id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.13236 | Time-Constrained Model Predictive Control for Autonomous Satellite
Rendezvous, Proximity Operations, and Docking | [
"eess.SY",
"cs.SY"
] | This paper presents a time-constrained model predictive control strategy for the six degree-of-freedom autonomous rendezvous, proximity, operations and docking problem between a controllable "deputy" satellite and an uncontrolled "chief" satellite. The objective is to achieve a docking configuration defined by both the... | {
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2501.13241 | State Combinatorial Generalization In Decision Making With Conditional
Diffusion Models | [
"cs.LG"
] | Many real-world decision-making problems are combinatorial in nature, where states (e.g., surrounding traffic of a self-driving car) can be seen as a combination of basic elements (e.g., pedestrians, trees, and other cars). Due to combinatorial complexity, observing all combinations of basic elements in the training se... | {
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2501.13242 | Distributed Multiple Testing with False Discovery Rate Control in the
Presence of Byzantines | [
"eess.SP",
"cs.IT",
"math.IT",
"stat.ME"
] | This work studies distributed multiple testing with false discovery rate (FDR) control in the presence of Byzantine attacks, where an adversary captures a fraction of the nodes and corrupts their reported p-values. We focus on two baseline attack models: an oracle model with the full knowledge of which hypotheses are t... | {
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2501.13247 | Multimodal AI on Wound Images and Clinical Notes for Home Patient
Referral | [
"cs.LG",
"cs.CV",
"eess.IV"
] | Chronic wounds affect 8.5 million Americans, particularly the elderly and patients with diabetes. These wounds can take up to nine months to heal, making regular care essential to ensure healing and prevent severe outcomes like limb amputations. Many patients receive care at home from visiting nurses with varying level... | {
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2501.13252 | Exploring the Technology Landscape through Topic Modeling, Expert
Involvement, and Reinforcement Learning | [
"cs.LG",
"cs.CR",
"quant-ph"
] | In today's rapidly evolving technological landscape, organizations face the challenge of integrating external insights into their decision-making processes to stay competitive. To address this issue, this study proposes a method that combines topic modeling, expert knowledge inputs, and reinforcement learning (RL) to e... | {
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2501.13255 | Stochastic Deep Learning Surrogate Models for Uncertainty Propagation in
Microstructure-Properties of Ceramic Aerogels | [
"cs.CE"
] | Deep learning surrogate models have become pivotal in enabling model-driven materials discovery to achieve exceptional properties. However, ensuring the accuracy and reliability of predictions from these models, trained on limited and sparse material datasets remains a significant challenge. This study introduces an in... | {
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2501.13261 | Exploring GPT's Ability as a Judge in Music Understanding | [
"cs.IR",
"cs.SD",
"eess.AS"
] | Recent progress in text-based Large Language Models (LLMs) and their extended ability to process multi-modal sensory data have led us to explore their applicability in addressing music information retrieval (MIR) challenges. In this paper, we use a systematic prompt engineering approach for LLMs to solve MIR problems. ... | {
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2501.13264 | RAG-Reward: Optimizing RAG with Reward Modeling and RLHF | [
"cs.CL"
] | Retrieval-augmented generation (RAG) enhances Large Language Models (LLMs) with relevant and up-to-date knowledge, improving their ability to answer knowledge-intensive questions. It has been shown to enhance both generation quality and trustworthiness. While numerous works have focused on improving retrieval, generati... | {
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2501.13268 | Threat-based Security Controls to Protect Industrial Control Systems | [
"cs.CR",
"cs.SY",
"eess.SY"
] | This paper analyzes the reported threats to Industrial Control Systems (ICS)/Operational Technology (OT) and identifies common tactics, techniques, and procedures (TTP) used by threat actors. The paper then uses the MITRE ATT&CK framework to map the common TTPs and provide an understanding of the security controls need... | {
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2501.13271 | Hybrid Two-Stage Reconstruction of Multiscale Subsurface Flow with
Physics-informed Residual Connected Neural Operator | [
"cs.LG"
] | The novel neural networks show great potential in solving partial differential equations. For single-phase flow problems in subsurface porous media with high-contrast coefficients, the key is to develop neural operators with accurate reconstruction capability and strict adherence to physical laws. In this study, we pro... | {
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2501.13272 | PCSI -- The Platform for Content-Structure Inference | [
"cs.IR",
"cs.CY"
] | The Platform for Content-Structure Inference (PCSI, pronounced "pixie") facilitates the sharing of information about the process of converting Web resources into structured content objects that conform to a predefined format. PCSI records encode methods for deriving structured content from classes of URLs, and report t... | {
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2501.13273 | Enhancing Robust Fairness via Confusional Spectral Regularization | [
"cs.LG"
