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arxiv-665501 | 2410.03221 | Learning to steer with Brownian noise | <|reference_start|>Learning to steer with Brownian noise: This paper considers an ergodic version of the bounded velocity follower problem, assuming that the decision maker lacks knowledge of the underlying system parameters and must learn them while simultaneously controlling. We propose algorithms based on moving empirical averages and develop a framework for integrating statistical methods with stochastic control theory. Our primary result is a logarithmic expected regret rate. To achieve this, we conduct a rigorous analysis of the ergodic convergence rates of the underlying processes and the risks of the considered estimators.<|reference_end|> | arxiv | @article{ankirchner2024learning,
title={Learning to steer with Brownian noise},
author={Stefan Ankirchner, S"oren Christensen, Jan Kallsen, Philip Le Borne,
Stefan Perko},
journal={arXiv preprint arXiv:2410.03221},
year={2024},
archivePrefix={arXiv},
eprint={2410.03221},
primaryClass={stat.ML cs.LG math.PR math.ST stat.TH}
} | ankirchner2024learning |
arxiv-665502 | 2410.03223 | Consultation on Industrial Machine Faults with Large language Models | <|reference_start|>Consultation on Industrial Machine Faults with Large language Models: Industrial machine fault diagnosis is a critical component of operational efficiency and safety in manufacturing environments. Traditional methods rely heavily on expert knowledge and specific machine learning models, which can be limited in their adaptability and require extensive labeled data. This paper introduces a novel approach leveraging Large Language Models (LLMs), specifically through a structured multi-round prompting technique, to improve fault diagnosis accuracy. By dynamically crafting prompts, our method enhances the model's ability to synthesize information from diverse data sources, leading to improved contextual understanding and actionable recommendations. Experimental results demonstrate that our approach outperforms baseline models, achieving an accuracy of 91% in diagnosing various fault types. The findings underscore the potential of LLMs in revolutionizing industrial fault consultation practices, paving the way for more effective maintenance strategies in complex environments.<|reference_end|> | arxiv | @article{boonmee2024consultation,
title={Consultation on Industrial Machine Faults with Large language Models},
author={Apiradee Boonmee, Kritsada Wongsuwan, Pimchanok Sukjai},
journal={arXiv preprint arXiv:2410.03223},
year={2024},
archivePrefix={arXiv},
eprint={2410.03223},
primaryClass={cs.CL}
} | boonmee2024consultation |
arxiv-665503 | 2410.03224 | ScriptViz: A Visualization Tool to Aid Scriptwriting based on a Large Movie Database | <|reference_start|>ScriptViz: A Visualization Tool to Aid Scriptwriting based on a Large Movie Database: Scriptwriters usually rely on their mental visualization to create a vivid story by using their imagination to see, feel, and experience the scenes they are writing. Besides mental visualization, they often refer to existing images or scenes in movies and analyze the visual elements to create a certain mood or atmosphere. In this paper, we develop ScriptViz to provide external visualization based on a large movie database for the screenwriting process. It retrieves reference visuals on the fly based on scripts' text and dialogue from a large movie database. The tool provides two types of control on visual elements that enable writers to 1) see exactly what they want with fixed visual elements and 2) see variances in uncertain elements. User evaluation among 15 scriptwriters shows that ScriptViz is able to present scriptwriters with consistent yet diverse visual possibilities, aligning closely with their scripts and helping their creation.<|reference_end|> | arxiv | @article{rao2024scriptviz:,
title={ScriptViz: A Visualization Tool to Aid Scriptwriting based on a Large
Movie Database},
author={Anyi Rao, Jean-Pe"ic Chou, Maneesh Agrawala},
journal={arXiv preprint arXiv:2410.03224},
year={2024},
archivePrefix={arXiv},
eprint={2410.03224},
primaryClass={cs.HC cs.AI cs.CV cs.GR}
} | rao2024scriptviz: |
arxiv-665504 | 2410.03225 | AutoPenBench: Benchmarking Generative Agents for Penetration Testing | <|reference_start|>AutoPenBench: Benchmarking Generative Agents for Penetration Testing: Generative AI agents, software systems powered by Large Language Models (LLMs), are emerging as a promising approach to automate cybersecurity tasks. Among the others, penetration testing is a challenging field due to the task complexity and the diverse strategies to simulate cyber-attacks. Despite growing interest and initial studies in automating penetration testing with generative agents, there remains a significant gap in the form of a comprehensive and standard framework for their evaluation and development. This paper introduces AutoPenBench, an open benchmark for evaluating generative agents in automated penetration testing. We present a comprehensive framework that includes 33 tasks, each representing a vulnerable system that the agent has to attack. Tasks are of increasing difficulty levels, including in-vitro and real-world scenarios. We assess the agent performance with generic and specific milestones that allow us to compare results in a standardised manner and understand the limits of the agent under test. We show the benefits of AutoPenBench by testing two agent architectures: a fully autonomous and a semi-autonomous supporting human interaction. We compare their performance and limitations. For example, the fully autonomous agent performs unsatisfactorily achieving a 21% Success Rate (SR) across the benchmark, solving 27% of the simple tasks and only one real-world task. In contrast, the assisted agent demonstrates substantial improvements, with 64% of SR. AutoPenBench allows us also to observe how different LLMs like GPT-4o or OpenAI o1 impact the ability of the agents to complete the tasks. We believe that our benchmark fills the gap with a standard and flexible framework to compare penetration testing agents on a common ground. We hope to extend AutoPenBench along with the research community by making it available under https://github.com/lucagioacchini/auto-pen-bench.<|reference_end|> | arxiv | @article{gioacchini2024autopenbench:,
title={AutoPenBench: Benchmarking Generative Agents for Penetration Testing},
author={Luca Gioacchini, Marco Mellia, Idilio Drago, Alexander Delsanto,
Giuseppe Siracusano, Roberto Bifulco},
journal={arXiv preprint arXiv:2410.03225},
year={2024},
archivePrefix={arXiv},
eprint={2410.03225},
primaryClass={cs.CR cs.AI}
} | gioacchini2024autopenbench: |
arxiv-665505 | 2410.03226 | Frame-Voyager: Learning to Query Frames for Video Large Language Models | <|reference_start|>Frame-Voyager: Learning to Query Frames for Video Large Language Models: Video Large Language Models (Video-LLMs) have made remarkable progress in video understanding tasks. However, they are constrained by the maximum length of input tokens, making it impractical to input entire videos. Existing frame selection approaches, such as uniform frame sampling and text-frame retrieval, fail to account for the information density variations in the videos or the complex instructions in the tasks, leading to sub-optimal performance. In this paper, we propose Frame-Voyager that learns to query informative frame combinations, based on the given textual queries in the task. To train Frame-Voyager, we introduce a new data collection and labeling pipeline, by ranking frame combinations using a pre-trained Video-LLM. Given a video of M frames, we traverse its T-frame combinations, feed them into a Video-LLM, and rank them based on Video-LLM's prediction losses. Using this ranking as supervision, we train Frame-Voyager to query the frame combinations with lower losses. In experiments, we evaluate Frame-Voyager on four Video Question Answering benchmarks by plugging it into two different Video-LLMs. The experimental results demonstrate that Frame-Voyager achieves impressive results in all settings, highlighting its potential as a plug-and-play solution for Video-LLMs.<|reference_end|> | arxiv | @article{yu2024frame-voyager:,
title={Frame-Voyager: Learning to Query Frames for Video Large Language Models},
author={Sicheng Yu, Chengkai Jin, Huanyu Wang, Zhenghao Chen, Sheng Jin,
Zhongrong Zuo, Xiaolei Xu, Zhenbang Sun, Bingni Zhang, Jiawei Wu, Hao Zhang,
Qianru Sun},
journal={arXiv preprint arXiv:2410.03226},
year={2024},
archivePrefix={arXiv},
eprint={2410.03226},
primaryClass={cs.CV cs.CL}
} | yu2024frame-voyager: |
arxiv-665506 | 2410.03227 | ALR$^2$: A Retrieve-then-Reason Framework for Long-context Question Answering | <|reference_start|>ALR$^2$: A Retrieve-then-Reason Framework for Long-context Question Answering: The context window of large language models (LLMs) has been extended significantly in recent years. However, while the context length that the LLM can process has grown, the capability of the model to accurately reason over that context degrades noticeably. This occurs because modern LLMs often become overwhelmed by the vast amount of information in the context; when answering questions, the model must identify and reason over relevant evidence sparsely distributed throughout the text. To alleviate the challenge of long-context reasoning, we develop a retrieve-then-reason framework, enabling LLMs to reason over relevant evidence collected during an intermediate retrieval step. We find that modern LLMs struggle to accurately retrieve relevant facts and instead, often hallucinate "retrieved facts", resulting in flawed reasoning and the production of incorrect answers. To address these issues, we introduce ALR$^2$, a method that augments the long-context reasoning capability of LLMs via an explicit two-stage procedure, i.e., aligning LLMs with the objectives of both retrieval and reasoning. We demonstrate the efficacy of ALR$^2$ for mitigating performance degradation in long-context reasoning tasks. Through extensive experiments on long-context QA benchmarks, we find our method to outperform competitive baselines by large margins, achieving at least 8.4 and 7.9 EM gains on the long-context versions of HotpotQA and SQuAD datasets, respectively.<|reference_end|> | arxiv | @article{li2024alr$^2$:,
title={ALR$^2$: A Retrieve-then-Reason Framework for Long-context Question
Answering},
author={Huayang Li, Pat Verga, Priyanka Sen, Bowen Yang, Vijay Viswanathan,
Patrick Lewis, Taro Watanabe, Yixuan Su},
journal={arXiv preprint arXiv:2410.03227},
year={2024},
archivePrefix={arXiv},
eprint={2410.03227},
primaryClass={cs.CL}
} | li2024alr$^2$: |
arxiv-665507 | 2410.03229 | Elucidating the Design Choice of Probability Paths in Flow Matching for Forecasting | <|reference_start|>Elucidating the Design Choice of Probability Paths in Flow Matching for Forecasting: Flow matching has recently emerged as a powerful paradigm for generative modeling and has been extended to probabilistic time series forecasting in latent spaces. However, the impact of the specific choice of probability path model on forecasting performance remains under-explored. In this work, we demonstrate that forecasting spatio-temporal data with flow matching is highly sensitive to the selection of the probability path model. Motivated by this insight, we propose a novel probability path model designed to improve forecasting performance. Our empirical results across various dynamical system benchmarks show that our model achieves faster convergence during training and improved predictive performance compared to existing probability path models. Importantly, our approach is efficient during inference, requiring only a few sampling steps. This makes our proposed model practical for real-world applications and opens new avenues for probabilistic forecasting.<|reference_end|> | arxiv | @article{lim2024elucidating,
title={Elucidating the Design Choice of Probability Paths in Flow Matching for
Forecasting},
author={Soon Hoe Lim, Yijin Wang, Annan Yu, Emma Hart, Michael W. Mahoney,
Xiaoye S. Li, N. Benjamin Erichson},
journal={arXiv preprint arXiv:2410.03229},
year={2024},
archivePrefix={arXiv},
eprint={2410.03229},
primaryClass={stat.ML cs.LG}
} | lim2024elucidating |
arxiv-665508 | 2410.03230 | Online Bandit Nonlinear Control with Dynamic Batch Length and Adaptive Learning Rate | <|reference_start|>Online Bandit Nonlinear Control with Dynamic Batch Length and Adaptive Learning Rate: This paper is concerned with the online bandit nonlinear control, which aims to learn the best stabilizing controller from a pool of stabilizing and destabilizing controllers of unknown types for a given nonlinear dynamical system. We develop an algorithm, named Dynamic Batch length and Adaptive learning Rate (DBAR), and study its stability and regret. Unlike the existing Exp3 algorithm requiring an exponentially stabilizing controller, DBAR only needs a significantly weaker notion of controller stability, in which case substantial time may be required to certify the system stability. Dynamic batch length in DBAR effectively addresses this issue and enables the system to attain asymptotic stability, where the algorithm behaves as if there were no destabilizing controllers. Moreover, adaptive learning rate in DBAR only uses the state norm information to achieve a tight regret bound even when none of the stabilizing controllers in the pool are exponentially stabilizing.<|reference_end|> | arxiv | @article{kim2024online,
title={Online Bandit Nonlinear Control with Dynamic Batch Length and Adaptive
Learning Rate},
author={Jihun Kim and Javad Lavaei},
journal={arXiv preprint arXiv:2410.03230},
year={2024},
archivePrefix={arXiv},
eprint={2410.03230},
primaryClass={eess.SY cs.SY}
} | kim2024online |
arxiv-665509 | 2410.03234 | Showing LLM-Generated Code Selectively Based on Confidence of LLMs | <|reference_start|>Showing LLM-Generated Code Selectively Based on Confidence of LLMs: Large Language Models (LLMs) have shown impressive abilities in code generation, but they may generate erroneous programs. Reading a program takes ten times longer than writing it. Showing these erroneous programs to developers will waste developers' energies and introduce security risks to software. To address the above limitations, we propose HonestCoder, a novel LLM-based code generation approach. HonestCoder selectively shows the generated programs to developers based on LLMs' confidence. The confidence provides valuable insights into the correctness of generated programs. To achieve this goal, we propose a novel approach to estimate LLMs' confidence in code generation. It estimates confidence by measuring the multi-modal similarity between LLMs-generated programs. We collect and release a multilingual benchmark named TruthCodeBench, which consists of 2,265 samples and covers two popular programming languages (i.e., Python and Java). We apply HonestCoder to four popular LLMs (e.g., DeepSeek-Coder and Code Llama) and evaluate it on TruthCodeBench. Based on the experiments, we obtain the following insights. (1) HonestCoder can effectively estimate LLMs' confidence and accurately determine the correctness of generated programs. For example, HonestCoder outperforms the state-of-the-art baseline by 27.79% in AUROC and 63.74% in AUCPR. (2) HonestCoder can decrease the number of erroneous programs shown to developers. Compared to eight baselines, it can show more correct programs and fewer erroneous programs to developers. (3) Compared to showing code indiscriminately, HonestCoder only adds slight time overhead (approximately 0.4 seconds per requirement). (4) We discuss future directions to facilitate the application of LLMs in software development. We hope this work can motivate broad discussions about measuring the reliability of LLMs' outputs in performing code-related tasks.<|reference_end|> | arxiv | @article{li2024showing,
title={Showing LLM-Generated Code Selectively Based on Confidence of LLMs},
author={Jia Li, Yuqi Zhu, Yongmin Li, Ge Li, Zhi Jin},
journal={arXiv preprint arXiv:2410.03234},
year={2024},
archivePrefix={arXiv},
eprint={2410.03234},
primaryClass={cs.SE cs.CL}
} | li2024showing |
arxiv-665510 | 2410.03235 | Enriching Ontologies with Disjointness Axioms using Large Language Models | <|reference_start|>Enriching Ontologies with Disjointness Axioms using Large Language Models: Ontologies often lack explicit disjointness declarations between classes, despite their usefulness for sophisticated reasoning and consistency checking in Knowledge Graphs. In this study, we explore the potential of Large Language Models (LLMs) to enrich ontologies by identifying and asserting class disjointness axioms. Our approach aims at leveraging the implicit knowledge embedded in LLMs, using prompt engineering to elicit this knowledge for classifying ontological disjointness. We validate our methodology on the DBpedia ontology, focusing on open-source LLMs. Our findings suggest that LLMs, when guided by effective prompt strategies, can reliably identify disjoint class relationships, thus streamlining the process of ontology completion without extensive manual input. For comprehensive disjointness enrichment, we propose a process that takes logical relationships between disjointness and subclass statements into account in order to maintain satisfiability and reduce the number of calls to the LLM. This work provides a foundation for future applications of LLMs in automated ontology enhancement and offers insights into optimizing LLM performance through strategic prompt design. Our code is publicly available on GitHub at https://github.com/n28div/llm-disjointness.<|reference_end|> | arxiv | @article{crum2024enriching,
title={Enriching Ontologies with Disjointness Axioms using Large Language
Models},
author={Elias Crum, Antonio De Santis, Manon Ovide, Jiaxin Pan, Alessia Pisu,
Nicolas Lazzari, Sebastian Rudolph},
journal={arXiv preprint arXiv:2410.03235},
year={2024},
archivePrefix={arXiv},
eprint={2410.03235},
primaryClass={cs.AI cs.LO}
} | crum2024enriching |
arxiv-665511 | 2410.03240 | Beyond Film Subtitles: Is YouTube the Best Approximation of Spoken Vocabulary? | <|reference_start|>Beyond Film Subtitles: Is YouTube the Best Approximation of Spoken Vocabulary?: Word frequency is a key variable in psycholinguistics, useful for modeling human familiarity with words even in the era of large language models (LLMs). Frequency in film subtitles has proved to be a particularly good approximation of everyday language exposure. For many languages, however, film subtitles are not easily available, or are overwhelmingly translated from English. We demonstrate that frequencies extracted from carefully processed YouTube subtitles provide an approximation comparable to, and often better than, the best currently available resources. Moreover, they are available for languages for which a high-quality subtitle or speech corpus does not exist. We use YouTube subtitles to construct frequency norms for five diverse languages, Chinese, English, Indonesian, Japanese, and Spanish, and evaluate their correlation with lexical decision time, word familiarity, and lexical complexity. In addition to being strongly correlated with two psycholinguistic variables, a simple linear regression on the new frequencies achieves a new high score on a lexical complexity prediction task in English and Japanese, surpassing both models trained on film subtitle frequencies and the LLM GPT-4. Our code, the frequency lists, fastText word embeddings, and statistical language models are freely available at https://github.com/naist-nlp/tubelex.<|reference_end|> | arxiv | @article{nohejl2024beyond,
title={Beyond Film Subtitles: Is YouTube the Best Approximation of Spoken
Vocabulary?},
author={Adam Nohejl, Frederikus Hudi, Eunike Andriani Kardinata, Shintaro
Ozaki, Maria Angelica Riera Machin, Hongyu Sun, Justin Vasselli, Taro
Watanabe},
journal={arXiv preprint arXiv:2410.03240},
year={2024},
archivePrefix={arXiv},
eprint={2410.03240},
primaryClass={cs.CL}
} | nohejl2024beyond |
arxiv-665512 | 2410.03246 | Latent Action Priors From a Single Gait Cycle Demonstration for Online Imitation Learning | <|reference_start|>Latent Action Priors From a Single Gait Cycle Demonstration for Online Imitation Learning: Deep Reinforcement Learning (DRL) in simulation often results in brittle and unrealistic learning outcomes. To push the agent towards more desirable solutions, prior information can be injected in the learning process through, for instance, reward shaping, expert data, or motion primitives. We propose an additional inductive bias for robot learning: latent actions learned from expert demonstration as priors in the action space. We show that these action priors can be learned from only a single open-loop gait cycle using a simple autoencoder. Using these latent action priors combined with established style rewards for imitation in DRL achieves above expert demonstration level of performance and leads to more desirable gaits. Further, action priors substantially improve the performance on transfer tasks, even leading to gait transitions for higher target speeds. Videos and code are available at https://sites.google.com/view/latent-action-priors.<|reference_end|> | arxiv | @article{hausdörfer2024latent,
title={Latent Action Priors From a Single Gait Cycle Demonstration for Online
Imitation Learning},
author={Oliver Hausd"orfer, Alexander von Rohr, 'Eric Lefort and Angela
Schoellig},
journal={arXiv preprint arXiv:2410.03246},
year={2024},
archivePrefix={arXiv},
eprint={2410.03246},
primaryClass={cs.RO cs.AI}
} | hausdörfer2024latent |
arxiv-665513 | 2410.03248 | 3D Segmentation of Neuronal Nuclei and Cell-Type Identification using Multi-channel Information | <|reference_start|>3D Segmentation of Neuronal Nuclei and Cell-Type Identification using Multi-channel Information: Background Analyzing images to accurately estimate the number of different cell types in the brain using automatic methods is a major objective in neuroscience. The automatic and selective detection and segmentation of neurons would be an important step in neuroanatomical studies. New method We present a method to improve the 3D reconstruction of neuronal nuclei that allows their segmentation, excluding the nuclei of non-neuronal cell types. Results We have tested the algorithm on stacks of images from rat neocortex, in a complex scenario (large stacks of images, uneven staining, and three different channels to visualize different cellular markers). It was able to provide a good identification ratio of neuronal nuclei and a 3D segmentation. Comparison with Existing Methods: Many automatic tools are in fact currently available, but different methods yield different cell count estimations, even in the same brain regions, due to differences in the labeling and imaging techniques, as well as in the algorithms used to detect cells. Moreover, some of the available automated software methods have provided estimations of cell numbers that have been reported to be inaccurate or inconsistent after evaluation by neuroanatomists. Conclusions It is critical to have a tool for automatic segmentation that allows discrimination between neurons, glial cells and perivascular cells. It would greatly speed up a task that is currently performed manually and would allow the cell counting to be systematic, avoiding human bias. Furthermore, the resulting 3D reconstructions of different cell types can be used to generate models of the spatial distribution of cells.<|reference_end|> | arxiv | @article{latorre20243d,
