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arxiv-664201 | 2410.00980 | Heterogeneous sound classification with the Broad Sound Taxonomy and Dataset | <|reference_start|>Heterogeneous sound classification with the Broad Sound Taxonomy and Dataset: Automatic sound classification has a wide range of applications in machine listening, enabling context-aware sound processing and understanding. This paper explores methodologies for automatically classifying heterogeneous sounds characterized by high intra-class variability. Our study evaluates the classification task using the Broad Sound Taxonomy, a two-level taxonomy comprising 28 classes designed to cover a heterogeneous range of sounds with semantic distinctions tailored for practical user applications. We construct a dataset through manual annotation to ensure accuracy, diverse representation within each class and relevance in real-world scenarios. We compare a variety of both traditional and modern machine learning approaches to establish a baseline for the task of heterogeneous sound classification. We investigate the role of input features, specifically examining how acoustically derived sound representations compare to embeddings extracted with pre-trained deep neural networks that capture both acoustic and semantic information about sounds. Experimental results illustrate that audio embeddings encoding acoustic and semantic information achieve higher accuracy in the classification task. After careful analysis of classification errors, we identify some underlying reasons for failure and propose actions to mitigate them. The paper highlights the need for deeper exploration of all stages of classification, understanding the data and adopting methodologies capable of effectively handling data complexity and generalizing in real-world sound environments.<|reference_end|> | arxiv | @article{anastasopoulou2024heterogeneous,
title={Heterogeneous sound classification with the Broad Sound Taxonomy and
Dataset},
author={Panagiota Anastasopoulou, Jessica Torrey, Xavier Serra, Frederic Font},
journal={arXiv preprint arXiv:2410.00980},
year={2024},
archivePrefix={arXiv},
eprint={2410.00980},
primaryClass={cs.SD cs.AI eess.AS}
} | anastasopoulou2024heterogeneous |
arxiv-664202 | 2410.00982 | ScVLM: a Vision-Language Model for Driving Safety Critical Event Understanding | <|reference_start|>ScVLM: a Vision-Language Model for Driving Safety Critical Event Understanding: Accurately identifying, understanding, and describing driving safety-critical events (SCEs), including crashes and near-crashes, is crucial for traffic safety, automated driving systems, and advanced driver assistance systems research and application. As SCEs are rare events, most general Vision-Language Models (VLMs) have not been trained sufficiently to link SCE videos and narratives, which could lead to hallucination and missing key safety characteristics. To tackle these challenges, we propose ScVLM, a hybrid approach that combines supervised learning and contrastive learning to improve driving video understanding and event description rationality for VLMs. The proposed approach is trained on and evaluated by more than 8,600 SCEs from the Second Strategic Highway Research Program Naturalistic Driving Study dataset, the largest publicly accessible driving dataset with videos and SCE annotations. The results demonstrate the superiority of the proposed approach in generating contextually accurate event descriptions and mitigate hallucinations from VLMs.<|reference_end|> | arxiv | @article{shi2024scvlm:,
title={ScVLM: a Vision-Language Model for Driving Safety Critical Event
Understanding},
author={Liang Shi, Boyu Jiang, Feng Guo},
journal={arXiv preprint arXiv:2410.00982},
year={2024},
archivePrefix={arXiv},
eprint={2410.00982},
primaryClass={cs.CV}
} | shi2024scvlm: |
arxiv-664203 | 2410.00983 | Robust Guided Diffusion for Offline Black-Box Optimization | <|reference_start|>Robust Guided Diffusion for Offline Black-Box Optimization: Offline black-box optimization aims to maximize a black-box function using an offline dataset of designs and their measured properties. Two main approaches have emerged: the forward approach, which learns a mapping from input to its value, thereby acting as a proxy to guide optimization, and the inverse approach, which learns a mapping from value to input for conditional generation. (a) Although proxy-free~(classifier-free) diffusion shows promise in robustly modeling the inverse mapping, it lacks explicit guidance from proxies, essential for generating high-performance samples beyond the training distribution. Therefore, we propose \textit{proxy-enhanced sampling} which utilizes the explicit guidance from a trained proxy to bolster proxy-free diffusion with enhanced sampling control. (b) Yet, the trained proxy is susceptible to out-of-distribution issues. To address this, we devise the module \textit{diffusion-based proxy refinement}, which seamlessly integrates insights from proxy-free diffusion back into the proxy for refinement. To sum up, we propose \textit{\textbf{R}obust \textbf{G}uided \textbf{D}iffusion for Offline Black-box Optimization}~(\textbf{RGD}), combining the advantages of proxy~(explicit guidance) and proxy-free diffusion~(robustness) for effective conditional generation. RGD achieves state-of-the-art results on various design-bench tasks, underscoring its efficacy. Our code is at https://anonymous.4open.science/r/RGD-27A5/README.md.<|reference_end|> | arxiv | @article{can2024robust,
title={Robust Guided Diffusion for Offline Black-Box Optimization},
author={Can (Sam) Chen, Christopher Beckham, Zixuan Liu, Xue Liu, Christopher
Pal},
journal={arXiv preprint arXiv:2410.00983},
year={2024},
archivePrefix={arXiv},
eprint={2410.00983},
primaryClass={cs.LG cs.AI}
} | can2024robust |
arxiv-664204 | 2410.00984 | Tackling the Accuracy-Interpretability Trade-off in a Hierarchy of Machine Learning Models for the Prediction of Extreme Heatwaves | <|reference_start|>Tackling the Accuracy-Interpretability Trade-off in a Hierarchy of Machine Learning Models for the Prediction of Extreme Heatwaves: When performing predictions that use Machine Learning (ML), we are mainly interested in performance and interpretability. This generates a natural trade-off, where complex models generally have higher skills but are harder to explain and thus trust. Interpretability is particularly important in the climate community, where we aim at gaining a physical understanding of the underlying phenomena. Even more so when the prediction concerns extreme weather events with high impact on society. In this paper, we perform probabilistic forecasts of extreme heatwaves over France, using a hierarchy of increasingly complex ML models, which allows us to find the best compromise between accuracy and interpretability. More precisely, we use models that range from a global Gaussian Approximation (GA) to deep Convolutional Neural Networks (CNNs), with the intermediate steps of a simple Intrinsically Interpretable Neural Network (IINN) and a model using the Scattering Transform (ScatNet). Our findings reveal that CNNs provide higher accuracy, but their black-box nature severely limits interpretability, even when using state-of-the-art Explainable Artificial Intelligence (XAI) tools. In contrast, ScatNet achieves similar performance to CNNs while providing greater transparency, identifying key scales and patterns in the data that drive predictions. This study underscores the potential of interpretability in ML models for climate science, demonstrating that simpler models can rival the performance of their more complex counterparts, all the while being much easier to understand. This gained interpretability is crucial for building trust in model predictions and uncovering new scientific insights, ultimately advancing our understanding and management of extreme weather events.<|reference_end|> | arxiv | @article{lovo2024tackling,
title={Tackling the Accuracy-Interpretability Trade-off in a Hierarchy of
Machine Learning Models for the Prediction of Extreme Heatwaves},
author={Alessandro Lovo, Amaury Lancelin, Corentin Herbert and Freddy Bouchet},
journal={arXiv preprint arXiv:2410.00984},
year={2024},
archivePrefix={arXiv},
eprint={2410.00984},
primaryClass={cs.LG physics.ao-ph}
} | lovo2024tackling |
arxiv-664205 | 2410.00986 | TransResNet: Integrating the Strengths of ViTs and CNNs for High Resolution Medical Image Segmentation via Feature Grafting | <|reference_start|>TransResNet: Integrating the Strengths of ViTs and CNNs for High Resolution Medical Image Segmentation via Feature Grafting: High-resolution images are preferable in medical imaging domain as they significantly improve the diagnostic capability of the underlying method. In particular, high resolution helps substantially in improving automatic image segmentation. However, most of the existing deep learning-based techniques for medical image segmentation are optimized for input images having small spatial dimensions and perform poorly on high-resolution images. To address this shortcoming, we propose a parallel-in-branch architecture called TransResNet, which incorporates Transformer and CNN in a parallel manner to extract features from multi-resolution images independently. In TransResNet, we introduce Cross Grafting Module (CGM), which generates the grafted features, enriched in both global semantic and low-level spatial details, by combining the feature maps from Transformer and CNN branches through fusion and self-attention mechanism. Moreover, we use these grafted features in the decoding process, increasing the information flow for better prediction of the segmentation mask. Extensive experiments on ten datasets demonstrate that TransResNet achieves either state-of-the-art or competitive results on several segmentation tasks, including skin lesion, retinal vessel, and polyp segmentation. The source code and pre-trained models are available at https://github.com/Sharifmhamza/TransResNet.<|reference_end|> | arxiv | @article{sharif2024transresnet:,
title={TransResNet: Integrating the Strengths of ViTs and CNNs for High
Resolution Medical Image Segmentation via Feature Grafting},
author={Muhammad Hamza Sharif, Dmitry Demidov, Asif Hanif, Mohammad Yaqub, Min
Xu},
journal={arXiv preprint arXiv:2410.00986},
year={2024},
archivePrefix={arXiv},
eprint={2410.00986},
primaryClass={eess.IV cs.CV}
} | sharif2024transresnet: |
arxiv-664206 | 2410.00988 | Creative and Context-Aware Translation of East Asian Idioms with GPT-4 | <|reference_start|>Creative and Context-Aware Translation of East Asian Idioms with GPT-4: As a type of figurative language, an East Asian idiom condenses rich cultural background into only a few characters. Translating such idioms is challenging for human translators, who often resort to choosing a context-aware translation from an existing list of candidates. However, compiling a dictionary of candidate translations demands much time and creativity even for expert translators. To alleviate such burden, we evaluate if GPT-4 can help generate high-quality translations. Based on automatic evaluations of faithfulness and creativity, we first identify Pareto-optimal prompting strategies that can outperform translation engines from Google and DeepL. Then, at a low cost, our context-aware translations can achieve far more high-quality translations per idiom than the human baseline. We open-source all code and data to facilitate further research.<|reference_end|> | arxiv | @article{tang2024creative,
title={Creative and Context-Aware Translation of East Asian Idioms with GPT-4},
author={Kenan Tang, Peiyang Song, Yao Qin, Xifeng Yan},
journal={arXiv preprint arXiv:2410.00988},
year={2024},
archivePrefix={arXiv},
eprint={2410.00988},
primaryClass={cs.CL}
} | tang2024creative |
arxiv-664207 | 2410.00990 | LaDTalk: Latent Denoising for Synthesizing Talking Head Videos with High Frequency Details | <|reference_start|>LaDTalk: Latent Denoising for Synthesizing Talking Head Videos with High Frequency Details: Audio-driven talking head generation is a pivotal area within film-making and Virtual Reality. Although existing methods have made significant strides following the end-to-end paradigm, they still encounter challenges in producing videos with high-frequency details due to their limited expressivity in this domain. This limitation has prompted us to explore an effective post-processing approach to synthesize photo-realistic talking head videos. Specifically, we employ a pretrained Wav2Lip model as our foundation model, leveraging its robust audio-lip alignment capabilities. Drawing on the theory of Lipschitz Continuity, we have theoretically established the noise robustness of Vector Quantised Auto Encoders (VQAEs). Our experiments further demonstrate that the high-frequency texture deficiency of the foundation model can be temporally consistently recovered by the Space-Optimised Vector Quantised Auto Encoder (SOVQAE) we introduced, thereby facilitating the creation of realistic talking head videos. We conduct experiments on both the conventional dataset and the High-Frequency TalKing head (HFTK) dataset that we curated. The results indicate that our method, LaDTalk, achieves new state-of-the-art video quality and out-of-domain lip synchronization performance.<|reference_end|> | arxiv | @article{yang2024ladtalk:,
title={LaDTalk: Latent Denoising for Synthesizing Talking Head Videos with High
Frequency Details},
author={Jian Yang, Xukun Wang, Wentao Wang, Guoming Li, Qihang Fang, Ruihong
Yuan, Tianyang Wang, Jason Zhaoxin Fan},
journal={arXiv preprint arXiv:2410.00990},
year={2024},
archivePrefix={arXiv},
eprint={2410.00990},
primaryClass={cs.CV}
} | yang2024ladtalk: |
arxiv-664208 | 2410.00993 | Tight Rates for Bandit Control Beyond Quadratics | <|reference_start|>Tight Rates for Bandit Control Beyond Quadratics: Unlike classical control theory, such as Linear Quadratic Control (LQC), real-world control problems are highly complex. These problems often involve adversarial perturbations, bandit feedback models, and non-quadratic, adversarially chosen cost functions. A fundamental yet unresolved question is whether optimal regret can be achieved for these general control problems. The standard approach to addressing this problem involves a reduction to bandit convex optimization with memory. In the bandit setting, constructing a gradient estimator with low variance is challenging due to the memory structure and non-quadratic loss functions. In this paper, we provide an affirmative answer to this question. Our main contribution is an algorithm that achieves an $\tilde{O}(\sqrt{T})$ optimal regret for bandit non-stochastic control with strongly-convex and smooth cost functions in the presence of adversarial perturbations, improving the previously known $\tilde{O}(T^{2/3})$ regret bound from (Cassel and Koren, 2020. Our algorithm overcomes the memory issue by reducing the problem to Bandit Convex Optimization (BCO) without memory and addresses general strongly-convex costs using recent advancements in BCO from (Suggala et al., 2024). Along the way, we develop an improved algorithm for BCO with memory, which may be of independent interest.<|reference_end|> | arxiv | @article{sun2024tight,
title={Tight Rates for Bandit Control Beyond Quadratics},
author={Y. Jennifer Sun, Zhou Lu},
journal={arXiv preprint arXiv:2410.00993},
year={2024},
archivePrefix={arXiv},
eprint={2410.00993},
primaryClass={cs.LG math.OC}
} | sun2024tight |
arxiv-664209 | 2410.00995 | CktGen: Specification-Conditioned Analog Circuit Generation | <|reference_start|>CktGen: Specification-Conditioned Analog Circuit Generation: Automatic synthesis of analog circuits presents significant challenges. Existing methods usually treat the task as optimization problems, which limits their transferability and reusability for new requirements. To address this limitation, we introduce a task that directly generates analog circuits based on specified specifications, termed specification-conditioned analog circuit generation. Specifically, we propose CktGen, a simple yet effective variational autoencoder (VAE) model, that maps specifications and circuits into a joint latent space, and reconstructs the circuit from the latent. Moreover, given that a single specification can correspond to multiple distinct circuits, simply minimizing the distance between the mapped latent representations of the circuit and specification does not capture these one-to-many relationships. To address this, we integrate contrastive learning and classifier guidance to prevent model collapse. We conduct comprehensive experiments on the Open Circuit Benchmark (OCB) and introduce new evaluation metrics for cross-model consistency in the specification-to-circuit generation task. Experimental results demonstrate substantial improvements over existing state-of-the-art methods.<|reference_end|> | arxiv | @article{hou2024cktgen:,
title={CktGen: Specification-Conditioned Analog Circuit Generation},
author={Yuxuan Hou, Jianrong Zhang, Hua Chen, Min Zhou, Faxin Yu, Hehe Fan, Yi
Yang},
journal={arXiv preprint arXiv:2410.00995},
year={2024},
archivePrefix={arXiv},
eprint={2410.00995},
primaryClass={cs.LG}
} | hou2024cktgen: |
arxiv-664210 | 2410.00996 | Approximating Klee's Measure Problem and a Lower Bound for Union Volume Estimation | <|reference_start|>Approximating Klee's Measure Problem and a Lower Bound for Union Volume Estimation: Union volume estimation is a classical algorithmic problem. Given a family of objects $O_1,\ldots,O_n \subseteq \mathbb{R}^d$, we want to approximate the volume of their union. In the special case where all objects are boxes (also known as hyperrectangles) this is known as Klee's measure problem. The state-of-the-art algorithm [Karp, Luby, Madras '89] for union volume estimation and Klee's measure problem in constant dimension $d$ computes a $(1+\varepsilon)$-approximation with constant success probability by using a total of $O(n/\varepsilon^2)$ queries of the form (i) ask for the volume of $O_i$, (ii) sample a point uniformly at random from $O_i$, and (iii) query whether a given point is contained in $O_i$. We show that if one can only interact with the objects via the aforementioned three queries, the query complexity of [Karp, Luby, Madras '89] is indeed optimal, i.e., $\Omega(n/\varepsilon^2)$ queries are necessary. Our lower bound already holds for estimating the union of equiponderous axis-aligned polygons in $\mathbb{R}^2$, and even if the algorithm is allowed to inspect the coordinates of the points sampled from the polygons, and still holds when a containment query can ask containment of an arbitrary (not sampled) point. Guided by the insights of the lower bound, we provide a more efficient approximation algorithm for Klee's measure problem improving the $O(n/\varepsilon^2)$ time to $O((n+\frac{1}{\varepsilon^2}) \cdot \log^{O(d)}n)$. We achieve this improvement by exploiting the geometry of Klee's measure problem in various ways: (1) Since we have access to the boxes' coordinates, we can split the boxes into classes of boxes of similar shape. (2) Within each class, we show how to sample from the union of all boxes, by using orthogonal range searching. And (3) we exploit that boxes of different classes have small intersection, for most pairs of classes.<|reference_end|> | arxiv | @article{bringmann2024approximating,
title={Approximating Klee's Measure Problem and a Lower Bound for Union Volume
Estimation},
author={Karl Bringmann, Kasper Green Larsen, Andr'e Nusser, Eva Rotenberg,
Yanheng Wang},
journal={arXiv preprint arXiv:2410.00996},
year={2024},
archivePrefix={arXiv},
eprint={2410.00996},
primaryClass={cs.CG cs.CC cs.DS}
} | bringmann2024approximating |
arxiv-664211 | 2410.00998 | "Hiding in Plain Sight": Designing Synthetic Dialog Generation for Uncovering Socially Situated Norms | <|reference_start|>"Hiding in Plain Sight": Designing Synthetic Dialog Generation for Uncovering Socially Situated Norms: Naturally situated conversations capture the underlying social norms appropriate for the topic of conversation, the relationship between interlocutors and their communicative intent. This paper proposes a framework for controlled generation of dialogues, spanning a wide range of interlocutors attributes (such as age group, profession and personality types), relationship types, conversation topics and conversational trajectories. We use this framework to generate NormHint, a collection of dialogues consistent with these rich settings and analyzed for norm violation leading to conflicts, and potential steps for avoiding these conflicts by adhering to social norms and preferring respectful utterances maintaining the communicative intents of the original utterance. We present the results of human validation and automated analysis of NormHint and show it captures a wide range of conversational topics and scored highly by humans for the naturalness of the conversations based on the prompted context.<|reference_end|> | arxiv | @article{wu2024"hiding,
