiv:2409.20002},
year={2024},
archivePrefix={arXiv},
eprint={2409.20002},
primaryClass={cs.CR}
}
|
song2024the
|
arxiv-663467
|
2409.20003
|
Multibiometrics Using a Single Face Image
|
<|reference_start|>Multibiometrics Using a Single Face Image: Multibiometrics, which uses multiple biometric traits to improve recognition performance instead of using only one biometric trait to authenticate individuals, has been investigated. Previous studies have combined individually acquired biometric traits or have not fully considered the convenience of the system.Focusing on a single face image, we propose a novel multibiometric method that combines five biometric traits, i.e., face, iris, periocular, nose, eyebrow, that can be extracted from a single face image. The proposed method does not sacrifice the convenience of biometrics since only a single face image is used as input.Through a variety of experiments using the CASIA Iris Distance database, we demonstrate the effectiveness of the proposed multibiometrics method.<|reference_end|>
|
arxiv
|
@article{ito2024multibiometrics,
title={Multibiometrics Using a Single Face Image},
author={Koichi Ito, Taito Tonosaki, Takafumi Aoki, Tetsushi Ohki, and
Masakatsu Nishigaki},
journal={arXiv preprint arXiv:2409.20003},
year={2024},
archivePrefix={arXiv},
eprint={2409.20003},
primaryClass={cs.CV}
}
|
ito2024multibiometrics
|
arxiv-663468
|
2409.20004
|
Numerically Robust Fixed-Point Smoothing Without State Augmentation
|
<|reference_start|>Numerically Robust Fixed-Point Smoothing Without State Augmentation: Practical implementations of Gaussian smoothing algorithms have received a great deal of attention in the last 60 years. However, almost all work focuses on estimating complete time series (''fixed-interval smoothing'', $\mathcal{O}(K)$ memory) through variations of the Rauch--Tung--Striebel smoother, rarely on estimating the initial states (''fixed-point smoothing'', $\mathcal{O}(1)$ memory). Since fixed-point smoothing is a crucial component of algorithms for dynamical systems with unknown initial conditions, we close this gap by introducing a new formulation of a Gaussian fixed-point smoother. In contrast to prior approaches, our perspective admits a numerically robust Cholesky-based form (without downdates) and avoids state augmentation, which would needlessly inflate the state-space model and reduce the numerical practicality of any fixed-point smoother code. The experiments demonstrate how a JAX implementation of our algorithm matches the runtime of the fastest methods and the robustness of the most robust techniques while existing implementations must always sacrifice one for the other.<|reference_end|>
|
arxiv
|
@article{krämer2024numerically,
title={Numerically Robust Fixed-Point Smoothing Without State Augmentation},
author={Nicholas Kr"amer},
journal={arXiv preprint arXiv:2409.20004},
year={2024},
archivePrefix={arXiv},
eprint={2409.20004},
primaryClass={math.NA cs.LG cs.NA cs.SY eess.SY stat.CO stat.ML}
}
|
krämer2024numerically
|
arxiv-663469
|
2409.20005
|
Model Selection with a Shapelet-based Distance Measure for Multi-source Transfer Learning in Time Series Classification
|
<|reference_start|>Model Selection with a Shapelet-based Distance Measure for Multi-source Transfer Learning in Time Series Classification: Transfer learning is a common practice that alleviates the need for extensive data to train neural networks. It is performed by pre-training a model using a source dataset and fine-tuning it for a target task. However, not every source dataset is appropriate for each target dataset, especially for time series. In this paper, we propose a novel method of selecting and using multiple datasets for transfer learning for time series classification. Specifically, our method combines multiple datasets as one source dataset for pre-training neural networks. Furthermore, for selecting multiple sources, our method measures the transferability of datasets based on shapelet discovery for effective source selection. While traditional transferability measures require considerable time for pre-training all the possible sources for source selection of each possible architecture, our method can be repeatedly used for every possible architecture with a single simple computation. Using the proposed method, we demonstrate that it is possible to increase the performance of temporal convolutional neural networks (CNN) on time series datasets.<|reference_end|>
|
arxiv
|
@article{lee2024model,
title={Model Selection with a Shapelet-based Distance Measure for Multi-source
Transfer Learning in Time Series Classification},
author={Jiseok Lee and Brian Kenji Iwana},
journal={arXiv preprint arXiv:2409.20005},
year={2024},
archivePrefix={arXiv},
eprint={2409.20005},
primaryClass={cs.LG cs.AI}
}
|
lee2024model
|
arxiv-663470
|
2409.20007
|
Developing Instruction-Following Speech Language Model Without Speech Instruction-Tuning Data
|
<|reference_start|>Developing Instruction-Following Speech Language Model Without Speech Instruction-Tuning Data: Recent end-to-end speech language models (SLMs) have expanded upon the capabilities of large language models (LLMs) by incorporating pre-trained speech models. However, these SLMs often undergo extensive speech instruction-tuning to bridge the gap between speech and text modalities. This requires significant annotation efforts and risks catastrophic forgetting of the original language capabilities. In this work, we present a simple yet effective automatic process for creating speech-text pair data that carefully injects speech paralinguistic understanding abilities into SLMs while preserving the inherent language capabilities of the text-based LLM. Our model demonstrates general capabilities for speech-related tasks without the need for speech instruction-tuning data, achieving impressive performance on Dynamic-SUPERB and AIR-Bench-Chat benchmarks. Furthermore, our model exhibits the ability to follow complex instructions derived from LLMs, such as specific output formatting and chain-of-thought reasoning. Our approach not only enhances the versatility and effectiveness of SLMs but also reduces reliance on extensive annotated datasets, paving the way for more efficient and capable speech understanding systems.<|reference_end|>
|
arxiv
|
@article{lu2024developing,
title={Developing Instruction-Following Speech Language Model Without Speech
Instruction-Tuning Data},
author={Ke-Han Lu, Zhehuai Chen, Szu-Wei Fu, Chao-Han Huck Yang, Jagadeesh
Balam, Boris Ginsburg, Yu-Chiang Frank Wang, Hung-yi Lee},
journal={arXiv preprint arXiv:2409.20007},
year={2024},
archivePrefix={arXiv},
eprint={2409.20007},
primaryClass={eess.AS cs.CL cs.SD}
}
|
lu2024developing
|
arxiv-663471
|
2409.20008
|
Computing Both Upper and Lower Eigenvalue Bounds by HDG Methods
|
<|reference_start|>Computing Both Upper and Lower Eigenvalue Bounds by HDG Methods: In this paper, we observe an interesting phenomenon for a hybridizable discontinuous Galerkin (HDG) method for eigenvalue problems. Specifically, using the same finite element method, we may achieve both upper and lower eigenvalue bounds simultaneously, simply by the fine tuning of the stabilization parameter. Based on this observation, a high accuracy algorithm for computing eigenvalues is designed to yield higher convergence rate at a lower computational cost. Meanwhile, we demonstrate that certain type of HDG methods can only provide upper bounds. As a by-product, the asymptotic upper bound property of the Brezzi-Douglas-Marini mixed finite element is also established. Numerical results supporting our theory are given.<|reference_end|>