] | Recent research has highlighted a critical issue known as ``robust fairness", where robust accuracy varies significantly across different classes, undermining the reliability of deep neural networks (DNNs). A common approach to address this has been to dynamically reweight classes during training, giving more weight to... | {
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2501.13274 | T-Graphormer: Using Transformers for Spatiotemporal Forecasting | [
"cs.LG"
] | Multivariate time series data is ubiquitous, and forecasting it has important applications in many domains. However, its complex spatial dependencies and non-linear temporal dynamics can be challenging for traditional techniques. Existing methods tackle these challenges by learning the two dimensions separately. Here, ... | {
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2501.13277 | MEDFORM: A Foundation Model for Contrastive Learning of CT Imaging and
Clinical Numeric Data in Multi-Cancer Analysis | [
"cs.CV"
] | Computed tomography (CT) and clinical numeric data are essential modalities for cancer evaluation, but building large-scale multimodal training datasets for developing medical foundation models remains challenging due to the structural complexity of multi-slice CT data and high cost of expert annotation. In this study,... | {
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2501.13278 | On Subset Retrieval and Group Testing Problems with Differential Privacy
Constraints | [
"cs.IT",
"math.IT"
] | This paper focuses on the design and analysis of privacy-preserving techniques for group testing and infection status retrieval. Our work is motivated by the need to provide accurate information on the status of disease spread among a group of individuals while protecting the privacy of the infection status of any sing... | {
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2501.13282 | Experience with GitHub Copilot for Developer Productivity at Zoominfo | [
"cs.SE",
"cs.AI"
] | This paper presents a comprehensive evaluation of GitHub Copilot's deployment and impact on developer productivity at Zoominfo, a leading Go-To-Market (GTM) Intelligence Platform. We describe our systematic four-phase approach to evaluating and deploying GitHub Copilot across our engineering organization, involving ove... | {
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2501.13284 | Toyteller: AI-powered Visual Storytelling Through Toy-Playing with
Character Symbols | [
"cs.HC",
"cs.AI",
"cs.CL"
] | We introduce Toyteller, an AI-powered storytelling system where users generate a mix of story text and visuals by directly manipulating character symbols like they are toy-playing. Anthropomorphized symbol motions can convey rich and nuanced social interactions; Toyteller leverages these motions (1) to let users steer ... | {
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2501.13288 | Task-Oriented Automatic Fact-Checking with Frame-Semantics | [
"cs.CL"
] | We propose a novel paradigm for automatic fact-checking that leverages frame semantics to enhance the structured understanding of claims and guide the process of fact-checking them. To support this, we introduce a pilot dataset of real-world claims extracted from PolitiFact, specifically annotated for large-scale struc... | {
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2501.13295 | Parallel Belief Contraction via Order Aggregation | [
"cs.AI"
] | The standard ``serial'' (aka ``singleton'') model of belief contraction models the manner in which an agent's corpus of beliefs responds to the removal of a single item of information. One salient extension of this model introduces the idea of ``parallel'' (aka ``package'' or ``multiple'') change, in which an entire se... | {
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2501.13296 | Exploring Variance Reduction in Importance Sampling for Efficient DNN
Training | [
"cs.LG",
"stat.ML"
] | Importance sampling is widely used to improve the efficiency of deep neural network (DNN) training by reducing the variance of gradient estimators. However, efficiently assessing the variance reduction relative to uniform sampling remains challenging due to computational overhead. This paper proposes a method for estim... | {
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2501.13297 | RAMQA: A Unified Framework for Retrieval-Augmented Multi-Modal Question
Answering | [
"cs.CL",
"cs.AI",
"cs.IR",
"cs.LG"
] | Multi-modal retrieval-augmented Question Answering (MRAQA), integrating text and images, has gained significant attention in information retrieval (IR) and natural language processing (NLP). Traditional ranking methods rely on small encoder-based language models, which are incompatible with modern decoder-based generat... | {
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2501.13298 | Collaborative Coded Caching for Partially Connected Networks | [
"cs.IT",
"math.IT"
] | Coded caching leverages the differences in user cache memories to achieve gains that scale with the total cache size, alleviating network congestion due to high-quality content requests. Additionally, distributing transmitters over a wide area can mitigate the adverse effects of path loss. In this work, we consider a p... | {
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2501.13299 | Hypothesis Generation for Materials Discovery and Design Using
Goal-Driven and Constraint-Guided LLM Agents | [
"cs.CL"
] | Materials discovery and design are essential for advancing technology across various industries by enabling the development of application-specific materials. Recent research has leveraged Large Language Models (LLMs) to accelerate this process. We explore the potential of LLMs to generate viable hypotheses that, once ... | {