title={"Hiding in Plain Sight": Designing Synthetic Dialog Generation for
Uncovering Socially Situated Norms},
author={Chengfei Wu, Dan Goldwasser},
journal={arXiv preprint arXiv:2410.00998},
year={2024},
archivePrefix={arXiv},
eprint={2410.00998},
primaryClass={cs.CL}
} | wu2024"hiding |
arxiv-664212 | 2410.01003 | Y-CA-Net: A Convolutional Attention Based Network for Volumetric Medical Image Segmentation | <|reference_start|>Y-CA-Net: A Convolutional Attention Based Network for Volumetric Medical Image Segmentation: Recent attention-based volumetric segmentation (VS) methods have achieved remarkable performance in the medical domain which focuses on modeling long-range dependencies. However, for voxel-wise prediction tasks, discriminative local features are key components for the performance of the VS models which is missing in attention-based VS methods. Aiming at resolving this issue, we deliberately incorporate the convolutional encoder branch with transformer backbone to extract local and global features in a parallel manner and aggregate them in Cross Feature Mixer Module (CFMM) for better prediction of segmentation mask. Consequently, we observe that the derived model, Y-CT-Net, achieves competitive performance on multiple medical segmentation tasks. For example, on multi-organ segmentation, Y-CT-Net achieves an 82.4% dice score, surpassing well-tuned VS Transformer/CNN-like baselines UNETR/ResNet-3D by 2.9%/1.4%. With the success of Y-CT-Net, we extend this concept with hybrid attention models, that derived Y-CH-Net model, which brings a 3% improvement in terms of HD95 score for same segmentation task. The effectiveness of both models Y-CT-Net and Y-CH-Net verifies our hypothesis and motivates us to initiate the concept of Y-CA-Net, a versatile generic architecture based upon any two encoders and a decoder backbones, to fully exploit the complementary strengths of both convolution and attention mechanisms. Based on experimental results, we argue Y-CA-Net is a key player in achieving superior results for volumetric segmentation.<|reference_end|> | arxiv | @article{sharif2024y-ca-net:,
title={Y-CA-Net: A Convolutional Attention Based Network for Volumetric Medical
Image Segmentation},
author={Muhammad Hamza Sharif, Muzammal Naseer, Mohammad Yaqub, Min Xu, Mohsen
Guizani},
journal={arXiv preprint arXiv:2410.01003},
year={2024},
archivePrefix={arXiv},
eprint={2410.01003},
primaryClass={cs.CV}
} | sharif2024y-ca-net: |
arxiv-664213 | 2410.01010 | Code Interviews: Design and Evaluation of a More Authentic Assessment for Introductory Programming Assignments | <|reference_start|>Code Interviews: Design and Evaluation of a More Authentic Assessment for Introductory Programming Assignments: Generative artificial intelligence poses new challenges around assessment and academic integrity, increasingly driving introductory programming educators to employ invigilated exams often conducted in-person on pencil-and-paper. But the structure of exams often fails to accommodate authentic programming experiences that involve planning, implementing, and debugging programs with computer interaction. In this experience report, we describe code interviews: a more authentic assessment method for take-home programming assignments. Through action research, we experimented with varying the number and type of questions as well as whether interviews were conducted individually or with groups of students. To scale the program, we converted most of our weekly teaching assistant (TA) sections to conduct code interviews on 5 major weekly take-home programming assignments. By triangulating data from 5 sources, we identified 4 themes. Code interviews (1) pushed students to discuss their work, motivating more nuanced but sometimes repetitive insights; (2) enabled peer learning, reducing stress in some ways but increasing stress in other ways; (3) scaled with TA-led sections, replacing familiar practice with an unfamiliar assessment; (4) focused on student contributions, limiting opportunities for TAs to give guidance and feedback. We conclude by discussing the different decisions about the design of code interviews with implications for student experience, academic integrity, and teaching workload.<|reference_end|> | arxiv | @article{kannam2024code,
title={Code Interviews: Design and Evaluation of a More Authentic Assessment
for Introductory Programming Assignments},
author={Suhas Kannam, Yuri Yang, Aarya Dharm, Kevin Lin},
journal={arXiv preprint arXiv:2410.01010},
year={2024},
archivePrefix={arXiv},
eprint={2410.01010},
primaryClass={cs.CY}
} | kannam2024code |
arxiv-664214 | 2410.01011 | Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies with an Integrated Statistical and Neural Framework | <|reference_start|>Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies with an Integrated Statistical and Neural Framework: Existing methods for anomaly detection often fall short due to their inability to handle the complexity, heterogeneity, and high dimensionality inherent in real-world mobility data. In this paper, we propose DeepBayesic, a novel framework that integrates Bayesian principles with deep neural networks to model the underlying multivariate distributions from sparse and complex datasets. Unlike traditional models, DeepBayesic is designed to manage heterogeneous inputs, accommodating both continuous and categorical data to provide a more comprehensive understanding of mobility patterns. The framework features customized neural density estimators and hybrid architectures, allowing for flexibility in modeling diverse feature distributions and enabling the use of specialized neural networks tailored to different data types. Our approach also leverages agent embeddings for personalized anomaly detection, enhancing its ability to distinguish between normal and anomalous behaviors for individual agents. We evaluate our approach on several mobility datasets, demonstrating significant improvements over state-of-the-art anomaly detection methods. Our results indicate that incorporating personalization and advanced sequence modeling techniques can substantially enhance the ability to detect subtle and complex anomalies in spatiotemporal event sequences.<|reference_end|> | arxiv | @article{duan2024back,
title={Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies
with an Integrated Statistical and Neural Framework},
author={Minxuan Duan, Yinlong Qian, Lingyi Zhao, Zihao Zhou, Zeeshan Rasheed,
Rose Yu, Khurram Shafique},
journal={arXiv preprint arXiv:2410.01011},
year={2024},
archivePrefix={arXiv},
eprint={2410.01011},
primaryClass={cs.LG}
} | duan2024back |
arxiv-664215 | 2410.01014 | "For Us By Us": Intentionally Designing Technology for Lived Black Experiences | <|reference_start|>"For Us By Us": Intentionally Designing Technology for Lived Black Experiences: HCI research to date has only scratched the surface of the unique approaches racially minoritized communities take to building, designing, and using technology systems. While there has been an increase in understanding how people across racial groups create community across different platforms, there is still a lack of studies that explicitly center on how Black technologists design with and for their own communities. In this paper, we present findings from a series of semi-structured interviews with Black technologists who have used, created, or curated resources to support lived Black experiences. From their experiences, we find a multifaceted approach to design as a means of survival, to stay connected, for cultural significance, and to bask in celebratory joy. Further, we provide considerations that emphasize the need for centering lived Black experiences in design and share approaches that can empower the broader research community to conduct further inquiries into design focused on those in the margins.<|reference_end|> | arxiv | @article{egede2024"for,
title={"For Us By Us": Intentionally Designing Technology for Lived Black
Experiences},
author={Lisa Egede, Leslie Coney, Brittany Johnson, Christina N. Harrington,
Denae Ford},
journal={arXiv preprint arXiv:2410.01014},
year={2024},
doi={10.1145/3643834.3661535},
archivePrefix={arXiv},
eprint={2410.01014},
primaryClass={cs.HC cs.CY}
} | egede2024"for |
arxiv-664216 | 2410.01016 | Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security | <|reference_start|>Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security: Attacks against the Internet of Things (IoT) are rising as devices, applications, and interactions become more networked and integrated. The increase in cyber-attacks that target IoT networks poses a considerable vulnerability and threat to the privacy, security, functionality, and availability of critical systems, which leads to operational disruptions, financial losses, identity thefts, and data breaches. To efficiently secure IoT devices, real-time detection of intrusion systems is critical, especially those using machine learning to identify threats and mitigate risks and vulnerabilities. This paper investigates the latest research on machine learning-based intrusion detection strategies for IoT security, concentrating on real-time responsiveness, detection accuracy, and algorithm efficiency. Key studies were reviewed from all well-known academic databases, and a taxonomy was provided for the existing approaches. This review also highlights existing research gaps and outlines the limitations of current IoT security frameworks to offer practical insights for future research directions and developments.<|reference_end|> | arxiv | @article{esmaeili2024machine,
title={Machine Learning-Assisted Intrusion Detection for Enhancing Internet of
Things Security},
author={Mona Esmaeili, Morteza Rahimi, Hadise Pishdast, Dorsa Farahmandazad,
Matin Khajavi, Hadi Jabbari Saray},
journal={arXiv preprint arXiv:2410.01016},
year={2024},
archivePrefix={arXiv},
eprint={2410.01016},
primaryClass={cs.CR cs.LG}
} | esmaeili2024machine |
arxiv-664217 | 2410.01017 | A Generalized Approach to Root-based Attacks against PLWE | <|reference_start|>A Generalized Approach to Root-based Attacks against PLWE: The Polynomial Learning With Errors problem (PLWE) serves as the background of two of the three cryptosystems standardized in August 2024 by the National Institute of Standards and Technology to replace non-quantum resistant current primitives like those based on RSA, Diffie-Hellman or its elliptic curve analogue. Although PLWE is highly believed to be quantum resistant, this fact has not yet been established, contrariwise to other post-quantum proposals like multivariate and some code based ones. Moreover, several vulnerabilities have been encountered for a number of specific instances. In a search for more flexibility, it becomes fully relevant to study the robustness of PLWE based on other polynomials, not necessarily cyclotomic. In 2015, Elias et al found a good number of attacks based on different features of the roots of the polynomial. In the present work we present an overview of the approximations made against PLWE derived from this and subsequent works, along with several new attacks which refine those by Elias et al. exploiting the order of the trace of roots over finite extensions of the finite field under the three scenarios laid out by Elias et al., allowing to generalize the setting in which the attacks can be carried out.<|reference_end|> | arxiv | @article{chacón2024a,
title={A Generalized Approach to Root-based Attacks against PLWE},
author={Iv'an Blanco Chac'on, Ra'ul Dur'an D'iaz and Rodrigo Mart'in
S'anchez-Ledesma},
journal={arXiv preprint arXiv:2410.01017},
year={2024},
archivePrefix={arXiv},
eprint={2410.01017},
primaryClass={cs.CR}
} | chacón2024a |
arxiv-664218 | 2410.01018 | Risk-Averse Planning and Plan Assessment for Marine Robots | <|reference_start|>Risk-Averse Planning and Plan Assessment for Marine Robots: Autonomous Underwater Vehicles (AUVs) need to operate for days without human intervention and thus must be able to do efficient and reliable task planning. Unfortunately, efficient task planning requires deliberately abstract domain models (for scalability reasons), which in practice leads to plans that might be unreliable or under performing in practice. An optimal abstract plan may turn out suboptimal or unreliable during physical execution. To overcome this, we introduce a method that first generates a selection of diverse high-level plans and then assesses them in a low-level simulation to select the optimal and most reliable candidate. We evaluate the method using a realistic underwater robot simulation, estimating the risk metrics for different scenarios, demonstrating feasibility and effectiveness of the approach.<|reference_end|> | arxiv | @article{kashani2024risk-averse,
title={Risk-Averse Planning and Plan Assessment for Marine Robots},
author={Mahya Mohammadi Kashani, Tobias John, Jeremy P. Coffelt, Einar Broch
Johnsen and Andrzej Wasowski},
journal={arXiv preprint arXiv:2410.01018},
year={2024},
archivePrefix={arXiv},
eprint={2410.01018},
primaryClass={cs.RO}
} | kashani2024risk-averse |
arxiv-664219 | 2410.01019 | Investigating the Synergistic Effects of Dropout and Residual Connections on Language Model Training | <|reference_start|>Investigating the Synergistic Effects of Dropout and Residual Connections on Language Model Training: This paper examines the pivotal role of dropout techniques in mitigating overfitting in language model training. It conducts a comprehensive investigation into the influence of variable dropout rates on both individual layers and residual connections within the context of language modeling. Our study conducts training of a decoder implementation on the classic Tiny Shakespeare data to examine the effects of the adjustments on training efficiency and validation error. Results not only confirm the benefits of dropout for regularization and residuals for convergence, but also reveal their interesting interactions. There exists an important trade-off between the depth of residual connections and the dropout on these connections for optimal deep neural network convergence and generalization.<|reference_end|> | arxiv | @article{li2024investigating,
title={Investigating the Synergistic Effects of Dropout and Residual
Connections on Language Model Training},
author={Qingyang Li and Weimao Ke},
journal={arXiv preprint arXiv:2410.01019},
year={2024},
archivePrefix={arXiv},
eprint={2410.01019},
primaryClass={cs.CL cs.LG}
} | li2024investigating |
arxiv-664220 | 2410.01020 | A Critical Assessment of Visual Sound Source Localization Models Including Negative Audio | <|reference_start|>A Critical Assessment of Visual Sound Source Localization Models Including Negative Audio: The task of Visual Sound Source Localization (VSSL) involves identifying the location of sound sources in visual scenes, integrating audio-visual data for enhanced scene understanding. Despite advancements in state-of-the-art (SOTA) models, we observe three critical flaws: i) The evaluation of the models is mainly focused in sounds produced by objects that are visible in the image, ii) The evaluation often assumes a prior knowledge of the size of the sounding object, and iii) No universal threshold for localization in real-world scenarios is established, as previous approaches only consider positive examples without accounting for both positive and negative cases. In this paper, we introduce a novel test set and metrics designed to complete the current standard evaluation of VSSL models by testing them in scenarios where none of the objects in the image corresponds to the audio input, i.e. a negative audio. We consider three types of negative audio: silence, noise and offscreen. Our analysis reveals that numerous SOTA models fail to appropriately adjust their predictions based on audio input, suggesting that these models may not be leveraging audio information as intended. Additionally, we provide a comprehensive analysis of the range of maximum values in the estimated audio-visual similarity maps, in both positive and negative audio cases, and show that most of the models are not discriminative enough, making them unfit to choose a universal threshold appropriate to perform sound localization without any a priori information of the sounding object, that is, object size and visibility.<|reference_end|> | arxiv | @article{juanola2024a,
title={A Critical Assessment of Visual Sound Source Localization Models
Including Negative Audio},
author={Xavier Juanola, Gloria Haro, Magdalena Fuentes},
journal={arXiv preprint arXiv:2410.01020},
year={2024},
archivePrefix={arXiv},
eprint={2410.01020},
primaryClass={cs.CV cs.SD eess.AS}
} | juanola2024a |
arxiv-664221 | 2410.01021 | A Naturally-Colored Translation from LTL to Parity and COCOA | <|reference_start|>A Naturally-Colored Translation from LTL to Parity and COCOA: Chains of co-B\"uchi automata (COCOA) have recently been introduced as a new canonical representation of omega-regular languages. The co-B\"uchi automata in a chain assign to each omega-word its natural color, which depends only on the language itself and not on its automaton representation. The automata in such a chain can be minimized in polynomial time and are good-for-games, making the representation attractive for verification and reactive synthesis applications. However, since in such applications, a specification is usually given in linear temporal logic (LTL), to make COCOA useful, the specification first has to be translated into such a chain of automata. The only currently known translation procedure goes through deterministic parity automata (LTL to DPW to COCOA), where the first step neglects the natural colors and requires intricate constructions by Safra or Esparza et al. This observation raises the question whether with the help of the definition of the natural color of words, such complex constructions can be avoided, leading to a more direct translation from LTL to COCOA. In this paper, we describe a surprisingly simple yet optimal translation from LTL to COCOA and a variant of it that translates from LTL to deterministic parity automata. It constitutes a novel path for translating from LTL to DPW as the translation procedure does not use any of the aforementioned intricate constructions. Instead, our procedure relies on standard operations on weak alternating automata, Miyano/Hayashi's breakpoint construction, an augmented subset construction, and some simple graph algorithms. With weak alternating automata as starting point, the procedure can also be applied to specifications in linear dynamic logic. The translation procedure runs in asymptotically-optimal doubly-exponential time and computes automata of asymptotically optimal size.<|reference_end|> | arxiv | @article{ehlers2024a,
title={A Naturally-Colored Translation from LTL to Parity and COCOA},
author={R"udiger Ehlers and Ayrat Khalimov},
journal={arXiv preprint arXiv:2410.01021},
year={2024},
archivePrefix={arXiv},
eprint={2410.01021},
primaryClass={cs.FL}
} | ehlers2024a |
arxiv-664222 | 2410.01023 | Can visual language models resolve textual ambiguity with visual cues? Let visual puns tell you! | <|reference_start|>Can visual language models resolve textual ambiguity with visual cues? Let visual puns tell you!: Humans possess multimodal literacy, allowing them to actively integrate information from various modalities to form reasoning. Faced with challenges like lexical ambiguity in text, we supplement this with other modalities, such as thumbnail images or textbook illustrations. Is it possible for machines to achieve a similar multimodal understanding capability? In response, we present Understanding Pun with Image Explanations (UNPIE), a novel benchmark designed to assess the impact of multimodal inputs in resolving lexical ambiguities. Puns serve as the ideal subject for this evaluation due to their intrinsic ambiguity. Our dataset includes 1,000 puns, each accompanied by an image that explains both meanings. We pose three multimodal challenges with the annotations to assess different aspects of multimodal literacy; Pun Grounding, Disambiguation, and Reconstruction. The results indicate that various Socratic Models and Visual-Language Models improve over the text-only models when given visual context, particularly as the complexity of the tasks increases.<|reference_end|> | arxiv | @article{chung2024can,
title={Can visual language models resolve textual ambiguity with visual cues?
