id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.05531 | CRC-Assisted Channel Codes for Integrated Passive Sensing and
Communications | [
"eess.SP",
"cs.IT",
"math.IT"
] | We propose a novel coded integrated passive sensing and communication (CIPSAC) system with orthogonal frequency division multiplexing (OFDM), where a multi-antenna base station (BS) passively senses the parameters of the targets and decodes the information bit sequences transmitted by a user. The transmitted signal is ... | {
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2411.05536 | Towards Active Flow Control Strategies Through Deep Reinforcement
Learning | [
"cs.LG",
"physics.flu-dyn"
] | This paper presents a deep reinforcement learning (DRL) framework for active flow control (AFC) to reduce drag in aerodynamic bodies. Tested on a 3D cylinder at Re = 100, the DRL approach achieved a 9.32% drag reduction and a 78.4% decrease in lift oscillations by learning advanced actuation strategies. The methodology... | {
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2411.05540 | CRepair: CVAE-based Automatic Vulnerability Repair Technology | [
"cs.SE",
"cs.AI"
] | Software vulnerabilities are flaws in computer software systems that pose significant threats to the integrity, security, and reliability of modern software and its application data. These vulnerabilities can lead to substantial economic losses across various industries. Manual vulnerability repair is not only time-con... | {
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2411.05544 | Towards Lifelong Few-Shot Customization of Text-to-Image Diffusion | [
"cs.CV",
"cs.LG"
] | Lifelong few-shot customization for text-to-image diffusion aims to continually generalize existing models for new tasks with minimal data while preserving old knowledge. Current customization diffusion models excel in few-shot tasks but struggle with catastrophic forgetting problems in lifelong generations. In this st... | {
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2411.05547 | Assessing the Answerability of Queries in Retrieval-Augmented Code
Generation | [
"cs.CL"
] | Thanks to unprecedented language understanding and generation capabilities of large language model (LLM), Retrieval-augmented Code Generation (RaCG) has recently been widely utilized among software developers. While this has increased productivity, there are still frequent instances of incorrect codes being provided. I... | {
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2411.05548 | Equivariant IMU Preintegration with Biases: a Galilean Group Approach | [
"cs.RO"
] | This letter proposes a new approach for Inertial Measurement Unit (IMU) preintegration, a fundamental building block that can be leveraged in different optimization-based Inertial Navigation System (INS) localization solutions. Inspired by recent advances in equivariant theory applied to biased INSs, we derive a discre... | {
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2411.05549 | Streaming Network for Continual Learning of Object Relocations under
Household Context Drifts | [
"cs.RO"
] | In most applications, robots need to adapt to new environments and be multi-functional without forgetting previous information. This requirement gains further importance in real-world scenarios where robots operate in coexistence with humans. In these complex environments, human actions inevitably lead to changes, requ... | {
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2411.05552 | DeepArUco++: Improved detection of square fiducial markers in
challenging lighting conditions | [
"cs.CV"
] | Fiducial markers are a computer vision tool used for object pose estimation and detection. These markers are highly useful in fields such as industry, medicine and logistics. However, optimal lighting conditions are not always available,and other factors such as blur or sensor noise can affect image quality. Classical ... | {
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2411.05554 | Time-to-reach Bounds for Verification of Dynamical Systems Using the
Koopman Spectrum | [
"eess.SY",
"cs.SY",
"math.DS"
] | In this work, we present a novel Koopman spectrum-based reachability verification method for nonlinear systems. Contrary to conventional methods that focus on characterizing all potential states of a dynamical system over a presupposed time span, our approach seeks to verify the reachability by assessing the non-emptin... | {
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2411.05557 | A Nerf-Based Color Consistency Method for Remote Sensing Images | [
"cs.CV",
"cs.AI"
] | Due to different seasons, illumination, and atmospheric conditions, the photometric of the acquired image varies greatly, which leads to obvious stitching seams at the edges of the mosaic image. Traditional methods can be divided into two categories, one is absolute radiation correction and the other is relative radiat... | {
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2411.05561 | Training objective drives the consistency of representational similarity
across datasets | [
"cs.CV",
"cs.AI",
"cs.LG"
] | The Platonic Representation Hypothesis claims that recent foundation models are converging to a shared representation space as a function of their downstream task performance, irrespective of the objectives and data modalities used to train these models. Representational similarity is generally measured for individual ... | {
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2411.05564 | Open-set object detection: towards unified problem formulation and
benchmarking | [
"cs.CV",
"cs.AI",
"cs.LG"
] | In real-world applications where confidence is key, like autonomous driving, the accurate detection and appropriate handling of classes differing from those used during training are crucial. Despite the proposal of various unknown object detection approaches, we have observed widespread inconsistencies among them regar... | {
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2411.05565 | Solving 7x7 Killall-Go with Seki Database | [
"cs.AI"
] | Game solving is the process of finding the theoretical outcome for a game, assuming that all player choices are optimal. This paper focuses on a technique that can reduce the heuristic search space significantly for 7x7 Killall-Go. In Go and Killall-Go, live patterns are stones that are protected from opponent capture.... | {