|
arxiv
|
@article{liang2024computing,
title={Computing Both Upper and Lower Eigenvalue Bounds by HDG Methods},
author={Qigang Liang, Xuejun Xu, Liuyao Yuan},
journal={arXiv preprint arXiv:2409.20008},
year={2024},
archivePrefix={arXiv},
eprint={2409.20008},
primaryClass={math.NA cs.NA}
}
|
liang2024computing
|
arxiv-663472
|
2409.20010
|
Customized Information and Domain-centric Knowledge Graph Construction with Large Language Models
|
<|reference_start|>Customized Information and Domain-centric Knowledge Graph Construction with Large Language Models: In this paper we propose a novel approach based on knowledge graphs to provide timely access to structured information, to enable actionable technology intelligence, and improve cyber-physical systems planning. Our framework encompasses a text mining process, which includes information retrieval, keyphrase extraction, semantic network creation, and topic map visualization. Following this data exploration process, we employ a selective knowledge graph construction (KGC) approach supported by an electronics and innovation ontology-backed pipeline for multi-objective decision-making with a focus on cyber-physical systems. We apply our methodology to the domain of automotive electrical systems to demonstrate the approach, which is scalable. Our results demonstrate that our construction process outperforms GraphGPT as well as our bi-LSTM and transformer REBEL with a pre-defined dataset by several times in terms of class recognition, relationship construction and correct "sublass of" categorization. Additionally, we outline reasoning applications and provide a comparison with Wikidata to show the differences and advantages of the approach.<|reference_end|>
|
arxiv
|
@article{wawrzik2024customized,
title={Customized Information and Domain-centric Knowledge Graph Construction
with Large Language Models},
author={Frank Wawrzik, Matthias Plaue, Savan Vekariya, Christoph Grimm},
journal={arXiv preprint arXiv:2409.20010},
year={2024},
archivePrefix={arXiv},
eprint={2409.20010},
primaryClass={cs.AI cs.CL}
}
|
wawrzik2024customized
|
arxiv-663473
|
2409.20011
|
Bug-locating Method based on Statistical Testing for Quantum Programs
|
<|reference_start|>Bug-locating Method based on Statistical Testing for Quantum Programs: When a bug is detected by testing a quantum program on a quantum computer, we want to determine its location to fix it. To locate the bug, the quantum program is divided into several segments, and each segment is tested. However, to prepare a quantum state that is input to a segment, it is necessary to execute all the segments ahead of that segment in a quantum computer. This means that the cost of testing each segment depends on its location. We can also locate a buggy segment only if it is confirmed that there are no bugs in all segments ahead of that buggy segment. Since a quantum program is tested statistically on the basis of measurement results, there is a tradeoff between testing accuracy and cost. These characteristics are unique to quantum programs and complicate locating bugs. We propose an efficient bug-locating method consisting of four approaches, cost-based binary search, early determination, finalization, and looking back, which take these characteristics into account. We present experimental results that indicate that the proposed method can reduce the bug-locating cost, represented as the number of executed quantum gates, compared to naive methods that do not use the four approaches. The limitation and usefulness of the proposed method are also discussed from the experimental results.<|reference_end|>
|
arxiv
|
@article{sato2024bug-locating,
title={Bug-locating Method based on Statistical Testing for Quantum Programs},
author={Naoto Sato and Ryota Katsube},
journal={arXiv preprint arXiv:2409.20011},
year={2024},
archivePrefix={arXiv},
eprint={2409.20011},
primaryClass={cs.SE quant-ph}
}
|
sato2024bug-locating
|
arxiv-663474
|
2409.20012
|
Towards Robust Multimodal Sentiment Analysis with Incomplete Data
|
<|reference_start|>Towards Robust Multimodal Sentiment Analysis with Incomplete Data: The field of Multimodal Sentiment Analysis (MSA) has recently witnessed an emerging direction seeking to tackle the issue of data incompleteness. Recognizing that the language modality typically contains dense sentiment information, we consider it as the dominant modality and present an innovative Language-dominated Noise-resistant Learning Network (LNLN) to achieve robust MSA. The proposed LNLN features a dominant modality correction (DMC) module and dominant modality based multimodal learning (DMML) module, which enhances the model's robustness across various noise scenarios by ensuring the quality of dominant modality representations. Aside from the methodical design, we perform comprehensive experiments under random data missing scenarios, utilizing diverse and meaningful settings on several popular datasets (\textit{e.g.,} MOSI, MOSEI, and SIMS), providing additional uniformity, transparency, and fairness compared to existing evaluations in the literature. Empirically, LNLN consistently outperforms existing baselines, demonstrating superior performance across these challenging and extensive evaluation metrics.<|reference_end|>
|
arxiv
|
@article{zhang2024towards,
title={Towards Robust Multimodal Sentiment Analysis with Incomplete Data},
author={Haoyu Zhang, Wenbin Wang, Tianshu Yu},
journal={arXiv preprint arXiv:2409.20012},
year={2024},
archivePrefix={arXiv},
eprint={2409.20012},
primaryClass={cs.CL cs.AI cs.MM}
}
|
zhang2024towards
|
arxiv-663475
|
2409.20013
|
Single-shot reconstruction of three-dimensional morphology of biological cells in digital holographic microscopy using a physics-driven neural network
|
<|reference_start|>Single-shot reconstruction of three-dimensional morphology of biological cells in digital holographic microscopy using a physics-driven neural network: Recent advances in deep learning-based image reconstruction techniques have led to significant progress in phase retrieval using digital in-line holographic microscopy (DIHM). However, existing deep learning-based phase retrieval methods have technical limitations in generalization performance and three-dimensional (3D) morphology reconstruction from a single-shot hologram of biological cells. In this study, we propose a novel deep learning model, named MorpHoloNet, for single-shot reconstruction of 3D morphology by integrating physics-driven and coordinate-based neural networks. By simulating the optical diffraction of coherent light through a 3D phase shift distribution, the proposed MorpHoloNet is optimized by minimizing the loss between the simulated and input holograms on the sensor plane. Compared to existing DIHM methods that face challenges with twin image and phase retrieval problems, MorpHoloNet enables direct reconstruction of 3D complex light field and 3D morphology of a test sample from its single-shot hologram without requiring multiple phase-shifted holograms or angle scanning. The performance of the proposed MorpHoloNet is validated by reconstructing 3D morphologies and refractive index distributions from synthetic holograms of ellipsoids and experimental holograms of biological cells. The proposed deep learning model is utilized to reconstruct spatiotemporal variations in 3D translational and rotational behaviors and morphological deformations of biological cells from consecutive single-shot holograms captured using DIHM. MorpHoloNet would pave the way for advancing label-free, real-time 3D imaging and dynamic analysis of biological cells under various cellular microenvironments in biomedical and engineering fields.<|reference_end|>