Let visual puns tell you!},
author={Jiwan Chung, Seungwon Lim, Jaehyun Jeon, Seungbeen Lee, Youngjae Yu},
journal={arXiv preprint arXiv:2410.01023},
year={2024},
archivePrefix={arXiv},
eprint={2410.01023},
primaryClass={cs.CV cs.AI}
} | chung2024can |
arxiv-664223 | 2410.01024 | GPTreeO: An R package for continual regression with dividing local Gaussian processes | <|reference_start|>GPTreeO: An R package for continual regression with dividing local Gaussian processes: We introduce GPTreeO, a flexible R package for scalable Gaussian process (GP) regression, particularly tailored to continual learning problems. GPTreeO builds upon the Dividing Local Gaussian Processes (DLGP) algorithm, in which a binary tree of local GP regressors is dynamically constructed using a continual stream of input data. In GPTreeO we extend the original DLGP algorithm by allowing continual optimisation of the GP hyperparameters, incorporating uncertainty calibration, and introducing new strategies for how the local partitions are created. Moreover, the modular code structure allows users to interface their favourite GP library to perform the local GP regression in GPTreeO. The flexibility of GPTreeO gives the user fine-grained control of the balance between computational speed, accuracy, stability and smoothness. We conduct a sensitivity analysis to show how GPTreeO's configurable features impact the regression performance in a continual learning setting.<|reference_end|> | arxiv | @article{braun2024gptreeo:,
title={GPTreeO: An R package for continual regression with dividing local
Gaussian processes},
author={Timo Braun, Anders Kvellestad and Riccardo De Bin},
journal={arXiv preprint arXiv:2410.01024},
year={2024},
archivePrefix={arXiv},
eprint={2410.01024},
primaryClass={cs.LG stat.CO}
} | braun2024gptreeo: |
arxiv-664224 | 2410.01026 | Understanding the Human-LLM Dynamic: A Literature Survey of LLM Use in Programming Tasks | <|reference_start|>Understanding the Human-LLM Dynamic: A Literature Survey of LLM Use in Programming Tasks: Large Language Models (LLMs) are transforming programming practices, offering significant capabilities for code generation activities. While researchers have explored the potential of LLMs in various domains, this paper focuses on their use in programming tasks, drawing insights from user studies that assess the impact of LLMs on programming tasks. We first examined the user interaction behaviors with LLMs observed in these studies, from the types of requests made to task completion strategies. Additionally, our analysis reveals both benefits and weaknesses of LLMs showing mixed effects on the human and task. Lastly, we looked into what factors from the human, LLM or the interaction of both, affect the human's enhancement as well as the task performance. Our findings highlight the variability in human-LLM interactions due to the non-deterministic nature of both parties (humans and LLMs), underscoring the need for a deeper understanding of these interaction patterns. We conclude by providing some practical suggestions for researchers as well as programmers.<|reference_end|> | arxiv | @article{etsenake2024understanding,
title={Understanding the Human-LLM Dynamic: A Literature Survey of LLM Use in
Programming Tasks},
author={Deborah Etsenake and Meiyappan Nagappan},
journal={arXiv preprint arXiv:2410.01026},
year={2024},
archivePrefix={arXiv},
eprint={2410.01026},
primaryClass={cs.SE cs.HC}
} | etsenake2024understanding |
arxiv-664225 | 2410.01027 | Graph-based Scalable Sampling of 3D Point Cloud Attributes | <|reference_start|>Graph-based Scalable Sampling of 3D Point Cloud Attributes: 3D Point clouds (PCs) are commonly used to represent 3D scenes. They can have millions of points, making subsequent downstream tasks such as compression and streaming computationally expensive. PC sampling (selecting a subset of points) can be used to reduce complexity. Existing PC sampling algorithms focus on preserving geometry features and often do not scale to handle large PCs. In this work, we develop scalable graph-based sampling algorithms for PC color attributes, assuming the full geometry is available. Our sampling algorithms are optimized for a signal reconstruction method that minimizes the graph Laplacian quadratic form. We first develop a global sampling algorithm that can be applied to PCs with millions of points by exploiting sparsity and sampling rate adaptive parameter selection. Further, we propose a block-based sampling strategy where each block is sampled independently. We show that sampling the corresponding sub-graphs with optimally chosen self-loop weights (node weights) will produce a sampling set that approximates the results of global sampling while reducing complexity by an order of magnitude. Our empirical results on two large PC datasets show that our algorithms outperform the existing fast PC subsampling techniques (uniform and geometry feature preserving random sampling) by 2dB. Our algorithm is up to 50 times faster than existing graph signal sampling algorithms while providing better reconstruction accuracy. Finally, we illustrate the efficacy of PC attribute sampling within a compression scenario, showing that pre-compression sampling of PC attributes can lower the bitrate by 11% while having minimal effect on reconstruction.<|reference_end|> | arxiv | @article{sridhara2024graph-based,
title={Graph-based Scalable Sampling of 3D Point Cloud Attributes},
author={Shashank N. Sridhara, Eduardo Pavez, Ajinkya Jayawant, Antonio Ortega,
Ryosuke Watanabe, and Keisuke Nonaka},
journal={arXiv preprint arXiv:2410.01027},
year={2024},
archivePrefix={arXiv},
eprint={2410.01027},
primaryClass={eess.IV cs.MM eess.SP}
} | sridhara2024graph-based |
arxiv-664226 | 2410.01028 | Draft on the Fly: Adaptive Self-Speculative Decoding using Cosine Similarity | <|reference_start|>Draft on the Fly: Adaptive Self-Speculative Decoding using Cosine Similarity: We present a simple on the fly method for faster inference of large language models. Unlike other (self-)speculative decoding techniques, our method does not require fine-tuning or black-box optimization to generate a fixed draft model, relying instead on simple rules to generate varying draft models adapted to the input context. We show empirically that our light-weight algorithm is competitive with the current SOTA for self-speculative decoding, while being a truly plug-and-play method.<|reference_end|> | arxiv | @article{metel2024draft,
title={Draft on the Fly: Adaptive Self-Speculative Decoding using Cosine
Similarity},
author={Michael R. Metel, Peng Lu, Boxing Chen, Mehdi Rezagholizadeh, Ivan
Kobyzev},
journal={arXiv preprint arXiv:2410.01028},
year={2024},
archivePrefix={arXiv},
eprint={2410.01028},
primaryClass={cs.CL}
} | metel2024draft |
arxiv-664227 | 2410.01030 | Dynamic Bipedal Loco-manipulation using Oracle Guided Multi-mode Policies with Mode-transition Preference | <|reference_start|>Dynamic Bipedal Loco-manipulation using Oracle Guided Multi-mode Policies with Mode-transition Preference: Loco-manipulation calls for effective whole-body control and contact-rich interactions with the object and the environment. Existing learning-based control frameworks rely on task-specific engineered rewards, training a set of low-level skill policies and explicitly switching between them with a high-level policy or FSM, leading to quasi-static and fragile transitions between skills. In contrast, for solving highly dynamic tasks such as soccer, the robot should run towards the ball, decelerating into an optimal approach configuration to seamlessly switch to dribbling and eventually score a goal - a continuum of smooth motion. To this end, we propose to learn a single Oracle Guided Multi-mode Policy (OGMP) for mastering all the required modes and transition maneuvers to solve uni-object bipedal loco-manipulation tasks. Specifically, we design a multi-mode oracle as a closed loop state-reference generator, viewing it as a hybrid automaton with continuous reference generating dynamics and discrete mode jumps. Given such an oracle, we then train an OGMP through bounded exploration around the generated reference. Furthermore, to enforce the policy to learn the desired sequence of mode transitions, we present a novel task-agnostic mode-switching preference reward that enhances performance. The proposed approach results in successful dynamic loco-manipulation in omnidirectional soccer and box-moving tasks with a 16-DoF bipedal robot HECTOR. Supplementary video results are available at https://www.youtube.com/watch?v=gfDaRqobheg<|reference_end|> | arxiv | @article{ravichandar2024dynamic,
title={Dynamic Bipedal Loco-manipulation using Oracle Guided Multi-mode
Policies with Mode-transition Preference},
author={Prashanth Ravichandar, Lokesh Krishna, Nikhil Sobanbabu, and Quan
Nguyen},
journal={arXiv preprint arXiv:2410.01030},
year={2024},
archivePrefix={arXiv},
eprint={2410.01030},
primaryClass={cs.RO}
} | ravichandar2024dynamic |
arxiv-664228 | 2410.01031 | FCE-YOLOv8: YOLOv8 with Feature Context Excitation Modules for Fracture Detection in Pediatric Wrist X-ray Images | <|reference_start|>FCE-YOLOv8: YOLOv8 with Feature Context Excitation Modules for Fracture Detection in Pediatric Wrist X-ray Images: Children often suffer wrist trauma in daily life, while they usually need radiologists to analyze and interpret X-ray images before surgical treatment by surgeons. The development of deep learning has enabled neural networks to serve as computer-assisted diagnosis (CAD) tools to help doctors and experts in medical image diagnostics. Since the You Only Look Once Version-8 (YOLOv8) model has obtained the satisfactory success in object detection tasks, it has been applied to various fracture detection. This work introduces four variants of Feature Contexts Excitation-YOLOv8 (FCE-YOLOv8) model, each incorporating a different FCE module (i.e., modules of Squeeze-and-Excitation (SE), Global Context (GC), Gather-Excite (GE), and Gaussian Context Transformer (GCT)) to enhance the model performance. Experimental results on GRAZPEDWRI-DX dataset demonstrate that our proposed YOLOv8+GC-M3 model improves the mAP@50 value from 65.78% to 66.32%, outperforming the state-of-the-art (SOTA) model while reducing inference time. Furthermore, our proposed YOLOv8+SE-M3 model achieves the highest mAP@50 value of 67.07%, exceeding the SOTA performance. The implementation of this work is available at https://github.com/RuiyangJu/FCE-YOLOv8.<|reference_end|> | arxiv | @article{ju2024pediatric,
title={Pediatric Wrist Fracture Detection Using Feature Context Excitation
Modules in X-ray Images},
author={Rui-Yang Ju, Chun-Tse Chien, Enkaer Xieerke, Jen-Shiun Chiang},
journal={arXiv preprint arXiv:2410.01031},
year={2024},
archivePrefix={arXiv},
eprint={2410.01031},
primaryClass={cs.CV}
} | ju2024pediatric |
arxiv-664229 | 2410.01032 | Teaching Cloud Infrastructure and Scalable Application Deployment in an Undergraduate Computer Science Program | <|reference_start|>Teaching Cloud Infrastructure and Scalable Application Deployment in an Undergraduate Computer Science Program: Making successful use of cloud computing for deploying scalable web applications requires nuanced approaches to both system design and deployment methodology, involving reasoning about the elasticity, cost, and security models of cloud services. Students commonly interact with cloud abstractions early in their technical careers, including during internships and academic research. Building cloud-native applications without a firm understanding of the fundamentals of cloud engineering can leave students susceptible to cost and security pitfalls common to cloud platforms. Yet, cloud computing is not commonly taught at the undergraduate level, because the technology and practices behind modern cloud deployment, such as containerization and infrastructure-as-code (IaC), have only recently matured into a set of general principles independent from specific providers' offerings. To address this gap, we designed an undergraduate-level course around these principles that framed cloud infrastructure deployment as a software engineering practice in support of scalable web applications, emphasizing the value of both cloud deployment and application design skills in building robust cloud-native systems. Our course featured a number of hands-on assignments that gave students experience with modern, best-practice concepts and tools such as IaC, containerization, observability, serverless computing, and continuous integration and deployment. We describe the design of the course, our experience teaching its initial offering in Winter 2024, and provide our reflections on what worked well and potential areas for improvement. Our course material is publicly available at https://infracourse.cloud.<|reference_end|> | arxiv | @article{saligrama2024teaching,
title={Teaching Cloud Infrastructure and Scalable Application Deployment in an
Undergraduate Computer Science Program},
author={Aditya Saligrama, Cody Ho, Benjamin Tripp, Michael Abbott, Christos
Kozyrakis},
journal={arXiv preprint arXiv:2410.01032},
year={2024},
archivePrefix={arXiv},
eprint={2410.01032},
primaryClass={cs.CY}
} | saligrama2024teaching |
arxiv-664230 | 2410.01033 | Single-Shot Learning of Stable Dynamical Systems for Long-Horizon Manipulation Tasks | <|reference_start|>Single-Shot Learning of Stable Dynamical Systems for Long-Horizon Manipulation Tasks: Mastering complex sequential tasks continues to pose a significant challenge in robotics. While there has been progress in learning long-horizon manipulation tasks, most existing approaches lack rigorous mathematical guarantees for ensuring reliable and successful execution. In this paper, we extend previous work on learning long-horizon tasks and stable policies, focusing on improving task success rates while reducing the amount of training data needed. Our approach introduces a novel method that (1) segments long-horizon demonstrations into discrete steps defined by waypoints and subgoals, and (2) learns globally stable dynamical system policies to guide the robot to each subgoal, even in the face of sensory noise and random disturbances. We validate our approach through both simulation and real-world experiments, demonstrating effective transfer from simulation to physical robotic platforms. Code is available at https://github.com/Alestaubin/stable-imitation-policy-with-waypoints<|reference_end|> | arxiv | @article{st-aubin2024single-shot,
title={Single-Shot Learning of Stable Dynamical Systems for Long-Horizon
Manipulation Tasks},
author={Alexandre St-Aubin, Amin Abyaneh, and Hsiu-Chin Lin},
journal={arXiv preprint arXiv:2410.01033},
year={2024},
archivePrefix={arXiv},
eprint={2410.01033},
primaryClass={cs.RO cs.LG}
} | st-aubin2024single-shot |
arxiv-664231 | 2410.01035 | Don't Stop Me Now: Embedding Based Scheduling for LLMs | <|reference_start|>Don't Stop Me Now: Embedding Based Scheduling for LLMs: Efficient scheduling is crucial for interactive Large Language Model (LLM) applications, where low request completion time directly impacts user engagement. Size-based scheduling algorithms like Shortest Remaining Process Time (SRPT) aim to reduce average request completion time by leveraging known or estimated request sizes and allowing preemption by incoming jobs with shorter service times. However, two main challenges arise when applying size-based scheduling to LLM systems. First, accurately predicting output lengths from prompts is challenging and often resource-intensive, making it impractical for many systems. As a result, the state-of-the-art LLM systems default to first-come, first-served scheduling, which can lead to head-of-line blocking and reduced system efficiency. Second, preemption introduces extra memory overhead to LLM systems as they must maintain intermediate states for unfinished (preempted) requests. In this paper, we propose TRAIL, a method to obtain output predictions from the target LLM itself. After generating each output token, we recycle the embedding of its internal structure as input for a lightweight classifier that predicts the remaining length for each running request. Using these predictions, we propose a prediction-based SRPT variant with limited preemption designed to account for memory overhead in LLM systems. This variant allows preemption early in request execution when memory consumption is low but restricts preemption as requests approach completion to optimize resource utilization. On the theoretical side, we derive a closed-form formula for this SRPT variant in an M/G/1 queue model, which demonstrates its potential value. In our system, we implement this preemption policy alongside our embedding-based prediction method.<|reference_end|> | arxiv | @article{shahout2024don't,
title={Don't Stop Me Now: Embedding Based Scheduling for LLMs},
author={Rana Shahout, Eran Malach, Chunwei Liu, Weifan Jiang, Minlan Yu,
Michael Mitzenmacher},
journal={arXiv preprint arXiv:2410.01035},
year={2024},
archivePrefix={arXiv},
eprint={2410.01035},
primaryClass={cs.LG}
} | shahout2024don't |
arxiv-664232 | 2410.01036 | MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languages | <|reference_start|>MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languages: The rise of foundation models (FMs), coupled with regulatory efforts addressing their risks and impacts, has sparked significant interest in open-source models. However, existing speech FMs (SFMs) fall short of full compliance with the open-source principles, even if claimed otherwise, as no existing SFM has model weights, code, and training data publicly available under open-source terms. In this work, we take the first step toward filling this gap by focusing on the 24 official languages of the European Union (EU). We collect suitable training data by surveying automatic speech recognition datasets and unlabeled speech corpora under open-source compliant licenses, for a total of 950k hours. Additionally, we release automatic transcripts for 441k hours of unlabeled data under the permissive CC-BY license, thereby facilitating the creation of open-source SFMs for the EU languages.<|reference_end|> | arxiv | @article{gaido2024mosel:,
title={MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation
Model Training on EU Languages},
author={Marco Gaido, Sara Papi, Luisa Bentivogli, Alessio Brutti, Mauro
Cettolo, Roberto Gretter, Marco Matassoni, Mohamed Nabih, Matteo Negri},
journal={arXiv preprint arXiv:2410.01036},
year={2024},
archivePrefix={arXiv},
eprint={2410.01036},
primaryClass={cs.CL cs.AI cs.SD eess.AS}
} | gaido2024mosel: |
arxiv-664233 | 2410.01038 | Safe Autonomy for Uncrewed Surface Vehicles Using Adaptive Control and Reachability Analysis | <|reference_start|>Safe Autonomy for Uncrewed Surface Vehicles Using Adaptive Control and Reachability Analysis: Marine robots must maintain precise control and ensure safety during tasks like ocean monitoring, even when encountering unpredictable disturbances that affect performance. Designing algorithms for uncrewed surface vehicles (USVs) requires accounting for these disturbances to control the vehicle and ensure it avoids obstacles. While adaptive control has addressed USV control challenges, real-world applications are limited, and certifying USV safety amidst unexpected disturbances remains difficult. To tackle control issues, we employ a model reference adaptive controller (MRAC) to stabilize the USV along a desired trajectory. For safety certification, we developed a reachability module with a moving horizon estimator (MHE) to estimate disturbances affecting the USV. This estimate is propagated through a forward reachable set calculation, predicting future states and enabling real-time safety certification. We tested our safe autonomy pipeline on a Clearpath Heron USV in the Charles River, near MIT. Our experiments demonstrated that the USV's MRAC controller and reachability module could adapt to disturbances like thruster failures and drag forces. The MRAC controller outperformed a PID baseline, showing a 45%-81% reduction in RMSE position error. Additionally, the reachability module provided real-time safety certification, ensuring the USV's safety. We further validated our pipeline's effectiveness in underway replenishment and canal scenarios, simulating relevant marine tasks.<|reference_end|> | arxiv | @article{mahesh2024safe,
title={Safe Autonomy for Uncrewed Surface Vehicles Using Adaptive Control and
Reachability Analysis},
author={Karan Mahesh, Tyler M. Paine, Max L. Greene, Nicholas Rober, Steven
Lee, Sildomar T. Monteiro, Anuradha Annaswamy, Michael R. Benjamin, Jonathan
P. How},
journal={arXiv preprint arXiv:2410.01038},
year={2024},
archivePrefix={arXiv},
eprint={2410.01038},
primaryClass={cs.RO cs.SY eess.SY}
} | mahesh2024safe |
arxiv-664234 | 2410.01039 | From Facts to Insights: A Study on the Generation and Evaluation of Analytical Reports for Deciphering Earnings Calls | <|reference_start|>From Facts to Insights: A Study on the Generation and Evaluation of Analytical Reports for Deciphering Earnings Calls: This paper explores the use of Large Language Models (LLMs) in the generation and evaluation of analytical reports derived from Earnings Calls (ECs). Addressing a current gap in research, we explore the generation of analytical reports with LLMs in a multi-agent framework, designing specialized agents that introduce diverse viewpoints and desirable topics of analysis into the report generation process. Through multiple analyses, we examine the alignment between generated and human-written reports and the impact of both individual and collective agents. Our findings suggest that the introduction of additional agents results in more insightful reports, although reports generated by human experts remain preferred in the majority of cases. Finally, we address the challenging issue of report evaluation, we examine the limitations and strengths of LLMs in assessing the quality of generated reports in different settings, revealing a significant correlation with human experts across multiple dimensions.<|reference_end|> | arxiv | @article{goldsack2024from,