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2411.05572 | Why These Documents? Explainable Generative Retrieval with Hierarchical
Category Paths | [
"cs.IR"
] | Generative retrieval has recently emerged as a new alternative of traditional information retrieval approaches. However, existing generative retrieval methods directly decode docid when a query is given, making it impossible to provide users with explanations as an answer for "Why this document is retrieved?". To addre... | {
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2411.05575 | Towards a Real-Time Simulation of Elastoplastic Deformation Using
Multi-Task Neural Networks | [
"cs.CE",
"cs.LG"
] | This study introduces a surrogate modeling framework merging proper orthogonal decomposition, long short-term memory networks, and multi-task learning, to accurately predict elastoplastic deformations in real-time. Superior to single-task neural networks, this approach achieves a mean absolute error below 0.40\% across... | {
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2411.05577 | Exploring Relationships Between Cryptocurrency News Outlets and
Influencers' Twitter Activity and Market Prices | [
"cs.SI"
] | Academics increasingly acknowledge the predictive power of social media for a wide variety of events and, more specifically, for financial markets. Anecdotal and empirical findings show that cryptocurrencies are among the financial assets that have been affected by news and influencers' activities on Twitter. However, ... | {
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2411.05586 | Tangled Program Graphs as an alternative to DRL-based control algorithms
for UAVs | [
"cs.RO",
"cs.AI",
"cs.SY",
"eess.SY"
] | Deep reinforcement learning (DRL) is currently the most popular AI-based approach to autonomous vehicle control. An agent, trained for this purpose in simulation, can interact with the real environment with a human-level performance. Despite very good results in terms of selected metrics, this approach has some signifi... | {
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2411.05591 | Network EM Algorithm for Gaussian Mixture Model in Decentralized
Federated Learning | [
"stat.ML",
"cs.LG"
] | We systematically study various network Expectation-Maximization (EM) algorithms for the Gaussian mixture model within the framework of decentralized federated learning. Our theoretical investigation reveals that directly extending the classical decentralized supervised learning method to the EM algorithm exhibits poor... | {
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2411.05593 | Evaluating and Adapting Large Language Models to Represent Folktales in
Low-Resource Languages | [
"cs.CL"
] | Folktales are a rich resource of knowledge about the society and culture of a civilisation. Digital folklore research aims to use automated techniques to better understand these folktales, and it relies on abstract representations of the textual data. Although a number of large language models (LLMs) claim to be able t... | {
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2411.05596 | Machine learning-driven Anomaly Detection and Forecasting for Euclid
Space Telescope Operations | [
"cs.LG",
"astro-ph.IM"
] | State-of-the-art space science missions increasingly rely on automation due to spacecraft complexity and the costs of human oversight. The high volume of data, including scientific and telemetry data, makes manual inspection challenging. Machine learning offers significant potential to meet these demands. The Euclid ... | {
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2411.05597 | Predicting Stroke through Retinal Graphs and Multimodal Self-supervised
Learning | [
"cs.CV",
"cs.LG"
] | Early identification of stroke is crucial for intervention, requiring reliable models. We proposed an efficient retinal image representation together with clinical information to capture a comprehensive overview of cardiovascular health, leveraging large multimodal datasets for new medical insights. Our approach is one... | {
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2411.05599 | Expectation vs. Reality: Towards Verification of Psychological Games | [
"cs.GT",
"cs.AI",
"cs.MA"
] | Game theory provides an effective way to model strategic interactions among rational agents. In the context of formal verification, these ideas can be used to produce guarantees on the correctness of multi-agent systems, with a diverse range of applications from computer security to autonomous driving. Psychological ga... | {
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2411.05603 | Efficient Audio-Visual Fusion for Video Classification | [
"cs.CV"
] | We present Attend-Fusion, a novel and efficient approach for audio-visual fusion in video classification tasks. Our method addresses the challenge of exploiting both audio and visual modalities while maintaining a compact model architecture. Through extensive experiments on the YouTube-8M dataset, we demonstrate that o... | {
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2411.05609 | A Two-Step Concept-Based Approach for Enhanced Interpretability and
Trust in Skin Lesion Diagnosis | [
"cs.CV",
"cs.LG"
] | The main challenges hindering the adoption of deep learning-based systems in clinical settings are the scarcity of annotated data and the lack of interpretability and trust in these systems. Concept Bottleneck Models (CBMs) offer inherent interpretability by constraining the final disease prediction on a set of human-u... | {
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2411.05614 | Acceleration for Deep Reinforcement Learning using Parallel and
Distributed Computing: A Survey | [
"cs.LG",
"cs.AI",
"cs.DC"
] | Deep reinforcement learning has led to dramatic breakthroughs in the field of artificial intelligence for the past few years. As the amount of rollout experience data and the size of neural networks for deep reinforcement learning have grown continuously, handling the training process and reducing the time consumption ... | {
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2411.05616 | Learning-based Nonlinear Model Predictive Control of Articulated Soft
Robots using Recurrent Neural Networks | [