title={3D Segmentation of Neuronal Nuclei and Cell-Type Identification using
Multi-channel Information},
author={Antonio LaTorre, Lidia Alonso-Nanclares, Jos'e Mar'ia Pe~na, Javier
De Felipe},
journal={Expert Systems with Applications, vol. 183, p. 115443, Nov. 2021},
year={2024},
doi={10.1016/j.eswa.2021.115443},
archivePrefix={arXiv},
eprint={2410.03248},
primaryClass={eess.IV cs.CV}
} | latorre20243d |
arxiv-665514 | 2410.03249 | How much can we forget about Data Contamination? | <|reference_start|>How much can we forget about Data Contamination?: The leakage of benchmark data into the training data has emerged as a significant challenge for evaluating the capabilities of large language models (LLMs). In this work, we use experimental evidence and theoretical estimates to challenge the common assumption that small-scale contamination renders benchmark evaluations invalid. First, we experimentally quantify the magnitude of benchmark overfitting based on scaling along three dimensions: The number of model parameters (up to 1.6B), the number of times an example is seen (up to 144), and the number of training tokens (up to 40B). We find that if model and data follow the Chinchilla scaling laws, minor contamination indeed leads to overfitting. At the same time, even 144 times of contamination can be forgotten if the training data is scaled beyond five times Chinchilla, a regime characteristic of many modern LLMs. We then derive a simple theory of example forgetting via cumulative weight decay. It allows us to bound the number of gradient steps required to forget past data for any training run where we know the hyperparameters of AdamW. This indicates that many LLMs, including Llama 3, have forgotten the data seen at the beginning of training. Experimentally, we demonstrate that forgetting occurs faster than what is predicted by our bounds. Taken together, our results suggest that moderate amounts of contamination can be forgotten at the end of realistically scaled training runs.<|reference_end|> | arxiv | @article{bordt2024how,
title={How much can we forget about Data Contamination?},
author={Sebastian Bordt, Suraj Srinivas, Valentyn Boreiko, Ulrike von Luxburg},
journal={arXiv preprint arXiv:2410.03249},
year={2024},
archivePrefix={arXiv},
eprint={2410.03249},
primaryClass={cs.LG cs.AI cs.CL}
} | bordt2024how |
arxiv-665515 | 2410.03252 | An egonet-based approach to effective weighted network comparison | <|reference_start|>An egonet-based approach to effective weighted network comparison: With the impressive growth of network models in practically every scientific and technological area, we are often faced with the need to compare graphs, i.e., to quantify their (dis)similarity using appropriate metrics. This is necessary, for example, to identify networks with comparable characteristics or to spot anomalous instants in a time sequence of graphs. While a large number of metrics are available for binary networks, the set of comparison methods capable of handling weighted graphs is much smaller. Yet, the strength of connections is often a key ingredient of the model, and ignoring this information could lead to misleading results. In this paper we introduce a family of dissimilarity measures to compare undirected weighted networks. They fall into the class of alignment-free metrics: as such, they do not require the correspondence of the nodes between the two graphs and can also compare networks of different sizes. In short, they are based on the distributions, on the graph, of a few egonet features which are easily defined and computed: the distance between two graphs is then the distance between the corresponding distributions. On a properly defined testbed with a pool of weighted network models with diversified characteristics, the proposed metrics are shown to achieve state-of-the-art performance in the model classification task. The effectiveness and applicability of the proposed metrics are then demonstrated on two examples. In the first, some ''filtering'' schemes -- designed to eliminate non-significant links while maintaining most of the total weight -- are evaluated in their ability to produce as output a graph faithful to the original, in terms of the local structure around nodes. In the second example, analyzing a timeline of stock market correlation graphs highlights anomalies associated with periods of financial instability.<|reference_end|> | arxiv | @article{piccardi2024an,
title={An egonet-based approach to effective weighted network comparison},
author={Carlo Piccardi},
journal={arXiv preprint arXiv:2410.03252},
year={2024},
archivePrefix={arXiv},
eprint={2410.03252},
primaryClass={cs.SI physics.data-an}
} | piccardi2024an |
arxiv-665516 | 2410.03253 | Dynamic Curvature Constrained Path Planning | <|reference_start|>Dynamic Curvature Constrained Path Planning: Effective path planning is a pivotal challenge across various domains, from robotics to logistics and beyond. This research is centred on the development and evaluation of the Dynamic Curvature-Constrained Path Planning Algorithm (DCCPPA) within two dimensional space. DCCPPA is designed to navigate constrained environments, optimising path solutions while accommodating curvature constraints.The study goes beyond algorithm development and conducts a comparative analysis with two established path planning methodologies: Rapidly Exploring Random Trees (RRT) and Probabilistic Roadmaps (PRM). These comparisons provide insights into the performance and adaptability of path planning algorithms across a range of applications.This research underscores the versatility of DCCPPA as a path planning algorithm tailored for 2D space, demonstrating its potential for addressing real-world path planning challenges across various domains. Index Terms Path Planning, PRM, RRT, Optimal Path, 2D Path Planning.<|reference_end|> | arxiv | @article{myadam2024dynamic,
title={Dynamic Curvature Constrained Path Planning},
author={Nishkal Gupta Myadam},
journal={arXiv preprint arXiv:2410.03253},
year={2024},
archivePrefix={arXiv},
eprint={2410.03253},
primaryClass={cs.RO}
} | myadam2024dynamic |
arxiv-665517 | 2410.03254 | Are Expert-Level Language Models Expert-Level Annotators? | <|reference_start|>Are Expert-Level Language Models Expert-Level Annotators?: Data annotation refers to the labeling or tagging of textual data with relevant information. A large body of works have reported positive results on leveraging LLMs as an alternative to human annotators. However, existing studies focus on classic NLP tasks, and the extent to which LLMs as data annotators perform in domains requiring expert knowledge remains underexplored. In this work, we investigate comprehensive approaches across three highly specialized domains and discuss practical suggestions from a cost-effectiveness perspective. To the best of our knowledge, we present the first systematic evaluation of LLMs as expert-level data annotators.<|reference_end|> | arxiv | @article{tseng2024are,
title={Are Expert-Level Language Models Expert-Level Annotators?},
author={Yu-Min Tseng, Wei-Lin Chen, Chung-Chi Chen, Hsin-Hsi Chen},
journal={arXiv preprint arXiv:2410.03254},
year={2024},
archivePrefix={arXiv},
eprint={2410.03254},
primaryClass={cs.CL}
} | tseng2024are |
arxiv-665518 | 2410.03255 | Towards a Benchmark for Large Language Models for Business Process Management Tasks | <|reference_start|>Towards a Benchmark for Large Language Models for Business Process Management Tasks: An increasing number of organizations are deploying Large Language Models (LLMs) for a wide range of tasks. Despite their general utility, LLMs are prone to errors, ranging from inaccuracies to hallucinations. To objectively assess the capabilities of existing LLMs, performance benchmarks are conducted. However, these benchmarks often do not translate to more specific real-world tasks. This paper addresses the gap in benchmarking LLM performance in the Business Process Management (BPM) domain. Currently, no BPM-specific benchmarks exist, creating uncertainty about the suitability of different LLMs for BPM tasks. This paper systematically compares LLM performance on four BPM tasks focusing on small open-source models. The analysis aims to identify task-specific performance variations, compare the effectiveness of open-source versus commercial models, and assess the impact of model size on BPM task performance. This paper provides insights into the practical applications of LLMs in BPM, guiding organizations in selecting appropriate models for their specific needs.<|reference_end|> | arxiv | @article{busch2024towards,
title={Towards a Benchmark for Large Language Models for Business Process
Management Tasks},
author={Kiran Busch and Henrik Leopold},
journal={arXiv preprint arXiv:2410.03255},
year={2024},
archivePrefix={arXiv},
eprint={2410.03255},
primaryClass={cs.AI cs.CL}
} | busch2024towards |
arxiv-665519 | 2410.03258 | Adaptive BPE Tokenization for Enhanced Vocabulary Adaptation in Finetuning Pretrained Language Models | <|reference_start|>Adaptive BPE Tokenization for Enhanced Vocabulary Adaptation in Finetuning Pretrained Language Models: In this work, we show a fundamental limitation in vocabulary adaptation approaches that use Byte-Pair Encoding (BPE) tokenization scheme for fine-tuning pretrained language models (PLMs) to expert domains. Current approaches trivially append the target domain-specific vocabulary at the end of the PLM vocabulary. This approach leads to a lower priority score and causes sub-optimal tokenization in BPE that iteratively uses merge rules to tokenize a given text. To mitigate this issue, we propose AdaptBPE where the BPE tokenization initialization phase is modified to first perform the longest string matching on the added (target) vocabulary before tokenizing at the character level. We perform an extensive evaluation of AdaptBPE versus the standard BPE over various classification and summarization tasks; AdaptBPE improves by 3.57% (in terms of accuracy) and 1.87% (in terms of Rouge-L), respectively. AdaptBPE for MEDVOC works particularly well when reference summaries have high OOV concentration or are longer in length. We also conduct a human evaluation, revealing that AdaptBPE generates more relevant and more faithful summaries as compared to MEDVOC. We make our codebase publicly available at https://github.com/gb-kgp/adaptbpe.<|reference_end|> | arxiv | @article{balde2024adaptive,
title={Adaptive BPE Tokenization for Enhanced Vocabulary Adaptation in
Finetuning Pretrained Language Models},
author={Gunjan Balde, Soumyadeep Roy, Mainack Mondal, Niloy Ganguly},
journal={arXiv preprint arXiv:2410.03258},
year={2024},
archivePrefix={arXiv},
eprint={2410.03258},
primaryClass={cs.CL}
} | balde2024adaptive |
arxiv-665520 | 2410.03261 | Simulated Eyeblink Artifact Removal with ICA: Effect of Measurement Uncertainty | <|reference_start|>Simulated Eyeblink Artifact Removal with ICA: Effect of Measurement Uncertainty: Independent Component Analysis (ICA) is commonly-used in electroencephalogram (EEG) signal processing to remove non-cerebral artifacts from cerebral data. Despite the ubiquity of ICA, the effect of measurement uncertainty on the artifact removal process has not been thoroughly investigated. We first characterize the measurement uncertainty distribution of a common ADC and show that it quantitatively conforms to a Gaussian distribution. We then evaluate the effect of measurement uncertainty on the artifact identification process through several computer simulations. These computer simulations evaluate the performance of two different ICA algorithms, FastICA and Infomax, in removing eyeblink artifacts from five different electrode configurations with varying levels of measurement uncertainty. FastICA and Infomax show similar performance in identifying the eyeblink artifacts for a given uncertainty level and electrode configuration. We quantify the correlation performance degradation with respect to SNR and show that in general, an SNR of greater than 15 dB results in less than a 5% degradation in performance. The biggest difference in performance between the two algorithms is in their execution time. FastICA's execution time is dependent on the amount of measurement uncertainty, with a 50% to 85% reduction in execution time over an SNR range of 20 dB. This contrasts with Infomax's execution time, which is unaffected by measurement uncertainty.<|reference_end|> | arxiv | @article{couchman2024simulated,
title={Simulated Eyeblink Artifact Removal with ICA: Effect of Measurement
Uncertainty},
author={Jennie Couchman, Orestis Kaparounakis, Chatura Samarakoon, Phillip
Stanley-Marbell},
journal={arXiv preprint arXiv:2410.03261},
year={2024},
archivePrefix={arXiv},
eprint={2410.03261},
primaryClass={eess.SY cs.SY}
} | couchman2024simulated |
arxiv-665521 | 2410.03263 | Test-time Adaptation for Regression by Subspace Alignment | <|reference_start|>Test-time Adaptation for Regression by Subspace Alignment: This paper investigates test-time adaptation (TTA) for regression, where a regression model pre-trained in a source domain is adapted to an unknown target distribution with unlabeled target data. Although regression is one of the fundamental tasks in machine learning, most of the existing TTA methods have classification-specific designs, which assume that models output class-categorical predictions, whereas regression models typically output only single scalar values. To enable TTA for regression, we adopt a feature alignment approach, which aligns the feature distributions between the source and target domains to mitigate the domain gap. However, we found that naive feature alignment employed in existing TTA methods for classification is ineffective or even worse for regression because the features are distributed in a small subspace and many of the raw feature dimensions have little significance to the output. For an effective feature alignment in TTA for regression, we propose Significant-subspace Alignment (SSA). SSA consists of two components: subspace detection and dimension weighting. Subspace detection finds the feature subspace that is representative and significant to the output. Then, the feature alignment is performed in the subspace during TTA. Meanwhile, dimension weighting raises the importance of the dimensions of the feature subspace that have greater significance to the output. We experimentally show that SSA outperforms various baselines on real-world datasets.<|reference_end|> | arxiv | @article{adachi2024test-time,
title={Test-time Adaptation for Regression by Subspace Alignment},
author={Kazuki Adachi, Shin'ya Yamaguchi, Atsutoshi Kumagai, Tomoki Hamagami},
journal={arXiv preprint arXiv:2410.03263},
year={2024},
archivePrefix={arXiv},
eprint={2410.03263},
primaryClass={cs.LG cs.AI}
} | adachi2024test-time |
arxiv-665522 | 2410.03264 | Enriching Music Descriptions with a Finetuned-LLM and Metadata for Text-to-Music Retrieval | <|reference_start|>Enriching Music Descriptions with a Finetuned-LLM and Metadata for Text-to-Music Retrieval: Text-to-Music Retrieval, finding music based on a given natural language query, plays a pivotal role in content discovery within extensive music databases. To address this challenge, prior research has predominantly focused on a joint embedding of music audio and text, utilizing it to retrieve music tracks that exactly match descriptive queries related to musical attributes (i.e. genre, instrument) and contextual elements (i.e. mood, theme). However, users also articulate a need to explore music that shares similarities with their favorite tracks or artists, such as \textit{I need a similar track to Superstition by Stevie Wonder}. To address these concerns, this paper proposes an improved Text-to-Music Retrieval model, denoted as TTMR++, which utilizes rich text descriptions generated with a finetuned large language model and metadata. To accomplish this, we obtained various types of seed text from several existing music tag and caption datasets and a knowledge graph dataset of artists and tracks. The experimental results show the effectiveness of TTMR++ in comparison to state-of-the-art music-text joint embedding models through a comprehensive evaluation involving various musical text queries.<|reference_end|> | arxiv | @article{doh2024enriching,
title={Enriching Music Descriptions with a Finetuned-LLM and Metadata for
Text-to-Music Retrieval},
author={SeungHeon Doh, Minhee Lee, Dasaem Jeong, Juhan Nam},
journal={arXiv preprint arXiv:2410.03264},
year={2024},
archivePrefix={arXiv},
eprint={2410.03264},
primaryClass={cs.SD cs.IR cs.MM eess.AS}
} | doh2024enriching |
arxiv-665523 | 2410.03265 | Multimodal Point-of-Interest Recommendation | <|reference_start|>Multimodal Point-of-Interest Recommendation: Large Language Models are applied to recommendation tasks such as items to buy and news articles to read. Point of Interest is quite a new area to sequential recommendation based on language representations of multimodal datasets. As a first step to prove our concepts, we focused on restaurant recommendation based on each user's past visit history. When choosing a next restaurant to visit, a user would consider genre and location of the venue and, if available, pictures of dishes served there. We created a pseudo restaurant check-in history dataset from the Foursquare dataset and the FoodX-251 dataset by converting pictures into text descriptions with a multimodal model called LLaVA, and used a language-based sequential recommendation framework named Recformer proposed in 2023. A model trained on this semi-multimodal dataset has outperformed another model trained on the same dataset without picture descriptions. This suggests that this semi-multimodal model reflects actual human behaviours and that our path to a multimodal recommendation model is in the right direction.<|reference_end|> | arxiv | @article{kanzawa2024multimodal,
title={Multimodal Point-of-Interest Recommendation},
author={Yuta Kanzawa, Toyotaro Suzumura, Hiroki Kanezashi, Jiawei Yong,
Shintaro Fukushima},
journal={arXiv preprint arXiv:2410.03265},
year={2024},
archivePrefix={arXiv},
eprint={2410.03265},
primaryClass={cs.IR cs.SI}
} | kanzawa2024multimodal |
arxiv-665524 | 2410.03267 | Optimal Transport for $\epsilon$-Contaminated Credal Sets | <|reference_start|>Optimal Transport for $\epsilon$-Contaminated Credal Sets: We provide a version for lower probabilities of Monge's and Kantorovich's optimal transport problems. We show that, when the lower probabilities are the lower envelopes of $\epsilon$-contaminated sets, then our version of Monge's, and a restricted version of our Kantorovich's problems, coincide with their respective classical versions. We also give sufficient conditions for the existence of our version of Kantorovich's optimal plan, and for the two problems to be equivalent. As a byproduct, we show that for $\epsilon$-contaminations the lower probability versions of Monge's and Kantorovich's optimal transport problems need not coincide. The applications of our results to Machine Learning and Artificial Intelligence are also discussed.<|reference_end|> | arxiv | @article{caprio2024optimal,
title={Optimal Transport for $\epsilon$-Contaminated Credal Sets},
author={Michele Caprio},
journal={arXiv preprint arXiv:2410.03267},
year={2024},
archivePrefix={arXiv},
eprint={2410.03267},
primaryClass={stat.ML cs.LG math.PR}
} | caprio2024optimal |
arxiv-665525 | 2410.03268 | Narrative Player: Reviving Data Narratives with Visuals | <|reference_start|>Narrative Player: Reviving Data Narratives with Visuals: Data-rich documents are commonly found across various fields such as business, finance, and science. However, a general limitation of these documents for reading is their reliance on text to convey data and facts. Visual representation of text aids in providing a satisfactory reading experience in comprehension and engagement. However, existing work emphasizes presenting the insights of local text context, rather than fully conveying data stories within the whole paragraphs and engaging readers. To provide readers with satisfactory data stories, this paper presents Narrative Player, a novel method that automatically revives data narratives with consistent and contextualized visuals. Specifically, it accepts a paragraph and corresponding data table as input and leverages LLMs to characterize the clauses and extract contextualized data facts. Subsequently, the facts are transformed into a coherent visualization sequence with a carefully designed optimization-based approach. Animations are also assigned between adjacent visualizations to enable seamless transitions. Finally, the visualization sequence, transition animations, and audio narration generated by text-to-speech technologies are rendered into a data video. The evaluation results showed that the automatic-generated data videos were well-received by participants and experts for enhancing reading.<|reference_end|> | arxiv | @article{shao2024narrative,
title={Narrative Player: Reviving Data Narratives with Visuals},
author={Zekai Shao, Leixian Shen, Haotian Li, Yi Shan, Huamin Qu, Yun Wang,
Siming Chen},
journal={arXiv preprint arXiv:2410.03268},
year={2024},
archivePrefix={arXiv},
eprint={2410.03268},
primaryClass={cs.HC}
} | shao2024narrative |
arxiv-665526 | 2410.03276 | Sm: enhanced localization in Multiple Instance Learning for medical imaging classification | <|reference_start|>Sm: enhanced localization in Multiple Instance Learning for medical imaging classification: Multiple Instance Learning (MIL) is widely used in medical imaging classification to reduce the labeling effort. While only bag labels are available for training, one typically seeks predictions at both bag and instance levels (classification and localization tasks, respectively). Early MIL methods treated the instances in a bag independently. Recent methods account for global and local dependencies among instances. Although they have yielded excellent results in classification, their performance in terms of localization is comparatively limited. We argue that these models have been designed to target the classification task, while implications at the instance level have not been deeply investigated. Motivated by a simple observation -- that neighboring instances are likely to have the same label -- we propose a novel, principled, and flexible mechanism to model local dependencies. It can be used alone or combined with any mechanism to model global dependencies (e.g., transformers). A thorough empirical validation shows that our module leads to state-of-the-art performance in localization while being competitive or superior in classification. Our code is at https://github.com/Franblueee/SmMIL.<|reference_end|> | arxiv | @article{castro-macías2024sm:,
title={Sm: enhanced localization in Multiple Instance Learning for medical
imaging classification},
author={Francisco M. Castro-Mac'ias and Pablo Morales-'Alvarez and Yunan Wu