title={From Facts to Insights: A Study on the Generation and Evaluation of
Analytical Reports for Deciphering Earnings Calls},
author={Tomas Goldsack, Yang Wang, Chenghua Lin, Chung-Chi Chen},
journal={arXiv preprint arXiv:2410.01039},
year={2024},
archivePrefix={arXiv},
eprint={2410.01039},
primaryClass={cs.CL}
} | goldsack2024from |
arxiv-664235 | 2410.01041 | On the simultaneous recovery of two coefficients in the Helmholtz equation for inverse scattering problems via neural networks | <|reference_start|>On the simultaneous recovery of two coefficients in the Helmholtz equation for inverse scattering problems via neural networks: Recently, deep neural networks (DNNs) have become powerful tools for solving inverse scattering problems. However, the approximation and generalization rates of DNNs for solving these problems remain largely under-explored. In this work, we introduce two types of combined DNNs (uncompressed and compressed) to reconstruct two coefficients in the Helmholtz equation for inverse scattering problems from the scattering data at two different frequencies. An analysis of the approximation and generalization capabilities of the proposed neural networks for simulating the regularized pseudo-inverses of the linearized forward operators in direct scattering problems is provided. The results show that, with sufficient training data and parameters, the proposed neural networks can effectively approximate the inverse process with desirable generalization. Preliminary numerical results show the feasibility of the proposed neural networks for recovering two types of isotropic inhomogeneous media. Furthermore, the trained neural network is capable of reconstructing the isotropic representation of certain types of anisotropic media.<|reference_end|> | arxiv | @article{zhou2024on,
title={On the simultaneous recovery of two coefficients in the Helmholtz
equation for inverse scattering problems via neural networks},
author={Zehui Zhou},
journal={arXiv preprint arXiv:2410.01041},
year={2024},
archivePrefix={arXiv},
eprint={2410.01041},
primaryClass={math.NA cs.NA}
} | zhou2024on |
arxiv-664236 | 2410.01044 | RATIONALYST: Pre-training Process-Supervision for Improving Reasoning | <|reference_start|>RATIONALYST: Pre-training Process-Supervision for Improving Reasoning: The reasoning steps generated by LLMs might be incomplete, as they mimic logical leaps common in everyday communication found in their pre-training data: underlying rationales are frequently left implicit (unstated). To address this challenge, we introduce RATIONALYST, a model for process-supervision of reasoning based on pre-training on a vast collection of rationale annotations extracted from unlabeled data. We extract 79k rationales from web-scale unlabelled dataset (the Pile) and a combination of reasoning datasets with minimal human intervention. This web-scale pre-training for reasoning allows RATIONALYST to consistently generalize across diverse reasoning tasks, including mathematical, commonsense, scientific, and logical reasoning. Fine-tuned from LLaMa-3-8B, RATIONALYST improves the accuracy of reasoning by an average of 3.9% on 7 representative reasoning benchmarks. It also demonstrates superior performance compared to significantly larger verifiers like GPT-4 and similarly sized models fine-tuned on matching training sets.<|reference_end|> | arxiv | @article{jiang2024rationalyst:,
title={RATIONALYST: Pre-training Process-Supervision for Improving Reasoning},
author={Dongwei Jiang, Guoxuan Wang, Yining Lu, Andrew Wang, Jingyu Zhang,
Chuyu Liu, Benjamin Van Durme, Daniel Khashabi},
journal={arXiv preprint arXiv:2410.01044},
year={2024},
archivePrefix={arXiv},
eprint={2410.01044},
primaryClass={cs.AI cs.CL}
} | jiang2024rationalyst: |
arxiv-664237 | 2410.01045 | A Unified Approach for Optimal Cruise Airspeed with Variable Cost Index for Fuel-powered and All-electric Aircraft | <|reference_start|>A Unified Approach for Optimal Cruise Airspeed with Variable Cost Index for Fuel-powered and All-electric Aircraft: This paper proposes for the first time a unified optimal approach to solve a direct operating cost (DOC) minimization problem where the cost index (CI) is time-varying. More specifically, the coefficient CI is modeled as a time-varying parameter commanded by Air Traffic Control (ATC). The proposed unified approach relies on the solution of an optimal control problem both for fuel-powered and all-electric aircraft. Furthermore, this paper demonstrates how a variable CI affects the solution of the optimization problem as it presents the equations that allow the computation of optimal constant cruise airspeed and flight time in response to step changes in the CI value. The proposed methodology is validated by a simulated flight scenario. In this scenario the inputs from the ATC are received during flight and the aircraft is required to adjust its optimal airspeed, flight time, and total energy consumption to comply with the operational restrictions imposed by the ATC. The optimal values of airspeed, flight time and energy consumption are computed for both a fuel-powered and an all-electric aircraft, thus enabling applications of the proposed approach to future air mobility all-electric vehicles.<|reference_end|> | arxiv | @article{silva2024a,
title={A Unified Approach for Optimal Cruise Airspeed with Variable Cost Index
for Fuel-powered and All-electric Aircraft},
author={Lucas Souza e Silva, Ali Akgunduz and Luis Rodrigues},
journal={arXiv preprint arXiv:2410.01045},
year={2024},
archivePrefix={arXiv},
eprint={2410.01045},
primaryClass={eess.SY cs.SY}
} | silva2024a |
arxiv-664238 | 2410.01046 | Addition of a peristaltic wave improves multi-legged locomotion performance on complex terrains | <|reference_start|>Addition of a peristaltic wave improves multi-legged locomotion performance on complex terrains: Characterized by their elongate bodies and relatively simple legs, multi-legged robots have the potential to locomote through complex terrains for applications such as search-and-rescue and terrain inspection. Prior work has developed effective and reliable locomotion strategies for multi-legged robots by propagating the two waves of lateral body undulation and leg stepping, which we will refer to as the two-wave template. However, these robots have limited capability to climb over obstacles with sizes comparable to their heights. We hypothesize that such limitations stem from the two-wave template that we used to prescribe the multi-legged locomotion. Seeking effective alternative waves for obstacle-climbing, we designed a five-segment robot with static (non-actuated) legs, where each cable-driven joint has a rotational degree-of-freedom (DoF) in the sagittal plane (vertical wave) and a linear DoF (peristaltic wave). We tested robot locomotion performance on a flat terrain and a rugose terrain. While the benefit of peristalsis on flat-ground locomotion is marginal, the inclusion of a peristaltic wave substantially improves the locomotion performance in rugose terrains: it not only enables obstacle-climbing capabilities with obstacles having a similar height as the robot, but it also significantly improves the traversing capabilities of the robot in such terrains. Our results demonstrate an alternative actuation mechanism for multi-legged robots, paving the way towards all-terrain multi-legged robots.<|reference_end|> | arxiv | @article{iaschi2024addition,
title={Addition of a peristaltic wave improves multi-legged locomotion
performance on complex terrains},
author={Massimiliano Iaschi and Baxi Chong and Tianyu Wang and Jianfeng Lin
and Juntao He and Daniel Soto and Zhaochen Xu and Daniel I Goldman},
journal={arXiv preprint arXiv:2410.01046},
year={2024},
archivePrefix={arXiv},
eprint={2410.01046},
primaryClass={cs.RO}
} | iaschi2024addition |
arxiv-664239 | 2410.01047 | Spherical Analysis of Learning Nonlinear Functionals | <|reference_start|>Spherical Analysis of Learning Nonlinear Functionals: In recent years, there has been growing interest in the field of functional neural networks. They have been proposed and studied with the aim of approximating continuous functionals defined on sets of functions on Euclidean domains. In this paper, we consider functionals defined on sets of functions on spheres. The approximation ability of deep ReLU neural networks is investigated by novel spherical analysis using an encoder-decoder framework. An encoder comes up first to accommodate the infinite-dimensional nature of the domain of functionals. It utilizes spherical harmonics to help us extract the latent finite-dimensional information of functions, which in turn facilitates in the next step of approximation analysis using fully connected neural networks. Moreover, real-world objects are frequently sampled discretely and are often corrupted by noise. Therefore, encoders with discrete input and those with discrete and random noise input are constructed, respectively. The approximation rates with different encoder structures are provided therein.<|reference_end|> | arxiv | @article{yang2024spherical,
title={Spherical Analysis of Learning Nonlinear Functionals},
author={Zhenyu Yang, Shuo Huang, Han Feng, Ding-Xuan Zhou},
journal={arXiv preprint arXiv:2410.01047},
year={2024},
archivePrefix={arXiv},
eprint={2410.01047},
primaryClass={cs.LG math.FA stat.ML}
} | yang2024spherical |
arxiv-664240 | 2410.01048 | The Telephone $k$-Multicast Problem | <|reference_start|>The Telephone $k$-Multicast Problem: We consider minimum time multicasting problems in directed and undirected graphs: given a root node and a subset of $t$ terminal nodes, multicasting seeks to find the minimum number of rounds within which all terminals can be informed with a message originating at the root. In each round, the telephone model we study allows the information to move via a matching from the informed nodes to the uninformed nodes. Since minimum time multicasting in digraphs is poorly understood compared to the undirected variant, we study an intermediate problem in undirected graphs that specifies a target $k < t$, and requires the only $k$ of the terminals be informed in the minimum number of rounds. For this problem, we improve implications of prior results and obtain an $\tilde{O}(t^{1/3})$ multiplicative approximation. For the directed version, we obtain an {\em additive} $\tilde{O}(k^{1/2})$ approximation algorithm (with a poly-logarithmic multiplicative factor). Our algorithms are based on reductions to the related problems of finding $k$-trees of minimum poise (sum of maximum degree and diameter) and applying a combination of greedy network decomposition techniques and set covering under partition matroid constraints.<|reference_end|> | arxiv | @article{hathcock2024the,
title={The Telephone $k$-Multicast Problem},
author={Daniel Hathcock, Guy Kortsarz, R. Ravi},
journal={arXiv preprint arXiv:2410.01048},
year={2024},
archivePrefix={arXiv},
eprint={2410.01048},
primaryClass={cs.DS}
} | hathcock2024the |
arxiv-664241 | 2410.01049 | List strong and list normal edge-coloring of (sub)cubic graphs | <|reference_start|>List strong and list normal edge-coloring of (sub)cubic graphs: A strong edge-coloring of a graph is a proper edge-coloring, in which the edges of every path of length 3 receive distinct colors; in other words, every pair of edges at distance at most 2 must be colored differently. The least number of colors needed for a strong edge-coloring of a graph is the strong chromatic index. We consider the list version of the coloring and prove that the list strong chromatic index of graphs with maximum degree 3 is at most 10. This bound is tight and improves the previous bound of 11 colors. We also consider the question whether the strong chromatic index and the list strong chromatic index always coincide. We answer it in negative by presenting an infinite family of graphs for which the two invariants differ. For the special case of the Petersen graph, we show that its list strong chromatic index equals 7, while its strong chromatic index is 5. Up to our best knowledge, this is the first known edge-coloring for which there are graphs with distinct values of the chromatic index and its list version. In relation to the above, we also initiate the study of the list version of the normal edge-coloring. A normal edge-coloring of a cubic graph is a proper edge-coloring, in which every edge is adjacent to edges colored with 4 colors or to edges colored with 2 colors. It is conjectured that 5 colors suffice for a normal edge-coloring of any bridgeless cubic graph which is equivalent to the Petersen Coloring Conjecture. Similarly to the strong edge-coloring, the list normal edge-coloring is much more restrictive and consequently for many graphs the list normal chromatic index is greater than the normal chromatic index. In particular, we show that there are cubic graphs with the list normal chromatic index at least 9, there are bridgeless cubic graphs with its value at least 8, and there are cyclically 4-edge-connected cubic graphs with value at least 7.<|reference_end|> | arxiv | @article{lužar2024list,
title={List strong and list normal edge-coloring of (sub)cubic graphs},
author={Borut Luv{z}ar and Edita M'av{c}ajov'a and Roman Sot'ak and Diana
v{S}vecov'a},
journal={arXiv preprint arXiv:2410.01049},
year={2024},
archivePrefix={arXiv},
eprint={2410.01049},
primaryClass={math.CO cs.DM}
} | lužar2024list |
arxiv-664242 | 2410.01050 | Steering Elongate Multi-legged Robots By Modulating Body Undulation Waves | <|reference_start|>Steering Elongate Multi-legged Robots By Modulating Body Undulation Waves: Centipedes exhibit great maneuverability in diverse environments due to their many legs and body-driven control. By leveraging similar morphologies, their robotic counterparts also demonstrate effective terrestrial locomotion. However, the success of these multi-legged robots is largely limited to forward locomotion; steering is substantially less studied, in part due to the challenges in coordinating their many body joints. Furthermore, steering behavior is complex and can include different combinations of desired rotational/translational displacement. In this paper, we explore steering strategies in multi-legged robots based on tools derived from geometric mechanics (GM). We characterize the steering motion in the plane by the rotation angle, the steering radius, and the heading direction angle. We identify an effective turning strategy by superimposing two traveling waves in the lateral body undulation and further explore variations of the "turning wave" to enable a broad spectrum of steering behaviors. By combining an amplitude modulation and a phase modulation, we develop a control strategy for steering behaviors that enables steering with a range of rotation angles (from 0{\deg} to 20{\deg}) and steering radius (from 0.28 to 0.38 body length) while keeping the heading direction angle close to 0. Lastly, we test our control framework on an elongate multi-legged robot model to verify the effectiveness of our proposed strategy. Our work demonstrates the generality of the two-wave template for effective steering of multi-legged elongate robots.<|reference_end|> | arxiv | @article{flores2024steering,
title={Steering Elongate Multi-legged Robots By Modulating Body Undulation
Waves},
author={Esteban Flores, Baxi Chong, Daniel Soto, Dan Tatulescu and Daniel I.
Goldman},
journal={arXiv preprint arXiv:2410.01050},
year={2024},
archivePrefix={arXiv},
eprint={2410.01050},
primaryClass={cs.RO}
} | flores2024steering |
arxiv-664243 | 2410.01054 | Divide et Impera: Learning impedance families for peg-in-hole assembly | <|reference_start|>Divide et Impera: Learning impedance families for peg-in-hole assembly: This paper addresses robotic peg-in-hole assembly using the framework of Elementary Dynamic Actions (EDA). Inspired by motor primitives in neuromotor control research, the method leverages three primitives: submovements, oscillations, and mechanical impedances (e.g., stiffness and damping), combined via a Norton equivalent network model. By focusing on impedance parameterization, we explore the adaptability of EDA in contact-rich tasks. Experimental results, conducted on a real robot setup with four different peg types, demonstrated a range of successful impedance parameters, challenging conventional methods that seek optimal parameters. We analyze our data in a lower-dimensional solution space. Clustering analysis shows the possibility to identify different individual strategies for each single peg, as well as common strategies across all pegs. A neural network model, trained on the experimental data, accurately predicted successful impedance parameters across all pegs. The practical utility of this work is enhanced by a success-predictor model and the public availability of all code and CAD files. These findings highlight the flexibility and robustness of EDA; show multiple equally-successful strategies for contact-rich manipulation; and offer valuable insights and tools for robotic assembly programming.<|reference_end|> | arxiv | @article{lachner2024divide,
title={Divide et Impera: Learning impedance families for peg-in-hole assembly},
author={Johannes Lachner, Federico Tessari, A. Michael West Jr., Moses C. Nah,
Neville Hogan},
journal={arXiv preprint arXiv:2410.01054},
year={2024},
archivePrefix={arXiv},
eprint={2410.01054},
primaryClass={cs.RO}
} | lachner2024divide |
arxiv-664244 | 2410.01055 | ARPOV: Expanding Visualization of Object Detection in AR with Panoramic Mosaic Stitching | <|reference_start|>ARPOV: Expanding Visualization of Object Detection in AR with Panoramic Mosaic Stitching: As the uses of augmented reality (AR) become more complex and widely available, AR applications will increasingly incorporate intelligent features that require developers to understand the user's behavior and surrounding environment (e.g. an intelligent assistant). Such applications rely on video captured by an AR headset, which often contains disjointed camera movement with a limited field of view that cannot capture the full scope of what the user sees at any given time. Moreover, standard methods of visualizing object detection model outputs are limited to capturing objects within a single frame and timestep, and therefore fail to capture the temporal and spatial context that is often necessary for various domain applications. We propose ARPOV, an interactive visual analytics tool for analyzing object detection model outputs tailored to video captured by an AR headset that maximizes user understanding of model performance. The proposed tool leverages panorama stitching to expand the view of the environment while automatically filtering undesirable frames, and includes interactive features that facilitate object detection model debugging. ARPOV was designed as part of a collaboration between visualization researchers and machine learning and AR experts; we validate our design choices through interviews with 5 domain experts.<|reference_end|> | arxiv | @article{mcgowan2024arpov:,
title={ARPOV: Expanding Visualization of Object Detection in AR with Panoramic
Mosaic Stitching},
author={Erin McGowan, Ethan Brewer, Claudio Silva},
journal={arXiv preprint arXiv:2410.01055},
year={2024},
archivePrefix={arXiv},
eprint={2410.01055},
primaryClass={cs.CV}
} | mcgowan2024arpov: |
arxiv-664245 | 2410.01056 | Effective self-righting strategies for elongate multi-legged robots | <|reference_start|>Effective self-righting strategies for elongate multi-legged robots: Centipede-like robots offer an effective and robust solution to navigation over complex terrain with minimal sensing. However, when climbing over obstacles, such multi-legged robots often elevate their center-of-mass into unstable configurations, where even moderate terrain uncertainty can cause tipping over. Robust mechanisms for such elongate multi-legged robots to self-right remain unstudied. Here, we developed a comparative biological and robophysical approach to investigate self-righting strategies. We first released \textit{S. polymorpha} upside down from a 10 cm height and recorded their self-righting behaviors using top and side view high-speed cameras. Using kinematic analysis, we hypothesize that these behaviors can be prescribed by two traveling waves superimposed in the body lateral and vertical planes, respectively. We tested our hypothesis on an elongate robot with static (non-actuated) limbs, and we successfully reconstructed these self-righting behaviors. We further evaluated how wave parameters affect self-righting effectiveness. We identified two key wave parameters: the spatial frequency, which characterizes the sequence of body-rolling, and the wave amplitude, which characterizes body curvature. By empirically obtaining a behavior diagram of spatial frequency and amplitude, we identify effective and versatile self-righting strategies for general elongate multi-legged robots, which greatly enhances these robots' mobility and robustness in practical applications such as agricultural terrain inspection and search-and-rescue.<|reference_end|> | arxiv | @article{teder2024effective,
title={Effective self-righting strategies for elongate multi-legged robots},
author={Erik Teder, Baxi Chong, Juntao He, Tianyu Wang, Massimiliano Iaschi,
Daniel Soto and Daniel I Goldman},
journal={arXiv preprint arXiv:2410.01056},