"cs.RO"
] | Soft robots pose difficulties in terms of control, requiring novel strategies to effectively manipulate their compliant structures. Model-based approaches face challenges due to the high dimensionality and nonlinearities such as hysteresis effects. In contrast, learning-based approaches provide nonlinear models of diff... | {
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2411.05618 | Knowledge Distillation Neural Network for Predicting Car-following
Behaviour of Human-driven and Autonomous Vehicles | [
"cs.LG",
"cs.AI"
] | As we move towards a mixed-traffic scenario of Autonomous vehicles (AVs) and Human-driven vehicles (HDVs), understanding the car-following behaviour is important to improve traffic efficiency and road safety. Using a real-world trajectory dataset, this study uses descriptive and statistical analysis to investigate the ... | {
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2411.05619 | WHALE: Towards Generalizable and Scalable World Models for Embodied
Decision-making | [
"cs.LG"
] | World models play a crucial role in decision-making within embodied environments, enabling cost-free explorations that would otherwise be expensive in the real world. To facilitate effective decision-making, world models must be equipped with strong generalizability to support faithful imagination in out-of-distributio... | {
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2411.05624 | Data-Driven Min-Max MPC for LPV Systems with Unknown Scheduling Signal | [
"eess.SY",
"cs.SY"
] | This paper presents a data-driven min-max model predictive control (MPC) scheme for linear parameter-varying (LPV) systems. Contrary to existing data-driven LPV control approaches, we assume that the scheduling signal is unknown during offline data collection and online system operation. Assuming a quadratic matrix ine... | {
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2411.05625 | Cross-validating causal discovery via Leave-One-Variable-Out | [
"stat.ML",
"cs.LG",
"stat.ME"
] | We propose a new approach to falsify causal discovery algorithms without ground truth, which is based on testing the causal model on a pair of variables that has been dropped when learning the causal model. To this end, we use the "Leave-One-Variable-Out (LOVO)" prediction where $Y$ is inferred from $X$ without any joi... | {
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2411.05627 | Large problems are not necessarily hard: A case study on distributed
NMPC paying off | [
"math.OC",
"cs.SY",
"eess.SY"
] | A key motivation in the development of distributed Model Predictive Control (MPC) is to widen the computational bottleneck of centralized MPC for large-scale systems. Parallelizing computations among individual subsystems, distributed MPC has the prospect of scaling well for large networks. However, the communication d... | {
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2411.05631 | Physics-constrained coupled neural differential equations for one
dimensional blood flow modeling | [
"physics.flu-dyn",
"cs.LG"
] | Computational cardiovascular flow modeling plays a crucial role in understanding blood flow dynamics. While 3D models provide acute details, they are computationally expensive, especially with fluid-structure interaction (FSI) simulations. 1D models offer a computationally efficient alternative, by simplifying the 3D N... | {
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2411.05633 | SynDroneVision: A Synthetic Dataset for Image-Based Drone Detection | [
"cs.CV",
"cs.AI",
"cs.RO"
] | Developing robust drone detection systems is often constrained by the limited availability of large-scale annotated training data and the high costs associated with real-world data collection. However, leveraging synthetic data generated via game engine-based simulations provides a promising and cost-effective solution... | {
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2411.05636 | Video RWKV:Video Action Recognition Based RWKV | [
"cs.CV",
"cs.LG"
] | To address the challenges of high computational costs and long-distance dependencies in exist ing video understanding methods, such as CNNs and Transformers, this work introduces RWKV to the video domain in a novel way. We propose a LSTM CrossRWKV (LCR) framework, designed for spatiotemporal representation learning to ... | {
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2411.05638 | Impact of Fake News on Social Media Towards Public Users of Different
Age Groups | [
"cs.CL"
] | This study examines how fake news affects social media users across a range of age groups and how machine learning (ML) and artificial intelligence (AI) can help reduce the spread of false information. The paper evaluates various machine learning models for their efficacy in identifying and categorizing fake news and e... | {
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2411.05639 | Assessing Open-Source Large Language Models on Argumentation Mining
Subtasks | [
"cs.CL"
] | We explore the capability of four open-sourcelarge language models (LLMs) in argumentation mining (AM). We conduct experiments on three different corpora; persuasive essays(PE), argumentative microtexts (AMT) Part 1 and Part 2, based on two argumentation mining sub-tasks: (i) argumentative discourse units classificatio... | {
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2411.05641 | Evaluating Large Language Model Capability in Vietnamese Fact-Checking
Data Generation | [
"cs.CL"
] | Large Language Models (LLMs), with gradually improving reading comprehension and reasoning capabilities, are being applied to a range of complex language tasks, including the automatic generation of language data for various purposes. However, research on applying LLMs for automatic data generation in low-resource lang... | {
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2411.05648 | Enhancing Model Fairness and Accuracy with Similarity Networks: A
Methodological Approach | [
"cs.LG"
] | In this paper, we propose an innovative approach to thoroughly explore dataset features that introduce bias in downstream machine-learning tasks. Depending on the data format, we use different techniques to map instances into a similarity feature space. Our method's ability to adjust the resolution of pairwise similari... | {