and Rafael Molina and Aggelos K. Katsaggelos},
journal={arXiv preprint arXiv:2410.03276},
year={2024},
archivePrefix={arXiv},
eprint={2410.03276},
primaryClass={cs.CV cs.LG}
} | castro-macías2024sm: |
arxiv-665527 | 2410.03277 | A Multi-task Learning Framework for Evaluating Machine Translation of Emotion-loaded User-generated Content | <|reference_start|>A Multi-task Learning Framework for Evaluating Machine Translation of Emotion-loaded User-generated Content: Machine translation (MT) of user-generated content (UGC) poses unique challenges, including handling slang, emotion, and literary devices like irony and sarcasm. Evaluating the quality of these translations is challenging as current metrics do not focus on these ubiquitous features of UGC. To address this issue, we utilize an existing emotion-related dataset that includes emotion labels and human-annotated translation errors based on Multi-dimensional Quality Metrics. We extend it with sentence-level evaluation scores and word-level labels, leading to a dataset suitable for sentence- and word-level translation evaluation and emotion classification, in a multi-task setting. We propose a new architecture to perform these tasks concurrently, with a novel combined loss function, which integrates different loss heuristics, like the Nash and Aligned losses. Our evaluation compares existing fine-tuning and multi-task learning approaches, assessing generalization with ablative experiments over multiple datasets. Our approach achieves state-of-the-art performance and we present a comprehensive analysis for MT evaluation of UGC.<|reference_end|> | arxiv | @article{qian2024a,
title={A Multi-task Learning Framework for Evaluating Machine Translation of
Emotion-loaded User-generated Content},
author={Shenbin Qian, Constantin Oru{a}san, Diptesh Kanojia, F'elix do Carmo},
journal={arXiv preprint arXiv:2410.03277},
year={2024},
archivePrefix={arXiv},
eprint={2410.03277},
primaryClass={cs.CL}
} | qian2024a |
arxiv-665528 | 2410.03278 | What do Large Language Models Need for Machine Translation Evaluation? | <|reference_start|>What do Large Language Models Need for Machine Translation Evaluation?: Leveraging large language models (LLMs) for various natural language processing tasks has led to superlative claims about their performance. For the evaluation of machine translation (MT), existing research shows that LLMs are able to achieve results comparable to fine-tuned multilingual pre-trained language models. In this paper, we explore what translation information, such as the source, reference, translation errors and annotation guidelines, is needed for LLMs to evaluate MT quality. In addition, we investigate prompting techniques such as zero-shot, Chain of Thought (CoT) and few-shot prompting for eight language pairs covering high-, medium- and low-resource languages, leveraging varying LLM variants. Our findings indicate the importance of reference translations for an LLM-based evaluation. While larger models do not necessarily fare better, they tend to benefit more from CoT prompting, than smaller models. We also observe that LLMs do not always provide a numerical score when generating evaluations, which poses a question on their reliability for the task. Our work presents a comprehensive analysis for resource-constrained and training-less LLM-based evaluation of machine translation. We release the accrued prompt templates, code and data publicly for reproducibility.<|reference_end|> | arxiv | @article{qian2024what,
title={What do Large Language Models Need for Machine Translation Evaluation?},
author={Shenbin Qian, Archchana Sindhujan, Minnie Kabra, Diptesh Kanojia,
Constantin Oru{a}san, Tharindu Ranasinghe, Fr'ed'eric Blain},
journal={arXiv preprint arXiv:2410.03278},
year={2024},
archivePrefix={arXiv},
eprint={2410.03278},
primaryClass={cs.CL}
} | qian2024what |
arxiv-665529 | 2410.03279 | Unisolvence of Kansa collocation for elliptic equations by polyharmonic splines with random fictitious centers | <|reference_start|>Unisolvence of Kansa collocation for elliptic equations by polyharmonic splines with random fictitious centers: We make a further step in the unisolvence open problem for unsymmetric Kansa collocation, proving nonsingularity of Kansa matrices with polyharmonic splines and random fictitious centers, for second-order elliptic equations with mixed boundary conditions. We also show some numerical tests, where the fictitious centers are local random perturbations of predetermined collocation points.<|reference_end|> | arxiv | @article{mohammadi2024unisolvence,
title={Unisolvence of Kansa collocation for elliptic equations by polyharmonic
splines with random fictitious centers},
author={Maryam Mohammadi, Alvise Sommariva, and Marco Vianello},
journal={arXiv preprint arXiv:2410.03279},
year={2024},
archivePrefix={arXiv},
eprint={2410.03279},
primaryClass={math.NA cs.NA math.AP}
} | mohammadi2024unisolvence |
arxiv-665530 | 2410.03280 | Manikin-Recorded Cardiopulmonary Sounds Dataset Using Digital Stethoscope | <|reference_start|>Manikin-Recorded Cardiopulmonary Sounds Dataset Using Digital Stethoscope: Heart and lung sounds are crucial for healthcare monitoring. Recent improvements in stethoscope technology have made it possible to capture patient sounds with enhanced precision. In this dataset, we used a digital stethoscope to capture both heart and lung sounds, including individual and mixed recordings. To our knowledge, this is the first dataset to offer both separate and mixed cardiorespiratory sounds. The recordings were collected from a clinical manikin, a patient simulator designed to replicate human physiological conditions, generating clean heart and lung sounds at different body locations. This dataset includes both normal sounds and various abnormalities (i.e., murmur, atrial fibrillation, tachycardia, atrioventricular block, third and fourth heart sound, wheezing, crackles, rhonchi, pleural rub, and gurgling sounds). The dataset includes audio recordings of chest examinations performed at different anatomical locations, as determined by specialist nurses. Each recording has been enhanced using frequency filters to highlight specific sound types. This dataset is useful for applications in artificial intelligence, such as automated cardiopulmonary disease detection, sound classification, unsupervised separation techniques, and deep learning algorithms related to audio signal processing.<|reference_end|> | arxiv | @article{torabi2024manikin-recorded,
title={Manikin-Recorded Cardiopulmonary Sounds Dataset Using Digital
Stethoscope},
author={Yasaman Torabi, Shahram Shirani, James P. Reilly},
journal={arXiv preprint arXiv:2410.03280},
year={2024},
archivePrefix={arXiv},
eprint={2410.03280},
primaryClass={eess.AS cs.AI cs.LG eess.SP}
} | torabi2024manikin-recorded |
arxiv-665531 | 2410.03281 | BN-SCAFFOLD: controlling the drift of Batch Normalization statistics in Federated Learning | <|reference_start|>BN-SCAFFOLD: controlling the drift of Batch Normalization statistics in Federated Learning: Federated Learning (FL) is gaining traction as a learning paradigm for training Machine Learning (ML) models in a decentralized way. Batch Normalization (BN) is ubiquitous in Deep Neural Networks (DNN), as it improves convergence and generalization. However, BN has been reported to hinder performance of DNNs in heterogeneous FL. Recently, the FedTAN algorithm has been proposed to mitigate the effect of heterogeneity on BN, by aggregating BN statistics and gradients from all the clients. However, it has a high communication cost, that increases linearly with the depth of the DNN. SCAFFOLD is a variance reduction algorithm, that estimates and corrects the client drift in a communication-efficient manner. Despite its promising results in heterogeneous FL settings, it has been reported to underperform for models with BN. In this work, we seek to revive SCAFFOLD, and more generally variance reduction, as an efficient way of training DNN with BN in heterogeneous FL. We introduce a unified theoretical framework for analyzing the convergence of variance reduction algorithms in the BN-DNN setting, inspired of by the work of Wang et al. 2023, and show that SCAFFOLD is unable to remove the bias introduced by BN. We thus propose the BN-SCAFFOLD algorithm, which extends the client drift correction of SCAFFOLD to BN statistics. We prove convergence using the aforementioned framework and validate the theoretical results with experiments on MNIST and CIFAR-10. BN-SCAFFOLD equals the performance of FedTAN, without its high communication cost, outperforming Federated Averaging (FedAvg), SCAFFOLD, and other FL algorithms designed to mitigate BN heterogeneity.<|reference_end|> | arxiv | @article{quintana2024bn-scaffold:,
title={BN-SCAFFOLD: controlling the drift of Batch Normalization statistics in
Federated Learning},
author={Gonzalo I~naki Quintana and Laurence Vancamberg and Vincent Jugnon
and Mathilde Mougeot and Agn`es Desolneux},
journal={arXiv preprint arXiv:2410.03281},
year={2024},
archivePrefix={arXiv},
eprint={2410.03281},
primaryClass={cs.LG}
} | quintana2024bn-scaffold: |
arxiv-665532 | 2410.03282 | Neural Sampling from Boltzmann Densities: Fisher-Rao Curves in the Wasserstein Geometry | <|reference_start|>Neural Sampling from Boltzmann Densities: Fisher-Rao Curves in the Wasserstein Geometry: We deal with the task of sampling from an unnormalized Boltzmann density $\rho_D$ by learning a Boltzmann curve given by energies $f_t$ starting in a simple density $\rho_Z$. First, we examine conditions under which Fisher-Rao flows are absolutely continuous in the Wasserstein geometry. Second, we address specific interpolations $f_t$ and the learning of the related density/velocity pairs $(\rho_t,v_t)$. It was numerically observed that the linear interpolation, which requires only a parametrization of the velocity field $v_t$, suffers from a "teleportation-of-mass" issue. Using tools from the Wasserstein geometry, we give an analytical example, where we can precisely measure the explosion of the velocity field. Inspired by M\'at\'e and Fleuret, who parametrize both $f_t$ and $v_t$, we propose an interpolation which parametrizes only $f_t$ and fixes an appropriate $v_t$. This corresponds to the Wasserstein gradient flow of the Kullback-Leibler divergence related to Langevin dynamics. We demonstrate by numerical examples that our model provides a well-behaved flow field which successfully solves the above sampling task.<|reference_end|> | arxiv | @article{chemseddine2024neural,
title={Neural Sampling from Boltzmann Densities: Fisher-Rao Curves in the
Wasserstein Geometry},
author={Jannis Chemseddine, Christian Wald, Richard Duong, Gabriele Steidl},
journal={arXiv preprint arXiv:2410.03282},
year={2024},
archivePrefix={arXiv},
eprint={2410.03282},
primaryClass={cs.LG math.AP math.PR}
} | chemseddine2024neural |
arxiv-665533 | 2410.03284 | uniINF: Best-of-Both-Worlds Algorithm for Parameter-Free Heavy-Tailed MABs | <|reference_start|>uniINF: Best-of-Both-Worlds Algorithm for Parameter-Free Heavy-Tailed MABs: In this paper, we present a novel algorithm, uniINF, for the Heavy-Tailed Multi-Armed Bandits (HTMAB) problem, demonstrating robustness and adaptability in both stochastic and adversarial environments. Unlike the stochastic MAB setting where loss distributions are stationary with time, our study extends to the adversarial setup, where losses are generated from heavy-tailed distributions that depend on both arms and time. Our novel algorithm `uniINF` enjoys the so-called Best-of-Both-Worlds (BoBW) property, performing optimally in both stochastic and adversarial environments without knowing the exact environment type. Moreover, our algorithm also possesses a Parameter-Free feature, i.e., it operates without the need of knowing the heavy-tail parameters $(\sigma, \alpha)$ a-priori. To be precise, uniINF ensures nearly-optimal regret in both stochastic and adversarial environments, matching the corresponding lower bounds when $(\sigma, \alpha)$ is known (up to logarithmic factors). To our knowledge, uniINF is the first parameter-free algorithm to achieve the BoBW property for the heavy-tailed MAB problem. Technically, we develop innovative techniques to achieve BoBW guarantees for Parameter-Free HTMABs, including a refined analysis for the dynamics of log-barrier, an auto-balancing learning rate scheduling scheme, an adaptive skipping-clipping loss tuning technique, and a stopping-time analysis for logarithmic regret.<|reference_end|> | arxiv | @article{chen2024uniinf:,
title={uniINF: Best-of-Both-Worlds Algorithm for Parameter-Free Heavy-Tailed
MABs},
author={Yu Chen and Jiatai Huang and Yan Dai and Longbo Huang},
journal={arXiv preprint arXiv:2410.03284},
year={2024},
archivePrefix={arXiv},
eprint={2410.03284},
primaryClass={cs.LG}
} | chen2024uniinf: |
arxiv-665534 | 2410.03286 | Computational Diplomacy: How "hackathons for good" feed a participatory future for multilateralism in the digital age | <|reference_start|>Computational Diplomacy: How "hackathons for good" feed a participatory future for multilateralism in the digital age: This article explores the role of hackathons for good in building a community of software and hardware developers focused on addressing global SDG challenges. We theorise this movement as computational diplomacy: a decentralised, participatory process for digital governance that leverages collective intelligence to tackle major global issues. Analysing Devpost and GitHub data reveals that 30% of hackathons since 2010 have addressed SDG topics, employing diverse technologies to create innovative solutions. Hackathons serve as crucial kairos moments, sparking innovation bursts that drive both immediate project outcomes and long-term production. We propose that these events harness the neurobiological basis of human cooperation and empathy, fostering a collective sense of purpose and reducing interpersonal prejudice. This bottom-up approach to digital governance integrates software development, human collective intelligence, and collective action, creating a dynamic model for transformative change. By leveraging kairos moments, computational diplomacy promotes a more inclusive and effective model for digital multilateral governance of the future.<|reference_end|> | arxiv | @article{maillart2024computational,
title={Computational Diplomacy: How "hackathons for good" feed a participatory
future for multilateralism in the digital age},
author={Thomas Maillart, Lucia Gomez, Ewa Lombard, Alexander Nolte, Francesco
Pisano},
journal={Phil. Trans. R. Soc. A.382:20240103 (2024)},
year={2024},
doi={10.1098/rsta.2024.0103},
archivePrefix={arXiv},
eprint={2410.03286},
primaryClass={cs.CY}
} | maillart2024computational |
arxiv-665535 | 2410.03287 | A Service Robot in the Wild: Analysis of Users Intentions, Robot Behaviors, and Their Impact on the Interaction | <|reference_start|>A Service Robot in the Wild: Analysis of Users Intentions, Robot Behaviors, and Their Impact on the Interaction: We consider a service robot that offers chocolate treats to people passing in its proximity: it has the capability of predicting in advance a person's intention to interact, and to actuate an "offering" gesture, subtly extending the tray of chocolates towards a given target. We run the system for more than 5 hours across 3 days and two different crowded public locations; the system implements three possible behaviors that are randomly toggled every few minutes: passive (e.g. never performing the offering gesture); or active, triggered by either a naive distance-based rule, or a smart approach that relies on various behavioral cues of the user. We collect a real-world dataset that includes information on 1777 users with several spontaneous human-robot interactions and study the influence of robot actions on people's behavior. Our comprehensive analysis suggests that users are more prone to engage with the robot when it proactively starts the interaction. We release the dataset and provide insights to make our work reproducible for the community. Also, we report qualitative observations collected during the acquisition campaign and identify future challenges and research directions in the domain of social human-robot interaction.<|reference_end|> | arxiv | @article{arreghini2024a,
title={A Service Robot in the Wild: Analysis of Users Intentions, Robot
Behaviors, and Their Impact on the Interaction},
author={Simone Arreghini, Gabriele Abbate, Alessandro Giusti, Antonio Paolillo},
journal={arXiv preprint arXiv:2410.03287},
year={2024},
archivePrefix={arXiv},
eprint={2410.03287},
primaryClass={cs.RO}
} | arreghini2024a |
arxiv-665536 | 2410.03289 | Semantic Segmentation Based Quality Control of Histopathology Whole Slide Images | <|reference_start|>Semantic Segmentation Based Quality Control of Histopathology Whole Slide Images: We developed a software pipeline for quality control (QC) of histopathology whole slide images (WSIs) that segments various regions, such as blurs of different levels, tissue regions, tissue folds, and pen marks. Given the necessity and increasing availability of GPUs for processing WSIs, the proposed pipeline comprises multiple lightweight deep learning models to strike a balance between accuracy and speed. The pipeline was evaluated in all TCGAs, which is the largest publicly available WSI dataset containing more than 11,000 histopathological images from 28 organs. It was compared to a previous work, which was not based on deep learning, and it showed consistent improvement in segmentation results across organs. To minimize annotation effort for tissue and blur segmentation, annotated images were automatically prepared by mosaicking patches (sub-images) from various WSIs whose labels were identified using a patch classification tool HistoROI. Due to the generality of our trained QC pipeline and its extensive testing the potential impact of this work is broad. It can be used for automated pre-processing any WSI cohort to enhance the accuracy and reliability of large-scale histopathology image analysis for both research and clinical use. We have made the trained models, training scripts, training data, and inference results publicly available at https://github.com/abhijeetptl5/wsisegqc, which should enable the research community to use the pipeline right out of the box or further customize it to new datasets and applications in the future.<|reference_end|> | arxiv | @article{patil2024semantic,
title={Semantic Segmentation Based Quality Control of Histopathology Whole
Slide Images},
author={Abhijeet Patil, Garima Jain, Harsh Diwakar, Jay Sawant, Tripti Bameta,
Swapnil Rane, Amit Sethi},
journal={arXiv preprint arXiv:2410.03289},
year={2024},
archivePrefix={arXiv},
eprint={2410.03289},
primaryClass={eess.IV cs.CV}
} | patil2024semantic |
arxiv-665537 | 2410.03290 | Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models | <|reference_start|>Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models: Video Large Language Models (Video-LLMs) have demonstrated remarkable capabilities in coarse-grained video understanding, however, they struggle with fine-grained temporal grounding. In this paper, we introduce Grounded-VideoLLM, a novel Video-LLM adept at perceiving and reasoning over specific video moments in a fine-grained manner. We identify that current Video-LLMs have limitations for fine-grained video understanding since they lack effective temporal modeling and timestamp representation. In light of this, we sharpen our model by incorporating (1) an additional temporal stream to encode the relationships between frames and (2) discrete temporal tokens enriched with specific time knowledge to represent timestamps. To optimize the training of Grounded-VideoLLM, we employ a multi-stage training scheme, beginning with simple video-captioning tasks and progressively introducing video temporal grounding tasks of increasing complexity. To further enhance Grounded-VideoLLM's temporal reasoning capability, we also curate a grounded VideoQA dataset by an automatic annotation pipeline. Extensive experiments demonstrate that Grounded-VideoLLM not only excels in fine-grained grounding tasks such as temporal sentence grounding, dense video captioning, and grounded VideoQA, but also shows great potential as a versatile video assistant for general video understanding.<|reference_end|> | arxiv | @article{wang2024grounded-videollm:,
title={Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video
Large Language Models},
author={Haibo Wang, Zhiyang Xu, Yu Cheng, Shizhe Diao, Yufan Zhou, Yixin Cao,
Qifan Wang, Weifeng Ge, Lifu Huang},
journal={arXiv preprint arXiv:2410.03290},
year={2024},
archivePrefix={arXiv},
eprint={2410.03290},
primaryClass={cs.CV cs.AI}
} | wang2024grounded-videollm: |
arxiv-665538 | 2410.03291 | Enhanced Transformer architecture for in-context learning of dynamical systems | <|reference_start|>Enhanced Transformer architecture for in-context learning of dynamical systems: Recently introduced by some of the authors, the in-context identification paradigm aims at estimating, offline and based on synthetic data, a meta-model that describes the behavior of a whole class of systems. Once trained, this meta-model is fed with an observed input/output sequence (context) generated by a real system to predict its behavior in a zero-shot learning fashion. In this paper, we enhance the original meta-modeling framework through three key innovations: by formulating the learning task within a probabilistic framework; by managing non-contiguous context and query windows; and by adopting recurrent patching to effectively handle long context sequences. The efficacy of these modifications is demonstrated through a numerical example focusing on the Wiener-Hammerstein system class, highlighting the model's enhanced performance and scalability.<|reference_end|> | arxiv | @article{rufolo2024enhanced,
title={Enhanced Transformer architecture for in-context learning of dynamical
systems},
author={Matteo Rufolo, Dario Piga, Gabriele Maroni, Marco Forgione},
journal={arXiv preprint arXiv:2410.03291},
year={2024},
archivePrefix={arXiv},
eprint={2410.03291},
primaryClass={cs.LG cs.AI cs.SY eess.SY}
} | rufolo2024enhanced |
arxiv-665539 | 2410.03292 | Demystifying the Token Dynamics of Deep Selective State Space Models | <|reference_start|>Demystifying the Token Dynamics of Deep Selective State Space Models: Selective state space models (SSM), such as Mamba, have gained prominence for their effectiveness in modeling sequential data. Despite their outstanding empirical performance, a comprehensive theoretical understanding of deep selective SSM remains elusive, hindering their further development and adoption for applications that need high fidelity. In this paper, we investigate the dynamical properties of tokens in a pre-trained Mamba model. In particular, we derive the dynamical system governing the continuous-time limit of the Mamba model and characterize the asymptotic behavior of its solutions. In the one-dimensional case, we prove that only one of the following two scenarios happens: either all tokens converge to zero, or all tokens diverge to infinity. We provide criteria based on model parameters to determine when each scenario occurs. For the convergent scenario, we empirically verify that this scenario negatively impacts the model's performance. For the divergent scenario, we prove that different tokens will diverge to infinity at different rates, thereby contributing unequally to the updates during model training. Based on these investigations, we propose two refinements for the model: excluding the convergent scenario and reordering tokens based on their importance scores, both aimed at improving practical performance. Our experimental results validate these refinements, offering insights into enhancing Mamba's effectiveness in real-world applications.<|reference_end|> | arxiv | @article{vo2024demystifying,