year={2024},
archivePrefix={arXiv},
eprint={2410.01056},
primaryClass={cs.RO}
} | teder2024effective |
arxiv-664246 | 2410.01057 | Uncertainty Modelling and Robust Observer Synthesis using the Koopman Operator | <|reference_start|>Uncertainty Modelling and Robust Observer Synthesis using the Koopman Operator: This paper proposes a robust nonlinear observer synthesis method for a population of systems modelled using the Koopman operator. The Koopman operator allows nonlinear systems to be rewritten as infinite-dimensional linear systems. A finite-dimensional approximation of the Koopman operator can be identified directly from data, yielding an approximately linear model of a nonlinear system. The proposed observer synthesis method is made possible by this linearity that in turn allows uncertainty within a population of Koopman models to be quantified in the frequency domain. Using this uncertainty model, linear robust control techniques are used to synthesize robust nonlinear Koopman observers. A population of several dozen motor drives is used to experimentally demonstrate the proposed method. Manufacturing variation is characterized in the frequency domain, and a robust Koopman observer is synthesized using mixed $\mathcal{H}_2$-$\mathcal{H}_\infty$ optimal control.<|reference_end|> | arxiv | @article{dahdah2024uncertainty,
title={Uncertainty Modelling and Robust Observer Synthesis using the Koopman
Operator},
author={Steven Dahdah, James Richard Forbes},
journal={arXiv preprint arXiv:2410.01057},
year={2024},
archivePrefix={arXiv},
eprint={2410.01057},
primaryClass={eess.SY cs.LG cs.SY math.DS}
} | dahdah2024uncertainty |
arxiv-664247 | 2410.01061 | Pose Estimation of Buried Deep-Sea Objects using 3D Vision Deep Learning Models | <|reference_start|>Pose Estimation of Buried Deep-Sea Objects using 3D Vision Deep Learning Models: We present an approach for pose and burial fraction estimation of debris field barrels found on the seabed in the Southern California San Pedro Basin. Our computational workflow leverages recent advances in foundation models for segmentation and a vision transformer-based approach to estimate the point cloud which defines the geometry of the barrel. We propose BarrelNet for estimating the 6-DOF pose and radius of buried barrels from the barrel point clouds as input. We train BarrelNet using synthetically generated barrel point clouds, and qualitatively demonstrate the potential of our approach using remotely operated vehicle (ROV) video footage of barrels found at a historic dump site. We compare our method to a traditional least squares fitting approach and show significant improvement according to our defined benchmarks.<|reference_end|> | arxiv | @article{yan2024pose,
title={Pose Estimation of Buried Deep-Sea Objects using 3D Vision Deep Learning
Models},
author={Jerry Yan, Chinmay Talegaonkar, Nicholas Antipa, Eric Terrill, Sophia
Merrifield},
journal={arXiv preprint arXiv:2410.01061},
year={2024},
archivePrefix={arXiv},
eprint={2410.01061},
primaryClass={cs.CV}
} | yan2024pose |
arxiv-664248 | 2410.01064 | Truth or Deceit? A Bayesian Decoding Game Enhances Consistency and Reliability | <|reference_start|>Truth or Deceit? A Bayesian Decoding Game Enhances Consistency and Reliability: Large Language Models (LLMs) often produce outputs that -- though plausible -- can lack consistency and reliability, particularly in ambiguous or complex scenarios. Challenges arise from ensuring that outputs align with both factual correctness and human intent. This is problematic in existing approaches that trade improved consistency for lower accuracy. To mitigate these challenges, we propose a novel game-theoretic approach to enhance consistency and reliability during the decoding stage of LLM output generation. Our method models the decoding process as a multistage Bayesian decoding game. This ensures consistency through Correctness Alignment and enhances reliability via Ambiguity Calibration. The model dynamically converges to a consensus on the most reliable outputs and distinguishes {Valid, Specious} outputs without human feedback or additional training. Our game design allows smaller models to outperform much larger models through game mechanisms (e.g., 78.1 LLaMA13B vs 76.6 PaLM540B), as well as integrating various LL strategies and models, demonstrating the potential of game-theoretic tools to improve the truthfulness and reliability of LLMs.<|reference_end|> | arxiv | @article{zhang2024truth,
title={Truth or Deceit? A Bayesian Decoding Game Enhances Consistency and
Reliability},
author={Weitong Zhang, Chengqi Zang, Bernhard Kainz},
journal={arXiv preprint arXiv:2410.01064},
year={2024},
archivePrefix={arXiv},
eprint={2410.01064},
primaryClass={cs.AI}
} | zhang2024truth |
arxiv-664249 | 2410.01065 | Structure-Preserving Operator Learning | <|reference_start|>Structure-Preserving Operator Learning: Learning complex dynamics driven by partial differential equations directly from data holds great promise for fast and accurate simulations of complex physical systems. In most cases, this problem can be formulated as an operator learning task, where one aims to learn the operator representing the physics of interest, which entails discretization of the continuous system. However, preserving key continuous properties at the discrete level, such as boundary conditions, and addressing physical systems with complex geometries is challenging for most existing approaches. We introduce a family of operator learning architectures, structure-preserving operator networks (SPONs), that allows to preserve key mathematical and physical properties of the continuous system by leveraging finite element (FE) discretizations of the input-output spaces. SPONs are encode-process-decode architectures that are end-to-end differentiable, where the encoder and decoder follows from the discretizations of the input-output spaces. SPONs can operate on complex geometries, enforce certain boundary conditions exactly, and offer theoretical guarantees. Our framework provides a flexible way of devising structure-preserving architectures tailored to specific applications, and offers an explicit trade-off between performance and efficiency, all thanks to the FE discretization of the input-output spaces. Additionally, we introduce a multigrid-inspired SPON architecture that yields improved performance at higher efficiency. Finally, we release a software to automate the design and training of SPON architectures.<|reference_end|> | arxiv | @article{bouziani2024structure-preserving,
title={Structure-Preserving Operator Learning},
author={Nacime Bouziani, Nicolas Boull'e},
journal={arXiv preprint arXiv:2410.01065},
year={2024},
archivePrefix={arXiv},
eprint={2410.01065},
primaryClass={cs.LG cs.CE cs.NA math.NA}
} | bouziani2024structure-preserving |
arxiv-664250 | 2410.01066 | From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems | <|reference_start|>From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems: Since the onset of LLMs, translating natural language queries to structured SQL commands is assuming increasing. Unlike the previous reviews, this survey provides a comprehensive study of the evolution of LLM-based text-to-SQL systems, from early rule-based models to advanced LLM approaches, and how LLMs impacted this field. We discuss benchmarks, evaluation methods and evaluation metrics. Also, we uniquely study the role of integration of knowledge graphs for better contextual accuracy and schema linking in these systems. The current techniques fall into two categories: in-context learning of corpus and fine-tuning, which then leads to approaches such as zero-shot, few-shot learning from the end, and data augmentation. Finally, we highlight key challenges such as computational efficiency, model robustness, and data privacy with perspectives toward their development and improvements in potential areas for future of LLM-based text-to-SQL system.<|reference_end|> | arxiv | @article{mohammadjafari2024from,
title={From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems},
author={Ali Mohammadjafari, Anthony S. Maida, Raju Gottumukkala},
journal={arXiv preprint arXiv:2410.01066},
year={2024},
archivePrefix={arXiv},
eprint={2410.01066},
primaryClass={cs.CL cs.AI}
} | mohammadjafari2024from |
arxiv-664251 | 2410.01068 | Convergent Privacy Loss of Noisy-SGD without Convexity and Smoothness | <|reference_start|>Convergent Privacy Loss of Noisy-SGD without Convexity and Smoothness: We study the Differential Privacy (DP) guarantee of hidden-state Noisy-SGD algorithms over a bounded domain. Standard privacy analysis for Noisy-SGD assumes all internal states are revealed, which leads to a divergent R'enyi DP bound with respect to the number of iterations. Ye & Shokri (2022) and Altschuler & Talwar (2022) proved convergent bounds for smooth (strongly) convex losses, and raise open questions about whether these assumptions can be relaxed. We provide positive answers by proving convergent R'enyi DP bound for non-convex non-smooth losses, where we show that requiring losses to have H\"older continuous gradient is sufficient. We also provide a strictly better privacy bound compared to state-of-the-art results for smooth strongly convex losses. Our analysis relies on the improvement of shifted divergence analysis in multiple aspects, including forward Wasserstein distance tracking, identifying the optimal shifts allocation, and the H"older reduction lemma. Our results further elucidate the benefit of hidden-state analysis for DP and its applicability.<|reference_end|> | arxiv | @article{chien2024convergent,
title={Convergent Privacy Loss of Noisy-SGD without Convexity and Smoothness},
author={Eli Chien, Pan Li},
journal={arXiv preprint arXiv:2410.01068},
year={2024},
archivePrefix={arXiv},
eprint={2410.01068},
primaryClass={cs.LG cs.CR}
} | chien2024convergent |
arxiv-664252 | 2410.01070 | Meta Learning Based Adaptive Cooperative Perception in Nonstationary Vehicular Networks | <|reference_start|>Meta Learning Based Adaptive Cooperative Perception in Nonstationary Vehicular Networks: To accommodate high network dynamics in real-time cooperative perception (CP), reinforcement learning (RL) based adaptive CP schemes have been proposed, to allow adaptive switchings between CP and stand-alone perception modes among connected and autonomous vehicles. The traditional offline-training online-execution RL framework suffers from performance degradation under nonstationary network conditions. To achieve fast and efficient model adaptation, we formulate a set of Markov decision processes for adaptive CP decisions in each stationary local vehicular network (LVN). A meta RL solution is proposed, which trains a meta RL model that captures the general features among LVNs, thus facilitating fast model adaptation for each LVN with the meta RL model as an initial point. Simulation results show the superiority of meta RL in terms of the convergence speed without reward degradation. The impact of the customization level of meta models on the model adaptation performance has also been evaluated.<|reference_end|> | arxiv | @article{qu2024meta,
title={Meta Learning Based Adaptive Cooperative Perception in Nonstationary
Vehicular Networks},
author={Kaige Qu, Zixiong Qin, Weihua Zhuang},
journal={arXiv preprint arXiv:2410.01070},
year={2024},
archivePrefix={arXiv},
eprint={2410.01070},
primaryClass={cs.NI eess.SP}
} | qu2024meta |
arxiv-664253 | 2410.01071 | An Approach to Elicit Human-Understandable Robot Expressions to Support Human-Robot Interaction | <|reference_start|>An Approach to Elicit Human-Understandable Robot Expressions to Support Human-Robot Interaction: Understanding the intentions of robots is essential for natural and seamless human-robot collaboration. Ensuring that robots have means for non-verbal communication is a basis for intuitive and implicit interaction. For this, we contribute an approach to elicit and design human-understandable robot expressions. We outline the approach in the context of non-humanoid robots. We paired human mimicking and enactment with research from gesture elicitation in two phases: first, to elicit expressions, and second, to ensure they are understandable. We present an example application through two studies (N=16 \& N=260) of our approach to elicit expressions for a simple 6-DoF robotic arm. We show that it enabled us to design robot expressions that signal curiosity and interest in getting attention. Our main contribution is an approach to generate and validate understandable expressions for robots, enabling more natural human-robot interaction.<|reference_end|> | arxiv | @article{leusmann2024an,
title={An Approach to Elicit Human-Understandable Robot Expressions to Support
Human-Robot Interaction},
author={Jan Leusmann, Steeven Villa, Thomas Liang, Chao Wang, Albrecht
Schmidt, Sven Mayer},
journal={arXiv preprint arXiv:2410.01071},
year={2024},
archivePrefix={arXiv},
eprint={2410.01071},
primaryClass={cs.RO cs.HC}
} | leusmann2024an |
arxiv-664254 | 2410.01072 | Generating Seamless Virtual Immunohistochemical Whole Slide Images with Content and Color Consistency | <|reference_start|>Generating Seamless Virtual Immunohistochemical Whole Slide Images with Content and Color Consistency: Immunohistochemical (IHC) stains play a vital role in a pathologist's analysis of medical images, providing crucial diagnostic information for various diseases. Virtual staining from hematoxylin and eosin (H&E)-stained whole slide images (WSIs) allows the automatic production of other useful IHC stains without the expensive physical staining process. However, current virtual WSI generation methods based on tile-wise processing often suffer from inconsistencies in content, texture, and color at tile boundaries. These inconsistencies lead to artifacts that compromise image quality and potentially hinder accurate clinical assessment and diagnoses. To address this limitation, we propose a novel consistent WSI synthesis network, CC-WSI-Net, that extends GAN models to produce seamless synthetic whole slide images. Our CC-WSI-Net integrates a content- and color-consistency supervisor, ensuring consistency across tiles and facilitating the generation of seamless synthetic WSIs while ensuring Sox10 immunohistochemistry accuracy in melanocyte detection. We validate our method through extensive image-quality analyses, objective detection assessments, and a subjective survey with pathologists. By generating high-quality synthetic WSIs, our method opens doors for advanced virtual staining techniques with broader applications in research and clinical care.<|reference_end|> | arxiv | @article{liu2024generating,
title={Generating Seamless Virtual Immunohistochemical Whole Slide Images with
Content and Color Consistency},
author={Sitong Liu, Kechun Liu, Samuel Margolis, Wenjun Wu, Stevan R.
Knezevich, David E Elder, Megan M. Eguchi, Joann G Elmore, Linda Shapiro},
journal={arXiv preprint arXiv:2410.01072},
year={2024},
archivePrefix={arXiv},
eprint={2410.01072},
primaryClass={eess.IV cs.CV q-bio.QM}
} | liu2024generating |
arxiv-664255 | 2410.01076 | Inferring Kernel $\epsilon$-Machines: Discovering Structure in Complex Systems | <|reference_start|>Inferring Kernel $\epsilon$-Machines: Discovering Structure in Complex Systems: Previously, we showed that computational mechanic's causal states -- predictively-equivalent trajectory classes for a stochastic dynamical system -- can be cast into a reproducing kernel Hilbert space. The result is a widely-applicable method that infers causal structure directly from very different kinds of observations and systems. Here, we expand this method to explicitly introduce the causal diffusion components it produces. These encode the kernel causal-state estimates as a set of coordinates in a reduced dimension space. We show how each component extracts predictive features from data and demonstrate their application on four examples: first, a simple pendulum -- an exactly solvable system; second, a molecular-dynamic trajectory of $n$-butane -- a high-dimensional system with a well-studied energy landscape; third, the monthly sunspot sequence -- the longest-running available time series of direct observations; and fourth, multi-year observations of an active crop field -- a set of heterogeneous observations of the same ecosystem taken for over a decade. In this way, we demonstrate that the empirical kernel causal-states algorithm robustly discovers predictive structures for systems with widely varying dimensionality and stochasticity.<|reference_end|> | arxiv | @article{jurgens2024inferring,
title={Inferring Kernel $\epsilon$-Machines: Discovering Structure in Complex
Systems},
author={Alexandra M. Jurgens, Nicolas Brodu},
journal={arXiv preprint arXiv:2410.01076},
year={2024},
archivePrefix={arXiv},
eprint={2410.01076},
primaryClass={cs.LG}
} | jurgens2024inferring |
arxiv-664256 | 2410.01078 | Two-Finger Soft Gripper Force Modulation via Kinesthetic Feedback | <|reference_start|>Two-Finger Soft Gripper Force Modulation via Kinesthetic Feedback: We investigate a method to modulate contact forces between the soft fingers of a two-finger gripper and an object, without relying on tactile sensors. This work is a follow-up to our previous results on contact detection. Here, our hypothesis is that once the contact between a finger and an object is detected, a controller that keeps a desired difference between the finger bending measurement and its bending at the moment of contact is sufficient to maintain and modulate the contact force. This approach can be simultaneously applied to both fingers while getting in contact with a single object. We successfully tested the hypothesis, and characterized the contact and peak pull-out force magnitude vs. the desired difference expressed by a multiplicative factor. All of the results are performed on a real physical device.<|reference_end|> | arxiv | @article{herrera2024two-finger,
title={Two-Finger Soft Gripper Force Modulation via Kinesthetic Feedback},
author={Stephanie O. Herrera, Tae Myung Huh, Dejan Milutinovic},
journal={arXiv preprint arXiv:2410.01078},
year={2024},
archivePrefix={arXiv},
eprint={2410.01078},
primaryClass={cs.RO}
} | herrera2024two-finger |
arxiv-664257 | 2410.01079 | Concept Space Alignment in Multilingual LLMs | <|reference_start|>Concept Space Alignment in Multilingual LLMs: Multilingual large language models (LLMs) seem to generalize somewhat across languages. We hypothesize this is a result of implicit vector space alignment. Evaluating such alignment, we see that larger models exhibit very high-quality linear alignments between corresponding concepts in different languages. Our experiments show that multilingual LLMs suffer from two familiar weaknesses: generalization works best for languages with similar typology, and for abstract concepts. For some models, e.g., the Llama-2 family of models, prompt-based embeddings align better than word embeddings, but the projections are less linear -- an observation that holds across almost all model families, indicating that some of the implicitly learned alignments are broken somewhat by prompt-based methods.<|reference_end|> | arxiv | @article{peng2024concept,
title={Concept Space Alignment in Multilingual LLMs},
author={Qiwei Peng and Anders S{o}gaard},
journal={arXiv preprint arXiv:2410.01079},
year={2024},
archivePrefix={arXiv},
eprint={2410.01079},
primaryClass={cs.CL}
} | peng2024concept |
arxiv-664258 | 2410.01083 | Deep Nets with Subsampling Layers Unwittingly Discard Useful Activations at Test-Time | <|reference_start|>Deep Nets with Subsampling Layers Unwittingly Discard Useful Activations at Test-Time: Subsampling layers play a crucial role in deep nets by discarding a portion of an activation map to reduce its spatial dimensions. This encourages the deep net to learn higher-level representations. Contrary to this motivation, we hypothesize that the discarded activations are useful and can be incorporated on the fly to improve models' prediction. To validate our hypothesis, we propose a search and aggregate method to find useful activation maps to be used at test time. We applied our approach to the task of image classification and semantic segmentation. Extensive experiments over nine different architectures on multiple datasets show that our method consistently improves model test-time performance, complementing existing test-time augmentation techniques. Our code is available at https://github.com/ca-joe-yang/discard-in-subsampling.<|reference_end|> | arxiv | @article{yang2024deep,
title={Deep Nets with Subsampling Layers Unwittingly Discard Useful Activations
at Test-Time},
author={Chiao-An Yang, Ziwei Liu, Raymond A. Yeh},
journal={arXiv preprint arXiv:2410.01083},
year={2024},
archivePrefix={arXiv},
eprint={2410.01083},
primaryClass={cs.CV cs.LG}
} | yang2024deep |
arxiv-664259 | 2410.01084 | Error exponent of activated non-signaling assisted classical-quantum channel coding | <|reference_start|>Error exponent of activated non-signaling assisted classical-quantum channel coding: We provide a tight asymptotic characterization of the error exponent for classical-quantum channel coding assisted by activated non-signaling correlations. Namely, we find that the optimal exponent--also called reliability function--is equal to the well-known sphere packing bound, which can be written as a single-letter formula optimized over Petz-R\'enyi divergences. Remarkably, there is no critical rate and as such our characterization remains tight for arbitrarily low rates below the capacity. On the achievability side, we further extend our results to fully quantum channels. Our proofs rely on semi-definite program duality and a dual representation of the Petz-R\'enyi divergences via Young inequalities.<|reference_end|> | arxiv | @article{oufkir2024error,