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2411.05649 | Harnessing High-Level Song Descriptors towards Natural Language-Based
Music Recommendation | [
"cs.IR"
] | Recommender systems relying on Language Models (LMs) have gained popularity in assisting users to navigate large catalogs. LMs often exploit item high-level descriptors, i.e. categories or consumption contexts, from training data or user preferences. This has been proven effective in domains like movies or products. Ho... | {
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2411.05653 | The influence of persona and conversational task on social interactions
with a LLM-controlled embodied conversational agent | [
"cs.HC",
"cs.AI"
] | Large Language Models (LLMs) have demonstrated remarkable capabilities in conversational tasks. Embodying an LLM as a virtual human allows users to engage in face-to-face social interactions in Virtual Reality. However, the influence of person- and task-related factors in social interactions with LLM-controlled agents ... | {
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2411.05659 | Investigation of Holographic Beamforming via Dynamic Metasurface
Antennas in QoS Guaranteed Power Efficient Networks | [
"cs.IT",
"eess.SP",
"math.IT"
] | This work focuses on designing a power-efficient network for Dynamic Metasurface Antennas (DMA)-aided multi-user multiple-input single-output (MISO) antenna systems. Power efficiency is achieved through holographic beamforming in a DMA-aided network, minimizing total transmission power while ensuring a guaranteed signa... | {
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2411.05661 | Multi-armed Bandits with Missing Outcome | [
"stat.ML",
"cs.LG"
] | While significant progress has been made in designing algorithms that minimize regret in online decision-making, real-world scenarios often introduce additional complexities, perhaps the most challenging of which is missing outcomes. Overlooking this aspect or simply assuming random missingness invariably leads to bias... | {
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2411.05663 | Online-LoRA: Task-free Online Continual Learning via Low Rank Adaptation | [
"cs.CV",
"cs.LG"
] | Catastrophic forgetting is a significant challenge in online continual learning (OCL), especially for non-stationary data streams that do not have well-defined task boundaries. This challenge is exacerbated by the memory constraints and privacy concerns inherent in rehearsal buffers. To tackle catastrophic forgetting, ... | {
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2411.05665 | Unmasking the Limits of Large Language Models: A Systematic Evaluation
of Masked Text Processing Ability through MskQA and MskCal | [
"cs.CL"
] | This paper sheds light on the limitations of Large Language Models (LLMs) by rigorously evaluating their ability to process masked text. We introduce two novel tasks: MskQA, measuring reasoning on masked question-answering datasets like RealtimeQA, and MskCal, assessing numerical reasoning on masked arithmetic problems... | {
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2411.05673 | Relationships between the degrees of freedom in the affine Gaussian
derivative model for visual receptive fields and 2-D affine image
transformations, with application to covariance properties of simple cells in
the primary visual cortex | [
"q-bio.NC",
"cs.CV"
] | When observing the surface patterns of objects delimited by smooth surfaces, the projections of the surface patterns to the image domain will be subject to substantial variabilities, as induced by variabilities in the geometric viewing conditions, and as generated by either monocular or binocular imaging conditions, or... | {
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2411.05676 | Improving Molecular Graph Generation with Flow Matching and Optimal
Transport | [
"cs.LG",
"cs.AI"
] | Generating molecular graphs is crucial in drug design and discovery but remains challenging due to the complex interdependencies between nodes and edges. While diffusion models have demonstrated their potentiality in molecular graph design, they often suffer from unstable training and inefficient sampling. To enhance g... | {
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2411.05679 | Tell What You Hear From What You See -- Video to Audio Generation
Through Text | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.SD",
"eess.AS"
] | The content of visual and audio scenes is multi-faceted such that a video can be paired with various audio and vice-versa. Thereby, in video-to-audio generation task, it is imperative to introduce steering approaches for controlling the generated audio. While Video-to-Audio generation is a well-established generative t... | {
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2411.05683 | Data-Driven Distributed Common Operational Picture from Heterogeneous
Platforms using Multi-Agent Reinforcement Learning | [
"cs.MA",
"cs.AI"
] | The integration of unmanned platforms equipped with advanced sensors promises to enhance situational awareness and mitigate the "fog of war" in military operations. However, managing the vast influx of data from these platforms poses a significant challenge for Command and Control (C2) systems. This study presents a no... | {
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2411.05685 | Beyond Pairwise Interactions: Unveiling the Role of Higher-Order
Interactions via Stepwise Reduction | [
"physics.soc-ph",
"cs.SI"
] | Complex systems, such as economic, social, biological, and ecological systems, usually feature interactions not only between pairwise entities but also among three or more entities. These multi-entity interactions are known as higher-order interactions. Hypergraph, as a mathematical tool, can effectively characterize h... | {
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2411.05689 | optipoly: A Python package for boxed-constrained multi-variable
polynomial cost functions optimization | [
"cs.CE"
] | In this paper, a new python package (optipoly) is described that solves box-constrained optimization problem over multivariate polynomial cost functions. The principle of the algorithm is described before its performance is compared to three general purpose NLP solvers implemented in the state-of-the-art Gekko and scip... | {