|
arxiv
|
@article{kim2024single-shot,
title={Single-shot reconstruction of three-dimensional morphology of biological
cells in digital holographic microscopy using a physics-driven neural network},
author={Jihwan Kim, Youngdo Kim, Hyo Seung Lee, Eunseok Seo, Sang Joon Lee},
journal={arXiv preprint arXiv:2409.20013},
year={2024},
archivePrefix={arXiv},
eprint={2409.20013},
primaryClass={cs.CV cs.LG physics.optics q-bio.QM}
}
|
kim2024single-shot
|
arxiv-663476
|
2409.20016
|
Personalisation via Dynamic Policy Fusion
|
<|reference_start|>Personalisation via Dynamic Policy Fusion: Deep reinforcement learning (RL) policies, although optimal in terms of task rewards, may not align with the personal preferences of human users. To ensure this alignment, a naive solution would be to retrain the agent using a reward function that encodes the user's specific preferences. However, such a reward function is typically not readily available, and as such, retraining the agent from scratch can be prohibitively expensive. We propose a more practical approach - to adapt the already trained policy to user-specific needs with the help of human feedback. To this end, we infer the user's intent through trajectory-level feedback and combine it with the trained task policy via a theoretically grounded dynamic policy fusion approach. As our approach collects human feedback on the very same trajectories used to learn the task policy, it does not require any additional interactions with the environment, making it a zero-shot approach. We empirically demonstrate in a number of environments that our proposed dynamic policy fusion approach consistently achieves the intended task while simultaneously adhering to user-specific needs.<|reference_end|>
|
arxiv
|
@article{palattuparambil2024personalisation,
title={Personalisation via Dynamic Policy Fusion},
author={Ajsal Shereef Palattuparambil, Thommen George Karimpanal, Santu Rana},
journal={arXiv preprint arXiv:2409.20016},
year={2024},
archivePrefix={arXiv},
eprint={2409.20016},
primaryClass={cs.AI cs.LG}
}
|
palattuparambil2024personalisation
|
arxiv-663477
|
2409.20018
|
Visual Context Window Extension: A New Perspective for Long Video Understanding
|
<|reference_start|>Visual Context Window Extension: A New Perspective for Long Video Understanding: Large Multimodal Models (LMMs) have demonstrated impressive performance in short video understanding tasks but face great challenges when applied to long video understanding. In contrast, Large Language Models (LLMs) exhibit outstanding capabilities in modeling long texts. Existing work attempts to address this issue by introducing long video-text pairs during training. However, these approaches require substantial computational and data resources. In this paper, we tackle the challenge of long video understanding from the perspective of context windows, aiming to apply LMMs to long video tasks without retraining on long video datasets. We first conduct an in-depth analysis of why pretrained LMMs struggle to understand lengthy video content, identifying that discrepancies between visual and language modalities lead to different context windows for visual and language tokens, making it difficult to directly extend the visual tokens to match the language context window. Based on this, we propose to adapt LMMs for long video understanding tasks by extending the visual context window, eliminating the need for retraining on large scalelong video datasets. To further mitigate the significant memory consumption caused by long sequences, we introduce a progressive pooling inference strategy that selectively adjusts the spatial resolution of frame embeddings, reducing the number of visual tokens while retaining important spatial information. Across multiple long video understanding benchmarks, our method consistently improves the performance as the number of video frames increases. On the MLVU benchmark, our method outperforms GPT-4o, even though our model size is only 7B. Additionally, in the 256-frame setting, our method reduces memory usage by approximately 45% compared to the baseline, without introducing any performance loss.<|reference_end|>
|
arxiv
|
@article{wei2024visual,
title={Visual Context Window Extension: A New Perspective for Long Video
Understanding},
author={Hongchen Wei and Zhenzhong Chen},
journal={arXiv preprint arXiv:2409.20018},
year={2024},
archivePrefix={arXiv},
eprint={2409.20018},
primaryClass={cs.CV}
}
|
wei2024visual
|
arxiv-663478
|
2409.20020
|
Optimal Infinite-Horizon Mixed $\mathitH_2/\mathitH_\infty$ Control
|
<|reference_start|>Optimal Infinite-Horizon Mixed $\mathitH_2/\mathitH_\infty$ Control: We study the problem of mixed $\mathit{H}_2/\mathit{H}_\infty$ control in the infinite-horizon setting. We identify the optimal causal controller that minimizes the $\mathit{H}_2$ cost of the closed-loop system subject to an $\mathit{H}_\infty$ constraint. Megretski proved that the optimal mixed $\mathit{H}_2/\mathit{H}_\infty$ controller is non-rational whenever the constraint is active without giving an explicit construction of the controller. In this work, we provide the first exact closed-form solution to the infinite-horizon mixed $\mathit{H}_2/\mathit{H}_\infty$ control in the frequency domain. While the optimal controller is non-rational, our formulation provides a finite-dimensional parameterization of the optimal controller. Leveraging this fact, we introduce an efficient iterative algorithm that finds the optimal causal controller in the frequency domain. We show that this algorithm is convergent when the system is scalar and present numerical evidence for exponential convergence of the proposed algorithm. Finally, we show how to find the best (in $\mathit{H}_\infty$ norm) fixed-order rational approximations of the optimal mixed $\mathit{H}_2/\mathit{H}_\infty$ controller and study its performance.<|reference_end|>
|
arxiv
|
@article{malik2024optimal,
title={Optimal Infinite-Horizon Mixed $\mathit{H}_2/\mathit{H}_\infty$ Control},
author={Vikrant Malik, Taylan Kargin, Joudi Hajar, and Babak Hassibi},
journal={arXiv preprint arXiv:2409.20020},
year={2024},
archivePrefix={arXiv},
eprint={2409.20020},
primaryClass={math.OC cs.SY eess.SY}
}
|
malik2024optimal
|
arxiv-663479
|
2409.20027
|
A Parallel-in-Time Newton's Method for Nonlinear Model Predictive Control
|
<|reference_start|>A Parallel-in-Time Newton's Method for Nonlinear Model Predictive Control: Model predictive control (MPC) is a powerful framework for optimal control of dynamical systems. However, MPC solvers suffer from a high computational burden that restricts their application to systems with low sampling frequency. This issue is further amplified in nonlinear and constrained systems that require nesting MPC solvers within iterative procedures. In this paper, we address these issues by developing parallel-in-time algorithms for constrained nonlinear optimization problems that take advantage of massively parallel hardware to achieve logarithmic computational time scaling over the planning horizon. We develop time-parallel second-order solvers based on interior point methods and the alternating direction method of multipliers, leveraging fast convergence and lower computational cost per iteration. The parallelization is based on a reformulation of the subproblems in terms of associative operations that can be parallelized using the associative scan algorithm. We validate our approach on numerical examples of nonlinear and constrained dynamical systems.<|reference_end|>