title={Demystifying the Token Dynamics of Deep Selective State Space Models},
author={Thieu N Vo, Tung D. Pham, Xin T. Tong, Tan Minh Nguyen},
journal={arXiv preprint arXiv:2410.03292},
year={2024},
archivePrefix={arXiv},
eprint={2410.03292},
primaryClass={cs.LG}
} | vo2024demystifying |
arxiv-665540 | 2410.03293 | Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis | <|reference_start|>Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis: The work presented in this paper makes three scientific contributions with a specific focus on mining and analysis of COVID-19-related posts on Instagram. First, it presents a multilingual dataset of 500,153 Instagram posts about COVID-19 published between January 2020 and September 2024. This dataset, available at https://dx.doi.org/10.21227/d46p-v480, contains Instagram posts in 161 different languages as well as 535,021 distinct hashtags. After the development of this dataset, multilingual sentiment analysis was performed, which involved classifying each post as positive, negative, or neutral. The results of sentiment analysis are presented as a separate attribute in this dataset. Second, it presents the results of performing sentiment analysis per year from 2020 to 2024. The findings revealed the trends in sentiment related to COVID-19 on Instagram since the beginning of the pandemic. For instance, between 2020 and 2024, the sentiment trends show a notable shift, with positive sentiment decreasing from 38.35% to 28.69%, while neutral sentiment rising from 44.19% to 58.34%. Finally, the paper also presents findings of language-specific sentiment analysis. This analysis highlighted similar and contrasting trends of sentiment across posts published in different languages on Instagram. For instance, out of all English posts, 49.68% were positive, 14.84% were negative, and 35.48% were neutral. In contrast, among Hindi posts, 4.40% were positive, 57.04% were negative, and 38.56% were neutral, reflecting distinct differences in the sentiment distribution between these two languages.<|reference_end|> | arxiv | @article{thakur2024five,
title={Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram
Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis},
author={Nirmalya Thakur},
journal={arXiv preprint arXiv:2410.03293},
year={2024},
archivePrefix={arXiv},
eprint={2410.03293},
primaryClass={cs.CL cs.AI cs.CY cs.LG cs.SI}
} | thakur2024five |
arxiv-665541 | 2410.03294 | Resource-aware Mixed-precision Quantization for Enhancing Deployability of Transformers for Time-series Forecasting on Embedded FPGAs | <|reference_start|>Resource-aware Mixed-precision Quantization for Enhancing Deployability of Transformers for Time-series Forecasting on Embedded FPGAs: This study addresses the deployment challenges of integer-only quantized Transformers on resource-constrained embedded FPGAs (Xilinx Spartan-7 XC7S15). We enhanced the flexibility of our VHDL template by introducing a selectable resource type for storing intermediate results across model layers, thereby breaking the deployment bottleneck by utilizing BRAM efficiently. Moreover, we developed a resource-aware mixed-precision quantization approach that enables researchers to explore hardware-level quantization strategies without requiring extensive expertise in Neural Architecture Search. This method provides accurate resource utilization estimates with a precision discrepancy as low as 3%, compared to actual deployment metrics. Compared to previous work, our approach has successfully facilitated the deployment of model configurations utilizing mixed-precision quantization, thus overcoming the limitations inherent in five previously non-deployable configurations with uniform quantization bitwidths. Consequently, this research enhances the applicability of Transformers in embedded systems, facilitating a broader range of Transformer-powered applications on edge devices.<|reference_end|> | arxiv | @article{ling2024resource-aware,
title={Resource-aware Mixed-precision Quantization for Enhancing Deployability
of Transformers for Time-series Forecasting on Embedded FPGAs},
author={Tianheng Ling, Chao Qian, Gregor Schiele},
journal={arXiv preprint arXiv:2410.03294},
year={2024},
archivePrefix={arXiv},
eprint={2410.03294},
primaryClass={cs.LG}
} | ling2024resource-aware |
arxiv-665542 | 2410.03296 | Comparing zero-shot self-explanations with human rationales in multilingual text classification | <|reference_start|>Comparing zero-shot self-explanations with human rationales in multilingual text classification: Instruction-tuned LLMs are able to provide an explanation about their output to users by generating self-explanations that do not require gradient computations or the application of possibly complex XAI methods. In this paper, we analyse whether this ability results in a good explanation by evaluating self-explanations in the form of input rationales with respect to their plausibility to humans as well as their faithfulness to models. For this, we apply two text classification tasks: sentiment classification and forced labour detection. Next to English, we further include Danish and Italian translations of the sentiment classification task and compare self-explanations to human annotations for all samples. To allow for direct comparisons, we also compute post-hoc feature attribution, i.e., layer-wise relevance propagation (LRP) and apply this pipeline to 4 LLMs (Llama2, Llama3, Mistral and Mixtral). Our results show that self-explanations align more closely with human annotations compared to LRP, while maintaining a comparable level of faithfulness.<|reference_end|> | arxiv | @article{brandl2024comparing,
title={Comparing zero-shot self-explanations with human rationales in
multilingual text classification},
author={Stephanie Brandl and Oliver Eberle},
journal={arXiv preprint arXiv:2410.03296},
year={2024},
archivePrefix={arXiv},
eprint={2410.03296},
primaryClass={cs.CL cs.AI}
} | brandl2024comparing |
arxiv-665543 | 2410.03302 | Action Selection Learning for Multi-label Multi-view Action Recognition | <|reference_start|>Action Selection Learning for Multi-label Multi-view Action Recognition: Multi-label multi-view action recognition aims to recognize multiple concurrent or sequential actions from untrimmed videos captured by multiple cameras. Existing work has focused on multi-view action recognition in a narrow area with strong labels available, where the onset and offset of each action are labeled at the frame-level. This study focuses on real-world scenarios where cameras are distributed to capture a wide-range area with only weak labels available at the video-level. We propose the method named MultiASL (Multi-view Action Selection Learning), which leverages action selection learning to enhance view fusion by selecting the most useful information from different viewpoints. The proposed method includes a Multi-view Spatial-Temporal Transformer video encoder to extract spatial and temporal features from multi-viewpoint videos. Action Selection Learning is employed at the frame-level, using pseudo ground-truth obtained from weak labels at the video-level, to identify the most relevant frames for action recognition. Experiments in a real-world office environment using the MM-Office dataset demonstrate the superior performance of the proposed method compared to existing methods.<|reference_end|> | arxiv | @article{nguyen2024action,
title={Action Selection Learning for Multi-label Multi-view Action Recognition},
author={Trung Thanh Nguyen, Yasutomo Kawanishi, Takahiro Komamizu, Ichiro Ide},
journal={arXiv preprint arXiv:2410.03302},
year={2024},
doi={10.1145/3696409.3700211},
archivePrefix={arXiv},
eprint={2410.03302},
primaryClass={cs.CV}
} | nguyen2024action |
arxiv-665544 | 2410.03303 | SELU: Self-Learning Embodied MLLMs in Unknown Environments | <|reference_start|>SELU: Self-Learning Embodied MLLMs in Unknown Environments: Recently, multimodal large language models (MLLMs) have demonstrated strong visual understanding and decision-making capabilities, enabling the exploration of autonomously improving MLLMs in unknown environments. However, external feedback like human or environmental feedback is not always available. To address this challenge, existing methods primarily focus on enhancing the decision-making capabilities of MLLMs through voting and scoring mechanisms, while little effort has been paid to improving the environmental comprehension of MLLMs in unknown environments. To fully unleash the self-learning potential of MLLMs, we propose a novel actor-critic self-learning paradigm, dubbed SELU, inspired by the actor-critic paradigm in reinforcement learning. The critic employs self-asking and hindsight relabeling to extract knowledge from interaction trajectories collected by the actor, thereby augmenting its environmental comprehension. Simultaneously, the actor is improved by the self-feedback provided by the critic, enhancing its decision-making. We evaluate our method in the AI2-THOR and VirtualHome environments, and SELU achieves critic improvements of approximately 28% and 30%, and actor improvements of about 20% and 24% via self-learning.<|reference_end|> | arxiv | @article{li2024selu:,
title={SELU: Self-Learning Embodied MLLMs in Unknown Environments},
author={Boyu Li, Haobin Jiang, Ziluo Ding, Xinrun Xu, Haoran Li, Dongbin Zhao,
and Zongqing Lu},
journal={arXiv preprint arXiv:2410.03303},
year={2024},
archivePrefix={arXiv},
eprint={2410.03303},
primaryClass={cs.LG cs.CV}
} | li2024selu: |
arxiv-665545 | 2410.03304 | Proportionality in Multiple Dimensions to Design Electoral Systems | <|reference_start|>Proportionality in Multiple Dimensions to Design Electoral Systems: How to elect the representatives in legislative bodies is a question that every modern democracy has to answer. This design task has to consider various elements so as to fulfill the citizens' expectations and contribute to the maintenance of a healthy democracy. The notion of proportionality, in that the support of a given idea in the house should be nearly proportional to its support in the general public, lies at the core of this design task. In the last decades, demographic aspects beyond political support have been incorporated by requiring that they are also fairly represented in the body, giving rise to a multidimensional version of the apportionment problem. In this work, we provide an axiomatic justification for a recently proposed notion of multidimensional proportionality and extend it to encompass two relevant constraints often used in electoral systems: a threshold on the number of votes that a list needs in order to be eligible and the election of the most-voted candidate in each district. We then build upon these results to design methods based on multidimensional proportionality. We use the Chilean Constitutional Convention election (May 15-16, 2021) results as a testing ground -- where the dimensions are given by political lists, districts, and genders -- and compare the apportionment obtained under each method according to three criteria: proportionality, representativeness, and voting power. While local and global methods exhibit a natural trade-off between local and global proportionality, including the election of most-voted candidates on top of methods based on 3-dimensional proportionality allows us to incorporate both notions while ensuring higher levels of representativeness and a balanced voting power.<|reference_end|> | arxiv | @article{cembrano2024proportionality,
title={Proportionality in Multiple Dimensions to Design Electoral Systems},
author={Javier Cembrano, Jos'e Correa, Gonzalo D'iaz, Victor Verdugo},
journal={arXiv preprint arXiv:2410.03304},
year={2024},
archivePrefix={arXiv},
eprint={2410.03304},
primaryClass={cs.GT econ.TH}
} | cembrano2024proportionality |
arxiv-665546 | 2410.03306 | Selective Test-Time Adaptation for Unsupervised Anomaly Detection using Neural Implicit Representations | <|reference_start|>Selective Test-Time Adaptation for Unsupervised Anomaly Detection using Neural Implicit Representations: Deep learning models in medical imaging often encounter challenges when adapting to new clinical settings unseen during training. Test-time adaptation offers a promising approach to optimize models for these unseen domains, yet its application in anomaly detection (AD) remains largely unexplored. AD aims to efficiently identify deviations from normative distributions; however, full adaptation, including pathological shifts, may inadvertently learn the anomalies it intends to detect. We introduce a novel concept of \emph{selective} test-time adaptation that utilizes the inherent characteristics of deep pre-trained features to adapt \emph{selectively} in a zero-shot manner to any test image from an unseen domain. This approach employs a model-agnostic, lightweight multi-layer perceptron for neural implicit representations, enabling the adaptation of outputs from any reconstruction-based AD method without altering the source-trained model. Rigorous validation in brain AD demonstrated that our strategy substantially enhances detection accuracy for multiple conditions and different target distributions. Specifically, our method improves the detection rates by up to 78\% for enlarged ventricles and 24\% for edemas.<|reference_end|> | arxiv | @article{ambekar2024selective,
title={Selective Test-Time Adaptation for Unsupervised Anomaly Detection using
Neural Implicit Representations},
author={Sameer Ambekar, Julia A. Schnabel, Cosmin I. Bercea},
journal={arXiv preprint arXiv:2410.03306},
year={2024},
archivePrefix={arXiv},
eprint={2410.03306},
primaryClass={cs.LG}
} | ambekar2024selective |
arxiv-665547 | 2410.03309 | Small Space Encoding and Recognition of $k$-Palindromic Prefixes | <|reference_start|>Small Space Encoding and Recognition of $k$-Palindromic Prefixes: Palindromes are non-empty strings that read the same forward and backward. The problem of recognizing strings that can be represented as the concatenation of even-length palindromes, the concatenation of palindromes of length greater than one, and the concatenation of exactly $k$ palindromes was introduced in the seminal paper of Knuth, Morris, and Pratt [SIAM J. Comput., 1977]. In this work, we study the problem of recognizing so-called $k$-palindromic strings, which can be represented as the concatenation of exactly $k$ palindromes. It was shown that the problem is solvable in linear space and time [Rubinchik and Schur, MFCS'2020]. We aim to develop a sublinear-space solution, and show the following results: (1) First, we show a structural characterization of the set of all $k$-palindromic prefixes of a string by representing it as a union of a small number of highly structured string sets, called affine prefix sets. We show that the size of this representation is of the right asymptotic form by constructing an almost matching lower bound. (2) Secondly, we derive a read-only algorithm that, given a string $T$ of length $n$ and an integer $k$, computes a compact representation of the $i$-palindromic prefixes of $T$, for all $1 \le i \le k$. (3) Finally, we also give a read-only algorithm for computing the palindromic length of $T$, which is the smallest $\ell$ such that $T$ is $\ell$-palindromic, given that $\ell \le k$. The algorithms use $\mathcal O(n \cdot 6^{k^2} \cdot \log^k n)$ time and $\mathcal O(6^{k^2} \cdot \log^k n)$ space. Our work is the first step toward a streaming algorithm for the recognition of $k$-palindromic prefixes.<|reference_end|> | arxiv | @article{bathie2024small,
title={Small Space Encoding and Recognition of $k$-Palindromic Prefixes},
author={Gabriel Bathie, Jonas Ellert, Pawe{l} Gawrychowski, Tatiana
Starikovskaya},
journal={arXiv preprint arXiv:2410.03309},
year={2024},
archivePrefix={arXiv},
eprint={2410.03309},
primaryClass={cs.DS}
} | bathie2024small |
arxiv-665548 | 2410.03311 | Quo Vadis, Motion Generation? From Large Language Models to Large Motion Models | <|reference_start|>Quo Vadis, Motion Generation? From Large Language Models to Large Motion Models: Inspired by the recent success of LLMs, the field of human motion understanding has increasingly shifted towards the development of large motion models. Despite some progress, current state-of-the-art works remain far from achieving truly generalist models, largely due to the lack of large-scale, high-quality motion data. To address this, we present MotionBase, the first million-level motion generation benchmark, offering 15 times the data volume of the previous largest dataset, and featuring multimodal data with hierarchically detailed text descriptions. By leveraging this vast dataset, our large motion model demonstrates strong performance across a broad range of motions, including unseen ones. Through systematic investigation, we underscore the importance of scaling both data and model size, with synthetic data and pseudo labels playing a crucial role in mitigating data acquisition costs. Moreover, our research reveals the limitations of existing evaluation metrics, particularly in handling out-of-domain text instructions -- an issue that has long been overlooked. In addition to these, we introduce a novel 2D lookup-free approach for motion tokenization, which preserves motion information and expands codebook capacity, further enhancing the representative ability of large motion models. The release of MotionBase and the insights gained from this study are expected to pave the way for the development of more powerful and versatile motion generation models.<|reference_end|> | arxiv | @article{wang2024quo,
title={Quo Vadis, Motion Generation? From Large Language Models to Large Motion
Models},
author={Ye Wang, Sipeng Zheng, Bin Cao, Qianshan Wei, Qin Jin, and Zongqing Lu},
journal={arXiv preprint arXiv:2410.03311},
year={2024},
archivePrefix={arXiv},
eprint={2410.03311},
primaryClass={cs.CV cs.LG}
} | wang2024quo |
arxiv-665549 | 2410.03312 | Context and System Fusion in Post-ASR Emotion Recognition with Large Language Models | <|reference_start|>Context and System Fusion in Post-ASR Emotion Recognition with Large Language Models: Large language models (LLMs) have started to play a vital role in modelling speech and text. To explore the best use of context and multiple systems' outputs for post-ASR speech emotion prediction, we study LLM prompting on a recent task named GenSEC. Our techniques include ASR transcript ranking, variable conversation context, and system output fusion. We show that the conversation context has diminishing returns and the metric used to select the transcript for prediction is crucial. Finally, our best submission surpasses the provided baseline by 20% in absolute accuracy.<|reference_end|> | arxiv | @article{stepachev2024context,
title={Context and System Fusion in Post-ASR Emotion Recognition with Large
Language Models},
author={Pavel Stepachev, Pinzhen Chen, Barry Haddow},
journal={arXiv preprint arXiv:2410.03312},
year={2024},
archivePrefix={arXiv},
eprint={2410.03312},
primaryClass={cs.CL eess.AS}
} | stepachev2024context |
arxiv-665550 | 2410.03314 | Evaluation of Study Plans using Partial Orders | <|reference_start|>Evaluation of Study Plans using Partial Orders: In higher education, data is collected that indicate the term(s) that a course is taken and when it is passed. Often, study plans propose a suggested course order to students. Study planners can adjust these based on detected deviations between the proposed and actual order of the courses being taken. In this work, we detect deviations by combining (1) the deviation between the proposed and actual course order with (2) the temporal difference between the expected and actual course-taking term(s). Partially ordered alignments identify the deviations between the proposed and actual order. We compute a partial order alignment by modeling a study plan as a process model and a student's course-taking behavior as a partial order. Using partial orders in such use cases allows one to relax the constraints of strictly ordered traces. This makes our approach less prone to the order in which courses are offered. Further, when modeling course-taking behavior as partial orders, we propose distinguishing intended course-taking behavior from actual course-passing behavior of students by including either all terms in which a course is attempted or only the term that a course is passed, respectively. This provides more perspectives when comparing the proposed and actual course-taking behavior. The proposed deviation measuring approach is evaluated on real-life data from RWTH Aachen University.<|reference_end|> | arxiv | @article{rennert2024evaluation,
title={Evaluation of Study Plans using Partial Orders},
author={Christian Rennert, Mahsa Pourbafrani, Wil van der Aalst},
journal={arXiv preprint arXiv:2410.03314},
year={2024},
archivePrefix={arXiv},
eprint={2410.03314},
primaryClass={cs.CY cs.DB}
} | rennert2024evaluation |
arxiv-665551 | 2410.03315 | Influence-oriented Personalized Federated Learning | <|reference_start|>Influence-oriented Personalized Federated Learning: Traditional federated learning (FL) methods often rely on fixed weighting for parameter aggregation, neglecting the mutual influence by others. Hence, their effectiveness in heterogeneous data contexts is limited. To address this problem, we propose an influence-oriented federated learning framework, namely FedC^2I, which quantitatively measures Client-level and Class-level Influence to realize adaptive parameter aggregation for each client. Our core idea is to explicitly model the inter-client influence within an FL system via the well-crafted influence vector and influence matrix. The influence vector quantifies client-level influence, enables clients to selectively acquire knowledge from others, and guides the aggregation of feature representation layers. Meanwhile, the influence matrix captures class-level influence in a more fine-grained manner to achieve personalized classifier aggregation. We evaluate the performance of FedC^2I against existing federated learning methods under non-IID settings and the results demonstrate the superiority of our method.<|reference_end|> | arxiv | @article{tan2024influence-oriented,
title={Influence-oriented Personalized Federated Learning},
author={Yue Tan, Guodong Long, Jing Jiang, and Chengqi Zhang},
journal={arXiv preprint arXiv:2410.03315},
year={2024},
archivePrefix={arXiv},
eprint={2410.03315},
primaryClass={cs.LG cs.AI cs.DC}
} | tan2024influence-oriented |