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2411.05691 | Asterisk*: Keep it Simple | [
"cs.CL",
"cs.AI"
] | This paper describes Asterisk, a compact GPT-based model for generating text embeddings. The model uses a minimalist architecture with two layers, two attention heads, and 256 embedding dimensions. By applying knowledge distillation from larger pretrained models, we explore the trade-offs between model size and perform... | {
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2411.05692 | Autoregressive Adaptive Hypergraph Transformer for Skeleton-based
Activity Recognition | [
"cs.CV"
] | Extracting multiscale contextual information and higher-order correlations among skeleton sequences using Graph Convolutional Networks (GCNs) alone is inadequate for effective action classification. Hypergraph convolution addresses the above issues but cannot harness the long-range dependencies. Transformer proves to b... | {
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2411.05693 | YOSO: You-Only-Sample-Once via Compressed Sensing for Graph Neural
Network Training | [
"cs.LG"
] | Graph neural networks (GNNs) have become essential tools for analyzing non-Euclidean data across various domains. During training stage, sampling plays an important role in reducing latency by limiting the number of nodes processed, particularly in large-scale applications. However, as the demand for better prediction ... | {
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2411.05697 | IPMN Risk Assessment under Federated Learning Paradigm | [
"eess.IV",
"cs.DC",
"cs.LG"
] | Accurate classification of Intraductal Papillary Mucinous Neoplasms (IPMN) is essential for identifying high-risk cases that require timely intervention. In this study, we develop a federated learning framework for multi-center IPMN classification utilizing a comprehensive pancreas MRI dataset. This dataset includes 65... | {
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2411.05698 | Visual-TCAV: Concept-based Attribution and Saliency Maps for Post-hoc
Explainability in Image Classification | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Convolutional Neural Networks (CNNs) have seen significant performance improvements in recent years. However, due to their size and complexity, they function as black-boxes, leading to transparency concerns. State-of-the-art saliency methods generate local explanations that highlight the area in the input image where a... | {
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2411.05705 | Image inpainting enhancement by replacing the original mask with a
self-attended region from the input image | [
"cs.CV",
"eess.IV"
] | Image inpainting, the process of restoring missing or corrupted regions of an image by reconstructing pixel information, has recently seen considerable advancements through deep learning-based approaches. In this paper, we introduce a novel deep learning-based pre-processing methodology for image inpainting utilizing t... | {
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2411.05706 | Image2Text2Image: A Novel Framework for Label-Free Evaluation of
Image-to-Text Generation with Text-to-Image Diffusion Models | [
"cs.CV",
"cs.CL"
] | Evaluating the quality of automatically generated image descriptions is a complex task that requires metrics capturing various dimensions, such as grammaticality, coverage, accuracy, and truthfulness. Although human evaluation provides valuable insights, its cost and time-consuming nature pose limitations. Existing aut... | {
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2411.05708 | Sample and Computationally Efficient Robust Learning of Gaussian
Single-Index Models | [
"cs.LG"
] | A single-index model (SIM) is a function of the form $\sigma(\mathbf{w}^{\ast} \cdot \mathbf{x})$, where $\sigma: \mathbb{R} \to \mathbb{R}$ is a known link function and $\mathbf{w}^{\ast}$ is a hidden unit vector. We study the task of learning SIMs in the agnostic (a.k.a. adversarial label noise) model with respect to... | {
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2411.05712 | Scaling Laws for Task-Optimized Models of the Primate Visual Ventral
Stream | [
"cs.LG",
"cs.CV",
"q-bio.NC"
] | When trained on large-scale object classification datasets, certain artificial neural network models begin to approximate core object recognition (COR) behaviors and neural response patterns in the primate visual ventral stream (VVS). While recent machine learning advances suggest that scaling model size, dataset size,... | {
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2411.05714 | STARS: Sensor-agnostic Transformer Architecture for Remote Sensing | [
"cs.CV",
"cs.LG",
"eess.IV"
] | We present a sensor-agnostic spectral transformer as the basis for spectral foundation models. To that end, we introduce a Universal Spectral Representation (USR) that leverages sensor meta-data, such as sensing kernel specifications and sensing wavelengths, to encode spectra obtained from any spectral instrument into ... | {
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2411.05715 | On the Role of Noise in AudioVisual Integration: Evidence from
Artificial Neural Networks that Exhibit the McGurk Effect | [
"cs.SD",
"cs.MM",
"cs.NE",
"eess.AS"
] | Humans are able to fuse information from both auditory and visual modalities to help with understanding speech. This is frequently demonstrated through an phenomenon known as the McGurk Effect, during which a listener is presented with incongruent auditory and visual speech that fuse together into the percept of an ill... | {
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2411.05718 | A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust,
Reliable, and Safe Learning Techniques for Real-world Robotics | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Machine learning methods have a groundbreaking impact in many application domains, but their application on real robotic platforms is still limited. Despite the many challenges associated with combining machine learning technology with robotics, robot learning remains one of the most promising directions for enhancing ... | {
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2411.05729 | Graph-Dictionary Signal Model for Sparse Representations of Multivariate