|
arxiv
|
@article{iacob2024a,
title={A Parallel-in-Time Newton's Method for Nonlinear Model Predictive
Control},
author={Casian Iacob, Hany Abdulsamad, Simo S"arkk"a},
journal={arXiv preprint arXiv:2409.20027},
year={2024},
archivePrefix={arXiv},
eprint={2409.20027},
primaryClass={math.OC cs.RO cs.SY eess.SY}
}
|
iacob2024a
|
arxiv-663480
|
2409.20028
|
A Quantum Unique Games Conjecture
|
<|reference_start|>A Quantum Unique Games Conjecture: After the NP-hardness of computational problems such as 3SAT and MaxCut was established, a natural next step was to explore whether these problems remain hard to approximate. While the quantum extensions of some of these problems are known to be hard-indeed undecidable-their inapproximability remains largely unresolved. In this work, we introduce definitions for the quantum extensions of Label-Cover and Unique-Label-Cover. We show that these problems play a similarly crucial role in studying the inapproximability of quantum constraint satisfaction problems as they do in the classical setting.<|reference_end|>
|
arxiv
|
@article{mousavi2024a,
title={A Quantum Unique Games Conjecture},
author={Hamoon Mousavi, Taro Spirig},
journal={arXiv preprint arXiv:2409.20028},
year={2024},
archivePrefix={arXiv},
eprint={2409.20028},
primaryClass={quant-ph cs.CC}
}
|
mousavi2024a
|
arxiv-663481
|
2409.20030
|
Acceleration Meets Inverse Maintenance: Faster $\ell_\infty$-Regression
|
<|reference_start|>Acceleration Meets Inverse Maintenance: Faster $\ell_\infty$-Regression: We propose a randomized multiplicative weight update (MWU) algorithm for $\ell_{\infty}$ regression that runs in $\widetilde{O}\left(n^{2+1/22.5} \text{poly}(1/\epsilon)\right)$ time when $\omega = 2+o(1)$, improving upon the previous best $\widetilde{O}\left(n^{2+1/18} \text{poly} \log(1/\epsilon)\right)$ runtime in the low-accuracy regime. Our algorithm combines state-of-the-art inverse maintenance data structures with acceleration. In order to do so, we propose a novel acceleration scheme for MWU that exhibits {\it stabiliy} and {\it robustness}, which are required for the efficient implementations of the inverse maintenance data structures. We also design a faster {\it deterministic} MWU algorithm that runs in $\widetilde{O}\left(n^{2+1/12}\text{poly}(1/\epsilon)\right))$ time when $\omega = 2+o(1)$, improving upon the previous best $\widetilde{O}\left(n^{2+1/6} \text{poly} \log(1/\epsilon)\right)$ runtime in the low-accuracy regime. We achieve this by showing a novel stability result that goes beyond the previous known works based on interior point methods (IPMs). Our work is the first to use acceleration and inverse maintenance together efficiently, finally making the two most important building blocks of modern structured convex optimization compatible.<|reference_end|>
|
arxiv
|
@article{adil2024acceleration,
title={Acceleration Meets Inverse Maintenance: Faster
$\ell_{\infty}$-Regression},
author={Deeksha Adil, Shunhua Jiang, Rasmus Kyng},
journal={arXiv preprint arXiv:2409.20030},
year={2024},
archivePrefix={arXiv},
eprint={2409.20030},
primaryClass={cs.DS math.OC}
}
|
adil2024acceleration
|
arxiv-663482
|
2409.20031
|
Adaptive high-precision sound source localization at low frequencies based on convolutional neural network
|
<|reference_start|>Adaptive high-precision sound source localization at low frequencies based on convolutional neural network: Sound source localization (SSL) technology plays a crucial role in various application areas such as fault diagnosis, speech separation, and vibration noise reduction. Although beamforming algorithms are widely used in SSL, their resolution at low frequencies is limited. In recent years, deep learning-based SSL methods have significantly improved their accuracy by employing large microphone arrays and training case specific neural networks, however, this could lead to narrow applicability. To address these issues, this paper proposes a convolutional neural network-based method for high-precision SSL, which is adaptive in the lower frequency range under 1kHz with varying numbers of sound sources and microphone array-to-scanning grid distances. It takes the pressure distribution on a relatively small microphone array as input to the neural network, and employs customized training labels and loss function to train the model. Prediction accuracy, adaptability and robustness of the trained model under certain signal-to-noise ratio (SNR) are evaluated using randomly generated test datasets, and compared with classical beamforming algorithms, CLEAN-SC and DAMAS. Results of both planar and spatial sound source distributions show that the proposed neural network model significantly improves low-frequency localization accuracy, demonstrating its effectiveness and potential in SSL.<|reference_end|>
|
arxiv
|
@article{ma2024adaptive,
title={Adaptive high-precision sound source localization at low frequencies
based on convolutional neural network},
author={Wenbo Ma, Yan Lu, Yijun Liu},
journal={arXiv preprint arXiv:2409.20031},
year={2024},
archivePrefix={arXiv},
eprint={2409.20031},
primaryClass={cs.SD eess.AS}
}
|
ma2024adaptive
|
arxiv-663483
|
2409.20033
|
Fuel tax loss in a world of electric mobility: A window of opportunity for congestion pricing
|
<|reference_start|>Fuel tax loss in a world of electric mobility: A window of opportunity for congestion pricing: The continued transition towards electric mobility will decrease energy tax revenues worldwide, which has substantial implications for government funds. At the same time, demand for transportation is ever increasing, which in turn increases congestion problems. Combining both challenges, this paper assesses the effectiveness of congestion pricing as a sustainable revenue stream to offset fuel tax loss in 2030 while simultaneously enhancing efficiency in the transport sector. A congestion-based toll that is road-and-time-variant is simulated for the greater Berlin area in Germany using the multi-agent transport simulation (MATSim) software. Through the simulation results, this paper quantifies the impacts of the toll on the governmental revenue, traffic management, environment, social welfare, and the distribution effects. We find that the revenue from congestion tolls in a metropolitan area can compensate the reduction in passenger car fuel tax. Furthermore, a remarkable welfare surplus is observed. The toll also successfully incentivises transport users to adjust their travel behaviour, which reduces traffic delay time by 28%. CO2 emissions as a key metric for decarbonisation of the transport sector decrease by more than 5%. The analysis of the distribution effects suggests that a redistribution plan with a focus on the middle-low-income residents and the outer boroughs could help the policy gain more public acceptance.<|reference_end|>
|
arxiv
|
@article{nguyen2024fuel,
title={Fuel tax loss in a world of electric mobility: A window of opportunity
for congestion pricing},
author={Thi Ngoc Nguyen, Felix Muesgens},
journal={arXiv preprint arXiv:2409.20033},
year={2024},
archivePrefix={arXiv},
eprint={2409.20033},
primaryClass={cs.MA econ.GN q-fin.EC}
}
|
nguyen2024fuel
|
arxiv-663484
|
2409.20034
|
Camera Calibration using a Collimator System
|