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2501.13302 | Watching the AI Watchdogs: A Fairness and Robustness Analysis of AI
Safety Moderation Classifiers | [
"cs.CL",
"cs.AI"
] | AI Safety Moderation (ASM) classifiers are designed to moderate content on social media platforms and to serve as guardrails that prevent Large Language Models (LLMs) from being fine-tuned on unsafe inputs. Owing to their potential for disparate impact, it is crucial to ensure that these classifiers: (1) do not unfairl... | {
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2501.13306 | OSUM: Advancing Open Speech Understanding Models with Limited Resources
in Academia | [
"cs.SD",
"cs.CL",
"eess.AS"
] | Large Language Models (LLMs) have made significant progress in various downstream tasks, inspiring the development of Speech Understanding Language Models (SULMs) to enable comprehensive speech-based interactions. However, most advanced SULMs are developed by the industry, leveraging large-scale datasets and computatio... | {
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2501.13307 | From Cross-Modal to Mixed-Modal Visible-Infrared Re-Identification | [
"cs.CV"
] | Visible-infrared person re-identification (VI-ReID) aims to match individuals across different camera modalities, a critical task in modern surveillance systems. While current VI-ReID methods focus on cross-modality matching, real-world applications often involve mixed galleries containing both V and I images, where st... | {
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2501.13312 | Tensor-Var: Variational Data Assimilation in Tensor Product Feature
Space | [
"cs.LG"
] | Variational data assimilation estimates the dynamical system states by minimizing a cost function that fits the numerical models with observational data. The widely used method, four-dimensional variational assimilation (4D-Var), has two primary challenges: (1) computationally demanding for complex nonlinear systems an... | {
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2501.13320 | Toward Ethical AI: A Qualitative Analysis of Stakeholder Perspectives | [
"cs.CY",
"cs.AI"
] | As Artificial Intelligence (AI) systems become increasingly integrated into various aspects of daily life, concerns about privacy and ethical accountability are gaining prominence. This study explores stakeholder perspectives on privacy in AI systems, focusing on educators, parents, and AI professionals. Using qualitat... | {
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2501.13321 | Investigation of the Privacy Concerns in AI Systems for Young Digital
Citizens: A Comparative Stakeholder Analysis | [
"cs.CY",
"cs.AI"
] | The integration of Artificial Intelligence (AI) systems into technologies used by young digital citizens raises significant privacy concerns. This study investigates these concerns through a comparative analysis of stakeholder perspectives. A total of 252 participants were surveyed, with the analysis focusing on 110 va... | {
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2501.13324 | Comparative Withholding Behavior Analysis of Historical Energy Storage
Bids in California | [
"eess.SY",
"cs.SY",
"econ.TH"
] | The rapid growth of battery energy storage in wholesale electricity markets calls for a deeper understanding of storage operators' bidding strategies and their market impacts. This study examines energy storage bidding data from the California Independent System Operator (CAISO) between July 1, 2023, and October 1, 202... | {
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2501.13329 | Sparse identification of nonlinear dynamics and Koopman operators with
Shallow Recurrent Decoder Networks | [
"cs.LG",
"cs.AI",
"math.DS"
] | Spatiotemporal modeling of real-world data poses a challenging problem due to inherent high dimensionality, measurement noise, and expensive data collection procedures. In this paper, we present Sparse Identification of Nonlinear Dynamics with SHallow REcurrent Decoder networks (SINDy-SHRED), a method to jointly solve ... | {
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2501.13331 | Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant
Data Razoring | [
"cs.LG"
] | Large-scale language models (LLMs) excel in language processing tasks but face deployment challenges due to high memory and computational demands. While low-bit quantization, such as 4-bit techniques, offers a potential solution, these methods often suffer from significant accuracy loss or require considerable effort f... | {
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2501.13332 | Co-Learning Bayesian Optimization | [
"cs.LG",
"stat.ML"
] | Bayesian optimization (BO) is well known to be sample-efficient for solving black-box problems. However, the BO algorithms can sometimes get stuck in suboptimal solutions even with plenty of samples. Intrinsically, such suboptimal problem of BO can attribute to the poor surrogate accuracy of the trained Gaussian proces... | {
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2501.13333 | AgentRec: Agent Recommendation Using Sentence Embeddings Aligned to
Human Feedback | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.MA"
] | Multi-agent systems must decide which agent is the most appropriate for a given task. We propose a novel architecture for recommending which LLM agent out of many should perform a task given a natural language prompt by extending the Sentence-BERT (SBERT) encoder model. On test data, we are able to achieve a top-1 accu... | {
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2501.13335 | Deblur-Avatar: Animatable Avatars from Motion-Blurred Monocular Videos | [
"cs.CV"