Data | [
"cs.LG",
"stat.ML"
] | Representing and exploiting multivariate signals require capturing complex relations between variables. We define a novel Graph-Dictionary signal model, where a finite set of graphs characterizes relationships in data distribution through a weighted sum of their Laplacians. We propose a framework to infer the graph dic... | {
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2411.05730 | Learning Subsystem Dynamics in Nonlinear Systems via Port-Hamiltonian
Neural Networks | [
"eess.SY",
"cs.LG",
"cs.SY"
] | Port-Hamiltonian neural networks (pHNNs) are emerging as a powerful modeling tool that integrates physical laws with deep learning techniques. While most research has focused on modeling the entire dynamics of interconnected systems, the potential for identifying and modeling individual subsystems while operating as pa... | {
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2411.05731 | PEP-GS: Perceptually-Enhanced Precise Structured 3D Gaussians for
View-Adaptive Rendering | [
"cs.CV"
] | Recently, 3D Gaussian Splatting (3D-GS) has achieved significant success in real-time, high-quality 3D scene rendering. However, it faces several challenges, including Gaussian redundancy, limited ability to capture view-dependent effects, and difficulties in handling complex lighting and specular reflections. Addition... | {
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2411.05733 | Differential Privacy Under Class Imbalance: Methods and Empirical
Insights | [
"cs.LG",
"cs.CR"
] | Imbalanced learning occurs in classification settings where the distribution of class-labels is highly skewed in the training data, such as when predicting rare diseases or in fraud detection. This class imbalance presents a significant algorithmic challenge, which can be further exacerbated when privacy-preserving tec... | {
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2411.05734 | Poze: Sports Technique Feedback under Data Constraints | [
"cs.CV"
] | Access to expert coaching is essential for developing technique in sports, yet economic barriers often place it out of reach for many enthusiasts. To bridge this gap, we introduce Poze, an innovative video processing framework that provides feedback on human motion, emulating the insights of a professional coach. Poze ... | {
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2411.05735 | Aioli: A Unified Optimization Framework for Language Model Data Mixing | [
"cs.LG",
"cs.AI",
"cs.CL",
"stat.ML"
] | Language model performance depends on identifying the optimal mixture of data groups to train on (e.g., law, code, math). Prior work has proposed a diverse set of methods to efficiently learn mixture proportions, ranging from fitting regression models over training runs to dynamically updating proportions throughout tr... | {
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2411.05738 | StdGEN: Semantic-Decomposed 3D Character Generation from Single Images | [
"cs.CV"
] | We present StdGEN, an innovative pipeline for generating semantically decomposed high-quality 3D characters from single images, enabling broad applications in virtual reality, gaming, and filmmaking, etc. Unlike previous methods which struggle with limited decomposability, unsatisfactory quality, and long optimization ... | {
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2411.05740 | Bias correction and instrumental variables for direct data-driven
model-reference control | [
"eess.SY",
"cs.SY"
] | Managing noisy data is a central challenge in direct data-driven control design. We propose an approach for synthesizing model-reference controllers for linear time-invariant (LTI) systems using noisy state-input data, employing novel noise mitigation techniques. Specifically, we demonstrate that using data-based covar... | {
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2411.05742 | Topology-aware Reinforcement Feature Space Reconstruction for Graph Data | [
"cs.LG",
"cs.AI"
] | Feature space is an environment where data points are vectorized to represent the original dataset. Reconstructing a good feature space is essential to augment the AI power of data, improve model generalization, and increase the availability of downstream ML models. Existing literature, such as feature transformation a... | {
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2411.05743 | Free Record-Level Privacy Risk Evaluation Through Artifact-Based Methods | [
"cs.LG",
"cs.CR"
] | Membership inference attacks (MIAs) are widely used to empirically assess privacy risks in machine learning models, both providing model-level vulnerability metrics and identifying the most vulnerable training samples. State-of-the-art methods, however, require training hundreds of shadow models with the same architect... | {
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2411.05746 | Continuous-Time Analysis of Adaptive Optimization and Normalization | [
"cs.LG",
"cs.AI"
] | Adaptive optimization algorithms, particularly Adam and its variant AdamW, are fundamental components of modern deep learning. However, their training dynamics lack comprehensive theoretical understanding, with limited insight into why common practices -- such as specific hyperparameter choices and normalization layers... | {
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2411.05747 | WavShadow: Wavelet Based Shadow Segmentation and Removal | [
"cs.CV"
] | Shadow removal and segmentation remain challenging tasks in computer vision, particularly in complex real world scenarios. This study presents a novel approach that enhances the ShadowFormer model by incorporating Masked Autoencoder (MAE) priors and Fast Fourier Convolution (FFC) blocks, leading to significantly faster... | {
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2411.05748 | Multi-Dimensional Reconfigurable, Physically Composable Hybrid
Diffractive Optical Neural Network | [
"physics.optics",
"cs.AI",
"cs.AR"
] | Diffractive optical neural networks (DONNs), leveraging free-space light wave propagation for ultra-parallel, high-efficiency computing, have emerged as promising artificial intelligence (AI) accelerators. However, their inherent lack of reconfigurability due to fixed optical structures post-fabrication hinders practic... | {