title={Error exponent of activated non-signaling assisted classical-quantum
channel coding},
author={Aadil Oufkir, Marco Tomamichel, Mario Berta},
journal={arXiv preprint arXiv:2410.01084},
year={2024},
archivePrefix={arXiv},
eprint={2410.01084},
primaryClass={quant-ph cs.IT math.IT}
} | oufkir2024error |
arxiv-664260 | 2410.01085 | RoTip: A Finger-Shaped Tactile Sensor with Active Rotation | <|reference_start|>RoTip: A Finger-Shaped Tactile Sensor with Active Rotation: In recent years, advancements in optical tactile sensor technology have primarily centred on enhancing sensing precision and expanding the range of sensing modalities. To meet the requirements for more skilful manipulation, there should be a movement towards making tactile sensors more dynamic. In this paper, we introduce RoTip, a novel vision-based tactile sensor that is uniquely designed with an independently controlled joint and the capability to sense contact over its entire surface. The rotational capability of the sensor is particularly crucial for manipulating everyday objects, especially thin and flexible ones, as it enables the sensor to mobilize while in contact with the object's surface. The manipulation experiments demonstrate the ability of our proposed RoTip to manipulate rigid and flexible objects, and the full-finger tactile feedback and active rotation capabilities have the potential to explore more complex and precise manipulation tasks.<|reference_end|> | arxiv | @article{zhang2024rotip:,
title={RoTip: A Finger-Shaped Tactile Sensor with Active Rotation},
author={Xuyang Zhang, Jiaqi Jiang, Shan Luo},
journal={arXiv preprint arXiv:2410.01085},
year={2024},
archivePrefix={arXiv},
eprint={2410.01085},
primaryClass={cs.RO}
} | zhang2024rotip: |
arxiv-664261 | 2410.01086 | An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event Outcomes | <|reference_start|>An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event Outcomes: Many applications involve reasoning about time durations before a critical event happens--also called time-to-event outcomes. When will a customer cancel a subscription, a coma patient wake up, or a convicted criminal reoffend? Time-to-event outcomes have been studied extensively within the field of survival analysis primarily by the statistical, medical, and reliability engineering communities, with textbooks already available in the 1970s and '80s. This monograph aims to provide a reasonably self-contained modern introduction to survival analysis. We focus on predicting time-to-event outcomes at the individual data point level with the help of neural networks. Our goal is to provide the reader with a working understanding of precisely what the basic time-to-event prediction problem is, how it differs from standard regression and classification, and how key "design patterns" have been used time after time to derive new time-to-event prediction models, from classical methods like the Cox proportional hazards model to modern deep learning approaches such as deep kernel Kaplan-Meier estimators and neural ordinary differential equation models. We further delve into two extensions of the basic time-to-event prediction setup: predicting which of several critical events will happen first along with the time until this earliest event happens (the competing risks setting), and predicting time-to-event outcomes given a time series that grows in length over time (the dynamic setting). We conclude with a discussion of a variety of topics such as fairness, causal reasoning, interpretability, and statistical guarantees. Our monograph comes with an accompanying code repository that implements every model and evaluation metric that we cover in detail.<|reference_end|> | arxiv | @article{chen2024an,
title={An Introduction to Deep Survival Analysis Models for Predicting
Time-to-Event Outcomes},
author={George H. Chen},
journal={arXiv preprint arXiv:2410.01086},
year={2024},
archivePrefix={arXiv},
eprint={2410.01086},
primaryClass={stat.ML cs.LG}
} | chen2024an |
arxiv-664262 | 2410.01087 | Development of a Platform to Enable Real Time, Non-disruptive Testing and Early Fault Detection of Critical High Voltage Transformers and Switchgears in High Speed-rail | <|reference_start|>Development of a Platform to Enable Real Time, Non-disruptive Testing and Early Fault Detection of Critical High Voltage Transformers and Switchgears in High Speed-rail: Partial discharge (PD) incidents can occur in critical components of high-speed rail electric systems, such as transformers and switchgears, due to localized insulation defects that cannot withstand electric stress, leading to potential flashovers. These incidents can escalate over time, resulting in breakdowns, downtime, and safety risks. Fortunately, PD activities emit radio frequency (RF) signals, allowing for the development of a hardware platform for real-time, non-invasive PD detection and monitoring. The system uses an RF antenna and high-speed data acquisition to scan signals across a configurable frequency range (100MHz to 3GHz), utilizing intermediate frequency modulation and sliding frequency windows for detailed analysis. When signals exceed a threshold, the system records the events, capturing both raw signal data and spectrum snapshots. Real-time data is streamed to a cloud server, offering remote access through a dedicated smartphone application, enabling maintenance teams to monitor and respond promptly. Laboratory testing has confirmed the system's ability to accurately capture RF signals and provide real-time PD monitoring, enhancing the reliability and safety of high-speed rail infrastructure.<|reference_end|> | arxiv | @article{fan2024development,
title={Development of a Platform to Enable Real Time, Non-disruptive Testing
and Early Fault Detection of Critical High Voltage Transformers and
Switchgears in High Speed-rail},
author={Jiawei Fan, Ming Zhu, Yingtao Jiang, Hualiang Teng},
journal={arXiv preprint arXiv:2410.01087},
year={2024},
archivePrefix={arXiv},
eprint={2410.01087},
primaryClass={eess.SY cs.SY eess.SP}
} | fan2024development |
arxiv-664263 | 2410.01088 | Exploring Empty Spaces: Human-in-the-Loop Data Augmentation | <|reference_start|>Exploring Empty Spaces: Human-in-the-Loop Data Augmentation: Data augmentation is crucial to make machine learning models more robust and safe. However, augmenting data can be challenging as it requires generating diverse data points to rigorously evaluate model behavior on edge cases and mitigate potential harms. Creating high-quality augmentations that cover these "unknown unknowns" is a time- and creativity-intensive task. In this work, we introduce Amplio, an interactive tool to help practitioners navigate "unknown unknowns" in unstructured text datasets and improve data diversity by systematically identifying empty data spaces to explore. Amplio includes three human-in-the-loop data augmentation techniques: Augment With Concepts, Augment by Interpolation, and Augment with Large Language Model. In a user study with 18 professional red teamers, we demonstrate the utility of our augmentation methods in helping generate high-quality, diverse, and relevant model safety prompts. We find that Amplio enabled red teamers to augment data quickly and creatively, highlighting the transformative potential of interactive augmentation workflows.<|reference_end|> | arxiv | @article{yeh2024exploring,
title={Exploring Empty Spaces: Human-in-the-Loop Data Augmentation},
author={Catherine Yeh, Donghao Ren, Yannick Assogba, Dominik Moritz, Fred
Hohman},
journal={arXiv preprint arXiv:2410.01088},
year={2024},
archivePrefix={arXiv},
eprint={2410.01088},
primaryClass={cs.HC cs.CL cs.LG}
} | yeh2024exploring |
arxiv-664264 | 2410.01089 | FMBench: Benchmarking Fairness in Multimodal Large Language Models on Medical Tasks | <|reference_start|>FMBench: Benchmarking Fairness in Multimodal Large Language Models on Medical Tasks: Advancements in Multimodal Large Language Models (MLLMs) have significantly improved medical task performance, such as Visual Question Answering (VQA) and Report Generation (RG). However, the fairness of these models across diverse demographic groups remains underexplored, despite its importance in healthcare. This oversight is partly due to the lack of demographic diversity in existing medical multimodal datasets, which complicates the evaluation of fairness. In response, we propose FMBench, the first benchmark designed to evaluate the fairness of MLLMs performance across diverse demographic attributes. FMBench has the following key features: 1: It includes four demographic attributes: race, ethnicity, language, and gender, across two tasks, VQA and RG, under zero-shot settings. 2: Our VQA task is free-form, enhancing real-world applicability and mitigating the biases associated with predefined choices. 3: We utilize both lexical metrics and LLM-based metrics, aligned with clinical evaluations, to assess models not only for linguistic accuracy but also from a clinical perspective. Furthermore, we introduce a new metric, Fairness-Aware Performance (FAP), to evaluate how fairly MLLMs perform across various demographic attributes. We thoroughly evaluate the performance and fairness of eight state-of-the-art open-source MLLMs, including both general and medical MLLMs, ranging from 7B to 26B parameters on the proposed benchmark. We aim for FMBench to assist the research community in refining model evaluation and driving future advancements in the field. All data and code will be released upon acceptance.<|reference_end|> | arxiv | @article{wu2024fmbench:,
title={FMBench: Benchmarking Fairness in Multimodal Large Language Models on
Medical Tasks},
author={Peiran Wu, Che Liu, Canyu Chen, Jun Li, Cosmin I. Bercea, Rossella
Arcucci},
journal={arXiv preprint arXiv:2410.01089},
year={2024},
archivePrefix={arXiv},
eprint={2410.01089},
primaryClass={cs.CV}
} | wu2024fmbench: |
arxiv-664265 | 2410.01091 | Efficient and Private Marginal Reconstruction with Local Non-Negativity | <|reference_start|>Efficient and Private Marginal Reconstruction with Local Non-Negativity: Differential privacy is the dominant standard for formal and quantifiable privacy and has been used in major deployments that impact millions of people. Many differentially private algorithms for query release and synthetic data contain steps that reconstruct answers to queries from answers to other queries measured by the mechanism. Reconstruction is an important subproblem for such mechanisms to economize the privacy budget, minimize error on reconstructed answers, and allow for scalability to high-dimensional datasets. In this paper, we introduce a principled and efficient postprocessing method ReM (Residuals-to-Marginals) for reconstructing answers to marginal queries. Our method builds on recent work on efficient mechanisms for marginal query release, based on making measurements using a residual query basis that admits efficient pseudoinversion, which is an important primitive used in reconstruction. An extension GReM-LNN (Gaussian Residuals-to-Marginals with Local Non-negativity) reconstructs marginals under Gaussian noise satisfying consistency and non-negativity, which often reduces error on reconstructed answers. We demonstrate the utility of ReM and GReM-LNN by applying them to improve existing private query answering mechanisms: ResidualPlanner and MWEM.<|reference_end|> | arxiv | @article{mullins2024efficient,
title={Efficient and Private Marginal Reconstruction with Local Non-Negativity},
author={Brett Mullins, Miguel Fuentes, Yingtai Xiao, Daniel Kifer, Cameron
Musco, Daniel Sheldon},
journal={arXiv preprint arXiv:2410.01091},
year={2024},
archivePrefix={arXiv},
eprint={2410.01091},
primaryClass={cs.LG cs.AI}
} | mullins2024efficient |
arxiv-664266 | 2410.01092 | Semantic Segmentation of Unmanned Aerial Vehicle Remote Sensing Images using SegFormer | <|reference_start|>Semantic Segmentation of Unmanned Aerial Vehicle Remote Sensing Images using SegFormer: The escalating use of Unmanned Aerial Vehicles (UAVs) as remote sensing platforms has garnered considerable attention, proving invaluable for ground object recognition. While satellite remote sensing images face limitations in resolution and weather susceptibility, UAV remote sensing, employing low-speed unmanned aircraft, offers enhanced object resolution and agility. The advent of advanced machine learning techniques has propelled significant strides in image analysis, particularly in semantic segmentation for UAV remote sensing images. This paper evaluates the effectiveness and efficiency of SegFormer, a semantic segmentation framework, for the semantic segmentation of UAV images. SegFormer variants, ranging from real-time (B0) to high-performance (B5) models, are assessed using the UAVid dataset tailored for semantic segmentation tasks. The research details the architecture and training procedures specific to SegFormer in the context of UAV semantic segmentation. Experimental results showcase the model's performance on benchmark dataset, highlighting its ability to accurately delineate objects and land cover features in diverse UAV scenarios, leading to both high efficiency and performance.<|reference_end|> | arxiv | @article{spasev2024semantic,
title={Semantic Segmentation of Unmanned Aerial Vehicle Remote Sensing Images
using SegFormer},
author={Vlatko Spasev, Ivica Dimitrovski, Ivan Chorbev, Ivan Kitanovski},
journal={arXiv preprint arXiv:2410.01092},
year={2024},
archivePrefix={arXiv},
eprint={2410.01092},
primaryClass={cs.CV}
} | spasev2024semantic |
arxiv-664267 | 2410.01093 | High-dimensional logistic regression with missing data: Imputation, regularization, and universality | <|reference_start|>High-dimensional logistic regression with missing data: Imputation, regularization, and universality: We study high-dimensional, ridge-regularized logistic regression in a setting in which the covariates may be missing or corrupted by additive noise. When both the covariates and the additive corruptions are independent and normally distributed, we provide exact characterizations of both the prediction error as well as the estimation error. Moreover, we show that these characterizations are universal: as long as the entries of the data matrix satisfy a set of independence and moment conditions, our guarantees continue to hold. Universality, in turn, enables the detailed study of several imputation-based strategies when the covariates are missing completely at random. We ground our study by comparing the performance of these strategies with the conjectured performance -- stemming from replica theory in statistical physics -- of the Bayes optimal procedure. Our analysis yields several insights including: (i) a distinction between single imputation and a simple variant of multiple imputation and (ii) that adding a simple ridge regularization term to single-imputed logistic regression can yield an estimator whose prediction error is nearly indistinguishable from the Bayes optimal prediction error. We supplement our findings with extensive numerical experiments.<|reference_end|> | arxiv | @article{verchand2024high-dimensional,
title={High-dimensional logistic regression with missing data: Imputation,
regularization, and universality},
author={Kabir Aladin Verchand, Andrea Montanari},
journal={arXiv preprint arXiv:2410.01093},
year={2024},
archivePrefix={arXiv},
eprint={2410.01093},
primaryClass={math.ST cs.LG stat.ML stat.TH}
} | verchand2024high-dimensional |
arxiv-664268 | 2410.01096 | Mechanic Maker: Accessible Game Development Via Symbolic Learning Program Synthesis | <|reference_start|>Mechanic Maker: Accessible Game Development Via Symbolic Learning Program Synthesis: Game development is a highly technical practice that traditionally requires programming skills. This serves as a barrier to entry for would-be developers or those hoping to use games as part of their creative expression. While there have been prior game development tools focused on accessibility, they generally still require programming, or have major limitations in terms of the kinds of games they can make. In this paper we introduce Mechanic Maker, a tool for creating a wide-range of game mechanics without programming. It instead relies on a backend symbolic learning system to synthesize game mechanics from examples. We conducted a user study to evaluate the benefits of the tool for participants with a variety of programming and game development experience. Our results demonstrated that participants' ability to use the tool was unrelated to programming ability. We conclude that tools like ours could help democratize game development, making the practice accessible regardless of programming skills.<|reference_end|> | arxiv | @article{sumner2024mechanic,
title={Mechanic Maker: Accessible Game Development Via Symbolic Learning
Program Synthesis},
author={Megan Sumner, Vardan Saini and Matthew Guzdial},
journal={arXiv preprint arXiv:2410.01096},
year={2024},
archivePrefix={arXiv},
eprint={2410.01096},
primaryClass={cs.HC cs.AI}
} | sumner2024mechanic |
arxiv-664269 | 2410.01098 | Generative AI Application for Building Industry | <|reference_start|>Generative AI Application for Building Industry: This paper investigates the transformative potential of generative AI technologies, particularly large language models (LLMs), within the building industry. By leveraging these advanced AI tools, the study explores their application across key areas such as energy code compliance, building design optimization, and workforce training. The research highlights how LLMs can automate labor-intensive processes, significantly improving efficiency, accuracy, and safety in building practices. The paper also addresses the challenges associated with interpreting complex visual and textual data in architectural plans and regulatory codes, proposing innovative solutions to enhance AI-driven compliance checking and design processes. Additionally, the study considers the broader implications of AI integration, including the development of AI-powered tools for comprehensive code compliance across various regulatory domains and the potential for AI to revolutionize workforce training through realistic simulations. This paper provides a comprehensive analysis of the current capabilities of generative AI in the building industry while outlining future directions for research and development, aiming to pave the way for smarter, more sustainable, and responsive construction practices.<|reference_end|> | arxiv | @article{wan2024generative,
title={Generative AI Application for Building Industry},
author={Hanlong Wan, Jian Zhang, Yan Chen, Weili Xu, Fan Feng},
journal={arXiv preprint arXiv:2410.01098},
year={2024},
number={PNNL-SA-203362},
archivePrefix={arXiv},
eprint={2410.01098},
primaryClass={cs.AI cs.SY eess.IV eess.SY}
} | wan2024generative |
arxiv-664270 | 2410.01100 | Unlocking Korean Verbs: A User-Friendly Exploration into the Verb Lexicon | <|reference_start|>Unlocking Korean Verbs: A User-Friendly Exploration into the Verb Lexicon: The Sejong dictionary dataset offers a valuable resource, providing extensive coverage of morphology, syntax, and semantic representation. This dataset can be utilized to explore linguistic information in greater depth. The labeled linguistic structures within this dataset form the basis for uncovering relationships between words and phrases and their associations with target verbs. This paper introduces a user-friendly web interface designed for the collection and consolidation of verb-related information, with a particular focus on subcategorization frames. Additionally, it outlines our efforts in mapping this information by aligning subcategorization frames with corresponding illustrative sentence examples. Furthermore, we provide a Python library that would simplify syntactic parsing and semantic role labeling. These tools are intended to assist individuals interested in harnessing the Sejong dictionary dataset to develop applications for Korean language processing.<|reference_end|> | arxiv | @article{song2024unlocking,
title={Unlocking Korean Verbs: A User-Friendly Exploration into the Verb
Lexicon},
author={Seohyun Song and Eunkyul Leah Jo and Yige Chen and Jeen-Pyo Hong and
Kyuwon Kim and Jin Wee and Miyoung Kang and KyungTae Lim and Jungyeul Park
and Chulwoo Park},
journal={arXiv preprint arXiv:2410.01100},
year={2024},
archivePrefix={arXiv},
eprint={2410.01100},
primaryClass={cs.CL}
} | song2024unlocking |
arxiv-664271 | 2410.01101 | Exploiting Structure in Offline Multi-Agent RL: The Benefits of Low Interaction Rank | <|reference_start|>Exploiting Structure in Offline Multi-Agent RL: The Benefits of Low Interaction Rank: We study the problem of learning an approximate equilibrium in the offline multi-agent reinforcement learning (MARL) setting. We introduce a structural assumption -- the interaction rank -- and establish that functions with low interaction rank are significantly more robust to distribution shift compared to general ones. Leveraging this observation, we demonstrate that utilizing function classes with low interaction rank, when combined with regularization and no-regret learning, admits decentralized, computationally and statistically efficient learning in offline MARL. Our theoretical results are complemented by experiments that showcase the potential of critic architectures with low interaction rank in offline MARL, contrasting with commonly used single-agent value decomposition architectures.<|reference_end|> | arxiv | @article{zhan2024exploiting,
title={Exploiting Structure in Offline Multi-Agent RL: The Benefits of Low
Interaction Rank},
author={Wenhao Zhan, Scott Fujimoto, Zheqing Zhu, Jason D. Lee, Daniel R.