arxiv-665552 | 2410.03320 | Lost in Tracking: Uncertainty-guided Cardiac Cine MRI Segmentation at Right Ventricle Base | <|reference_start|>Lost in Tracking: Uncertainty-guided Cardiac Cine MRI Segmentation at Right Ventricle Base: Accurate biventricular segmentation of cardiac magnetic resonance (CMR) cine images is essential for the clinical evaluation of heart function. However, compared to left ventricle (LV), right ventricle (RV) segmentation is still more challenging and less reproducible. Degenerate performance frequently occurs at the RV base, where the in-plane anatomical structures are complex (with atria, valve, and aorta) and vary due to the strong interplanar motion. In this work, we propose to address the currently unsolved issues in CMR segmentation, specifically at the RV base, with two strategies: first, we complemented the public resource by reannotating the RV base in the ACDC dataset, with refined delineation of the right ventricle outflow tract (RVOT), under the guidance of an expert cardiologist. Second, we proposed a novel dual encoder U-Net architecture that leverages temporal incoherence to inform the segmentation when interplanar motions occur. The inter-planar motion is characterized by loss-of-tracking, via Bayesian uncertainty of a motion-tracking model. Our experiments showed that our method significantly improved RV base segmentation taking into account temporal incoherence. Furthermore, we investigated the reproducibility of deep learning-based segmentation and showed that the combination of consistent annotation and loss of tracking could enhance the reproducibility of RV segmentation, potentially facilitating a large number of clinical studies focusing on RV.<|reference_end|> | arxiv | @article{zhao2024lost,
title={Lost in Tracking: Uncertainty-guided Cardiac Cine MRI Segmentation at
Right Ventricle Base},
author={Yidong Zhao, Yi Zhang, Orlando Simonetti, Yuchi Han, Qian Tao},
journal={arXiv preprint arXiv:2410.03320},
year={2024},
doi={10.1007/978-3-031-72114-4_40},
archivePrefix={arXiv},
eprint={2410.03320},
primaryClass={eess.IV cs.CV}
} | zhao2024lost |
arxiv-665553 | 2410.03321 | Visual-O1: Understanding Ambiguous Instructions via Multi-modal Multi-turn Chain-of-thoughts Reasoning | <|reference_start|>Visual-O1: Understanding Ambiguous Instructions via Multi-modal Multi-turn Chain-of-thoughts Reasoning: As large-scale models evolve, language instructions are increasingly utilized in multi-modal tasks. Due to human language habits, these instructions often contain ambiguities in real-world scenarios, necessitating the integration of visual context or common sense for accurate interpretation. However, even highly intelligent large models exhibit significant performance limitations on ambiguous instructions, where weak reasoning abilities of disambiguation can lead to catastrophic errors. To address this issue, this paper proposes Visual-O1, a multi-modal multi-turn chain-of-thought reasoning framework. It simulates human multi-modal multi-turn reasoning, providing instantial experience for highly intelligent models or empirical experience for generally intelligent models to understand ambiguous instructions. Unlike traditional methods that require models to possess high intelligence to understand long texts or perform lengthy complex reasoning, our framework does not significantly increase computational overhead and is more general and effective, even for generally intelligent models. Experiments show that our method not only significantly enhances the performance of models of different intelligence levels on ambiguous instructions but also improves their performance on general datasets. Our work highlights the potential of artificial intelligence to work like humans in real-world scenarios with uncertainty and ambiguity. We will release our data and code.<|reference_end|> | arxiv | @article{ni2024visual-o1:,
title={Visual-O1: Understanding Ambiguous Instructions via Multi-modal
Multi-turn Chain-of-thoughts Reasoning},
author={Minheng Ni, Yutao Fan, Lei Zhang, Wangmeng Zuo},
journal={arXiv preprint arXiv:2410.03321},
year={2024},
archivePrefix={arXiv},
eprint={2410.03321},
primaryClass={cs.CV}
} | ni2024visual-o1: |
arxiv-665554 | 2410.03323 | Does SpatioTemporal information benefit Two video summarization benchmarks? | <|reference_start|>Does SpatioTemporal information benefit Two video summarization benchmarks?: An important aspect of summarizing videos is understanding the temporal context behind each part of the video to grasp what is and is not important. Video summarization models have in recent years modeled spatio-temporal relationships to represent this information. These models achieved state-of-the-art correlation scores on important benchmark datasets. However, what has not been reviewed is whether spatio-temporal relationships are even required to achieve state-of-the-art results. Previous work in activity recognition has found biases, by prioritizing static cues such as scenes or objects, over motion information. In this paper we inquire if similar spurious relationships might influence the task of video summarization. To do so, we analyse the role that temporal information plays on existing benchmark datasets. We first estimate a baseline with temporally invariant models to see how well such models rank on benchmark datasets (TVSum and SumMe). We then disrupt the temporal order of the videos to investigate the impact it has on existing state-of-the-art models. One of our findings is that the temporally invariant models achieve competitive correlation scores that are close to the human baselines on the TVSum dataset. We also demonstrate that existing models are not affected by temporal perturbations. Furthermore, with certain disruption strategies that shuffle fixed time segments, we can actually improve their correlation scores. With these results, we find that spatio-temporal relationship play a minor role and we raise the question whether these benchmarks adequately model the task of video summarization. Code available at: https://github.com/AashGan/TemporalPerturbSum<|reference_end|> | arxiv | @article{ganesh2024does,
title={Does SpatioTemporal information benefit Two video summarization
benchmarks?},
author={Aashutosh Ganesh, Mirela Popa, Daan Odijk and Nava Tintarev},
journal={arXiv preprint arXiv:2410.03323},
year={2024},
archivePrefix={arXiv},
eprint={2410.03323},
primaryClass={cs.CV cs.MM}
} | ganesh2024does |
arxiv-665555 | 2410.03331 | EmojiHeroVR: A Study on Facial Expression Recognition under Partial Occlusion from Head-Mounted Displays | <|reference_start|>EmojiHeroVR: A Study on Facial Expression Recognition under Partial Occlusion from Head-Mounted Displays: Emotion recognition promotes the evaluation and enhancement of Virtual Reality (VR) experiences by providing emotional feedback and enabling advanced personalization. However, facial expressions are rarely used to recognize users' emotions, as Head-Mounted Displays (HMDs) occlude the upper half of the face. To address this issue, we conducted a study with 37 participants who played our novel affective VR game EmojiHeroVR. The collected database, EmoHeVRDB (EmojiHeroVR Database), includes 3,556 labeled facial images of 1,778 reenacted emotions. For each labeled image, we also provide 29 additional frames recorded directly before and after the labeled image to facilitate dynamic Facial Expression Recognition (FER). Additionally, EmoHeVRDB includes data on the activations of 63 facial expressions captured via the Meta Quest Pro VR headset for each frame. Leveraging our database, we conducted a baseline evaluation on the static FER classification task with six basic emotions and neutral using the EfficientNet-B0 architecture. The best model achieved an accuracy of 69.84% on the test set, indicating that FER under HMD occlusion is feasible but significantly more challenging than conventional FER.<|reference_end|> | arxiv | @article{ortmann2024emojiherovr:,
title={EmojiHeroVR: A Study on Facial Expression Recognition under Partial
Occlusion from Head-Mounted Displays},
author={Thorben Ortmann, Qi Wang, Larissa Putzar},
journal={arXiv preprint arXiv:2410.03331},
year={2024},
archivePrefix={arXiv},
eprint={2410.03331},
primaryClass={cs.CV}
} | ortmann2024emojiherovr: |
arxiv-665556 | 2410.03333 | Comparative Analysis and Ensemble Enhancement of Leading CNN Architectures for Breast Cancer Classification | <|reference_start|>Comparative Analysis and Ensemble Enhancement of Leading CNN Architectures for Breast Cancer Classification: This study introduces a novel and accurate approach to breast cancer classification using histopathology images. It systematically compares leading Convolutional Neural Network (CNN) models across varying image datasets, identifies their optimal hyperparameters, and ranks them based on classification efficacy. To maximize classification accuracy for each model we explore, the effects of data augmentation, alternative fully-connected layers, model training hyperparameter settings, and, the advantages of retraining models versus using pre-trained weights. Our methodology includes several original concepts, including serializing generated datasets to ensure consistent data conditions across training runs and significantly reducing training duration. Combined with automated curation of results, this enabled the exploration of over 2,000 training permutations -- such a comprehensive comparison is as yet unprecedented. Our findings establish the settings required to achieve exceptional classification accuracy for standalone CNN models and rank them by model efficacy. Based on these results, we propose ensemble architectures that stack three high-performing standalone CNN models together with diverse classifiers, resulting in improved classification accuracy. The ability to systematically run so many model permutations to get the best outcomes gives rise to very high quality results, including 99.75% for BreakHis x40 and BreakHis x200 and 95.18% for the Bach datasets when split into train, validation and test datasets. The Bach Online blind challenge, yielded 89% using this approach. Whilst this study is based on breast cancer histopathology image datasets, the methodology is equally applicable to other medical image datasets.<|reference_end|> | arxiv | @article{murphy2024comparative,
title={Comparative Analysis and Ensemble Enhancement of Leading CNN
Architectures for Breast Cancer Classification},
author={Gary Murphy, Raghubir Singh},
journal={arXiv preprint arXiv:2410.03333},
year={2024},
archivePrefix={arXiv},
eprint={2410.03333},
primaryClass={cs.CV cs.AI}
} | murphy2024comparative |
arxiv-665557 | 2410.03334 | An X-Ray Is Worth 15 Features: Sparse Autoencoders for Interpretable Radiology Report Generation | <|reference_start|>An X-Ray Is Worth 15 Features: Sparse Autoencoders for Interpretable Radiology Report Generation: Radiological services are experiencing unprecedented demand, leading to increased interest in automating radiology report generation. Existing Vision-Language Models (VLMs) suffer from hallucinations, lack interpretability, and require expensive fine-tuning. We introduce SAE-Rad, which uses sparse autoencoders (SAEs) to decompose latent representations from a pre-trained vision transformer into human-interpretable features. Our hybrid architecture combines state-of-the-art SAE advancements, achieving accurate latent reconstructions while maintaining sparsity. Using an off-the-shelf language model, we distil ground-truth reports into radiological descriptions for each SAE feature, which we then compile into a full report for each image, eliminating the need for fine-tuning large models for this task. To the best of our knowledge, SAE-Rad represents the first instance of using mechanistic interpretability techniques explicitly for a downstream multi-modal reasoning task. On the MIMIC-CXR dataset, SAE-Rad achieves competitive radiology-specific metrics compared to state-of-the-art models while using significantly fewer computational resources for training. Qualitative analysis reveals that SAE-Rad learns meaningful visual concepts and generates reports aligning closely with expert interpretations. Our results suggest that SAEs can enhance multimodal reasoning in healthcare, providing a more interpretable alternative to existing VLMs.<|reference_end|> | arxiv | @article{abdulaal2024an,
title={An X-Ray Is Worth 15 Features: Sparse Autoencoders for Interpretable
Radiology Report Generation},
author={Ahmed Abdulaal, Hugo Fry, Nina Monta~na-Brown, Ayodeji Ijishakin,
Jack Gao, Stephanie Hyland, Daniel C. Alexander, Daniel C. Castro},
journal={arXiv preprint arXiv:2410.03334},
year={2024},
archivePrefix={arXiv},
eprint={2410.03334},
primaryClass={cs.CV cs.AI}
} | abdulaal2024an |
arxiv-665558 | 2410.03335 | Audio-Agent: Leveraging LLMs For Audio Generation, Editing and Composition | <|reference_start|>Audio-Agent: Leveraging LLMs For Audio Generation, Editing and Composition: We introduce Audio-Agent, a multimodal framework for audio generation, editing and composition based on text or video inputs. Conventional approaches for text-to-audio (TTA) tasks often make single-pass inferences from text descriptions. While straightforward, this design struggles to produce high-quality audio when given complex text conditions. In our method, we utilize a pre-trained TTA diffusion network as the audio generation agent to work in tandem with GPT-4, which decomposes the text condition into atomic, specific instructions, and calls the agent for audio generation. Consequently, Audio-Agent generates high-quality audio that is closely aligned with the provided text or video while also supporting variable-length generation. For video-to-audio (VTA) tasks, most existing methods require training a timestamp detector to synchronize video events with generated audio, a process that can be tedious and time-consuming. We propose a simpler approach by fine-tuning a pre-trained Large Language Model (LLM), e.g., Gemma2-2B-it, to obtain both semantic and temporal conditions to bridge video and audio modality. Thus our framework provides a comprehensive solution for both TTA and VTA tasks without substantial computational overhead in training.<|reference_end|> | arxiv | @article{wang2024audio-agent:,
title={Audio-Agent: Leveraging LLMs For Audio Generation, Editing and
Composition},
author={Zixuan Wang, Yu-Wing Tai, Chi-Keung Tang},
journal={arXiv preprint arXiv:2410.03335},
year={2024},
archivePrefix={arXiv},
eprint={2410.03335},
primaryClass={cs.SD cs.CV cs.LG eess.AS}
} | wang2024audio-agent: |
arxiv-665559 | 2410.03339 | Tarzan: Passively-Learned Real-Time Rate Control for Video Conferencing | <|reference_start|>Tarzan: Passively-Learned Real-Time Rate Control for Video Conferencing: Rate control algorithms are at the heart of video conferencing platforms, determining target bitrates that match dynamic network characteristics for high quality. Recent data-driven strategies have shown promise for this challenging task, but the performance degradation they introduce during training has been a nonstarter for many production services, precluding adoption. This paper aims to bolster the practicality of data-driven rate control by presenting an alternative avenue for experiential learning: leveraging purely existing telemetry logs produced by the incumbent algorithm in production. We observe that these logs contain effective decisions, although often at the wrong times or in the wrong order. To realize this approach despite the inherent uncertainty that log-based learning brings (i.e., lack of feedback for new decisions), our system, Tarzan, combines a variety of robust learning techniques (i.e., conservatively reasoning about alternate behavior to minimize risk and using a richer model formulation to account for environmental noise). Across diverse networks (emulated and real-world), Tarzan outperforms the widely deployed GCC algorithm, increasing average video bitrates by 15-39% while reducing freeze rates by 60-100%.<|reference_end|> | arxiv | @article{agarwal2024tarzan:,
title={Tarzan: Passively-Learned Real-Time Rate Control for Video Conferencing},
author={Neil Agarwal, Rui Pan, Francis Y. Yan, Ravi Netravali},
journal={arXiv preprint arXiv:2410.03339},
year={2024},
archivePrefix={arXiv},
eprint={2410.03339},
primaryClass={cs.NI}
} | agarwal2024tarzan: |
arxiv-665560 | 2410.03341 | Zero-Shot Fact Verification via Natural Logic and Large Language Models | <|reference_start|>Zero-Shot Fact Verification via Natural Logic and Large Language Models: The recent development of fact verification systems with natural logic has enhanced their explainability by aligning claims with evidence through set-theoretic operators, providing faithful justifications. Despite these advancements, such systems often rely on a large amount of training data annotated with natural logic. To address this issue, we propose a zero-shot method that utilizes the generalization capabilities of instruction-tuned large language models. To comprehensively assess the zero-shot capabilities of our method and other fact verification systems, we evaluate all models on both artificial and real-world claims, including multilingual datasets. We also compare our method against other fact verification systems in two setups. First, in the zero-shot generalization setup, we demonstrate that our approach outperforms other systems that were not specifically trained on natural logic data, achieving an average accuracy improvement of 8.96 points over the best-performing baseline. Second, in the zero-shot transfer setup, we show that current systems trained on natural logic data do not generalize well to other domains, and our method outperforms these systems across all datasets with real-world claims.<|reference_end|> | arxiv | @article{strong2024zero-shot,
title={Zero-Shot Fact Verification via Natural Logic and Large Language Models},
author={Marek Strong, Rami Aly, Andreas Vlachos},
journal={arXiv preprint arXiv:2410.03341},
year={2024},
archivePrefix={arXiv},
eprint={2410.03341},
primaryClass={cs.CL}
} | strong2024zero-shot |
arxiv-665561 | 2410.03342 | Impact factors of astrophysics journals revisited | <|reference_start|>Impact factors of astrophysics journals revisited: We calculate the 2024 impact factors of 36 most widely used journals in Astrophysics, using the citations collated by NASA/ADS (Astrophysics Data System) and compare them to the official impact factors. This includes journals which publish papers outside of astrophysics such as PRD, EPJC, Nature etc. We also propose a new metric to gauge the impact factor based on the median number of citations in a journal and calculate the same for all the journals. We find that the ADS-based impact factors are mostly in agreement, albeit higher than the official impact factors for most journals. The journals with the maximum fractional difference in median-based and old impact factors are JHEAP and PTEP. We find the maximum difference between the ADS and official impact factor for Nature.<|reference_end|> | arxiv | @article{rithvik2024impact,
title={Impact factors of astrophysics journals revisited},
author={Rayani Venkat Sai Rithvik, Shantanu Desai},
journal={arXiv preprint arXiv:2410.03342},
year={2024},
archivePrefix={arXiv},
eprint={2410.03342},
primaryClass={astro-ph.IM cs.DL physics.soc-ph}
} | rithvik2024impact |
arxiv-665562 | 2410.03347 | Practical Light Clients for Committee-Based Blockchains | <|reference_start|>Practical Light Clients for Committee-Based Blockchains: Light clients are gaining increasing attention in the literature since they obviate the need for users to set up dedicated blockchain full nodes. While the literature features a number of light client instantiations, most light client protocols optimize for long offline phases and implicitly assume that the block headers to be verified are signed by highly dynamic validators. In this paper, we show that (i) most light clients are rarely offline for more than a week, and (ii) validators are unlikely to drastically change in most permissioned blockchains and in a number of permissionless blockchains, such as Cosmos and Polkadot. Motivated by these findings, we propose a novel practical system that optimizes for such realistic assumptions and achieves minimal communication and computational costs for light clients when compared to existing protocols. By means of a prototype implementation of our solution, we show that our protocol achieves a reduction by up to $90$ and $40000\times$ (respectively) in end-to-end latency and up to $1000$ and $10000\times$ (respectively) smaller proof size when compared to two state-of-the-art light client instantiations from the literature.<|reference_end|> | arxiv | @article{armknecht2024practical,
title={Practical Light Clients for Committee-Based Blockchains},
author={Frederik Armknecht, Ghassan Karame, Malcom Mohamed, Christiane Weis},
journal={arXiv preprint arXiv:2410.03347},
year={2024},
archivePrefix={arXiv},
eprint={2410.03347},
primaryClass={cs.CR}
} | armknecht2024practical |
arxiv-665563 | 2410.03348 | Dolphin: A Programmable Framework for Scalable Neurosymbolic Learning | <|reference_start|>Dolphin: A Programmable Framework for Scalable Neurosymbolic Learning: Neurosymbolic learning has emerged as a promising paradigm to incorporate symbolic reasoning into deep learning models. However, existing frameworks are limited in scalability with respect to both the training data and the complexity of symbolic programs. We propose Dolphin, a framework to scale neurosymbolic learning at a fundamental level by mapping both forward chaining and backward gradient propagation in symbolic programs to vectorized computations. For this purpose, Dolphin introduces a set of abstractions and primitives built directly on top of a high-performance deep learning framework like PyTorch, effectively enabling symbolic programs to be written as PyTorch modules. It thereby enables neurosymbolic programs to be written in a language like Python that is familiar to developers and compile them to computation graphs that are amenable to end-to-end differentiation on GPUs. We evaluate Dolphin on a suite of 13 benchmarks across 5 neurosymbolic tasks that combine deep learning models for text, image, or video processing with symbolic programs that involve multi-hop reasoning, recursion, and even black-box functions like Python eval(). Dolphin only takes 0.33%-37.17% of the time (and 2.77% on average) to train these models on the largest input per task compared to baselines Scallop, ISED, and IndeCateR+, which time out on most of these inputs. Models written in Dolphin also achieve state-of-the-art accuracies even on the largest benchmarks.<|reference_end|> | arxiv | @article{naik2024dolphin:,
title={Dolphin: A Programmable Framework for Scalable Neurosymbolic Learning},
author={Aaditya Naik, Jason Liu, Claire Wang, Saikat Dutta, Mayur Naik, Eric
Wong},
journal={arXiv preprint arXiv:2410.03348},
year={2024},
archivePrefix={arXiv},
eprint={2410.03348},
primaryClass={cs.LG}
} | naik2024dolphin: |