<|reference_start|>Camera Calibration using a Collimator System: Camera calibration is a crucial step in photogrammetry and 3D vision applications. In practical scenarios with a long working distance to cover a wide area, target-based calibration methods become complicated and inflexible due to site limitations. This paper introduces a novel camera calibration method using a collimator system, which can provide a reliable and controllable calibration environment for cameras with varying working distances. Based on the optical geometry of the collimator system, we prove that the relative motion between the target and camera conforms to the spherical motion model, reducing the original 6DOF relative motion to 3DOF pure rotation motion. Furthermore, a closed-form solver for multiple views and a minimal solver for two views are proposed for camera calibration. The performance of our method is evaluated in both synthetic and real-world experiments, which verify the feasibility of calibration using the collimator system and demonstrate that our method is superior to the state-of-the-art methods. Demo code is available at https://github.com/LiangSK98/CollimatorCalibration.<|reference_end|>
|
arxiv
|
@article{liang2024camera,
title={Camera Calibration using a Collimator System},
author={Shunkun Liang, Banglei Guan, Zhenbao Yu, Pengju Sun, Yang Shang},
journal={arXiv preprint arXiv:2409.20034},
year={2024},
archivePrefix={arXiv},
eprint={2409.20034},
primaryClass={cs.CV}
}
|
liang2024camera
|
arxiv-663485
|
2409.20038
|
Robot Design Optimization with Rotational and Prismatic Joints using Black-Box Multi-Objective Optimization
|
<|reference_start|>Robot Design Optimization with Rotational and Prismatic Joints using Black-Box Multi-Objective Optimization: Robots generally have a structure that combines rotational joints and links in a serial fashion. On the other hand, various joint mechanisms are being utilized in practice, such as prismatic joints, closed links, and wire-driven systems. Previous research have focused on individual mechanisms, proposing methods to design robots capable of achieving given tasks by optimizing the length of links and the arrangement of the joints. In this study, we propose a method for the design optimization of robots that combine different types of joints, specifically rotational and prismatic joints. The objective is to automatically generate a robot that minimizes the number of joints and link lengths while accomplishing a desired task, by utilizing a black-box multi-objective optimization approach. This enables the simultaneous observation of a diverse range of body designs through the obtained Pareto solutions. Our findings confirm the emergence of practical and known combinations of rotational and prismatic joints, as well as the discovery of novel joint combinations.<|reference_end|>
|
arxiv
|
@article{kawaharazuka2024robot,
title={Robot Design Optimization with Rotational and Prismatic Joints using
Black-Box Multi-Objective Optimization},
author={Kento Kawaharazuka and Kei Okada and Masayuki Inaba},
journal={arXiv preprint arXiv:2409.20038},
year={2024},
archivePrefix={arXiv},
eprint={2409.20038},
primaryClass={cs.RO}
}
|
kawaharazuka2024robot
|
arxiv-663486
|
2409.20042
|
Beyond Scores: A Modular RAG-Based System for Automatic Short Answer Scoring with Feedback
|
<|reference_start|>Beyond Scores: A Modular RAG-Based System for Automatic Short Answer Scoring with Feedback: Automatic short answer scoring (ASAS) helps reduce the grading burden on educators but often lacks detailed, explainable feedback. Existing methods in ASAS with feedback (ASAS-F) rely on fine-tuning language models with limited datasets, which is resource-intensive and struggles to generalize across contexts. Recent approaches using large language models (LLMs) have focused on scoring without extensive fine-tuning. However, they often rely heavily on prompt engineering and either fail to generate elaborated feedback or do not adequately evaluate it. In this paper, we propose a modular retrieval augmented generation based ASAS-F system that scores answers and generates feedback in strict zero-shot and few-shot learning scenarios. We design our system to be adaptable to various educational tasks without extensive prompt engineering using an automatic prompt generation framework. Results show an improvement in scoring accuracy by 9\% on unseen questions compared to fine-tuning, offering a scalable and cost-effective solution.<|reference_end|>
|
arxiv
|
@article{fateen2024beyond,
title={Beyond Scores: A Modular RAG-Based System for Automatic Short Answer
Scoring with Feedback},
author={Menna Fateen, Bo Wang, Tsunenori Mine},
journal={arXiv preprint arXiv:2409.20042},
year={2024},
archivePrefix={arXiv},
eprint={2409.20042},
primaryClass={cs.CL cs.AI}
}
|
fateen2024beyond
|
arxiv-663487
|
2409.20043
|
OPONeRF: One-Point-One NeRF for Robust Neural Rendering
|
<|reference_start|>OPONeRF: One-Point-One NeRF for Robust Neural Rendering: In this paper, we propose a One-Point-One NeRF (OPONeRF) framework for robust scene rendering. Existing NeRFs are designed based on a key assumption that the target scene remains unchanged between the training and test time. However, small but unpredictable perturbations such as object movements, light changes and data contaminations broadly exist in real-life 3D scenes, which lead to significantly defective or failed rendering results even for the recent state-of-the-art generalizable methods. To address this, we propose a divide-and-conquer framework in OPONeRF that adaptively responds to local scene variations via personalizing appropriate point-wise parameters, instead of fitting a single set of NeRF parameters that are inactive to test-time unseen changes. Moreover, to explicitly capture the local uncertainty, we decompose the point representation into deterministic mapping and probabilistic inference. In this way, OPONeRF learns the sharable invariance and unsupervisedly models the unexpected scene variations between the training and testing scenes. To validate the effectiveness of the proposed method, we construct benchmarks from both realistic and synthetic data with diverse test-time perturbations including foreground motions, illumination variations and multi-modality noises, which are more challenging than conventional generalization and temporal reconstruction benchmarks. Experimental results show that our OPONeRF outperforms state-of-the-art NeRFs on various evaluation metrics through benchmark experiments and cross-scene evaluations. We further show the efficacy of the proposed method via experimenting on other existing generalization-based benchmarks and incorporating the idea of One-Point-One NeRF into other advanced baseline methods.<|reference_end|>
|
arxiv
|
@article{zheng2024oponerf:,
title={OPONeRF: One-Point-One NeRF for Robust Neural Rendering},
author={Yu Zheng, Yueqi Duan, Kangfu Zheng, Hongru Yan, Jiwen Lu, Jie Zhou},
journal={arXiv preprint arXiv:2409.20043},
year={2024},
archivePrefix={arXiv},
eprint={2409.20043},
primaryClass={cs.CV}
}
|
zheng2024oponerf:
|
arxiv-663488
|
2409.20047
|
Building Touch-Less Trust in IoT Devices
|
<|reference_start|>Building Touch-Less Trust in IoT Devices: Trust mechanisms for Internet of Things (IoT) devices are commonly used by manufacturers and other ecosystem participants. However, end users face a challenge in establishing trust in devices, particularly as device encounters become more frequent thanks to the proliferation of new and unique products. Communication or even physical interaction with a device can expose a user to various threats, such as biometric theft or exploit of their own device. To address this, we propose a mechanism for verifying the integrity and trustworthiness of an IoT device before physical interaction or any significant communication has taken place.<|reference_end|>