] | We introduce Deblur-Avatar, a novel framework for modeling high-fidelity, animatable 3D human avatars from motion-blurred monocular video inputs. Motion blur is prevalent in real-world dynamic video capture, especially due to human movements in 3D human avatar modeling. Existing methods either (1) assume sharp image in... | {
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2501.13336 | Gradient-Free Adversarial Purification with Diffusion Models | [
"cs.CV",
"eess.IV"
] | Adversarial training and adversarial purification are two effective and practical defense methods to enhance a model's robustness against adversarial attacks. However, adversarial training necessitates additional training, while adversarial purification suffers from low time efficiency. More critically, current defense... | {
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2501.13337 | Generative Multi-Form Bayesian Optimization | [
"cs.CE"
] | Many real-world problems, such as airfoil design, involve optimizing a black-box expensive objective function over complex structured input space (e.g., discrete space or non-Euclidean space). By mapping the complex structured input space into a latent space of dozens of variables, a two-stage procedure labeled as gene... | {
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2501.13338 | CuriousBot: Interactive Mobile Exploration via Actionable 3D Relational
Object Graph | [
"cs.RO",
"cs.CV",
"cs.LG"
] | Mobile exploration is a longstanding challenge in robotics, yet current methods primarily focus on active perception instead of active interaction, limiting the robot's ability to interact with and fully explore its environment. Existing robotic exploration approaches via active interaction are often restricted to tabl... | {
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2501.13340 | Retrievals Can Be Detrimental: A Contrastive Backdoor Attack Paradigm on
Retrieval-Augmented Diffusion Models | [
"cs.CV"
] | Diffusion models (DMs) have recently demonstrated remarkable generation capability. However, their training generally requires huge computational resources and large-scale datasets. To solve these, recent studies empower DMs with the advanced Retrieval-Augmented Generation (RAG) technique and propose retrieval-augmente... | {
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2501.13341 | Multi-aspect Knowledge Distillation with Large Language Model | [
"cs.CV"
] | Recent advancements in deep learning have significantly improved performance on computer vision tasks. Previous image classification methods primarily modify model architectures or add features, and they optimize models using cross-entropy loss on class logits. Since they focus on classifying images with considering cl... | {
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2501.13343 | YOLOSCM: An improved YOLO algorithm for cars detection | [
"cs.CV"
] | Detecting objects in urban traffic images presents considerable difficulties because of the following reasons: 1) These images are typically immense in size, encompassing millions or even hundreds of millions of pixels, yet computational resources are constrained. 2) The small size of vehicles in certain scenarios lead... | {
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2501.13344 | Full-Stack Optimized Large Language Models for Lifelong Sequential
Behavior Comprehension in Recommendation | [
"cs.IR",
"cs.AI"
] | In this paper, we address the lifelong sequential behavior incomprehension problem in large language models (LLMs) for recommendation, where LLMs struggle to extract useful information from long user behavior sequences, even within their context limits. To tackle this, we propose ReLLaX (Retrieval-enhanced Large Langua... | {
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2501.13347 | One Fits All: General Mobility Trajectory Modeling via Masked
Conditional Diffusion | [
"cs.LG",
"cs.AI"
] | Trajectory data play a crucial role in many applications, ranging from network optimization to urban planning. Existing studies on trajectory data are task-specific, and their applicability is limited to the specific tasks on which they have been trained, such as generation, recovery, or prediction. However, the potent... | {
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2501.13349 | MSF: Efficient Diffusion Model Via Multi-Scale Latent Factorize | [
"cs.CV"
] | Diffusion-based generative models have achieved remarkable progress in visual content generation. However, traditional diffusion models directly denoise the entire image from noisy inputs, disregarding the hierarchical structure present in visual signals. This method is computationally intensive, especially for high-re... | {
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2501.13350 | DoMINO: A Decomposable Multi-scale Iterative Neural Operator for
Modeling Large Scale Engineering Simulations | [
"cs.LG",
"physics.comp-ph"
] | Numerical simulations play a critical role in design and development of engineering products and processes. Traditional computational methods, such as CFD, can provide accurate predictions but are computationally expensive, particularly for complex geometries. Several machine learning (ML) models have been proposed in ... | {
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2501.13352 | Polyhedra Encoding Transformers: Enhancing Diffusion MRI Analysis Beyond
Voxel and Volumetric Embedding | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Diffusion-weighted Magnetic Resonance Imaging (dMRI) is an essential tool in neuroimaging. It is arguably the sole noninvasive technique for examining the microstructural properties and structural connectivity of the brain. Recent years have seen the emergence of machine learning and data-driven approaches that enhance... | {