arxiv-665564 | 2410.03351 | Generating Equivalent Representations of Code By A Self-Reflection Approach | <|reference_start|>Generating Equivalent Representations of Code By A Self-Reflection Approach: Equivalent Representations (ERs) of code are textual representations that preserve the same semantics as the code itself, e.g., natural language comments and pseudocode. ERs play a critical role in software development and maintenance. However, how to automatically generate ERs of code remains an open challenge. In this paper, we propose a self-reflection approach to generating ERs of code. It enables two Large Language Models (LLMs) to work mutually and produce an ER through a reflection process. Depending on whether constraints on ERs are applied, our approach generates ERs in both open and constrained settings. We conduct a empirical study to generate ERs in two settings and obtain eight findings. (1) Generating ERs in the open setting. In the open setting, we allow LLMs to represent code without any constraints, analyzing the resulting ERs and uncovering five key findings. These findings shed light on how LLMs comprehend syntactic structures, APIs, and numerical computations in code. (2) Generating ERs in the constrained setting. In the constrained setting, we impose constraints on ERs, such as natural language comments, pseudocode, and flowcharts. This allows our approach to address a range of software engineering tasks. Based on our experiments, we have three findings demonstrating that our approach can effectively generate ERs that adhere to specific constraints, thus supporting various software engineering tasks. (3) Future directions. We also discuss potential future research directions, such as deriving intermediate languages for code generation, exploring LLM-friendly requirement descriptions, and further supporting software engineering tasks. We believe that this paper will spark discussions in research communities and inspire many follow-up studies.<|reference_end|> | arxiv | @article{li2024generating,
title={Generating Equivalent Representations of Code By A Self-Reflection
Approach},
author={Jia Li, Ge Li, Lecheng Wang, Hao Zhu, Zhi Jin},
journal={arXiv preprint arXiv:2410.03351},
year={2024},
archivePrefix={arXiv},
eprint={2410.03351},
primaryClass={cs.CL cs.PL cs.SE}
} | li2024generating |
arxiv-665565 | 2410.03355 | LANTERN: Accelerating Visual Autoregressive Models with Relaxed Speculative Decoding | <|reference_start|>LANTERN: Accelerating Visual Autoregressive Models with Relaxed Speculative Decoding: Auto-Regressive (AR) models have recently gained prominence in image generation, often matching or even surpassing the performance of diffusion models. However, one major limitation of AR models is their sequential nature, which processes tokens one at a time, slowing down generation compared to models like GANs or diffusion-based methods that operate more efficiently. While speculative decoding has proven effective for accelerating LLMs by generating multiple tokens in a single forward, its application in visual AR models remains largely unexplored. In this work, we identify a challenge in this setting, which we term \textit{token selection ambiguity}, wherein visual AR models frequently assign uniformly low probabilities to tokens, hampering the performance of speculative decoding. To overcome this challenge, we propose a relaxed acceptance condition referred to as LANTERN that leverages the interchangeability of tokens in latent space. This relaxation restores the effectiveness of speculative decoding in visual AR models by enabling more flexible use of candidate tokens that would otherwise be prematurely rejected. Furthermore, by incorporating a total variation distance bound, we ensure that these speed gains are achieved without significantly compromising image quality or semantic coherence. Experimental results demonstrate the efficacy of our method in providing a substantial speed-up over speculative decoding. In specific, compared to a na\"ive application of the state-of-the-art speculative decoding, LANTERN increases speed-ups by $\mathbf{1.75}\times$ and $\mathbf{1.76}\times$, as compared to greedy decoding and random sampling, respectively, when applied to LlamaGen, a contemporary visual AR model.<|reference_end|> | arxiv | @article{jang2024lantern:,
title={LANTERN: Accelerating Visual Autoregressive Models with Relaxed
Speculative Decoding},
author={Doohyuk Jang, Sihwan Park, June Yong Yang, Yeonsung Jung, Jihun Yun,
Souvik Kundu, Sung-Yub Kim, Eunho Yang},
journal={arXiv preprint arXiv:2410.03355},
year={2024},
archivePrefix={arXiv},
eprint={2410.03355},
primaryClass={cs.CV cs.AI}
} | jang2024lantern: |
arxiv-665566 | 2410.03357 | Should Cross-Lingual AMR Parsing go Meta? An Empirical Assessment of Meta-Learning and Joint Learning AMR Parsing | <|reference_start|>Should Cross-Lingual AMR Parsing go Meta? An Empirical Assessment of Meta-Learning and Joint Learning AMR Parsing: Cross-lingual AMR parsing is the task of predicting AMR graphs in a target language when training data is available only in a source language. Due to the small size of AMR training data and evaluation data, cross-lingual AMR parsing has only been explored in a small set of languages such as English, Spanish, German, Chinese, and Italian. Taking inspiration from Langedijk et al. (2022), who apply meta-learning to tackle cross-lingual syntactic parsing, we investigate the use of meta-learning for cross-lingual AMR parsing. We evaluate our models in $k$-shot scenarios (including 0-shot) and assess their effectiveness in Croatian, Farsi, Korean, Chinese, and French. Notably, Korean and Croatian test sets are developed as part of our work, based on the existing The Little Prince English AMR corpus, and made publicly available. We empirically study our method by comparing it to classical joint learning. Our findings suggest that while the meta-learning model performs slightly better in 0-shot evaluation for certain languages, the performance gain is minimal or absent when $k$ is higher than 0.<|reference_end|> | arxiv | @article{kang2024should,
title={Should Cross-Lingual AMR Parsing go Meta? An Empirical Assessment of
Meta-Learning and Joint Learning AMR Parsing},
author={Jeongwoo Kang, Maximin Coavoux, C'edric Lopez, Didier Schwab},
journal={arXiv preprint arXiv:2410.03357},
year={2024},
archivePrefix={arXiv},
eprint={2410.03357},
primaryClass={cs.CL}
} | kang2024should |
arxiv-665567 | 2410.03358 | Oracle Separation Between Quantum Commitments and Quantum One-wayness | <|reference_start|>Oracle Separation Between Quantum Commitments and Quantum One-wayness: We show that there exists a unitary quantum oracle relative to which quantum commitments exist but no (efficiently verifiable) one-way state generators exist. Both have been widely considered candidates for replacing one-way functions as the minimal assumption for cryptography: the weakest cryptographic assumption implied by all of computational cryptography. Recent work has shown that commitments can be constructed from one-way state generators, but the other direction has remained open. Our results rule out any black-box construction, and thus settle this crucial open problem, suggesting that quantum commitments (as well as its equivalency class of EFI pairs, quantum oblivious transfer, and secure quantum multiparty computation) appear to be strictly weakest among all known cryptographic primitives.<|reference_end|> | arxiv | @article{bostanci2024oracle,
title={Oracle Separation Between Quantum Commitments and Quantum One-wayness},
author={John Bostanci, Boyang Chen, Barak Nehoran},
journal={arXiv preprint arXiv:2410.03358},
year={2024},
archivePrefix={arXiv},
eprint={2410.03358},
primaryClass={quant-ph cs.CR}
} | bostanci2024oracle |
arxiv-665568 | 2410.03359 | An Enhanced Harmonic Densely Connected Hybrid Transformer Network Architecture for Chronic Wound Segmentation Utilising Multi-Colour Space Tensor Merging | <|reference_start|>An Enhanced Harmonic Densely Connected Hybrid Transformer Network Architecture for Chronic Wound Segmentation Utilising Multi-Colour Space Tensor Merging: Chronic wounds and associated complications present ever growing burdens for clinics and hospitals world wide. Venous, arterial, diabetic, and pressure wounds are becoming increasingly common globally. These conditions can result in highly debilitating repercussions for those affected, with limb amputations and increased mortality risk resulting from infection becoming more common. New methods to assist clinicians in chronic wound care are therefore vital to maintain high quality care standards. This paper presents an improved HarDNet segmentation architecture which integrates a contrast-eliminating component in the initial layers of the network to enhance feature learning. We also utilise a multi-colour space tensor merging process and adjust the harmonic shape of the convolution blocks to facilitate these additional features. We train our proposed model using wound images from light-skinned patients and test the model on two test sets (one set with ground truth, and one without) comprising only darker-skinned cases. Subjective ratings are obtained from clinical wound experts with intraclass correlation coefficient used to determine inter-rater reliability. For the dark-skin tone test set with ground truth, we demonstrate improvements in terms of Dice similarity coefficient (+0.1221) and intersection over union (+0.1274). Qualitative analysis showed high expert ratings, with improvements of >3% demonstrated when comparing the baseline model with the proposed model. This paper presents the first study to focus on darker-skin tones for chronic wound segmentation using models trained only on wound images exhibiting lighter skin. Diabetes is highly prevalent in countries where patients have darker skin tones, highlighting the need for a greater focus on such cases. Additionally, we conduct the largest qualitative study to date for chronic wound segmentation.<|reference_end|> | arxiv | @article{cassidy2024an,
title={An Enhanced Harmonic Densely Connected Hybrid Transformer Network
Architecture for Chronic Wound Segmentation Utilising Multi-Colour Space
Tensor Merging},
author={Bill Cassidy, Christian Mcbride, Connah Kendrick, Neil D. Reeves,
Joseph M. Pappachan, Cornelius J. Fernandez, Elias Chacko, Raphael Br"ungel,
Christoph M. Friedrich, Metib Alotaibi, Abdullah Abdulaziz AlWabel, Mohammad
Alderwish, Kuan-Ying Lai, Moi Hoon Yap},
journal={arXiv preprint arXiv:2410.03359},
year={2024},
archivePrefix={arXiv},
eprint={2410.03359},
primaryClass={eess.IV cs.AI cs.CV}
} | cassidy2024an |
arxiv-665569 | 2410.03364 | Error Correction Code Transformer: From Non-Unified to Unified | <|reference_start|>Error Correction Code Transformer: From Non-Unified to Unified: Channel coding is vital for reliable data transmission in modern wireless systems, and its significance will increase with the emergence of sixth-generation (6G) networks, which will need to support various error correction codes. However, traditional decoders were typically designed as fixed hardware circuits tailored to specific decoding algorithms, leading to inefficiencies and limited flexibility. To address these challenges, this paper proposes a unified, code-agnostic Transformer-based decoding architecture capable of handling multiple linear block codes, including Polar, Low-Density Parity-Check (LDPC), and Bose-Chaudhuri-Hocquenghem (BCH), within a single framework. To achieve this, standardized units are employed to harmonize parameters across different code types, while the redesigned unified attention module compresses the structural information of various codewords. Additionally, a sparse mask, derived from the sparsity of the parity-check matrix, is introduced to enhance the model's ability to capture inherent constraints between information and parity-check bits, resulting in improved decoding accuracy and robustness. Extensive experimental results demonstrate that the proposed unified Transformer-based decoder not only outperforms existing methods but also provides a flexible, efficient, and high-performance solution for next-generation wireless communication systems.<|reference_end|> | arxiv | @article{yan2024error,
title={Error Correction Code Transformer: From Non-Unified to Unified},
author={Yongli Yan, Jieao Zhu, Tianyue Zheng, Jiaqi He, Linglong Dai},
journal={arXiv preprint arXiv:2410.03364},
year={2024},
archivePrefix={arXiv},
eprint={2410.03364},
primaryClass={cs.IT cs.LG math.IT}
} | yan2024error |
arxiv-665570 | 2410.03365 | Large Synthetic Datasets for Machine Learning Applications in Power Systems | <|reference_start|>Large Synthetic Datasets for Machine Learning Applications in Power Systems: With the ongoing energy transition, power grids are evolving fast. They operate more and more often close to their technical limit, under more and more volatile conditions. Fast, essentially real-time computational approaches to evaluate their operational safety, stability and reliability are therefore highly desirable. Machine Learning methods have been advocated to solve this challenge, however they are heavy consumers of training and testing data, while historical operational data for real-world power grids are hard if not impossible to access. This manuscript describes an algorithmic approach for generating large datasets of power injections in electric power grids. The method allows one to generate arbitrarily large time series from the knowledge of the grid -- the admittance of its lines as well as the location, type and capacity of its power generators -- and aggregated power consumption data, such as the national load data given by ENTSO-E. The obtained datasets are statistically validated against real-world data.<|reference_end|> | arxiv | @article{gillioz2024large,
title={Large Synthetic Datasets for Machine Learning Applications in Power
Systems},
author={Marc Gillioz, Guillaume Dubuis, Philippe Jacquod},
journal={arXiv preprint arXiv:2410.03365},
year={2024},
archivePrefix={arXiv},
eprint={2410.03365},
primaryClass={eess.SY cs.SY}
} | gillioz2024large |
arxiv-665571 | 2410.03367 | Discretizing the Fokker-Planck equation with second-order accuracy: a dissipation driven approach | <|reference_start|>Discretizing the Fokker-Planck equation with second-order accuracy: a dissipation driven approach: We propose a fully discrete finite volume scheme for the standard Fokker-Planck equation. The space discretization relies on the well-known square-root approximation, which falls into the framework of two-point flux approximations. Our time discretization is novel and relies on a tailored nonlinear mid-point rule, designed to accurately capture the dissipative structure of the model. We establish well-posedness for the scheme, positivity of the solutions, as well as a fully discrete energy-dissipation inequality mimicking the continuous one. We then prove the rigorous convergence of the scheme under mildly restrictive conditions on the unstructured grids, which can be easily satisfied in practice. Numerical simulations show that our scheme is second order accurate both in time and space, and that one can solve the discrete nonlinear systems arising at each time step using Newton's method with low computational cost.<|reference_end|> | arxiv | @article{cancès2024discretizing,
title={Discretizing the Fokker-Planck equation with second-order accuracy: a
dissipation driven approach},
author={Cl'ement Canc`es (RAPSODI, LPP), L'eonard Monsaingeon (IECL),
Andrea Natale (RAPSODI, LPP)},
journal={arXiv preprint arXiv:2410.03367},
year={2024},
archivePrefix={arXiv},
eprint={2410.03367},
primaryClass={math.AP cs.NA math.NA}
} | cancès2024discretizing |
arxiv-665572 | 2410.03368 | Latent Abstractions in Generative Diffusion Models | <|reference_start|>Latent Abstractions in Generative Diffusion Models: In this work we study how diffusion-based generative models produce high-dimensional data, such as an image, by implicitly relying on a manifestation of a low-dimensional set of latent abstractions, that guide the generative process. We present a novel theoretical framework that extends NLF, and that offers a unique perspective on SDE-based generative models. The development of our theory relies on a novel formulation of the joint (state and measurement) dynamics, and an information-theoretic measure of the influence of the system state on the measurement process. According to our theory, diffusion models can be cast as a system of SDE, describing a non-linear filter in which the evolution of unobservable latent abstractions steers the dynamics of an observable measurement process (corresponding to the generative pathways). In addition, we present an empirical study to validate our theory and previous empirical results on the emergence of latent abstractions at different stages of the generative process.<|reference_end|> | arxiv | @article{franzese2024latent,
title={Latent Abstractions in Generative Diffusion Models},
author={Giulio Franzese, Mattia Martini, Giulio Corallo, Paolo Papotti, Pietro
Michiardi},
journal={arXiv preprint arXiv:2410.03368},
year={2024},
archivePrefix={arXiv},
eprint={2410.03368},
primaryClass={cs.LG stat.ML}
} | franzese2024latent |
arxiv-665573 | 2410.03370 | Collision-Aware Traversability Analysis for Autonomous Vehicles in the Context of Agricultural Robotics | <|reference_start|>Collision-Aware Traversability Analysis for Autonomous Vehicles in the Context of Agricultural Robotics: In this paper, we introduce a novel method for safe navigation in agricultural robotics. As global environmental challenges intensify, robotics offers a powerful solution to reduce chemical usage while meeting the increasing demands for food production. However, significant challenges remain in ensuring the autonomy and resilience of robots operating in unstructured agricultural environments. Obstacles such as crops and tall grass, which are deformable, must be identified as safely traversable, compared to rigid obstacles. To address this, we propose a new traversability analysis method based on a 3D spectral map reconstructed using a LIDAR and a multispectral camera. This approach enables the robot to distinguish between safe and unsafe collisions with deformable obstacles. We perform a comprehensive evaluation of multispectral metrics for vegetation detection and incorporate these metrics into an augmented environmental map. Utilizing this map, we compute a physics-based traversability metric that accounts for the robot's weight and size, ensuring safe navigation over deformable obstacles.<|reference_end|> | arxiv | @article{philippe2024collision-aware,
title={Collision-Aware Traversability Analysis for Autonomous Vehicles in the
Context of Agricultural Robotics},
author={Florian Philippe, Johann Laconte, Pierre-Jean Lapray, Matthias Spisser
and Jean-Philippe Lauffenburger},
journal={arXiv preprint arXiv:2410.03370},
year={2024},
archivePrefix={arXiv},
eprint={2410.03370},
primaryClass={cs.RO}
} | philippe2024collision-aware |
arxiv-665574 | 2410.03373 | Make Interval Bound Propagation great again | <|reference_start|>Make Interval Bound Propagation great again: In various scenarios motivated by real life, such as medical data analysis, autonomous driving, and adversarial training, we are interested in robust deep networks. A network is robust when a relatively small perturbation of the input cannot lead to drastic changes in output (like change of class, etc.). This falls under the broader scope field of Neural Network Certification (NNC). Two crucial problems in NNC are of profound interest to the scientific community: how to calculate the robustness of a given pre-trained network and how to construct robust networks. The common approach to constructing robust networks is Interval Bound Propagation (IBP). This paper demonstrates that IBP is sub-optimal in the first case due to its susceptibility to the wrapping effect. Even for linear activation, IBP gives strongly sub-optimal bounds. Consequently, one should use strategies immune to the wrapping effect to obtain bounds close to optimal ones. We adapt two classical approaches dedicated to strict computations -- Dubleton Arithmetic and Affine Arithmetic -- to mitigate the wrapping effect in neural networks. These techniques yield precise results for networks with linear activation functions, thus resisting the wrapping effect. As a result, we achieve bounds significantly closer to the optimal level than IBPs.<|reference_end|> | arxiv | @article{krukowski2024make,
title={Make Interval Bound Propagation great again},
author={Patryk Krukowski, Daniel Wilczak, Jacek Tabor, Anna Bielawska,
Przemys{l}aw Spurek},
journal={arXiv preprint arXiv:2410.03373},
year={2024},
archivePrefix={arXiv},
eprint={2410.03373},
primaryClass={cs.LG cs.AI}
} | krukowski2024make |
arxiv-665575 | 2410.03375 | SoundSignature: What Type of Music Do You Like? | <|reference_start|>SoundSignature: What Type of Music Do You Like?: SoundSignature is a music application that integrates a custom OpenAI Assistant to analyze users' favorite songs. The system incorporates state-of-the-art Music Information Retrieval (MIR) Python packages to combine extracted acoustic/musical features with the assistant's extensive knowledge of the artists and bands. Capitalizing on this combined knowledge, SoundSignature leverages semantic audio and principles from the emerging Internet of Sounds (IoS) ecosystem, integrating MIR with AI to provide users with personalized insights into the acoustic properties of their music, akin to a musical preference personality report. Users can then interact with the chatbot to explore deeper inquiries about the acoustic analyses performed and how they relate to their musical taste. This interactivity transforms the application, acting not only as an informative resource about familiar and/or favorite songs, but also as an educational platform that enables users to deepen their understanding of musical features, music theory, acoustic properties commonly used in signal processing, and the artists behind the music. Beyond general usability, the application also incorporates several well-established open-source musician-specific tools, such as a chord recognition algorithm (CREMA), a source separation algorithm (DEMUCS), and an audio-to-MIDI converter (basic-pitch). These features allow users without coding skills to access advanced, open-source music processing algorithms simply by interacting with the chatbot (e.g., can you give me the stems of this song?). In this paper, we highlight the application's innovative features and educational potential, and present findings from a pilot user study that evaluates its efficacy and usability.<|reference_end|> | arxiv | @article{carone2024soundsignature:,
title={SoundSignature: What Type of Music Do You Like?},
author={Brandon James Carone and Pablo Ripoll'es},
journal={arXiv preprint arXiv:2410.03375},
year={2024},
archivePrefix={arXiv},
eprint={2410.03375},
primaryClass={cs.SD cs.AI cs.IR eess.AS}
} | carone2024soundsignature: |