|
arxiv
|
@article{kerrison2024building,
title={Building Touch-Less Trust in IoT Devices},
author={Steve Kerrison},
journal={arXiv preprint arXiv:2409.20047},
year={2024},
archivePrefix={arXiv},
eprint={2409.20047},
primaryClass={cs.CR}
}
|
kerrison2024building
|
arxiv-663489
|
2409.20048
|
Depression detection in social media posts using transformer-based models and auxiliary features
|
<|reference_start|>Depression detection in social media posts using transformer-based models and auxiliary features: The detection of depression in social media posts is crucial due to the increasing prevalence of mental health issues. Traditional machine learning algorithms often fail to capture intricate textual patterns, limiting their effectiveness in identifying depression. Existing studies have explored various approaches to this problem but often fall short in terms of accuracy and robustness. To address these limitations, this research proposes a neural network architecture leveraging transformer-based models combined with metadata and linguistic markers. The study employs DistilBERT, extracting information from the last four layers of the transformer, applying learned weights, and averaging them to create a rich representation of the input text. This representation, augmented by metadata and linguistic markers, enhances the model's comprehension of each post. Dropout layers prevent overfitting, and a Multilayer Perceptron (MLP) is used for final classification. Data augmentation techniques, inspired by the Easy Data Augmentation (EDA) methods, are also employed to improve model performance. Using BERT, random insertion and substitution of phrases generate additional training data, focusing on balancing the dataset by augmenting underrepresented classes. The proposed model achieves weighted Precision, Recall, and F1-scores of 84.26%, 84.18%, and 84.15%, respectively. The augmentation techniques significantly enhance model performance, increasing the weighted F1-score from 72.59% to 84.15%.<|reference_end|>
|
arxiv
|
@article{kerasiotis2024depression,
title={Depression detection in social media posts using transformer-based
models and auxiliary features},
author={Marios Kerasiotis, Loukas Ilias, Dimitris Askounis},
journal={Volume 14, article number 196, (2024)},
year={2024},
doi={10.1007/s13278-024-01360-4},
archivePrefix={arXiv},
eprint={2409.20048},
primaryClass={cs.CL}
}
|
kerasiotis2024depression
|
arxiv-663490
|
2409.20052
|
Mitigating Propensity Bias of Large Language Models for Recommender Systems
|
<|reference_start|>Mitigating Propensity Bias of Large Language Models for Recommender Systems: The rapid development of Large Language Models (LLMs) creates new opportunities for recommender systems, especially by exploiting the side information (e.g., descriptions and analyses of items) generated by these models. However, aligning this side information with collaborative information from historical interactions poses significant challenges. The inherent biases within LLMs can skew recommendations, resulting in distorted and potentially unfair user experiences. On the other hand, propensity bias causes side information to be aligned in such a way that it often tends to represent all inputs in a low-dimensional subspace, leading to a phenomenon known as dimensional collapse, which severely restricts the recommender system's ability to capture user preferences and behaviours. To address these issues, we introduce a novel framework named Counterfactual LLM Recommendation (CLLMR). Specifically, we propose a spectrum-based side information encoder that implicitly embeds structural information from historical interactions into the side information representation, thereby circumventing the risk of dimension collapse. Furthermore, our CLLMR approach explores the causal relationships inherent in LLM-based recommender systems. By leveraging counterfactual inference, we counteract the biases introduced by LLMs. Extensive experiments demonstrate that our CLLMR approach consistently enhances the performance of various recommender models.<|reference_end|>
|
arxiv
|
@article{zhang2024mitigating,
title={Mitigating Propensity Bias of Large Language Models for Recommender
Systems},
author={Guixian Zhang, Guan Yuan, Debo Cheng, Lin Liu, Jiuyong Li, Shichao
Zhang},
journal={arXiv preprint arXiv:2409.20052},
year={2024},
archivePrefix={arXiv},
eprint={2409.20052},
primaryClass={cs.IR cs.AI}
}
|
zhang2024mitigating
|
arxiv-663491
|
2409.20053
|
GUNDAM: Aligning Large Language Models with Graph Understanding
|
<|reference_start|>GUNDAM: Aligning Large Language Models with Graph Understanding: Large Language Models (LLMs) have achieved impressive results in processing text data, which has sparked interest in applying these models beyond textual data, such as graphs. In the field of graph learning, there is a growing interest in harnessing LLMs to comprehend and manipulate graph-structured data. Existing research predominantly focuses on graphs with rich textual features, such as knowledge graphs or text attribute graphs, leveraging LLMs' ability to process text but inadequately addressing graph structure. This work specifically aims to assess and enhance LLMs' abilities to comprehend and utilize the structural knowledge inherent in graph data itself, rather than focusing solely on graphs rich in textual content. To achieve this, we introduce the \textbf{G}raph \textbf{U}nderstanding for \textbf{N}atural Language \textbf{D}riven \textbf{A}nalytical \textbf{M}odel (\model). This model adapts LLMs to better understand and engage with the structure of graph data, enabling them to perform complex reasoning tasks by leveraging the graph's structure itself. Our experimental evaluations on graph reasoning benchmarks not only substantiate that \model~ outperforms the SOTA baselines for comparisons. But also reveals key factors affecting the graph reasoning capabilities of LLMs. Moreover, we provide a theoretical analysis illustrating how reasoning paths can enhance LLMs' reasoning capabilities.<|reference_end|>
|
arxiv
|
@article{ouyang2024gundam:,
title={GUNDAM: Aligning Large Language Models with Graph Understanding},
author={Sheng Ouyang and Yulan Hu and Ge Chen and Yong Liu},
journal={arXiv preprint arXiv:2409.20053},
year={2024},
archivePrefix={arXiv},
eprint={2409.20053},
primaryClass={cs.AI cs.CL cs.LG}
}
|
ouyang2024gundam:
|
arxiv-663492
|
2409.20054
|
Evaluating and explaining training strategies for zero-shot cross-lingual news sentiment analysis
|
<|reference_start|>Evaluating and explaining training strategies for zero-shot cross-lingual news sentiment analysis: We investigate zero-shot cross-lingual news sentiment detection, aiming to develop robust sentiment classifiers that can be deployed across multiple languages without target-language training data. We introduce novel evaluation datasets in several less-resourced languages, and experiment with a range of approaches including the use of machine translation; in-context learning with large language models; and various intermediate training regimes including a novel task objective, POA, that leverages paragraph-level information. Our results demonstrate significant improvements over the state of the art, with in-context learning generally giving the best performance, but with the novel POA approach giving a competitive alternative with much lower computational overhead. We also show that language similarity is not in itself sufficient for predicting the success of cross-lingual transfer, but that similarity in semantic content and structure can be equally important.<|reference_end|>