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2501.13353 | Contrast: A Hybrid Architecture of Transformers and State Space Models
for Low-Level Vision | [
"cs.CV"
] | Transformers have become increasingly popular for image super-resolution (SR) tasks due to their strong global context modeling capabilities. However, their quadratic computational complexity necessitates the use of window-based attention mechanisms, which restricts the receptive field and limits effective context expa... | {
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2501.13354 | NUDT4MSTAR: A Large Dataset and Benchmark Towards Remote Sensing Object
Recognition in the Wild | [
"cs.CV"
] | As an indispensable sensor for Remote sensing, Synthetic Aperture Radar (SAR) has a unique capability for all-day imaging. Nevertheless, in a data-driven era, the scarcity of large-scale datasets poses a significant bottleneck to advancing SAR automatic target recognition (ATR) technology. This paper introduces NUDT4MS... | {
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2501.13357 | A light-weight model to generate NDWI from Sentinel-1 | [
"cs.CV",
"eess.IV"
] | The use of Sentinel-2 images to compute Normalized Difference Water Index (NDWI) has many applications, including water body area detection. However, cloud cover poses significant challenges in this regard, which hampers the effectiveness of Sentinel-2 images in this context. In this paper, we present a deep learning m... | {
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2501.13358 | Learning to Bid in Non-Stationary Repeated First-Price Auctions | [
"cs.LG",
"cs.GT",
"cs.IT",
"math.IT",
"stat.ML"
] | First-price auctions have recently gained significant traction in digital advertising markets, exemplified by Google's transition from second-price to first-price auctions. Unlike in second-price auctions, where bidding one's private valuation is a dominant strategy, determining an optimal bidding strategy in first-pri... | {
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2501.13364 | Task Allocation in Customer-led Two-sided Markets with Satellite
Constellation Services | [
"cs.GT",
"cs.MA"
] | Multi-agent systems (MAS) are increasingly applied to complex task allocation in two-sided markets, where agents such as companies and customers interact dynamically. Traditional company-led Stackelberg game models, where companies set service prices, and customers respond, struggle to accommodate diverse and personali... | {
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2501.13365 | Enhanced Extractor-Selector Framework and Symmetrization Weighted Binary
Cross-Entropy for Edge Detections | [
"cs.CV",
"cs.AI"
] | Recent advancements have demonstrated the effectiveness of the extractor-selector (E-S) framework in edge detection (ED) tasks, which achieves state-of-the-art (SOTA) performance in both quantitative metrics and perceptual quality. However, this method still falls short of fully exploiting the potential of feature extr... | {
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2501.13368 | Meta-Feature Adapter: Integrating Environmental Metadata for Enhanced
Animal Re-identification | [
"cs.CV",
"cs.LG"
] | Identifying individual animals within large wildlife populations is essential for effective wildlife monitoring and conservation efforts. Recent advancements in computer vision have shown promise in animal re-identification (Animal ReID) by leveraging data from camera traps. However, existing methods rely exclusively o... | {
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2501.13369 | A review on development of eco-friendly filters in Nepal for use in
cigarettes and masks and Air Pollution Analysis with Machine Learning and
SHAP Interpretability | [
"cs.LG",
"cs.AI"
] | In Nepal, air pollution is a serious public health concern, especially in cities like Kathmandu where particulate matter (PM2.5 and PM10) has a major influence on respiratory health and air quality. The Air Quality Index (AQI) is predicted in this work using a Random Forest Regressor, and the model's predictions are in... | {
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2501.13370 | Unraveling Normal Anatomy via Fluid-Driven Anomaly Randomization | [
"eess.IV",
"cs.CV"
] | Data-driven machine learning has made significant strides in medical image analysis. However, most existing methods are tailored to specific modalities and assume a particular resolution (often isotropic). This limits their generalizability in clinical settings, where variations in scan appearance arise from difference... | {
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2501.13372 | Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for
Personalized Speech Enhancement | [
"eess.AS",
"cs.AI"
] | This paper presents a new challenge that calls for zero-shot text-to-speech (TTS) systems to augment speech data for the downstream task, personalized speech enhancement (PSE), as part of the Generative Data Augmentation workshop at ICASSP 2025. Collecting high-quality personalized data is challenging due to privacy co... | {
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2501.13373 | Advancing Carbon Capture using AI: Design of permeable membrane and
estimation of parameters for Carbon Capture using linear regression and
membrane-based equations | [
"physics.chem-ph",
"cs.LG"
] | This study focuses on membrane-based systems for CO$_2$ separation, addressing the urgent need for efficient carbon capture solutions to mitigate climate change. Linear regression models, based on membrane equations, were utilized to estimate key parameters, including porosity ($\epsilon$) of 0.4805, Kozeny constant (K... | {