arxiv-665576 | 2410.03376 | Mitigating Adversarial Perturbations for Deep Reinforcement Learning via Vector Quantization | <|reference_start|>Mitigating Adversarial Perturbations for Deep Reinforcement Learning via Vector Quantization: Recent studies reveal that well-performing reinforcement learning (RL) agents in training often lack resilience against adversarial perturbations during deployment. This highlights the importance of building a robust agent before deploying it in the real world. Most prior works focus on developing robust training-based procedures to tackle this problem, including enhancing the robustness of the deep neural network component itself or adversarially training the agent on strong attacks. In this work, we instead study an input transformation-based defense for RL. Specifically, we propose using a variant of vector quantization (VQ) as a transformation for input observations, which is then used to reduce the space of adversarial attacks during testing, resulting in the transformed observations being less affected by attacks. Our method is computationally efficient and seamlessly integrates with adversarial training, further enhancing the robustness of RL agents against adversarial attacks. Through extensive experiments in multiple environments, we demonstrate that using VQ as the input transformation effectively defends against adversarial attacks on the agent's observations.<|reference_end|> | arxiv | @article{luu2024mitigating,
title={Mitigating Adversarial Perturbations for Deep Reinforcement Learning via
Vector Quantization},
author={Tung M. Luu, Thanh Nguyen, Tee Joshua Tian Jin, Sungwoon Kim, and
Chang D. Yoo},
journal={arXiv preprint arXiv:2410.03376},
year={2024},
archivePrefix={arXiv},
eprint={2410.03376},
primaryClass={cs.LG cs.AI}
} | luu2024mitigating |
arxiv-665577 | 2410.03378 | Neural networks meet anisotropic hyperelasticity: A framework based on generalized structure tensors and isotropic tensor functions | <|reference_start|>Neural networks meet anisotropic hyperelasticity: A framework based on generalized structure tensors and isotropic tensor functions: We present a data-driven framework for the multiscale modeling of anisotropic finite strain elasticity based on physics-augmented neural networks (PANNs). Our approach allows the efficient simulation of materials with complex underlying microstructures which reveal an overall anisotropic and nonlinear behavior on the macroscale. By using a set of invariants as input, an energy-type output and by adding several correction terms to the overall energy density functional, the model fulfills multiple physical principles by construction. The invariants are formed from the right Cauchy-Green deformation tensor and fully symmetric 2nd, 4th or 6th order structure tensors which enables to describe a wide range of symmetry groups. Besides the network parameters, the structure tensors are simultaneously calibrated during training so that the underlying anisotropy of the material is reproduced most accurately. In addition, sparsity of the model with respect to the number of invariants is enforced by adding a trainable gate layer and using lp regularization. Our approach works for data containing tuples of deformation, stress and material tangent, but also for data consisting only of tuples of deformation and stress, as is the case in real experiments. The developed approach is exemplarily applied to several representative examples, where necessary data for the training of the PANN surrogate model are collected via computational homogenization. We show that the proposed model achieves excellent interpolation and extrapolation behaviors. In addition, the approach is benchmarked against an NN model based on the components of the right Cauchy-Green deformation tensor.<|reference_end|> | arxiv | @article{kalina2024neural,
title={Neural networks meet anisotropic hyperelasticity: A framework based on
generalized structure tensors and isotropic tensor functions},
author={Karl A. Kalina, J"org Brummund, WaiChing Sun, Markus K"astner},
journal={arXiv preprint arXiv:2410.03378},
year={2024},
archivePrefix={arXiv},
eprint={2410.03378},
primaryClass={cs.CE}
} | kalina2024neural |
arxiv-665578 | 2410.03380 | Predicting perturbation targets with causal differential networks | <|reference_start|>Predicting perturbation targets with causal differential networks: Rationally identifying variables responsible for changes to a biological system can enable myriad applications in disease understanding and cell engineering. From a causality perspective, we are given two datasets generated by the same causal model, one observational (control) and one interventional (perturbed). The goal is to isolate the subset of measured variables (e.g. genes) that were the targets of the intervention, i.e. those whose conditional independencies have changed. Knowing the causal graph would limit the search space, allowing us to efficiently pinpoint these variables. However, current algorithms that infer causal graphs in the presence of unknown intervention targets scale poorly to the hundreds or thousands of variables in biological data, as they must jointly search the combinatorial spaces of graphs and consistent intervention targets. In this work, we propose a causality-inspired approach for predicting perturbation targets that decouples the two search steps. First, we use an amortized causal discovery model to separately infer causal graphs from the observational and interventional datasets. Then, we learn to map these paired graphs to the sets of variables that were intervened upon, in a supervised learning framework. This approach consistently outperforms baselines for perturbation modeling on seven single-cell transcriptomics datasets, each with thousands of measured variables. We also demonstrate significant improvements over six causal discovery algorithms in predicting intervention targets across a variety of tractable, synthetic datasets.<|reference_end|> | arxiv | @article{wu2024predicting,
title={Predicting perturbation targets with causal differential networks},
author={Menghua Wu, Umesh Padia, Sean H. Murphy, Regina Barzilay, Tommi
Jaakkola},
journal={arXiv preprint arXiv:2410.03380},
year={2024},
archivePrefix={arXiv},
eprint={2410.03380},
primaryClass={cs.LG cs.AI q-bio.QM}
} | wu2024predicting |
arxiv-665579 | 2410.03381 | Cogs in a Machine, Doing What They're Meant to Do -- The AMI Submission to the WMT24 General Translation Task | <|reference_start|>Cogs in a Machine, Doing What They're Meant to Do -- The AMI Submission to the WMT24 General Translation Task: This paper presents the submission of the \'Arni Magnusson Institute's team to the WMT24 General translation task. We work on the English->Icelandic translation direction. Our system comprises four translation models and a grammar correction model. For training our models we carefully curate our datasets, aggressively filtering out sentence pairs that may detrimentally affect the quality of our system's output. Some of our data are collected from human translations and some are synthetically generated. A part of the synthetic data is generated using an LLM, and we find that it increases the translation capability of our system significantly.<|reference_end|> | arxiv | @article{jasonarson2024cogs,
title={Cogs in a Machine, Doing What They're Meant to Do -- The AMI Submission
to the WMT24 General Translation Task},
author={Atli Jasonarson, Hinrik Hafsteinsson, Bjarki 'Armannsson,
Stein{th}'or Steingr'imsson},
journal={arXiv preprint arXiv:2410.03381},
year={2024},
archivePrefix={arXiv},
eprint={2410.03381},
primaryClass={cs.CL}
} | jasonarson2024cogs |
arxiv-665580 | 2410.03383 | Performance Analysis of 6TiSCH Networks Using Discrete Events Simulator | <|reference_start|>Performance Analysis of 6TiSCH Networks Using Discrete Events Simulator: The Internet of Things (IoT) empowers small devices to sense, react, and communicate, with applications ranging from smart ordinary household objects to complex industrial processes. To provide access to an increasing number of IoT devices, particularly in long-distance communication scenarios, a robust low-power wide area network (LPWAN) protocol becomes essential. A widely adopted protocol for this purpose is 6TiSCH, which builds upon the IEEE 802.15.4 standard. It introduces time-slotted channel hopping (TSCH) mode as a new medium access control (MAC) layer operating mode, in conjunction with IEEE 802.15.4g, which also defines both MAC and physical layer (PHY) layers and provides IPv6 connectivity for LPWAN. Notably, 6TiSCH has gained adoption in significant standards such as Wireless Intelligent Ubiquitous Networks (Wi-SUN). This study evaluates the scalability of 6TiSCH, with a focus on key parameters such as queue size, the maximum number of single-hop retries, and the slotframe length. Computational simulations were performed using an open-source simulator and obtained the following results: increasing the transmission queue size, along with adjusting the number of retries and slotframe length, leads to a reduction in the packet error rate (PER). Notably, the impact of the number of retries is particularly pronounced. Furthermore, the effect on latency varies based on the specific combination of these parameters as the network scales.<|reference_end|> | arxiv | @article{peron2024performance,
title={Performance Analysis of 6TiSCH Networks Using Discrete Events Simulator},
author={Guilherme de Santi Peron, Marcos Eduardo Pivaro Monteiro, Jo~ao
Lu'is Verdegay de Barros, Jamil Farhat and Glauber Brante},
journal={arXiv preprint arXiv:2410.03383},
year={2024},
archivePrefix={arXiv},
eprint={2410.03383},
primaryClass={cs.NI eess.SP}
} | peron2024performance |
arxiv-665581 | 2410.03385 | From Epilepsy Seizures Classification to Detection: A Deep Learning-based Approach for Raw EEG Signals | <|reference_start|>From Epilepsy Seizures Classification to Detection: A Deep Learning-based Approach for Raw EEG Signals: Epilepsy represents the most prevalent neurological disease in the world. One-third of people suffering from mesial temporal lobe epilepsy (MTLE) exhibit drug resistance, urging the need to develop new treatments. A key part in anti-seizure medication (ASM) development is the capability of detecting and quantifying epileptic seizures occurring in electroencephalogram (EEG) signals, which is crucial for treatment efficacy evaluation. In this study, we introduced a seizure detection pipeline based on deep learning models applied to raw EEG signals. This pipeline integrates: a new pre-processing technique which segments continuous raw EEG signals without prior distinction between seizure and seizure-free activities; a post-processing algorithm developed to reassemble EEG segments and allow the identification of seizures start/end; and finally, a new evaluation procedure based on a strict seizure events comparison between predicted and real labels. Models training have been performed using a data splitting strategy which addresses the potential for data leakage. We demonstrated the fundamental differences between a seizure classification and a seizure detection task and showed the differences in performance between the two tasks. Finally, we demonstrated the generalization capabilities across species of our best architecture, combining a Convolutional Neural Network and a Transformer encoder. The model was trained on animal EEGs and tested on human EEGs with a F1-score of 93% on a balanced Bonn dataset.<|reference_end|> | arxiv | @article{darankoum2024from,
title={From Epilepsy Seizures Classification to Detection: A Deep
Learning-based Approach for Raw EEG Signals},
author={Davy Darankoum, Manon Villalba, Clelia Allioux, Baptiste Caraballo,
Carine Dumont, Eloise Gronlier, Corinne Roucard, Yann Roche, Chloe
Habermacher, Sergei Grudinin and Julien Volle},
journal={arXiv preprint arXiv:2410.03385},
year={2024},
archivePrefix={arXiv},
eprint={2410.03385},
primaryClass={cs.LG q-bio.NC}
} | darankoum2024from |
arxiv-665582 | 2410.03386 | Chronic Disease Diagnoses Using Behavioral Data | <|reference_start|>Chronic Disease Diagnoses Using Behavioral Data: Early detection of chronic diseases is beneficial to healthcare by providing a golden opportunity for timely interventions. Although numerous prior studies have successfully used machine learning (ML) models for disease diagnoses, they highly rely on medical data, which are scarce for most patients in the early stage of the chronic diseases. In this paper, we aim to diagnose hyperglycemia (diabetes), hyperlipidemia, and hypertension (collectively known as 3H) using own collected behavioral data, thus, enable the early detection of 3H without using medical data collected in clinical settings. Specifically, we collected daily behavioral data from 629 participants over a 3-month study period, and trained various ML models after data preprocessing. Experimental results show that only using the participants' uploaded behavioral data, we can achieve accurate 3H diagnoses: 80.2\%, 71.3\%, and 81.2\% for diabetes, hyperlipidemia, and hypertension, respectively. Furthermore, we conduct Shapley analysis on the trained models to identify the most influential features for each type of diseases. The identified influential features are consistent with those reported in the literature.<|reference_end|> | arxiv | @article{wang2024chronic,
title={Chronic Disease Diagnoses Using Behavioral Data},
author={Di Wang, Yidan Hu, Eng Sing Lee, Hui Hwang Teong, Ray Tian Rui Lai,
Wai Han Hoi, Chunyan Miao},
journal={arXiv preprint arXiv:2410.03386},
year={2024},
archivePrefix={arXiv},
eprint={2410.03386},
primaryClass={cs.CY}
} | wang2024chronic |
arxiv-665583 | 2410.03390 | Lightning UQ Box: A Comprehensive Framework for Uncertainty Quantification in Deep Learning | <|reference_start|>Lightning UQ Box: A Comprehensive Framework for Uncertainty Quantification in Deep Learning: Uncertainty quantification (UQ) is an essential tool for applying deep neural networks (DNNs) to real world tasks, as it attaches a degree of confidence to DNN outputs. However, despite its benefits, UQ is often left out of the standard DNN workflow due to the additional technical knowledge required to apply and evaluate existing UQ procedures. Hence there is a need for a comprehensive toolbox that allows the user to integrate UQ into their modelling workflow, without significant overhead. We introduce \texttt{Lightning UQ Box}: a unified interface for applying and evaluating various approaches to UQ. In this paper, we provide a theoretical and quantitative comparison of the wide range of state-of-the-art UQ methods implemented in our toolbox. We focus on two challenging vision tasks: (i) estimating tropical cyclone wind speeds from infrared satellite imagery and (ii) estimating the power output of solar panels from RGB images of the sky. By highlighting the differences between methods our results demonstrate the need for a broad and approachable experimental framework for UQ, that can be used for benchmarking UQ methods. The toolbox, example implementations, and further information are available at: https://github.com/lightning-uq-box/lightning-uq-box<|reference_end|> | arxiv | @article{lehmann2024lightning,
title={Lightning UQ Box: A Comprehensive Framework for Uncertainty
Quantification in Deep Learning},
author={Nils Lehmann, Jakob Gawlikowski, Adam J. Stewart, Vytautas Jancauskas,
Stefan Depeweg, Eric Nalisnick, Nina Maria Gottschling},
journal={arXiv preprint arXiv:2410.03390},
year={2024},
archivePrefix={arXiv},
eprint={2410.03390},
primaryClass={cs.CV cs.LG}
} | lehmann2024lightning |
arxiv-665584 | 2410.03392 | Probabilistic Allocation of Payload Code Rate and Header Copies in LR-FHSS Networks | <|reference_start|>Probabilistic Allocation of Payload Code Rate and Header Copies in LR-FHSS Networks: We evaluate the performance of the LoRaWAN Long-Range Frequency Hopping Spread Spectrum (LR-FHSS) technique using a device-level probabilistic strategy for code rate and header replica allocation. Specifically, we investigate the effects of different header replica and code rate allocations at each end-device, guided by a probability distribution provided by the network server. As a benchmark, we compare the proposed strategy with the standardized LR-FHSS data rates DR8 and DR9. Our numerical results demonstrate that the proposed strategy consistently outperforms the DR8 and DR9 standard data rates across all considered scenarios. Notably, our findings reveal that the optimal distribution rarely includes data rate DR9, while data rate DR8 significantly contributes to the goodput and energy efficiency optimizations.<|reference_end|> | arxiv | @article{farhat2024probabilistic,
title={Probabilistic Allocation of Payload Code Rate and Header Copies in
LR-FHSS Networks},
author={Jamil de Araujo Farhat, Jean Michel de Souza Sant'Ana, Jo~ao Luiz
Rebelatto, Nurul Huda Mahmood, Gianni Pasolini and Richard Demo Souza},
journal={arXiv preprint arXiv:2410.03392},
year={2024},
archivePrefix={arXiv},
eprint={2410.03392},
primaryClass={eess.SP cs.NI}
} | farhat2024probabilistic |
arxiv-665585 | 2410.03394 | Killing Two Flies with One Stone: An Attempt to Break LLMs Using English->Icelandic Idioms and Proper Names | <|reference_start|>Killing Two Flies with One Stone: An Attempt to Break LLMs Using English->Icelandic Idioms and Proper Names: This paper presents the submission of the \'Arni Magn\'usson Institute's team to the WMT24 test suite subtask, focusing on idiomatic expressions and proper names for the English->Icelandic translation direction. Intuitively and empirically, idioms and proper names are known to be a significant challenge for modern translation models. We create two different test suites. The first evaluates the competency of MT systems in translating common English idiomatic expressions, as well as testing whether systems can distinguish between those expressions and the same phrases when used in a literal context. The second test suite consists of place names that should be translated into their Icelandic exonyms (and correctly inflected) and pairs of Icelandic names that share a surface form between the male and female variants, so that incorrect translations impact meaning as well as readability. The scores reported are relatively low, especially for idiomatic expressions and place names, and indicate considerable room for improvement.<|reference_end|> | arxiv | @article{ármannsson2024killing,
title={Killing Two Flies with One Stone: An Attempt to Break LLMs Using
English->Icelandic Idioms and Proper Names},
author={Bjarki 'Armannsson, Hinrik Hafsteinsson, Atli Jasonarson,
Stein{th}'or Steingr'imsson},
journal={arXiv preprint arXiv:2410.03394},
year={2024},
archivePrefix={arXiv},
eprint={2410.03394},
primaryClass={cs.CL}
} | ármannsson2024killing |
arxiv-665586 | 2410.03395 | Receptors cluster in high-curvature membrane regions for optimal spatial gradient sensing | <|reference_start|>Receptors cluster in high-curvature membrane regions for optimal spatial gradient sensing: Spatial information from cell-surface receptors is crucial for processes that require signal processing and sensing of the environment. Here, we investigate the optimal placement of such receptors through a theoretical model that minimizes uncertainty in gradient estimation. Without requiring a priori knowledge of the physical limits of sensing or biochemical processes, we reproduce the emergence of clusters that closely resemble those observed in real cells. On perfect spherical surfaces, optimally placed receptors spread uniformly. When perturbations break their symmetry, receptors cluster in regions of high curvature, massively reducing estimation uncertainty. This agrees with mechanistic models that minimize elastic preference discrepancies between receptors and cell membranes. We further extend our model to motile receptors responding to cell-shape changes and external fluid flow, demonstrating the relevance of our model in realistic scenarios. Our findings provide a simple and utilitarian explanation for receptor clustering at high-curvature regions when high sensing accuracy is paramount.<|reference_end|> | arxiv | @article{alonso2024receptors,
title={Receptors cluster in high-curvature membrane regions for optimal spatial
gradient sensing},
author={Albert Alonso, Robert G. Endres and Julius B. Kirkegaard},
journal={arXiv preprint arXiv:2410.03395},
year={2024},
archivePrefix={arXiv},
eprint={2410.03395},
primaryClass={physics.bio-ph cond-mat.soft cs.IT math.IT q-bio.CB}
} | alonso2024receptors |
arxiv-665587 | 2410.03396 | GraphCroc: Cross-Correlation Autoencoder for Graph Structural Reconstruction | <|reference_start|>GraphCroc: Cross-Correlation Autoencoder for Graph Structural Reconstruction: Graph-structured data is integral to many applications, prompting the development of various graph representation methods. Graph autoencoders (GAEs), in particular, reconstruct graph structures from node embeddings. Current GAE models primarily utilize self-correlation to represent graph structures and focus on node-level tasks, often overlooking multi-graph scenarios. Our theoretical analysis indicates that self-correlation generally falls short in accurately representing specific graph features such as islands, symmetrical structures, and directional edges, particularly in smaller or multiple graph contexts. To address these limitations, we introduce a cross-correlation mechanism that significantly enhances the GAE representational capabilities. Additionally, we propose GraphCroc, a new GAE that supports flexible encoder architectures tailored for various downstream tasks and ensures robust structural reconstruction, through a mirrored encoding-decoding process. This model also tackles the challenge of representation bias during optimization by implementing a loss-balancing strategy. Both theoretical analysis and numerical evaluations demonstrate that our methodology significantly outperforms existing self-correlation-based GAEs in graph structure reconstruction.<|reference_end|> | arxiv | @article{duan2024graphcroc:,
title={GraphCroc: Cross-Correlation Autoencoder for Graph Structural
Reconstruction},
author={Shijin Duan, Ruyi Ding, Jiaxing He, Aidong Adam Ding, Yunsi Fei, and
Xiaolin Xu},
journal={arXiv preprint arXiv:2410.03396},
year={2024},
archivePrefix={arXiv},
eprint={2410.03396},
primaryClass={cs.LG cs.AI}
} | duan2024graphcroc: |
arxiv-665588 | 2410.03397 | Strategic Utilization of Cellular Operator Energy Storages for Smart Grid Frequency Regulation | <|reference_start|>Strategic Utilization of Cellular Operator Energy Storages for Smart Grid Frequency Regulation: The innovative use of cellular operator energy storage enhances smart grid resilience and efficiency. Traditionally used to ensure uninterrupted operation of cellular base stations (BSs) during grid outages, these storages can now dynamically participate in the energy flexibility market. This dual utilization enhances the economic viability of BS storage systems and supports sustainable energy management. In this paper, we explore the potential of BS storages for supporting grid ancillary services by allocating a portion of their capacity while ensuring Ultra Reliable Low Latency (URLLC) requirements, such as meeting delay and reliability requirements. This includes feeding BS stored energy back into the grid during high-demand periods or powering BSs to regulate grid frequency. We investigate the impacts of URLLC requirements on grid frequency regulation, formulating a joint resource allocation problem. This problem maximizes total revenues of cellular networks, considering both the total sum rate in the communication network and BS storages participation in frequency regulation, while considering battery aging and cycling constraints. Simulation results show that a network with 1500 BSs can increase power vacancy compensation from 31% to 46% by reducing reliability from 10^(-8) to 10^(-3). For a power vacancy of -30 MW, this varies from 9.3 MW to 13.5 MW, exceeding a wind turbines capacity.<|reference_end|> | arxiv | @article{gholipoor2024strategic,