Jiang, Yonathan Efroni},
journal={arXiv preprint arXiv:2410.01101},
year={2024},
archivePrefix={arXiv},
eprint={2410.01101},
primaryClass={cs.LG}
} | zhan2024exploiting |
arxiv-664272 | 2410.01102 | Exploring How Non-Prehensile Manipulation Expands Capability in Robots Experiencing Multi-Joint Failure | <|reference_start|>Exploring How Non-Prehensile Manipulation Expands Capability in Robots Experiencing Multi-Joint Failure: This work explores non-prehensile manipulation (NPM) and whole-body interaction as strategies for enabling robotic manipulators to conduct manipulation tasks despite experiencing locked multi-joint (LMJ) failures. LMJs are critical system faults where two or more joints become inoperable; they impose constraints on the robot's configuration and control spaces, consequently limiting the capability and reach of a prehensile-only approach. This approach involves three components: i) modeling the failure-constrained workspace of the robot, ii) generating a kinodynamic map of NPM actions within this workspace, and iii) a manipulation action planner that uses a sim-in-the-loop approach to select the best actions to take from the kinodynamic map. The experimental evaluation shows that our approach can increase the failure-constrained reachable area in LMJ cases by 79%. Further, it demonstrates the ability to complete real-world manipulation with up to 88.9% success when the end-effector is unusable and up to 100% success when it is usable.<|reference_end|> | arxiv | @article{briscoe-martinez2024exploring,
title={Exploring How Non-Prehensile Manipulation Expands Capability in Robots
Experiencing Multi-Joint Failure},
author={Gilberto Briscoe-Martinez, Anuj Pasricha, Ava Abderezaei, Santosh
Chaganti, Sarath Chandra Vajrala, Sri Kanth Popuri, and Alessandro Roncone},
journal={arXiv preprint arXiv:2410.01102},
year={2024},
archivePrefix={arXiv},
eprint={2410.01102},
primaryClass={cs.RO}
} | briscoe-martinez2024exploring |
arxiv-664273 | 2410.01103 | Approximately Aligned Decoding | <|reference_start|>Approximately Aligned Decoding: It is common to reject undesired outputs of Large Language Models (LLMs); however, current methods to do so require an excessive amount of computation, or severely distort the distribution of outputs. We present a method to balance the distortion of the output distribution with computational efficiency, allowing for the generation of long sequences of text with difficult-to-satisfy constraints, with less amplification of low probability outputs compared to existing methods. We show through a series of experiments that the task-specific performance of our method is comparable to methods that do not distort the output distribution, while being much more computationally efficient.<|reference_end|> | arxiv | @article{melcer2024approximately,
title={Approximately Aligned Decoding},
author={Daniel Melcer, Sujan Gonugondla, Pramuditha Perera, Haifeng Qian,
Wen-Hao Chiang, Yanjun Wang, Nihal Jain, Pranav Garg, Xiaofei Ma, Anoop
Deoras},
journal={arXiv preprint arXiv:2410.01103},
year={2024},
archivePrefix={arXiv},
eprint={2410.01103},
primaryClass={cs.CL cs.AI}
} | melcer2024approximately |
arxiv-664274 | 2410.01104 | softmax is not enough (for sharp out-of-distribution) | <|reference_start|>softmax is not enough (for sharp out-of-distribution): A key property of reasoning systems is the ability to make sharp decisions on their input data. For contemporary AI systems, a key carrier of sharp behaviour is the softmax function, with its capability to perform differentiable query-key lookups. It is a common belief that the predictive power of networks leveraging softmax arises from "circuits" which sharply perform certain kinds of computations consistently across many diverse inputs. However, for these circuits to be robust, they would need to generalise well to arbitrary valid inputs. In this paper, we dispel this myth: even for tasks as simple as finding the maximum key, any learned circuitry must disperse as the number of items grows at test time. We attribute this to a fundamental limitation of the softmax function to robustly approximate sharp functions, prove this phenomenon theoretically, and propose adaptive temperature as an ad-hoc technique for improving the sharpness of softmax at inference time.<|reference_end|> | arxiv | @article{veličković2024softmax,
title={softmax is not enough (for sharp out-of-distribution)},
author={Petar Veliv{c}kovi'c, Christos Perivolaropoulos, Federico Barbero,
Razvan Pascanu},
journal={arXiv preprint arXiv:2410.01104},
year={2024},
archivePrefix={arXiv},
eprint={2410.01104},
primaryClass={cs.LG cs.AI cs.IT math.IT}
} | veličković2024softmax |
arxiv-664275 | 2410.01105 | M2P2: A Multi-Modal Passive Perception Dataset for Off-Road Mobility in Extreme Low-Light Conditions | <|reference_start|>M2P2: A Multi-Modal Passive Perception Dataset for Off-Road Mobility in Extreme Low-Light Conditions: Long-duration, off-road, autonomous missions require robots to continuously perceive their surroundings regardless of the ambient lighting conditions. Most existing autonomy systems heavily rely on active sensing, e.g., LiDAR, RADAR, and Time-of-Flight sensors, or use (stereo) visible light imaging sensors, e.g., color cameras, to perceive environment geometry and semantics. In scenarios where fully passive perception is required and lighting conditions are degraded to an extent that visible light cameras fail to perceive, most downstream mobility tasks such as obstacle avoidance become impossible. To address such a challenge, this paper presents a Multi-Modal Passive Perception dataset, M2P2, to enable off-road mobility in low-light to no-light conditions. We design a multi-modal sensor suite including thermal, event, and stereo RGB cameras, GPS, two Inertia Measurement Units (IMUs), as well as a high-resolution LiDAR for ground truth, with a novel multi-sensor calibration procedure that can efficiently transform multi-modal perceptual streams into a common coordinate system. Our 10-hour, 32 km dataset also includes mobility data such as robot odometry and actions and covers well-lit, low-light, and no-light conditions, along with paved, on-trail, and off-trail terrain. Our results demonstrate that off-road mobility is possible through only passive perception in extreme low-light conditions using end-to-end learning and classical planning. The project website can be found at https://cs.gmu.edu/~xiao/Research/M2P2/<|reference_end|> | arxiv | @article{datar2024m2p2:,
title={M2P2: A Multi-Modal Passive Perception Dataset for Off-Road Mobility in
Extreme Low-Light Conditions},
author={Aniket Datar, Anuj Pokhrel, Mohammad Nazeri, Madhan B. Rao, Chenhui
Pan, Yufan Zhang, Andre Harrison, Maggie Wigness, Philip R. Osteen, Jinwei
Ye, and Xuesu Xiao},
journal={arXiv preprint arXiv:2410.01105},
year={2024},
archivePrefix={arXiv},
eprint={2410.01105},
primaryClass={cs.RO}
} | datar2024m2p2: |
arxiv-664276 | 2410.01106 | Embedding-based statistical inference on generative models | <|reference_start|>Embedding-based statistical inference on generative models: The recent cohort of publicly available generative models can produce human expert level content across a variety of topics and domains. Given a model in this cohort as a base model, methods such as parameter efficient fine-tuning, in-context learning, and constrained decoding have further increased generative capabilities and improved both computational and data efficiency. Entire collections of derivative models have emerged as a byproduct of these methods and each of these models has a set of associated covariates such as a score on a benchmark, an indicator for if the model has (or had) access to sensitive information, etc. that may or may not be available to the user. For some model-level covariates, it is possible to use "similar" models to predict an unknown covariate. In this paper we extend recent results related to embedding-based representations of generative models -- the data kernel perspective space -- to classical statistical inference settings. We demonstrate that using the perspective space as the basis of a notion of "similar" is effective for multiple model-level inference tasks.<|reference_end|> | arxiv | @article{helm2024embedding-based,
title={Embedding-based statistical inference on generative models},
author={Hayden Helm and Aranyak Acharyya and Brandon Duderstadt and Youngser
Park and Carey E. Priebe},
journal={arXiv preprint arXiv:2410.01106},
year={2024},
archivePrefix={arXiv},
eprint={2410.01106},
primaryClass={cs.LG stat.ML}
} | helm2024embedding-based |
arxiv-664277 | 2410.01107 | Count of Monte Crypto: Accounting-based Defenses for Cross-Chain Bridges | <|reference_start|>Count of Monte Crypto: Accounting-based Defenses for Cross-Chain Bridges: Between 2021 and 2023, crypto assets valued at over \$US2.6 billion were stolen via attacks on "bridges" -- decentralized services designed to allow inter-blockchain exchange. While the individual exploits in each attack vary, a single design flaw underlies them all: the lack of end-to-end value accounting in cross-chain transactions. In this paper, we empirically analyze twenty million transactions used by key bridges during this period. We show that a simple invariant that balances cross-chain inflows and outflows is compatible with legitimate use, yet precisely identifies every known attack (and several likely attacks) in this data. Further, we show that this approach is not only sufficient for post-hoc audits, but can be implemented in-line in existing bridge designs to provide generic protection against a broad array of bridge vulnerabilities.<|reference_end|> | arxiv | @article{liu2024count,
title={Count of Monte Crypto: Accounting-based Defenses for Cross-Chain Bridges},
author={Enze Liu, Elisa Luo, Jian Chen Yan, Katherine Izhikevich, Stewart
Grant, Deian Stefan, Geoffrey M Voelker, Stefan Savage},
journal={arXiv preprint arXiv:2410.01107},
year={2024},
archivePrefix={arXiv},
eprint={2410.01107},
primaryClass={cs.CR}
} | liu2024count |
arxiv-664278 | 2410.01108 | Augmentation through Laundering Attacks for Audio Spoof Detection | <|reference_start|>Augmentation through Laundering Attacks for Audio Spoof Detection: Recent text-to-speech (TTS) developments have made voice cloning (VC) more realistic, affordable, and easily accessible. This has given rise to many potential abuses of this technology, including Joe Biden's New Hampshire deepfake robocall. Several methodologies have been proposed to detect such clones. However, these methodologies have been trained and evaluated on relatively clean databases. Recently, ASVspoof 5 Challenge introduced a new crowd-sourced database of diverse acoustic conditions including various spoofing attacks and codec conditions. This paper is our submission to the ASVspoof 5 Challenge and aims to investigate the performance of Audio Spoof Detection, trained using data augmentation through laundering attacks, on the ASVSpoof 5 database. The results demonstrate that our system performs worst on A18, A19, A20, A26, and A30 spoofing attacks and in the codec and compression conditions of C08, C09, and C10.<|reference_end|> | arxiv | @article{ali2024augmentation,
title={Augmentation through Laundering Attacks for Audio Spoof Detection},
author={Hashim Ali, Surya Subramani, and Hafiz Malik},
journal={arXiv preprint arXiv:2410.01108},
year={2024},
archivePrefix={arXiv},
eprint={2410.01108},
primaryClass={eess.AS cs.AI cs.SD}
} | ali2024augmentation |
arxiv-664279 | 2410.01109 | Mixing It Up: The Cocktail Effect of Multi-Task Fine-Tuning on LLM Performance -- A Case Study in Finance | <|reference_start|>Mixing It Up: The Cocktail Effect of Multi-Task Fine-Tuning on LLM Performance -- A Case Study in Finance: The application of large language models (LLMs) in domain-specific contexts, including finance, has expanded rapidly. Domain-specific LLMs are typically evaluated based on their performance in various downstream tasks relevant to the domain. In this work, we present a detailed analysis of fine-tuning LLMs for such tasks. Somewhat counterintuitively, we find that in domain-specific cases, fine-tuning exclusively on the target task is not always the most effective strategy. Instead, multi-task fine-tuning - where models are trained on a cocktail of related tasks - can significantly enhance performance. We demonstrate how this approach enables a small model, such as Phi-3-Mini, to achieve state-of-the-art results, even surpassing the much larger GPT-4-o model on financial benchmarks. Our study involves a large-scale experiment, training over 200 models using several widely adopted LLMs as baselines, and empirically confirms the benefits of multi-task fine-tuning. Additionally, we explore the use of general instruction data as a form of regularization, suggesting that it helps minimize performance degradation. We also investigate the inclusion of mathematical data, finding improvements in numerical reasoning that transfer effectively to financial tasks. Finally, we note that while fine-tuning for downstream tasks leads to targeted improvements in task performance, it does not necessarily result in broader gains in domain knowledge or complex domain reasoning abilities.<|reference_end|> | arxiv | @article{brief2024mixing,
title={Mixing It Up: The Cocktail Effect of Multi-Task Fine-Tuning on LLM
Performance -- A Case Study in Finance},
author={Meni Brief, Oded Ovadia, Gil Shenderovitz, Noga Ben Yoash, Rachel
Lemberg, Eitam Sheetrit},
journal={arXiv preprint arXiv:2410.01109},
year={2024},
archivePrefix={arXiv},
eprint={2410.01109},
primaryClass={cs.AI cs.CE cs.CL}
} | brief2024mixing |
arxiv-664280 | 2410.01110 | RobustEMD: Domain Robust Matching for Cross-domain Few-shot Medical Image Segmentation | <|reference_start|>RobustEMD: Domain Robust Matching for Cross-domain Few-shot Medical Image Segmentation: Few-shot medical image segmentation (FSMIS) aims to perform the limited annotated data learning in the medical image analysis scope. Despite the progress has been achieved, current FSMIS models are all trained and deployed on the same data domain, as is not consistent with the clinical reality that medical imaging data is always across different data domains (e.g. imaging modalities, institutions and equipment sequences). How to enhance the FSMIS models to generalize well across the different specific medical imaging domains? In this paper, we focus on the matching mechanism of the few-shot semantic segmentation models and introduce an Earth Mover's Distance (EMD) calculation based domain robust matching mechanism for the cross-domain scenario. Specifically, we formulate the EMD transportation process between the foreground support-query features, the texture structure aware weights generation method, which proposes to perform the sobel based image gradient calculation over the nodes, is introduced in the EMD matching flow to restrain the domain relevant nodes. Besides, the point set level distance measurement metric is introduced to calculated the cost for the transportation from support set nodes to query set nodes. To evaluate the performance of our model, we conduct experiments on three scenarios (i.e., cross-modal, cross-sequence and cross-institution), which includes eight medical datasets and involves three body regions, and the results demonstrate that our model achieves the SoTA performance against the compared models.<|reference_end|> | arxiv | @article{zhu2024robustemd:,
title={RobustEMD: Domain Robust Matching for Cross-domain Few-shot Medical
Image Segmentation},
author={Yazhou Zhu, Minxian Li, Qiaolin Ye, Shidong Wang, Tong Xin, Haofeng
Zhang},
journal={arXiv preprint arXiv:2410.01110},
year={2024},
archivePrefix={arXiv},
eprint={2410.01110},
primaryClass={cs.CV}
} | zhu2024robustemd: |
arxiv-664281 | 2410.01111 | Learning to Build by Building Your Own Instructions | <|reference_start|>Learning to Build by Building Your Own Instructions: Structural understanding of complex visual objects is an important unsolved component of artificial intelligence. To study this, we develop a new technique for the recently proposed Break-and-Make problem in LTRON where an agent must learn to build a previously unseen LEGO assembly using a single interactive session to gather information about its components and their structure. We attack this problem by building an agent that we call \textbf{\ours} that is able to make its own visual instruction book. By disassembling an unseen assembly and periodically saving images of it, the agent is able to create a set of instructions so that it has the information necessary to rebuild it. These instructions form an explicit memory that allows the model to reason about the assembly process one step at a time, avoiding the need for long-term implicit memory. This in turn allows us to train on much larger LEGO assemblies than has been possible in the past. To demonstrate the power of this model, we release a new dataset of procedurally built LEGO vehicles that contain an average of 31 bricks each and require over one hundred steps to disassemble and reassemble. We train these models using online imitation learning which allows the model to learn from its own mistakes. Finally, we also provide some small improvements to LTRON and the Break-and-Make problem that simplify the learning environment and improve usability.<|reference_end|> | arxiv | @article{walsman2024learning,
title={Learning to Build by Building Your Own Instructions},
author={Aaron Walsman, Muru Zhang, Adam Fishman, Ali Farhadi, Dieter Fox},
journal={arXiv preprint arXiv:2410.01111},
year={2024},
archivePrefix={arXiv},
eprint={2410.01111},
primaryClass={cs.AI cs.RO}
} | walsman2024learning |
arxiv-664282 | 2410.01112 | Almost Free: Self-concordance in Natural Exponential Families and an Application to Bandits | <|reference_start|>Almost Free: Self-concordance in Natural Exponential Families and an Application to Bandits: We prove that single-parameter natural exponential families with subexponential tails are self-concordant with polynomial-sized parameters. For subgaussian natural exponential families we establish an exact characterization of the growth rate of the self-concordance parameter. Applying these findings to bandits allows us to fill gaps in the literature: We show that optimistic algorithms for generalized linear bandits enjoy regret bounds that are both second-order (scale with the variance of the optimal arm's reward distribution) and free of an exponential dependence on the bound of the problem parameter in the leading term. To the best of our knowledge, ours is the first regret bound for generalized linear bandits with subexponential tails, broadening the class of problems to include Poisson, exponential and gamma bandits.<|reference_end|> | arxiv | @article{liu2024almost,
title={Almost Free: Self-concordance in Natural Exponential Families and an
Application to Bandits},
author={Shuai Liu, Alex Ayoub, Flore Sentenac, Xiaoqi Tan, Csaba Szepesv'ari},
journal={arXiv preprint arXiv:2410.01112},
year={2024},
archivePrefix={arXiv},
eprint={2410.01112},
primaryClass={cs.LG stat.ML}
} | liu2024almost |
arxiv-664283 | 2410.01118 | Sparse Actuation for LPV Systems with Full-State Feedback in $\mathcalH_2/\mathcalH_\infty$ Framework | <|reference_start|>Sparse Actuation for LPV Systems with Full-State Feedback in $\mathcalH_2/\mathcalH_\infty$ Framework: This paper addresses the sparse actuation problem for nonlinear systems represented in the Linear Parameter-Varying (LPV) form. We propose a convex optimization framework that concurrently determines actuator magnitude limits and the state-feedback law that guarantees a user-specified closed-loop performance in the $\mathcal{H}_2/\mathcal{H}_\infty$ sense. We also demonstrate that sparse actuation is achieved when the actuator magnitude-limits are minimized in the $l_1$ sense. This is the first paper that addresses this problem for LPV systems. The formulation is demonstrated in a vibration control problem for a flexible wing.<|reference_end|> | arxiv | @article{kumar2024sparse,
title={Sparse Actuation for LPV Systems with Full-State Feedback in
$\mathcal{H}_2/\mathcal{H}_\infty$ Framework},
author={Tanay Kumar, Raktim Bhattacharya},
journal={arXiv preprint arXiv:2410.01118},
year={2024},
archivePrefix={arXiv},
eprint={2410.01118},
primaryClass={eess.SY cs.SY math.OC}
} | kumar2024sparse |
arxiv-664284 | 2410.01124 | Synthetic imagery for fuzzy object detection: A comparative study | <|reference_start|>Synthetic imagery for fuzzy object detection: A comparative study: The fuzzy object detection is a challenging field of research in computer vision (CV). Distinguishing between fuzzy and non-fuzzy object detection in CV is important. Fuzzy objects such as fire, smoke, mist, and steam present significantly greater complexities in terms of visual features, blurred edges, varying shapes, opacity, and volume compared to non-fuzzy objects such as trees and cars. Collection of a balanced and diverse dataset and accurate annotation is crucial to achieve better ML models for fuzzy objects, however, the task of collection and annotation is still highly manual. In this research, we propose and leverage an alternative method of generating and automatically annotating fully synthetic fire images based on 3D models for training an object detection model. Moreover, the performance, and efficiency of the trained ML models on synthetic images is compared with ML models trained on real imagery and mixed imagery. Findings proved the effectiveness of the synthetic data for fire detection, while the performance improves as the test dataset covers a broader spectrum of real fires. Our findings illustrates that when synthetic imagery and real imagery is utilized in a mixed training set the resulting ML model outperforms models trained on real imagery as well as models trained on synthetic imagery for detection of a broad spectrum of fires. The proposed method for automating the annotation of synthetic fuzzy objects imagery carries substantial implications for reducing both time and cost in creating computer vision models specifically tailored for detecting fuzzy objects.<|reference_end|> | arxiv | @article{khajavi2024synthetic,
title={Synthetic imagery for fuzzy object detection: A comparative study},
author={Siavash H. Khajavi, Mehdi Moshtaghi, Dikai Yu, Zixuan Liu, Kary
Fr"amling, Jan Holmstr"om},
journal={arXiv preprint arXiv:2410.01124},
year={2024},
archivePrefix={arXiv},
eprint={2410.01124},
primaryClass={cs.CV}
} | khajavi2024synthetic |
arxiv-664285 | 2410.01128 | Using Interleaved Ensemble Unlearning to Keep Backdoors at Bay for Finetuning Vision Transformers | <|reference_start|>Using Interleaved Ensemble Unlearning to Keep Backdoors at Bay for Finetuning Vision Transformers: Vision Transformers (ViTs) have become popular in computer vision tasks. Backdoor attacks, which trigger undesirable behaviours in models during inference, threaten ViTs' performance, particularly in security-sensitive tasks. Although backdoor defences have been developed for Convolutional Neural Networks (CNNs), they are less effective for ViTs, and defences tailored to ViTs are scarce. To address this, we present Interleaved Ensemble Unlearning (IEU), a method for finetuning clean ViTs on backdoored datasets. In stage 1, a shallow ViT is finetuned to have high confidence on backdoored data and low confidence on clean data. In stage 2, the shallow ViT acts as a ``gate'' to block potentially poisoned data from the defended ViT. This data is added to an unlearn set and asynchronously unlearned via gradient ascent. We demonstrate IEU's effectiveness on three datasets against 11 state-of-the-art backdoor attacks and show its versatility by applying it to different model architectures.<|reference_end|> | arxiv | @article{li2024using,
title={Using Interleaved Ensemble Unlearning to Keep Backdoors at Bay for
Finetuning Vision Transformers},
author={Zeyu Michael Li},
journal={arXiv preprint arXiv:2410.01128},
year={2024},
archivePrefix={arXiv},
eprint={2410.01128},
primaryClass={cs.CV cs.LG}
} | li2024using |