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2411.05750 | On Differentially Private String Distances | [
"cs.DS",
"cs.AI",
"cs.CR",
"cs.LG",
"stat.ML"
] | Given a database of bit strings $A_1,\ldots,A_m\in \{0,1\}^n$, a fundamental data structure task is to estimate the distances between a given query $B\in \{0,1\}^n$ with all the strings in the database. In addition, one might further want to ensure the integrity of the database by releasing these distance statistics in... | {
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2411.05752 | FisherMask: Enhancing Neural Network Labeling Efficiency in Image
Classification Using Fisher Information | [
"cs.LG",
"cs.CL",
"cs.CV"
] | Deep learning (DL) models are popular across various domains due to their remarkable performance and efficiency. However, their effectiveness relies heavily on large amounts of labeled data, which are often time-consuming and labor-intensive to generate manually. To overcome this challenge, it is essential to develop s... | {
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2411.05755 | End-to-End Navigation with Vision Language Models: Transforming Spatial
Reasoning into Question-Answering | [
"cs.RO",
"cs.CL",
"cs.CV"
] | We present VLMnav, an embodied framework to transform a Vision-Language Model (VLM) into an end-to-end navigation policy. In contrast to prior work, we do not rely on a separation between perception, planning, and control; instead, we use a VLM to directly select actions in one step. Surprisingly, we find that a VLM ca... | {
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2411.05757 | Tract-RLFormer: A Tract-Specific RL policy based Decoder-only
Transformer Network | [
"cs.LG"
] | Fiber tractography is a cornerstone of neuroimaging, enabling the detailed mapping of the brain's white matter pathways through diffusion MRI. This is crucial for understanding brain connectivity and function, making it a valuable tool in neurological applications. Despite its importance, tractography faces challenges ... | {
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2411.05762 | Multi-hop Evidence Pursuit Meets the Web: Team Papelo at FEVER 2024 | [
"cs.CL"
] | Separating disinformation from fact on the web has long challenged both the search and the reasoning powers of humans. We show that the reasoning power of large language models (LLMs) and the retrieval power of modern search engines can be combined to automate this process and explainably verify claims. We integrate LL... | {
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2411.05763 | Frequency stability of grid-forming power-limiting droop control | [
"eess.SY",
"cs.SY"
] | In this paper, we analyze power-limiting grid-forming droop control used for grid-connected power converters. Compared to conventional grid-forming droop control, power-limiting droop control explicitly accounts for active power limits of the generation (e.g., renewables) interfaced by the converter. While power-limiti... | {
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2411.05764 | FinDVer: Explainable Claim Verification over Long and Hybrid-Content
Financial Documents | [
"cs.CL",
"cs.LG"
] | We introduce FinDVer, a comprehensive benchmark specifically designed to evaluate the explainable claim verification capabilities of LLMs in the context of understanding and analyzing long, hybrid-content financial documents. FinDVer contains 2,400 expert-annotated examples, divided into three subsets: information extr... | {
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2411.05771 | Sketched Equivariant Imaging Regularization and Deep Internal Learning
for Inverse Problems | [
"eess.IV",
"cs.CV",
"cs.LG",
"math.OC"
] | Equivariant Imaging (EI) regularization has become the de-facto technique for unsupervised training of deep imaging networks, without any need of ground-truth data. Observing that the EI-based unsupervised training paradigm currently has significant computational redundancy leading to inefficiency in high-dimensional a... | {
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2411.05775 | Fact or Fiction? Can LLMs be Reliable Annotators for Political Truths? | [
"cs.CL",
"cs.AI"
] | Political misinformation poses significant challenges to democratic processes, shaping public opinion and trust in media. Manual fact-checking methods face issues of scalability and annotator bias, while machine learning models require large, costly labelled datasets. This study investigates the use of state-of-the-art... | {
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2411.05777 | Quantitative Assessment of Intersectional Empathetic Bias and
Understanding | [
"cs.CL",
"cs.AI",
"cs.HC"
] | A growing amount of literature critiques the current operationalizations of empathy based on loose definitions of the construct. Such definitions negatively affect dataset quality, model robustness, and evaluation reliability. We propose an empathy evaluation framework that operationalizes empathy close to its psycholo... | {
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2411.05778 | LLMs as Method Actors: A Model for Prompt Engineering and Architecture | [
"cs.AI",
"cs.CL"
] | We introduce "Method Actors" as a mental model for guiding LLM prompt engineering and prompt architecture. Under this mental model, LLMs should be thought of as actors; prompts as scripts and cues; and LLM responses as performances. We apply this mental model to the task of improving LLM performance at playing Connecti... | {
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2411.05779 | Curriculum Learning for Few-Shot Domain Adaptation in CT-based Airway
Tree Segmentation | [
"cs.CV",
"cs.LG"
] | Despite advances with deep learning (DL), automated airway segmentation from chest CT scans continues to face challenges in segmentation quality and generalization across cohorts. To address these, we propose integrating Curriculum Learning (CL) into airway segmentation networks, distributing the training set into batc... | {
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2411.05780 | GazeSearch: Radiology Findings Search Benchmark | [
"cs.CV",
"cs.AI"
] | Medical eye-tracking data is an important information source for understanding how radiologists visually interpret medical images. This information not only improves the accuracy of deep learning models for X-ray analysis but also their interpretability, enhancing transparency in decision-making. However, the current e... | {