|
arxiv
|
@article{andrenšek2024evaluating,
title={Evaluating and explaining training strategies for zero-shot
cross-lingual news sentiment analysis},
author={Luka Andrenv{s}ek and Boshko Koloski and Andrav{z} Pelicon and Nada
Lavrav{c} and Senja Pollak and Matthew Purver},
journal={arXiv preprint arXiv:2409.20054},
year={2024},
archivePrefix={arXiv},
eprint={2409.20054},
primaryClass={cs.CL cs.AI}
}
|
andrenšek2024evaluating
|
arxiv-663493
|
2409.20055
|
Neural Click Models for Recommender Systems
|
<|reference_start|>Neural Click Models for Recommender Systems: We develop and evaluate neural architectures to model the user behavior in recommender systems (RS) inspired by click models for Web search but going beyond standard click models. Proposed architectures include recurrent networks, Transformer-based models that alleviate the quadratic complexity of self-attention, adversarial and hierarchical architectures. Our models outperform baselines on the ContentWise and RL4RS datasets and can be used in RS simulators to model user response for RS evaluation and pretraining.<|reference_end|>
|
arxiv
|
@article{shirokikh2024neural,
title={Neural Click Models for Recommender Systems},
author={Mikhail Shirokikh, Ilya Shenbin, Anton Alekseev, Anna Volodkevich,
Alexey Vasilev, Andrey V. Savchenko, Sergey Nikolenko},
journal={arXiv preprint arXiv:2409.20055},
year={2024},
doi={10.1145/3626772.3657939},
archivePrefix={arXiv},
eprint={2409.20055},
primaryClass={cs.IR cs.LG}
}
|
shirokikh2024neural
|
arxiv-663494
|
2409.20056
|
Reasoning About Exceptional Behavior At the Level of Java Bytecode
|
<|reference_start|>Reasoning About Exceptional Behavior At the Level of Java Bytecode: A program's exceptional behavior can substantially complicate its control flow, and hence accurately reasoning about the program's correctness. On the other hand, formally verifying realistic programs is likely to involve exceptions -- a ubiquitous feature in modern programming languages. In this paper, we present a novel approach to verify the exceptional behavior of Java programs, which extends our previous work on ByteBack. ByteBack works on a program's bytecode, while providing means to specify the intended behavior at the source-code level; this approach sets ByteBack apart from most state-of-the-art verifiers that target source code. To explicitly model a program's exceptional behavior in a way that is amenable to formal reasoning, we introduce Vimp: a high-level bytecode representation that extends the Soot framework's Grimp with verification-oriented features, thus serving as an intermediate layer between bytecode and the Boogie intermediate verification language. Working on bytecode through this intermediate layer brings flexibility and adaptability to new language versions and variants: as our experiments demonstrate, ByteBack can verify programs involving exceptional behavior in all versions of Java, as well as in Scala and Kotlin (two other popular JVM languages).<|reference_end|>
|
arxiv
|
@article{paganoni2024reasoning,
title={Reasoning About Exceptional Behavior At the Level of Java Bytecode},
author={Marco Paganoni and Carlo A. Furia},
journal={arXiv preprint arXiv:2409.20056},
year={2024},
doi={10.1007/978-3-031-47705-8_7},
archivePrefix={arXiv},
eprint={2409.20056},
primaryClass={cs.PL cs.LO}
}
|
paganoni2024reasoning
|
arxiv-663495
|
2409.20059
|
Is Preference Alignment Always the Best Option to Enhance LLM-Based Translation? An Empirical Analysis
|
<|reference_start|>Is Preference Alignment Always the Best Option to Enhance LLM-Based Translation? An Empirical Analysis: Neural metrics for machine translation (MT) evaluation have become increasingly prominent due to their superior correlation with human judgments compared to traditional lexical metrics. Researchers have therefore utilized neural metrics through quality-informed decoding strategies, achieving better results than likelihood-based methods. With the rise of Large Language Models (LLMs), preference-based alignment techniques have gained attention for their potential to enhance translation quality by optimizing model weights directly on preferences induced by quality estimators. This study focuses on Contrastive Preference Optimization (CPO) and conducts extensive experiments to evaluate the impact of preference-based alignment on translation quality. Our findings indicate that while CPO consistently outperforms Supervised Fine-Tuning (SFT) on high-quality data with regard to the alignment metric, it may lead to instability across downstream evaluation metrics, particularly between neural and lexical ones. Additionally, we demonstrate that relying solely on the base model for generating candidate translations achieves performance comparable to using multiple external systems, while ensuring better consistency across downstream metrics.<|reference_end|>
|
arxiv
|
@article{gisserot-boukhlef2024is,
title={Is Preference Alignment Always the Best Option to Enhance LLM-Based
Translation? An Empirical Analysis},
author={Hippolyte Gisserot-Boukhlef, Ricardo Rei, Emmanuel Malherbe, C'eline
Hudelot, Pierre Colombo, Nuno M. Guerreiro},
journal={arXiv preprint arXiv:2409.20059},
year={2024},
archivePrefix={arXiv},
eprint={2409.20059},
primaryClass={cs.CL}
}
|
gisserot-boukhlef2024is
|
arxiv-663496
|
2409.20060
|
Lightweight Neural Architecture Search for Cerebral Palsy Detection
|
<|reference_start|>Lightweight Neural Architecture Search for Cerebral Palsy Detection: The neurological condition known as cerebral palsy (CP) first manifests in infancy or early childhood and has a lifelong impact on motor coordination and body movement. CP is one of the leading causes of childhood disabilities, and early detection is crucial for providing appropriate treatment. However, such detection relies on assessments by human experts trained in methods like general movement assessment (GMA). These are not widely accessible, especially in developing countries. Conventional machine learning approaches offer limited predictive performance on CP detection tasks, and the approaches developed by the few available domain experts are generally dataset-specific, restricting their applicability beyond the context for which these were created. To address these challenges, we propose a neural architecture search (NAS) algorithm applying a reinforcement learning update scheme capable of efficiently optimizing for the best architectural and hyperparameter combination to discover the most suitable neural network configuration for detecting CP. Our method performs better on a real-world CP dataset than other approaches in the field, which rely on large ensembles. As our approach is less resource-demanding and performs better, it is particularly suitable for implementation in resource-constrained settings, including rural or developing areas with limited access to medical experts and the required diagnostic tools. The resulting model's lightweight architecture and efficient computation time allow for deployment on devices with limited processing power, reducing the need for expensive infrastructure, and can, therefore, be integrated into clinical workflows to provide timely and accurate support for early CP diagnosis.<|reference_end|>