arxiv-664286 | 2410.01129 | The Value of Compromising Strategic Intent in General Lotto Games | <|reference_start|>The Value of Compromising Strategic Intent in General Lotto Games: Resource allocation in adversarial environments is a fundamental challenge across various domains, from corporate competition to military strategy. This article examines the impact of compromising an opponent's strategic intent in the context of General Lotto games, a class of resource allocation problems. We consider a scenario where one player, termed the "Breaker", has access to partial information about their opponent's strategy through a binary sensor. This sensor reveals whether the opponent's allocated resources exceed a certain threshold. Our analysis provides a comprehensive characterization of equilibrium strategies and payoffs for both players under this information structure. Through numerical studies, we demonstrate that the information provided by the sensor can significantly improve the Breaker's performance.<|reference_end|> | arxiv | @article{diaz-garcia2024the,
title={The Value of Compromising Strategic Intent in General Lotto Games},
author={Gilberto Diaz-Garcia, Keith Paarporn, Jason R. Marden},
journal={arXiv preprint arXiv:2410.01129},
year={2024},
archivePrefix={arXiv},
eprint={2410.01129},
primaryClass={cs.GT}
} | diaz-garcia2024the |
arxiv-664287 | 2410.01130 | H-DES: a Quantum-Classical Hybrid Differential Equation Solver | <|reference_start|>H-DES: a Quantum-Classical Hybrid Differential Equation Solver: In this article, we introduce an original hybrid quantum-classical algorithm based on a variational quantum algorithm for solving systems of differential equations. The algorithm relies on a spectral method, which involves encoding the solution functions in the amplitudes of the quantum states generated by different parametrized circuits and transforms the task of solving the differential equations into an optimization problem. We first describe the principle of the algorithm from a theoretical point of view. We provide a detailed pseudo-code of the algorithm, on which we conduct a complexity analysis to highlight its scaling properties. We apply it to a set of examples, showcasing its applicability across diverse sets of differential equations. We discuss the advantages of our method and potential avenues for further exploration and refinement.<|reference_end|> | arxiv | @article{jaffali2024h-des:,
title={H-DES: a Quantum-Classical Hybrid Differential Equation Solver},
author={Hamza Jaffali, Jonas Bastos de Araujo, Nadia Milazzo, Marta Reina,
Henri de Boutray, Karla Baumann, Fr'ed'eric Holweck},
journal={arXiv preprint arXiv:2410.01130},
year={2024},
archivePrefix={arXiv},
eprint={2410.01130},
primaryClass={quant-ph cs.NA math.AP math.NA math.OC}
} | jaffali2024h-des: |
arxiv-664288 | 2410.01131 | nGPT: Normalized Transformer with Representation Learning on the Hypersphere | <|reference_start|>nGPT: Normalized Transformer with Representation Learning on the Hypersphere: We propose a novel neural network architecture, the normalized Transformer (nGPT) with representation learning on the hypersphere. In nGPT, all vectors forming the embeddings, MLP, attention matrices and hidden states are unit norm normalized. The input stream of tokens travels on the surface of a hypersphere, with each layer contributing a displacement towards the target output predictions. These displacements are defined by the MLP and attention blocks, whose vector components also reside on the same hypersphere. Experiments show that nGPT learns much faster, reducing the number of training steps required to achieve the same accuracy by a factor of 4 to 20, depending on the sequence length.<|reference_end|> | arxiv | @article{loshchilov2024ngpt:,
title={nGPT: Normalized Transformer with Representation Learning on the
Hypersphere},
author={Ilya Loshchilov, Cheng-Ping Hsieh, Simeng Sun, Boris Ginsburg},
journal={arXiv preprint arXiv:2410.01131},
year={2024},
archivePrefix={arXiv},
eprint={2410.01131},
primaryClass={cs.LG cs.AI}
} | loshchilov2024ngpt: |
arxiv-664289 | 2410.01137 | Explain Like I'm Five: Using LLMs to Improve PDE Surrogate Models with Text | <|reference_start|>Explain Like I'm Five: Using LLMs to Improve PDE Surrogate Models with Text: Solving Partial Differential Equations (PDEs) is ubiquitous in science and engineering. Computational complexity and difficulty in writing numerical solvers has motivated the development of machine learning techniques to generate solutions quickly. Many existing methods are purely data driven, relying solely on numerical solution fields, rather than known system information such as boundary conditions and governing equations. However, the recent rise in popularity of Large Language Models (LLMs) has enabled easy integration of text in multimodal machine learning models. In this work, we use pretrained LLMs to integrate various amounts known system information into PDE learning. Our multimodal approach significantly outperforms our baseline model, FactFormer, in both next-step prediction and autoregressive rollout performance on the 2D Heat, Burgers, Navier-Stokes, and Shallow Water equations. Further analysis shows that pretrained LLMs provide highly structured latent space that is consistent with the amount of system information provided through text.<|reference_end|> | arxiv | @article{lorsung2024explain,
title={Explain Like I'm Five: Using LLMs to Improve PDE Surrogate Models with
Text},
author={Cooper Lorsung, Amir Barati Farimani},
journal={arXiv preprint arXiv:2410.01137},
year={2024},
archivePrefix={arXiv},
eprint={2410.01137},
primaryClass={cs.LG physics.comp-ph}
} | lorsung2024explain |
arxiv-664290 | 2410.01138 | Impact of Knowledge Silos on Responsible AI Practices in Journalism | <|reference_start|>Impact of Knowledge Silos on Responsible AI Practices in Journalism: The effective adoption of responsible AI practices in journalism requires a concerted effort to bridge different perspectives, including technological, editorial, journalistic, and managerial. Among the many challenges that could impact information sharing around responsible AI inside news organizations are knowledge silos, where information is isolated within one part of the organization and not easily shared with others. This study aims to explore if, and if so, how, knowledge silos affect the adoption of responsible AI practices in journalism through a cross-case study of four major Dutch media outlets. We examine the individual and organizational barriers to AI knowledge sharing and the extent to which knowledge silos could impede the operationalization of responsible AI initiatives inside newsrooms. To address this question, we conducted 14 semi-structured interviews with editors, managers, and journalists at de Telegraaf, de Volkskrant, the Nederlandse Omroep Stichting (NOS), and RTL Nederland. The interviews aimed to uncover insights into the existence of knowledge silos, their effects on responsible AI practice adoption, and the organizational practices influencing these dynamics. Our results emphasize the importance of creating better structures for sharing information on AI across all layers of news organizations.<|reference_end|> | arxiv | @article{dodds2024the,
title={The Impact of Knowledge Silos on Responsible AI Practices in Journalism},
author={Tom'as Dodds, Astrid Vandendaele, Felix M. Simon, Natali Helberger,
Valeria Resendez, Wang Ngai Yeung},
journal={arXiv preprint arXiv:2410.01138},
year={2024},
archivePrefix={arXiv},
eprint={2410.01138},
primaryClass={cs.CY}
} | dodds2024the |
arxiv-664291 | 2410.01140 | A simple linear convergence analysis of the reshuffling Kaczmarz method | <|reference_start|>A simple linear convergence analysis of the reshuffling Kaczmarz method: The Kaczmarz method and its variants, which are types of stochastic gradient descent (SGD) methods, have been extensively studied for their simplicity and efficiency in solving linear systems. Random reshuffling (RR), also known as SGD without replacement, is typically faster in practice than traditional SGD method. Although some convergence analysis results for RR apply to the reshuffling Kaczmarz method, they do not comprehensively characterize its convergence. In this paper, we present a new convergence analysis of the reshuffling Kaczmarz method and demonstrate that it can converge linearly to the unique least-norm solution of the linear system. Furthermore, the convergence upper bound is tight and does not depend on the dimension of the coefficient matrix.<|reference_end|> | arxiv | @article{han2024a,
title={A simple linear convergence analysis of the reshuffling Kaczmarz method},
author={Deren Han and Jiaxin Xie},
journal={arXiv preprint arXiv:2410.01140},
year={2024},
archivePrefix={arXiv},
eprint={2410.01140},
primaryClass={math.NA cs.NA}
} | han2024a |
arxiv-664292 | 2410.01141 | Evaluating Deduplication Techniques for Economic Research Paper Titles with a Focus on Semantic Similarity using NLP and LLMs | <|reference_start|>Evaluating Deduplication Techniques for Economic Research Paper Titles with a Focus on Semantic Similarity using NLP and LLMs: This study investigates efficient deduplication techniques for a large NLP dataset of economic research paper titles. We explore various pairing methods alongside established distance measures (Levenshtein distance, cosine similarity) and a sBERT model for semantic evaluation. Our findings suggest a potentially low prevalence of duplicates based on the observed semantic similarity across different methods. Further exploration with a human-annotated ground truth set is completed for a more conclusive assessment. The result supports findings from the NLP, LLM based distance metrics.<|reference_end|> | arxiv | @article{you2024evaluating,
title={Evaluating Deduplication Techniques for Economic Research Paper Titles
with a Focus on Semantic Similarity using NLP and LLMs},
author={Doohee You, Karim Lasri, Samuel Fraiberger},
journal={arXiv preprint arXiv:2410.01141},
year={2024},
archivePrefix={arXiv},
eprint={2410.01141},
primaryClass={cs.CL cs.AI}
} | you2024evaluating |
arxiv-664293 | 2410.01143 | StraightTrack: Towards Mixed Reality Navigation System for Percutaneous K-wire Insertion | <|reference_start|>StraightTrack: Towards Mixed Reality Navigation System for Percutaneous K-wire Insertion: In percutaneous pelvic trauma surgery, accurate placement of Kirschner wires (K-wires) is crucial to ensure effective fracture fixation and avoid complications due to breaching the cortical bone along an unsuitable trajectory. Surgical navigation via mixed reality (MR) can help achieve precise wire placement in a low-profile form factor. Current approaches in this domain are as yet unsuitable for real-world deployment because they fall short of guaranteeing accurate visual feedback due to uncontrolled bending of the wire. To ensure accurate feedback, we introduce StraightTrack, an MR navigation system designed for percutaneous wire placement in complex anatomy. StraightTrack features a marker body equipped with a rigid access cannula that mitigates wire bending due to interactions with soft tissue and a covered bony surface. Integrated with an Optical See-Through Head-Mounted Display (OST HMD) capable of tracking the cannula body, StraightTrack offers real-time 3D visualization and guidance without external trackers, which are prone to losing line-of-sight. In phantom experiments with two experienced orthopedic surgeons, StraightTrack improves wire placement accuracy, achieving the ideal trajectory within $5.26 \pm 2.29$ mm and $2.88 \pm 1.49$ degree, compared to over 12.08 mm and 4.07 degree for comparable methods. As MR navigation systems continue to mature, StraightTrack realizes their potential for internal fracture fixation and other percutaneous orthopedic procedures.<|reference_end|> | arxiv | @article{zhang2024straighttrack:,
title={StraightTrack: Towards Mixed Reality Navigation System for Percutaneous
K-wire Insertion},
author={Han Zhang, Benjamin D. Killeen, Yu-Chun Ku, Lalithkumar Seenivasan,
Yuxuan Zhao, Mingxu Liu, Yue Yang, Suxi Gu, Alejandro Martin-Gomez, Russell
H. Taylor, Greg Osgood, and Mathias Unberath},
journal={arXiv preprint arXiv:2410.01143},
year={2024},
archivePrefix={arXiv},
eprint={2410.01143},
primaryClass={cs.RO}
} | zhang2024straighttrack: |
arxiv-664294 | 2410.01144 | Uncertainty-Guided Enhancement on Driving Perception System via Foundation Models | <|reference_start|>Uncertainty-Guided Enhancement on Driving Perception System via Foundation Models: Multimodal foundation models offer promising advancements for enhancing driving perception systems, but their high computational and financial costs pose challenges. We develop a method that leverages foundation models to refine predictions from existing driving perception models -- such as enhancing object classification accuracy -- while minimizing the frequency of using these resource-intensive models. The method quantitatively characterizes uncertainties in the perception model's predictions and engages the foundation model only when these uncertainties exceed a pre-specified threshold. Specifically, it characterizes uncertainty by calibrating the perception model's confidence scores into theoretical lower bounds on the probability of correct predictions using conformal prediction. Then, it sends images to the foundation model and queries for refining the predictions only if the theoretical bound of the perception model's outcome is below the threshold. Additionally, we propose a temporal inference mechanism that enhances prediction accuracy by integrating historical predictions, leading to tighter theoretical bounds. The method demonstrates a 10 to 15 percent improvement in prediction accuracy and reduces the number of queries to the foundation model by 50 percent, based on quantitative evaluations from driving datasets.<|reference_end|> | arxiv | @article{yang2024uncertainty-guided,
title={Uncertainty-Guided Enhancement on Driving Perception System via
Foundation Models},
author={Yunhao Yang, Yuxin Hu, Mao Ye, Zaiwei Zhang, Zhichao Lu, Yi Xu, Ufuk
Topcu, Ben Snyder},
journal={arXiv preprint arXiv:2410.01144},
year={2024},
archivePrefix={arXiv},
eprint={2410.01144},
primaryClass={cs.CV}
} | yang2024uncertainty-guided |
arxiv-664295 | 2410.01145 | ProxiMix: Enhancing Fairness with Proximity Samples in Subgroups | <|reference_start|>ProxiMix: Enhancing Fairness with Proximity Samples in Subgroups: Many bias mitigation methods have been developed for addressing fairness issues in machine learning. We found that using linear mixup alone, a data augmentation technique, for bias mitigation, can still retain biases present in dataset labels. Research presented in this paper aims to address this issue by proposing a novel pre-processing strategy in which both an existing mixup method and our new bias mitigation algorithm can be utilized to improve the generation of labels of augmented samples, which are proximity aware. Specifically, we proposed ProxiMix which keeps both pairwise and proximity relationships for fairer data augmentation. We conducted thorough experiments with three datasets, three ML models, and different hyperparameters settings. Our experimental results showed the effectiveness of ProxiMix from both fairness of predictions and fairness of recourse perspectives.<|reference_end|> | arxiv | @article{hu2024proximix:,
title={ProxiMix: Enhancing Fairness with Proximity Samples in Subgroups},
author={Jingyu Hu, Jun Hong, Mengnan Du, Weiru Liu},
journal={arXiv preprint arXiv:2410.01145},
year={2024},
archivePrefix={arXiv},
eprint={2410.01145},
primaryClass={cs.LG cs.AI}
} | hu2024proximix: |
arxiv-664296 | 2410.01148 | Automatic Image Unfolding and Stitching Framework for Esophageal Lining Video Based on Density-Weighted Feature Matching | <|reference_start|>Automatic Image Unfolding and Stitching Framework for Esophageal Lining Video Based on Density-Weighted Feature Matching: Endoscopy is a crucial tool for diagnosing the gastrointestinal tract, but its effectiveness is often limited by a narrow field of view and the dynamic nature of the internal environment, especially in the esophagus, where complex and repetitive patterns make image stitching challenging. This paper introduces a novel automatic image unfolding and stitching framework tailored for esophageal videos captured during endoscopy. The method combines feature matching algorithms, including LoFTR, SIFT, and ORB, to create a feature filtering pool and employs a Density-Weighted Homography Optimization (DWHO) algorithm to enhance stitching accuracy. By merging consecutive frames, the framework generates a detailed panoramic view of the esophagus, enabling thorough and accurate visual analysis. Experimental results show the framework achieves low Root Mean Square Error (RMSE) and high Structural Similarity Index (SSIM) across extensive video sequences, demonstrating its potential for clinical use and improving the quality and continuity of endoscopic visual data.<|reference_end|> | arxiv | @article{li2024automatic,
title={Automatic Image Unfolding and Stitching Framework for Esophageal Lining
Video Based on Density-Weighted Feature Matching},
author={Muyang Li, Juming Xiong, Ruining Deng, Tianyuan Yao, Regina N Tyree,
Girish Hiremath, Yuankai Huo},
journal={arXiv preprint arXiv:2410.01148},
year={2024},
archivePrefix={arXiv},
eprint={2410.01148},
primaryClass={cs.CV}
} | li2024automatic |
arxiv-664297 | 2410.01149 | Recovering Manifold Structure Using Ollivier-Ricci Curvature | <|reference_start|>Recovering Manifold Structure Using Ollivier-Ricci Curvature: We introduce ORC-ManL, a new algorithm to prune spurious edges from nearest neighbor graphs using a criterion based on Ollivier-Ricci curvature and estimated metric distortion. Our motivation comes from manifold learning: we show that when the data generating the nearest-neighbor graph consists of noisy samples from a low-dimensional manifold, edges that shortcut through the ambient space have more negative Ollivier-Ricci curvature than edges that lie along the data manifold. We demonstrate that our method outperforms alternative pruning methods and that it significantly improves performance on many downstream geometric data analysis tasks that use nearest neighbor graphs as input. Specifically, we evaluate on manifold learning, persistent homology, dimension estimation, and others. We also show that ORC-ManL can be used to improve clustering and manifold learning of single-cell RNA sequencing data. Finally, we provide empirical convergence experiments that support our theoretical findings.<|reference_end|> | arxiv | @article{saidi2024recovering,
title={Recovering Manifold Structure Using Ollivier-Ricci Curvature},
author={Tristan Luca Saidi, Abigail Hickok, Andrew J. Blumberg},
journal={arXiv preprint arXiv:2410.01149},
year={2024},
archivePrefix={arXiv},
eprint={2410.01149},
primaryClass={cs.LG cs.AI cs.CG}
} | saidi2024recovering |
arxiv-664298 | 2410.01150 | Restorative Speech Enhancement: A Progressive Approach Using SE and Codec Modules | <|reference_start|>Restorative Speech Enhancement: A Progressive Approach Using SE and Codec Modules: In challenging environments with significant noise and reverberation, traditional speech enhancement (SE) methods often lead to over-suppressed speech, creating artifacts during listening and harming downstream tasks performance. To overcome these limitations, we propose a novel approach called Restorative SE (RestSE), which combines a lightweight SE module with a generative codec module to progressively enhance and restore speech quality. The SE module initially reduces noise, while the codec module subsequently performs dereverberation and restores speech using generative capabilities. We systematically explore various quantization techniques within the codec module to optimize performance. Additionally, we introduce a weighted loss function and feature fusion that merges the SE output with the original mixture, particularly at segments where the SE output is heavily distorted. Experimental results demonstrate the effectiveness of our proposed method in enhancing speech quality under adverse conditions. Audio demos are available at: https://sophie091524.github.io/RestorativeSE/.<|reference_end|> | arxiv | @article{chiang2024restorative,
title={Restorative Speech Enhancement: A Progressive Approach Using SE and
Codec Modules},
author={Hsin-Tien Chiang, Hao Zhang, Yong Xu, Meng Yu, Dong Yu},
journal={arXiv preprint arXiv:2410.01150},
year={2024},
archivePrefix={arXiv},
eprint={2410.01150},
primaryClass={eess.AS cs.SD}
} | chiang2024restorative |
arxiv-664299 | 2410.01153 | Text2PDE: Latent Diffusion Models for Accessible Physics Simulation | <|reference_start|>Text2PDE: Latent Diffusion Models for Accessible Physics Simulation: Recent advances in deep learning have inspired numerous works on data-driven solutions to partial differential equation (PDE) problems. These neural PDE solvers can often be much faster than their numerical counterparts; however, each presents its unique limitations and generally balances training cost, numerical accuracy, and ease of applicability to different problem setups. To address these limitations, we introduce several methods to apply latent diffusion models to physics simulation. Firstly, we introduce a mesh autoencoder to compress arbitrarily discretized PDE data, allowing for efficient diffusion training across various physics. Furthermore, we investigate full spatio-temporal solution generation to mitigate autoregressive error accumulation. Lastly, we investigate conditioning on initial physical quantities, as well as conditioning solely on a text prompt to introduce text2PDE generation. We show that language can be a compact, interpretable, and accurate modality for generating physics simulations, paving the way for more usable and accessible PDE solvers. Through experiments on both uniform and structured grids, we show that the proposed approach is competitive with current neural PDE solvers in both accuracy and efficiency, with promising scaling behavior up to $\sim$3 billion parameters. By introducing a scalable, accurate, and usable physics simulator, we hope to bring neural PDE solvers closer to practical use.<|reference_end|> | arxiv | @article{zhou2024text2pde:,
title={Text2PDE: Latent Diffusion Models for Accessible Physics Simulation},
author={Anthony Zhou, Zijie Li, Michael Schneier, John R Buchanan Jr, Amir
Barati Farimani},
journal={arXiv preprint arXiv:2410.01153},
year={2024},
archivePrefix={arXiv},
eprint={2410.01153},
primaryClass={cs.LG}
} | zhou2024text2pde: |
arxiv-664300 | 2410.01154 | Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting | <|reference_start|>Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting: Recent research in zero-shot Relation Extraction (RE) has focused on using Large Language Models (LLMs) due to their impressive zero-shot capabilities. However, current methods often perform suboptimally, mainly due to a lack of detailed, context-specific prompts needed for understanding various sentences and relations. To address this, we introduce the Self-Prompting framework, a novel method designed to fully harness the embedded RE knowledge within LLMs. Specifically, our framework employs a three-stage diversity approach to prompt LLMs, generating multiple synthetic samples that encapsulate specific relations from scratch. These generated samples act as in-context learning samples, offering explicit and context-specific guidance to efficiently prompt LLMs for RE. Experimental evaluations on benchmark datasets show our approach outperforms existing LLM-based zero-shot RE methods. Additionally, our experiments confirm the effectiveness of our generation pipeline in producing high-quality synthetic data that enhances performance.<|reference_end|> | arxiv | @article{liu2024unleashing,
title={Unleashing the Power of Large Language Models in Zero-shot Relation
Extraction via Self-Prompting},
author={Siyi Liu, Yang Li, Jiang Li, Shan Yang, Yunshi Lan},
journal={arXiv preprint arXiv:2410.01154},
year={2024},
archivePrefix={arXiv},
eprint={2410.01154},
primaryClass={cs.IR cs.CL}
} | liu2024unleashing |
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