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2501.13375 | Bridging The Multi-Modality Gaps of Audio, Visual and Linguistic for
Speech Enhancement | [
"cs.SD",
"cs.LG",
"cs.MM",
"eess.AS"
] | Speech Enhancement (SE) aims to improve the quality of noisy speech. It has been shown that additional visual cues can further improve performance. Given that speech communication involves audio, visual, and linguistic modalities, it is natural to expect another performance boost by incorporating linguistic information... | {
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2501.13376 | Scalable Evaluation Framework for Foundation Models in Musculoskeletal
MRI Bridging Computational Innovation with Clinical Utility | [
"eess.IV",
"cs.CV"
] | Foundation models hold transformative potential for medical imaging, but their clinical utility requires rigorous evaluation to address their strengths and limitations. This study introduces an evaluation framework for assessing the clinical impact and translatability of SAM, MedSAM, and SAM2, using musculoskeletal MRI... | {
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2501.13377 | Concentration in Governance Control Across Decentralised Finance
Protocols | [
"cs.CE"
] | Blockchain-based systems are frequently governed through tokens that grant their holders voting rights over core protocol functions and funds. The centralisation occurring in Decentralised Finance (DeFi) protocols' token-based voting systems is typically analysed by examining token holdings' distribution across address... | {
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2501.13380 | On the Massive MIMO Channel Polarization | [
"cs.IT",
"math.IT"
] | In this work, we demonstrate that an $n \times n$ massive multiple-input multiple-output (MIMO) channel can be polarized using common matrix decomposition techniques: singular value decomposition (SVD) and QR decomposition. With full channel state information (CSI), we show that channel capacity is always attained by f... | {
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2501.13381 | Do as We Do, Not as You Think: the Conformity of Large Language Models | [
"cs.CL"
] | Recent advancements in large language models (LLMs) revolutionize the field of intelligent agents, enabling collaborative multi-agent systems capable of tackling complex problems across various domains. However, the potential of conformity within these systems, analogous to phenomena like conformity bias and groupthink... | {
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2501.13385 | Fast and Provable Tensor-Train Format Tensor Completion via
Precondtioned Riemannian Gradient Descent | [
"cs.LG",
"cs.NA",
"math.NA",
"math.OC"
] | Low-rank tensor completion aims to recover a tensor from partially observed entries, and it is widely applicable in fields such as quantum computing and image processing. Due to the significant advantages of the tensor train (TT) format in handling structured high-order tensors, this paper investigates the low-rank ten... | {
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2501.13387 | From Images to Point Clouds: An Efficient Solution for Cross-media Blind
Quality Assessment without Annotated Training | [
"cs.CV",
"eess.IV"
] | We present a novel quality assessment method which can predict the perceptual quality of point clouds from new scenes without available annotations by leveraging the rich prior knowledge in images, called the Distribution-Weighted Image-Transferred Point Cloud Quality Assessment (DWIT-PCQA). Recognizing the human visua... | {
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2501.13389 | AEON: Adaptive Estimation of Instance-Dependent In-Distribution and
Out-of-Distribution Label Noise for Robust Learning | [
"cs.CV"
] | Robust training with noisy labels is a critical challenge in image classification, offering the potential to reduce reliance on costly clean-label datasets. Real-world datasets often contain a mix of in-distribution (ID) and out-of-distribution (OOD) instance-dependent label noise, a challenge that is rarely addressed ... | {
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2501.13390 | Beyond Task Diversity: Provable Representation Transfer for Sequential
Multi-Task Linear Bandits | [
"cs.LG"
] | We study lifelong learning in linear bandits, where a learner interacts with a sequence of linear bandit tasks whose parameters lie in an $m$-dimensional subspace of $\mathbb{R}^d$, thereby sharing a low-rank representation. Current literature typically assumes that the tasks are diverse, i.e., their parameters uniform... | {
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2501.13391 | Can Large Language Models Understand Preferences in Personalized
Recommendation? | [
"cs.CL"
] | Large Language Models (LLMs) excel in various tasks, including personalized recommendations. Existing evaluation methods often focus on rating prediction, relying on regression errors between actual and predicted ratings. However, user rating bias and item quality, two influential factors behind rating scores, can obsc... | {
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2501.13392 | Time Series Embedding Methods for Classification Tasks: A Review | [
"cs.LG"
] | Time series analysis has become crucial in various fields, from engineering and finance to healthcare and social sciences. In this paper, we present a comprehensive review and evaluation of time series embedding methods for effective representations in machine learning and deep learning models. We introduce a taxonomy ... | {
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2501.13394 | Concurrent Learning with Aggregated States via Randomized Least Squares