|
arxiv
|
@article{tempel2024lightweight,
title={Lightweight Neural Architecture Search for Cerebral Palsy Detection},
author={Felix Tempel, Espen Alexander F. Ihlen, Inga Str"umke},
journal={arXiv preprint arXiv:2409.20060},
year={2024},
archivePrefix={arXiv},
eprint={2409.20060},
primaryClass={cs.CV}
}
|
tempel2024lightweight
|
arxiv-663497
|
2409.20063
|
Q-Bench-Video: Benchmarking the Video Quality Understanding of LMMs
|
<|reference_start|>Q-Bench-Video: Benchmarking the Video Quality Understanding of LMMs: With the rising interest in research on Large Multi-modal Models (LMMs) for video understanding, many studies have emphasized general video comprehension capabilities, neglecting the systematic exploration into video quality understanding. To address this oversight, we introduce Q-Bench-Video in this paper, a new benchmark specifically designed to evaluate LMMs' proficiency in discerning video quality. a) To ensure video source diversity, Q-Bench-Video encompasses videos from natural scenes, AI-generated Content (AIGC), and Computer Graphics (CG). b) Building on the traditional multiple-choice questions format with the Yes-or-No and What-How categories, we include Open-ended questions to better evaluate complex scenarios. Additionally, we incorporate the video pair quality comparison question to enhance comprehensiveness. c) Beyond the traditional Technical, Aesthetic, and Temporal distortions, we have expanded our evaluation aspects to include the dimension of AIGC distortions, which addresses the increasing demand for video generation. Finally, we collect a total of 2,378 question-answer pairs and test them on 12 open-source & 5 proprietary LMMs. Our findings indicate that while LMMs have a foundational understanding of video quality, their performance remains incomplete and imprecise, with a notable discrepancy compared to human performance. Through Q-Bench-Video, we seek to catalyze community interest, stimulate further research, and unlock the untapped potential of LMMs to close the gap in video quality understanding.<|reference_end|>
|
arxiv
|
@article{zhang2024q-bench-video:,
title={Q-Bench-Video: Benchmarking the Video Quality Understanding of LMMs},
author={Zicheng Zhang, Ziheng Jia, Haoning Wu, Chunyi Li, Zijian Chen, Yingjie
Zhou, Wei Sun, Xiaohong Liu, Xiongkuo Min, Weisi Lin, Guangtao Zhai},
journal={arXiv preprint arXiv:2409.20063},
year={2024},
archivePrefix={arXiv},
eprint={2409.20063},
primaryClass={cs.CV}
}
|
zhang2024q-bench-video:
|
arxiv-663498
|
2409.20064
|
Knowledge Discovery using Unsupervised Cognition
|
<|reference_start|>Knowledge Discovery using Unsupervised Cognition: Knowledge discovery is key to understand and interpret a dataset, as well as to find the underlying relationships between its components. Unsupervised Cognition is a novel unsupervised learning algorithm that focus on modelling the learned data. This paper presents three techniques to perform knowledge discovery over an already trained Unsupervised Cognition model. Specifically, we present a technique for pattern mining, a technique for feature selection based on the previous pattern mining technique, and a technique for dimensionality reduction based on the previous feature selection technique. The final goal is to distinguish between relevant and irrelevant features and use them to build a model from which to extract meaningful patterns. We evaluated our proposals with empirical experiments and found that they overcome the state-of-the-art in knowledge discovery.<|reference_end|>
|
arxiv
|
@article{ibias2024knowledge,
title={Knowledge Discovery using Unsupervised Cognition},
author={Alfredo Ibias, Hector Antona, Guillem Ramirez-Miranda, Enric Guinovart},
journal={arXiv preprint arXiv:2409.20064},
year={2024},
archivePrefix={arXiv},
eprint={2409.20064},
primaryClass={cs.LG cs.AI}
}
|
ibias2024knowledge
|
arxiv-663499
|
2409.20067
|
Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning
|
<|reference_start|>Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning: Standard multi-agent reinforcement learning (MARL) algorithms are vulnerable to sim-to-real gaps. To address this, distributionally robust Markov games (RMGs) have been proposed to enhance robustness in MARL by optimizing the worst-case performance when game dynamics shift within a prescribed uncertainty set. Solving RMGs remains under-explored, from problem formulation to the development of sample-efficient algorithms. A notorious yet open challenge is if RMGs can escape the curse of multiagency, where the sample complexity scales exponentially with the number of agents. In this work, we propose a natural class of RMGs where the uncertainty set of each agent is shaped by both the environment and other agents' strategies in a best-response manner. We first establish the well-posedness of these RMGs by proving the existence of game-theoretic solutions such as robust Nash equilibria and coarse correlated equilibria (CCE). Assuming access to a generative model, we then introduce a sample-efficient algorithm for learning the CCE whose sample complexity scales polynomially with all relevant parameters. To the best of our knowledge, this is the first algorithm to break the curse of multiagency for RMGs.<|reference_end|>
|
arxiv
|
@article{shi2024breaking,
title={Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement
Learning},
author={Laixi Shi, Jingchu Gai, Eric Mazumdar, Yuejie Chi, Adam Wierman},
journal={arXiv preprint arXiv:2409.20067},
year={2024},
archivePrefix={arXiv},
eprint={2409.20067},
primaryClass={cs.LG cs.GT cs.MA stat.ML}
}
|
shi2024breaking
|
arxiv-663500
|
2409.20071
|
Verifying Functional Correctness Properties At the Level of Java Bytecode
|
<|reference_start|>Verifying Functional Correctness Properties At the Level of Java Bytecode: The breakneck evolution of modern programming languages aggravates the development of deductive verification tools, which struggle to timely and fully support all new language features. To address this challenge, we present ByteBack: a verification technique that works on Java bytecode. Compared to high-level languages, intermediate representations such as bytecode offer a much more limited and stable set of features; hence, they may help decouple the verification process from changes in the source-level language. ByteBack offers a library to specify functional correctness properties at the level of the source code, so that the bytecode is only used as an intermediate representation that the end user does not need to work with. Then, ByteBack reconstructs some of the information about types and expressions that is erased during compilation into bytecode but is necessary to correctly perform verification. Our experiments with an implementation of ByteBack demonstrate that it can successfully verify bytecode compiled from different versions of Java, and including several modern language features that even state-of-the-art Java verifiers (such as KeY and OpenJML) do not directly support$\unicode{x2013}$thus revealing how ByteBack's approach can help keep up verification technology with language evolution.<|reference_end|>
|
arxiv
|
@article{paganoni2024verifying,
title={Verifying Functional Correctness Properties At the Level of Java
Bytecode},
author={Marco Paganoni and Carlo A. Furia},
journal={arXiv preprint arXiv:2409.20071},
year={2024},
doi={10.1007/978-3-031-27481-7_20},
archivePrefix={arXiv},
eprint={2409.20071},
primaryClass={cs.PL cs.LO}
}
|
paganoni2024verifying
|
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