title={Strategic Utilization of Cellular Operator Energy Storages for Smart
Grid Frequency Regulation},
author={Narges Gholipoor, Farid Hamzeh Aghdam, Mehdi Rasti},
journal={arXiv preprint arXiv:2410.03397},
year={2024},
archivePrefix={arXiv},
eprint={2410.03397},
primaryClass={eess.SY cs.SY}
} | gholipoor2024strategic |
arxiv-665589 | 2410.03399 | EBES: Easy Benchmarking for Event Sequences | <|reference_start|>EBES: Easy Benchmarking for Event Sequences: Event sequences, characterized by irregular sampling intervals and a mix of categorical and numerical features, are common data structures in various real-world domains such as healthcare, finance, and user interaction logs. Despite advances in temporal data modeling techniques, there is no standardized benchmarks for evaluating their performance on event sequences. This complicates result comparison across different papers due to varying evaluation protocols, potentially misleading progress in this field. We introduce EBES, a comprehensive benchmarking tool with standardized evaluation scenarios and protocols, focusing on regression and classification problems with sequence-level targets. Our library simplifies benchmarking, dataset addition, and method integration through a unified interface. It includes a novel synthetic dataset and provides preprocessed real-world datasets, including the largest publicly available banking dataset. Our results provide an in-depth analysis of datasets, identifying some as unsuitable for model comparison. We investigate the importance of modeling temporal and sequential components, as well as the robustness and scaling properties of the models. These findings highlight potential directions for future research. Our benchmark aim is to facilitate reproducible research, expediting progress and increasing real-world impacts.<|reference_end|> | arxiv | @article{osin2024ebes:,
title={EBES: Easy Benchmarking for Event Sequences},
author={Dmitry Osin, Igor Udovichenko, Viktor Moskvoretskii, Egor Shvetsov,
Evgeny Burnaev},
journal={arXiv preprint arXiv:2410.03399},
year={2024},
archivePrefix={arXiv},
eprint={2410.03399},
primaryClass={cs.LG cs.AI}
} | osin2024ebes: |
arxiv-665590 | 2410.03403 | Distributed Networked Multi-task Learning | <|reference_start|>Distributed Networked Multi-task Learning: We consider a distributed multi-task learning scheme that accounts for multiple linear model estimation tasks with heterogeneous and/or correlated data streams. We assume that nodes can be partitioned into groups corresponding to different learning tasks and communicate according to a directed network topology. Each node estimates a linear model asynchronously and is subject to local (within-group) regularization and global (across groups) regularization terms targeting noise reduction and generalization performance improvement respectively. We provide a finite-time characterization of convergence of the estimators and task relation and illustrate the scheme's general applicability in two examples: random field temperature estimation and modeling student performance from different academic districts.<|reference_end|> | arxiv | @article{hong2024distributed,
title={Distributed Networked Multi-task Learning},
author={Lingzhou Hong, Alfredo Garcia},
journal={arXiv preprint arXiv:2410.03403},
year={2024},
archivePrefix={arXiv},
eprint={2410.03403},
primaryClass={cs.MA cs.LG}
} | hong2024distributed |
arxiv-665591 | 2410.03406 | Conformal confidence sets for biomedical image segmentation | <|reference_start|>Conformal confidence sets for biomedical image segmentation: We develop confidence sets which provide spatial uncertainty guarantees for the output of a black-box machine learning model designed for image segmentation. To do so we adapt conformal inference to the imaging setting, obtaining thresholds on a calibration dataset based on the distribution of the maximum of the transformed logit scores within and outside of the ground truth masks. We prove that these confidence sets, when applied to new predictions of the model, are guaranteed to contain the true unknown segmented mask with desired probability. We show that learning appropriate score transformations on a learning dataset before performing calibration is crucial for optimizing performance. We illustrate and validate our approach on a polpys tumor dataset. To do so we obtain the logit scores from a deep neural network trained for polpys segmentation and show that using distance transformed scores to obtain outer confidence sets and the original scores for inner confidence sets enables tight bounds on tumor location whilst controlling the false coverage rate.<|reference_end|> | arxiv | @article{davenport2024conformal,
title={Conformal confidence sets for biomedical image segmentation},
author={Samuel Davenport},
journal={arXiv preprint arXiv:2410.03406},
year={2024},
archivePrefix={arXiv},
eprint={2410.03406},
primaryClass={stat.ML cs.LG}
} | davenport2024conformal |
arxiv-665592 | 2410.03407 | Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential Privacy | <|reference_start|>Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential Privacy: Federated learning (FL) has rapidly become a compelling paradigm that enables multiple clients to jointly train a model by sharing only gradient updates for aggregation, without revealing their local private data. In order to protect the gradient updates which could also be privacy-sensitive, there has been a line of work studying local differential privacy (LDP) mechanisms to provide a formal privacy guarantee. With LDP mechanisms, clients locally perturb their gradient updates before sharing them out for aggregation. However, such approaches are known for greatly degrading the model utility, due to heavy noise addition. To enable a better privacy-utility tradeoff, a recently emerging trend is to apply the shuffle model of DP in FL, which relies on an intermediate shuffling operation on the perturbed gradient updates to achieve privacy amplification. Following this trend, in this paper, we present Camel, a new communication-efficient and maliciously secure FL framework in the shuffle model of DP. Camel first departs from existing works by ambitiously supporting integrity check for the shuffle computation, achieving security against malicious adversary. Specifically, Camel builds on the trending cryptographic primitive of secret-shared shuffle, with custom techniques we develop for optimizing system-wide communication efficiency, and for lightweight integrity checks to harden the security of server-side computation. In addition, we also derive a significantly tighter bound on the privacy loss through analyzing the Renyi differential privacy (RDP) of the overall FL process. Extensive experiments demonstrate that Camel achieves better privacy-utility trade-offs than the state-of-the-art work, with promising performance.<|reference_end|> | arxiv | @article{xu2024camel:,
title={Camel: Communication-Efficient and Maliciously Secure Federated Learning
in the Shuffle Model of Differential Privacy},
author={Shuangqing Xu, Yifeng Zheng, Zhongyun Hua},
journal={arXiv preprint arXiv:2410.03407},
year={2024},
archivePrefix={arXiv},
eprint={2410.03407},
primaryClass={cs.CR}
} | xu2024camel: |
arxiv-665593 | 2410.03408 | Predictive Coding for Decision Transformer | <|reference_start|>Predictive Coding for Decision Transformer: Recent work in offline reinforcement learning (RL) has demonstrated the effectiveness of formulating decision-making as return-conditioned supervised learning. Notably, the decision transformer (DT) architecture has shown promise across various domains. However, despite its initial success, DTs have underperformed on several challenging datasets in goal-conditioned RL. This limitation stems from the inefficiency of return conditioning for guiding policy learning, particularly in unstructured and suboptimal datasets, resulting in DTs failing to effectively learn temporal compositionality. Moreover, this problem might be further exacerbated in long-horizon sparse-reward tasks. To address this challenge, we propose the Predictive Coding for Decision Transformer (PCDT) framework, which leverages generalized future conditioning to enhance DT methods. PCDT utilizes an architecture that extends the DT framework, conditioned on predictive codings, enabling decision-making based on both past and future factors, thereby improving generalization. Through extensive experiments on eight datasets from the AntMaze and FrankaKitchen environments, our proposed method achieves performance on par with or surpassing existing popular value-based and transformer-based methods in offline goal-conditioned RL. Furthermore, we also evaluate our method on a goal-reaching task with a physical robot.<|reference_end|> | arxiv | @article{luu2024predictive,
title={Predictive Coding for Decision Transformer},
author={Tung M. Luu, Donghoon Lee, and Chang D. Yoo},
journal={arXiv preprint arXiv:2410.03408},
year={2024},
archivePrefix={arXiv},
eprint={2410.03408},
primaryClass={cs.LG}
} | luu2024predictive |
arxiv-665594 | 2410.03409 | Comparative study of regression vs pairwise models for surrogate-based heuristic optimisation | <|reference_start|>Comparative study of regression vs pairwise models for surrogate-based heuristic optimisation: Heuristic optimisation algorithms explore the search space by sampling solutions, evaluating their fitness, and biasing the search in the direction of promising solutions. However, in many cases, this fitness function involves executing expensive computational calculations, drastically reducing the reasonable number of evaluations. In this context, surrogate models have emerged as an excellent alternative to alleviate these computational problems. This paper addresses the formulation of surrogate problems as both regression models that approximate fitness (surface surrogate models) and a novel way to connect classification models (pairwise surrogate models). The pairwise approach can be directly exploited by some algorithms, such as Differential Evolution, in which the fitness value is not actually needed to drive the search, and it is sufficient to know whether a solution is better than another one or not. Based on these modelling approaches, we have conducted a multidimensional analysis of surrogate models under different configurations: different machine learning algorithms (regularised regression, neural networks, decision trees, boosting methods, and random forests), different surrogate strategies (encouraging diversity or relaxing prediction thresholds), and compare them for both surface and pairwise surrogate models. The experimental part of the article includes the benchmark problems already proposed for the SOCO2011 competition in continuous optimisation and a simulation problem included in the recent GECCO2021 Industrial Challenge. This paper shows that the performance of the overall search, when using online machine learning-based surrogate models, depends not only on the accuracy of the predictive model but also on both the kind of bias towards positive or negative cases and how the optimisation uses those predictions to decide whether to execute the actual fitness function.<|reference_end|> | arxiv | @article{naharro2024comparative,
title={Comparative study of regression vs pairwise models for surrogate-based
heuristic optimisation},
author={Pablo S. Naharro, Pablo Toharia, Antonio LaTorre, Jos'e-Mar'ia
Pe~na},
journal={Swarm and Evolutionary Computation, vol. 75, p. 101176, Dec. 2022},
year={2024},
doi={10.1016/j.swevo.2022.101176},
archivePrefix={arXiv},
eprint={2410.03409},
primaryClass={cs.NE cs.AI}
} | naharro2024comparative |
arxiv-665595 | 2410.03411 | Benchmarking the Fidelity and Utility of Synthetic Relational Data | <|reference_start|>Benchmarking the Fidelity and Utility of Synthetic Relational Data: Synthesizing relational data has started to receive more attention from researchers, practitioners, and industry. The task is more difficult than synthesizing a single table due to the added complexity of relationships between tables. For the same reason, benchmarking methods for synthesizing relational data introduces new challenges. Our work is motivated by a lack of an empirical evaluation of state-of-the-art methods and by gaps in the understanding of how such an evaluation should be done. We review related work on relational data synthesis, common benchmarking datasets, and approaches to measuring the fidelity and utility of synthetic data. We combine the best practices and a novel robust detection approach into a benchmarking tool and use it to compare six methods, including two commercial tools. While some methods are better than others, no method is able to synthesize a dataset that is indistinguishable from original data. For utility, we typically observe moderate correlation between real and synthetic data for both model predictive performance and feature importance.<|reference_end|> | arxiv | @article{hudovernik2024benchmarking,
title={Benchmarking the Fidelity and Utility of Synthetic Relational Data},
author={Valter Hudovernik, Martin Jurkoviv{c}, Erik v{S}trumbelj},
journal={arXiv preprint arXiv:2410.03411},
year={2024},
archivePrefix={arXiv},
eprint={2410.03411},
primaryClass={cs.DB cs.LG}
} | hudovernik2024benchmarking |
arxiv-665596 | 2410.03412 | Team MTS @ AutoMin 2021: An Overview of Existing Summarization Approaches and Comparison to Unsupervised Summarization Techniques | <|reference_start|>Team MTS @ AutoMin 2021: An Overview of Existing Summarization Approaches and Comparison to Unsupervised Summarization Techniques: Remote communication through video or audio conferences has become more popular than ever because of the worldwide pandemic. These events, therefore, have provoked the development of systems for automatic minuting of spoken language leading to AutoMin 2021 challenge. The following paper illustrates the results of the research that team MTS has carried out while participating in the Automatic Minutes challenge. In particular, in this paper we analyze existing approaches to text and speech summarization, propose an unsupervised summarization technique based on clustering and provide a pipeline that includes an adapted automatic speech recognition block able to run on real-life recordings. The proposed unsupervised technique outperforms pre-trained summarization models on the automatic minuting task with Rouge 1, Rouge 2 and Rouge L values of 0.21, 0.02 and 0.2 on the dev set, with Rouge 1, Rouge 2, Rouge L, Adequacy, Grammatical correctness and Fluency values of 0.180, 0.035, 0.098, 1.857, 2.304, 1.911 on the test set accordingly<|reference_end|> | arxiv | @article{iakovenko2024team,
title={Team MTS @ AutoMin 2021: An Overview of Existing Summarization
Approaches and Comparison to Unsupervised Summarization Techniques},
author={Olga Iakovenko, Anna Andreeva, Anna Lapidus, Liana Mikaelyan},
journal={arXiv preprint arXiv:2410.03412},
year={2024},
doi={10.21437/automin.2021-7},
archivePrefix={arXiv},
eprint={2410.03412},
primaryClass={cs.CL}
} | iakovenko2024team |
arxiv-665597 | 2410.03413 | A Simple Framework for Secure Key Leasing | <|reference_start|>A Simple Framework for Secure Key Leasing: Secure key leasing (a.k.a. key-revocable cryptography) enables us to lease a cryptographic key as a quantum state in such a way that the key can be later revoked in a verifiable manner. We propose a simple framework for constructing cryptographic primitives with secure key leasing via the certified deletion property of BB84 states. Based on our framework, we obtain the following schemes. - A public key encryption scheme with secure key leasing that has classical revocation based on any IND-CPA secure public key encryption scheme. Prior works rely on either quantum revocation or stronger assumptions such as the quantum hardness of the learning with errors (LWE) problem. - A pseudorandom function with secure key leasing that has classical revocation based on one-way functions. Prior works rely on stronger assumptions such as the quantum hardness of the LWE problem. - A digital signature scheme with secure key leasing that has classical revocation based on the quantum hardness of the short integer solution (SIS) problem. Our construction has static signing keys, i.e., the state of a signing key almost does not change before and after signing. Prior constructions either rely on non-static signing keys or indistinguishability obfuscation to achieve a stronger goal of copy-protection. In addition, all of our schemes remain secure even if a verification key for revocation is leaked after the adversary submits a valid certificate of deletion. To our knowledge, all prior constructions are totally broken in this setting. Moreover, in our view, our security proofs are much simpler than those for existing schemes.<|reference_end|> | arxiv | @article{kitagawa2024a,
title={A Simple Framework for Secure Key Leasing},
author={Fuyuki Kitagawa and Tomoyuki Morimae and Takashi Yamakawa},
journal={arXiv preprint arXiv:2410.03413},
year={2024},
number={YITP-24-128},
archivePrefix={arXiv},
eprint={2410.03413},
primaryClass={quant-ph cs.CR}
} | kitagawa2024a |
arxiv-665598 | 2410.03414 | A 9T4R RRAM-Based ACAM for Analogue Template Matching at the Edge | <|reference_start|>A 9T4R RRAM-Based ACAM for Analogue Template Matching at the Edge: The continuous shift of computational bottlenecks to the memory access and data transfer, especially for AI applications, poses the urgent needs of re-engineering the computer architecture fundamentals. Many edge computing applications, like wearable and implantable medical devices, introduce increasingly more challenges to conventional computing systems due to the strict requirements of area and power at the edge. Emerging technologies, like Resistive RAM (RRAM), have shown a promising momentum in developing neuro-inspired analogue computing paradigms capable of achieving high classification capabilities alongside high energy efficiency. In this work, we present a novel RRAM-based Analogue Content Addressable Memory (ACAM) for on-line analogue template matching applications. This ACAM-based template matching architecture aims to achieve energy-efficient classification where low energy is of utmost importance. We are showcasing a highly tuneable novel RRAM-based ACAM pixel implemented using a commercial 180nm CMOS technology and in-house RRAM technology and exhibiting low energy dissipation of approximately 0.036pJ and 0.16pJ for mismatch and match, respectively, at 66MHz with 3V voltage supply. A proof-of-concept system-level implementation based on this novel pixel design is also implemented in 180nm.<|reference_end|> | arxiv | @article{papandroulidakis2024a,
title={A 9T4R RRAM-Based ACAM for Analogue Template Matching at the Edge},
author={Georgios Papandroulidakis, Shady Agwa, Ahmet Cirakoglu and Themis
Prodromakis},
journal={arXiv preprint arXiv:2410.03414},
year={2024},
archivePrefix={arXiv},
eprint={2410.03414},
primaryClass={eess.SY cs.AR cs.ET cs.SY}
} | papandroulidakis2024a |
arxiv-665599 | 2410.03415 | Surgical, Cheap, and Flexible: Mitigating False Refusal in Language Models via Single Vector Ablation | <|reference_start|>Surgical, Cheap, and Flexible: Mitigating False Refusal in Language Models via Single Vector Ablation: Training a language model to be both helpful and harmless requires careful calibration of refusal behaviours: Models should refuse to follow malicious instructions or give harmful advice (e.g. "how do I kill someone?"), but they should not refuse safe requests, even if they superficially resemble unsafe ones (e.g. "how do I kill a Python process?"). Avoiding such false refusal, as prior work has shown, is challenging even for highly-capable language models. In this paper, we propose a simple and surgical method for mitigating false refusal in language models via single vector ablation. For a given model, we extract a false refusal vector and show that ablating this vector reduces false refusal rate without negatively impacting model safety and general model capabilities. We also show that our approach can be used for fine-grained calibration of model safety. Our approach is training-free and model-agnostic, making it useful for mitigating the problem of false refusal in current and future language models.<|reference_end|> | arxiv | @article{wang2024surgical,,
title={Surgical, Cheap, and Flexible: Mitigating False Refusal in Language
Models via Single Vector Ablation},
author={Xinpeng Wang, Chengzhi Hu, Paul R"ottger, Barbara Plank},
journal={arXiv preprint arXiv:2410.03415},
year={2024},
archivePrefix={arXiv},
eprint={2410.03415},
primaryClass={cs.CL}
} | wang2024surgical, |
arxiv-665600 | 2410.03416 | Asymptotic Inapproximability of Reconfiguration Problems: Maxmin $k$-Cut and Maxmin E$k$-SAT | <|reference_start|>Asymptotic Inapproximability of Reconfiguration Problems: Maxmin $k$-Cut and Maxmin E$k$-SAT: We study the hardness of approximating two reconfiguration problems. One problem is Maxmin $k$-Cut Reconfiguration, which is a reconfiguration analogue of Max $k$-Cut. The other is Maxmin E$k$-SAT Reconfiguration, which is a reconfiguration analogue of Max E$k$-SAT. The Probabilistically Checkable Reconfiguration Proof theorem due to Hirahara and Ohsaka (STOC 2024) and Karthik C. S. and Manurangsi (2023) implies that Maxmin 4-Cut Reconfiguration and Maxmin E3-SAT Reconfiguration are PSPACE-hard to approximate within a constant factor. However, the asymptotic behavior of approximability for these problems with respect to $k$ is not well understood. In this paper, we present the following hardness-of-approximation results and approximation algorithms for Maxmin $k$-Cut Reconfiguration and Maxmin E$k$-SAT Reconfiguration: $\bullet$ For every $k \geq 2$, Maxmin $k$-Cut Reconfiguration is PSPACE-hard to approximate within a factor of $1 - \Omega\left(\frac{1}{k}\right)$, whereas it can be approximated within a factor of $1-\frac{2}{k}$. Our lower and upper bounds demonstrate that Maxmin $k$-Cut Reconfiguration exhibits the asymptotically same approximability as Max $k$-Cut. $\bullet$ For every $k \geq 3$, Maxmin E$k$-SAT Reconfiguration is PSPACE-hard (resp. NP-hard) to approximate within a factor of $1-\Omega\left(\frac{1}{9^{\sqrt{k}}}\right)$ (resp. $1-\frac{1}{8k}$). On the other hand, it admits a deterministic $\left(1-\frac{2.5}{k}\right)$-factor approximation algorithm, implying that Maxmin E$k$-SAT Reconfiguration displays an asymptotically approximation threshold different from Max E$k$-SAT.<|reference_end|> | arxiv | @article{hirahara2024asymptotic,
title={Asymptotic Inapproximability of Reconfiguration Problems: Maxmin $k$-Cut
and Maxmin E$k$-SAT},
author={Shuichi Hirahara, Naoto Ohsaka},
journal={arXiv preprint arXiv:2410.03416},
year={2024},
archivePrefix={arXiv},
eprint={2410.03416},
primaryClass={cs.CC cs.DM cs.DS}
} | hirahara2024asymptotic |
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