Value Iteration | [
"cs.LG",
"cs.AI"
] | Designing learning agents that explore efficiently in a complex environment has been widely recognized as a fundamental challenge in reinforcement learning. While a number of works have demonstrated the effectiveness of techniques based on randomized value functions on a single agent, it remains unclear, from a theoret... | {
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2501.13396 | Towards Intelligent Design: A Self-driven Framework for Collocated
Clothing Synthesis Leveraging Fashion Styles and Textures | [
"cs.CV"
] | Collocated clothing synthesis (CCS) has emerged as a pivotal topic in fashion technology, primarily concerned with the generation of a clothing item that harmoniously matches a given item. However, previous investigations have relied on using paired outfits, such as a pair of matching upper and lower clothing, to train... | {
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2501.13397 | ExLM: Rethinking the Impact of [MASK] Tokens in Masked Language Models | [
"cs.CL",
"cs.LG"
] | Masked Language Models (MLMs) have achieved remarkable success in many self-supervised representation learning tasks. MLMs are trained by randomly masking portions of the input sequences with [MASK] tokens and learning to reconstruct the original content based on the remaining context. This paper explores the impact of... | {
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2501.13400 | YOLOv8 to YOLO11: A Comprehensive Architecture In-depth Comparative
Review | [
"cs.CV",
"cs.AI"
] | In the field of deep learning-based computer vision, YOLO is revolutionary. With respect to deep learning models, YOLO is also the one that is evolving the most rapidly. Unfortunately, not every YOLO model possesses scholarly publications. Moreover, there exists a YOLO model that lacks a publicly accessible official ar... | {
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2501.13402 | VIGS SLAM: IMU-based Large-Scale 3D Gaussian Splatting SLAM | [
"cs.RO",
"cs.CV",
"cs.LG"
] | Recently, map representations based on radiance fields such as 3D Gaussian Splatting and NeRF, which excellent for realistic depiction, have attracted considerable attention, leading to attempts to combine them with SLAM. While these approaches can build highly realistic maps, large-scale SLAM still remains a challenge... | {
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2501.13403 | ROMA: ROtary and Movable Antenna | [
"eess.SP",
"cs.IT",
"math.IT"
] | The rotary and movable antenna (ROMA) architecture represents a next-generation multi-antenna technology that enables flexible adjustment of antenna position and array rotation angles of the transceiver. In this letter, we propose a ROMA-aided multi-user MIMO communication system to fully enhance the efficiency and rel... | {
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2501.13405 | Performance Analysis of Fluid Antenna Multiple Access Assisted Wireless
Powered Communication Network | [
"cs.IT",
"eess.SP",
"math.IT"
] | This paper investigates a novel fluid antenna multiple access (FAMA)-assisted wireless powered communication network (WPCN), in which a hybrid access point (HAP) equipped with multiple fixed position antennas (FPAs) provides integrated data and energy transfer (IDET) services towards low-power devices that are equipped... | {
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2501.13412 | Load and Renewable Energy Forecasting Using Deep Learning for Grid
Stability | [
"cs.LG",
"cs.AI"
] | As the energy landscape changes quickly, grid operators face several challenges, especially when integrating renewable energy sources with the grid. The most important challenge is to balance supply and demand because the solar and wind energy are highly unpredictable. When dealing with such uncertainty, trustworthy sh... | {
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2501.13414 | Physics-Aware Sparse Signal Recovery Through PDE-Governed Measurement
Systems | [
"cs.IT",
"math.IT"
] | This paper introduces a novel framework for physics-aware sparse signal recovery in measurement systems governed by partial differential equations (PDEs). Unlike conventional compressed sensing approaches that treat measurement systems as simple linear systems, our method explicitly incorporates the underlying physics ... | {
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2501.13416 | M3PT: A Transformer for Multimodal, Multi-Party Social Signal Prediction
with Person-aware Blockwise Attention | [
"cs.LG",
"cs.AI",
"cs.RO"
] | Understanding social signals in multi-party conversations is important for human-robot interaction and artificial social intelligence. Social signals include body pose, head pose, speech, and context-specific activities like acquiring and taking bites of food when dining. Past work in multi-party interaction tends to b... | {
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2501.13417 | GeomGS: LiDAR-Guided Geometry-Aware Gaussian Splatting for Robot
Localization | [
"cs.RO",
"cs.CV",
"cs.LG"
] | Mapping and localization are crucial problems in robotics and autonomous driving. Recent advances in 3D Gaussian Splatting (3DGS) have enabled precise 3D mapping and scene understanding by rendering photo-realistic images. However, existing 3DGS methods often struggle to accurately reconstruct a 3D map that reflects th... | {
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2501.13418 | Rethinking the Sample Relations for Few-Shot Classification | [
"cs.CV",
"cs.AI"
] | Feature quality is paramount for classification performance, particularly in few-shot scenarios. Contrastive learning, a widely adopted technique for enhancing feature quality, leverages sample relations to extract intrinsic features that capture semantic information and has achieved remarkable success in Few-Shot Lear... | {