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2411.05781 | Using Language Models to Disambiguate Lexical Choices in Translation | [
"cs.CL",
"cs.AI"
] | In translation, a concept represented by a single word in a source language can have multiple variations in a target language. The task of lexical selection requires using context to identify which variation is most appropriate for a source text. We work with native speakers of nine languages to create DTAiLS, a datase... | {
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2411.05783 | ASL STEM Wiki: Dataset and Benchmark for Interpreting STEM Articles | [
"cs.CL",
"cs.AI",
"cs.CV",
"cs.HC"
] | Deaf and hard-of-hearing (DHH) students face significant barriers in accessing science, technology, engineering, and mathematics (STEM) education, notably due to the scarcity of STEM resources in signed languages. To help address this, we introduce ASL STEM Wiki: a parallel corpus of 254 Wikipedia articles on STEM topi... | {
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} |
2411.05784 | Safe Reinforcement Learning of Robot Trajectories in the Presence of
Moving Obstacles | [
"cs.RO"
] | In this paper, we present an approach for learning collision-free robot trajectories in the presence of moving obstacles. As a first step, we train a backup policy to generate evasive movements from arbitrary initial robot states using model-free reinforcement learning. When learning policies for other tasks, the backu... | {
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} |
2411.05787 | Recycled Attention: Efficient inference for long-context language models | [
"cs.CL"
] | Generating long sequences of tokens given a long-context input imposes a heavy computational burden for large language models (LLMs). One of the computational bottleneck comes from computing attention over a long sequence of input at each generation step. In this paper, we propose Recycled Attention, an inference-time ... | {
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} |
2411.05788 | News-Driven Stock Price Forecasting in Indian Markets: A Comparative
Study of Advanced Deep Learning Models | [
"q-fin.ST",
"cs.LG"
] | Forecasting stock market prices remains a complex challenge for traders, analysts, and engineers due to the multitude of factors that influence price movements. Recent advancements in artificial intelligence (AI) and natural language processing (NLP) have significantly enhanced stock price prediction capabilities. AI's... | {
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} |
2411.05789 | Semantic Information G Theory for Range Control with Tradeoff between
Purposiveness and Efficiency | [
"cs.IT",
"cs.LG",
"math.IT",
"math.OC"
] | Recent advances in deep learning suggest that we need to maximize and minimize two different kinds of information simultaneously. The Information Max-Min (IMM) method has been used in deep learning, reinforcement learning, and maximum entropy control. Shannon's information rate-distortion function is the theoretical ba... | {
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} |
2411.05790 | Comparative Analysis of LSTM, GRU, and Transformer Models for Stock
Price Prediction | [
"q-fin.ST",
"cs.LG"
] | In recent fast-paced financial markets, investors constantly seek ways to gain an edge and make informed decisions. Although achieving perfect accuracy in stock price predictions remains elusive, artificial intelligence (AI) advancements have significantly enhanced our ability to analyze historical data and identify po... | {
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} |
2411.05791 | Forecasting Company Fundamentals | [
"q-fin.ST",
"cs.LG",
"econ.GN",
"q-fin.EC",
"stat.AP"
] | Company fundamentals are key to assessing companies' financial and overall success and stability. Forecasting them is important in multiple fields, including investing and econometrics. While statistical and contemporary machine learning methods have been applied to many time series tasks, there is a lack of comparison... | {
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} |
2411.05793 | A Comprehensive Survey of Time Series Forecasting: Architectural
Diversity and Open Challenges | [
"cs.LG",
"cs.AI"
] | Time series forecasting is a critical task that provides key information for decision-making across various fields. Recently, various fundamental deep learning architectures such as MLPs, CNNs, RNNs, and GNNs have been developed and applied to solve time series forecasting problems. However, the structural limitations ... | {
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} |
2411.05797 | What is Metaheuristics? A Primer for the Epidemiologists | [
"cs.NE",
"stat.CO"
] | Optimization plays an important role in tackling public health problems. Animal instincts can be used effectively to solve complex public health management issues by providing optimal or approximately optimal solutions to complicated optimization problems common in public health. BAT algorithm is an exemplary member of... | {
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} |
2411.05798 | A Genetic Algorithm for Multi-Capacity Fixed-Charge Flow Network Design | [
"cs.NE",
"cs.AI"
] | The Multi-Capacity Fixed-Charge Network Flow (MC-FCNF) problem, a generalization of the Fixed-Charge Network Flow problem, aims to assign capacities to edges in a flow network such that a target amount of flow can be hosted at minimum cost. The cost model for both problems dictates that the fixed cost of an edge is inc... | {
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} |
2411.05799 | NeoPhysIx: An Ultra Fast 3D Physical Simulator as Development Tool for
AI Algorithms | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Traditional AI algorithms, such as Genetic Programming and Reinforcement Learning, often require extensive computational resources to simulate real-world physical scenarios effectively. While advancements in multi-core processing have been made, the inherent limitations of parallelizing rigid body dynamics lead to sign... | {
"Other": 0,
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} |
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