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2501.13419 | A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future
Directions | [
"cs.CL"
] | Language in the Arab world presents a complex diglossic and multilingual setting, involving the use of Modern Standard Arabic, various dialects and sub-dialects, as well as multiple European languages. This diverse linguistic landscape has given rise to code-switching, both within Arabic varieties and between Arabic an... | {
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2501.13420 | LVFace: Large Vision model for Face Recogniton | [
"cs.CV"
] | Recently, large vision models have demonstrated powerful representation capabilities in the field of computer vision. However, we unexpectedly found that face recognition research is still mainly focused on CNN-based model architectures, which may lead to suboptimal state-of-the-art (SOTA) performance in face recogniti... | {
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2501.13421 | Perceived Fairness of the Machine Learning Development Process: Concept
Scale Development | [
"cs.HC",
"cs.CY",
"cs.LG"
] | In machine learning (ML) applications, unfairness is triggered due to bias in the data, the data curation process, erroneous assumptions, and implicit bias rendered during the development process. It is also well-accepted by researchers that fairness in ML application development is highly subjective, with a lack of cl... | {
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2501.13422 | Atmospheric Noise-Resilient Image Classification in a Real-World
Scenario: Using Hybrid CNN and Pin-GTSVM | [
"cs.CV"
] | Parking space occupation detection using deep learning frameworks has seen significant advancements over the past few years. While these approaches effectively detect partial obstructions and adapt to varying lighting conditions, their performance significantly diminishes when haze is present. This paper proposes a nov... | {
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2501.13426 | Auto-Prompting SAM for Weakly Supervised Landslide Extraction | [
"cs.CV"
] | Weakly supervised landslide extraction aims to identify landslide regions from remote sensing data using models trained with weak labels, particularly image-level labels. However, it is often challenged by the imprecise boundaries of the extracted objects due to the lack of pixel-wise supervision and the properties of ... | {
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2501.13428 | Softplus Attention with Re-weighting Boosts Length Extrapolation in
Large Language Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Large language models have achieved remarkable success in recent years, primarily due to the implementation of self-attention mechanisms. However, traditional Softmax attention suffers from numerical instability and reduced performance as the length of inference tokens increases. This paper addresses these issues by de... | {
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2501.13430 | Wasserstein-regularized Conformal Prediction under General Distribution
Shift | [
"cs.LG",
"stat.ML"
] | Conformal prediction yields a prediction set with guaranteed $1-\alpha$ coverage of the true target under the i.i.d. assumption, which may not hold and lead to a gap between $1-\alpha$ and the actual coverage. Prior studies bound the gap using total variation distance, which cannot identify the gap changes under distri... | {
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2501.13431 | Optimizing the Trade-off Between Throughput and PAoI Outage Exponents | [
"cs.NI",
"cs.IT",
"math.IT"
] | This paper investigates the trade-off between throughput and peak age of information (PAoI) outage probability in a multi-sensor information collection system. Each sensor monitors a physical process, periodically samples its status, and transmits the updates to a central access point over a shared radio resource. The ... | {
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2501.13432 | Emotion estimation from video footage with LSTM | [
"cs.CV",
"cs.LG",
"cs.RO"
] | Emotion estimation in general is a field that has been studied for a long time, and several approaches exist using machine learning. in this paper, we present an LSTM model, that processes the blend-shapes produced by the library MediaPipe, for a face detected in a live stream of a camera, to estimate the main emotion ... | {
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2501.13435 | GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for
Robust Deepfake Detection | [
"cs.CV"
] | The rapid development of Deepfake technology has enabled the generation of highly realistic manipulated videos, posing severe social and ethical challenges. Existing Deepfake detection methods primarily focused on either spatial or temporal inconsistencies, often neglecting the interplay between the two or suffering fr... | {
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"cs.SI": 0,
"cs.SY": 0
} |
2501.13439 | One-cycle Structured Pruning with Stability Driven Structure Search | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Existing structured pruning typically involves multi-stage training procedures that often demand heavy computation. Pruning at initialization, which aims to address this limitation, reduces training costs but struggles with performance. To address these challenges, we propose an efficient framework for one-cycle struct... | {
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} |
2501.13442 | Billion-scale Similarity Search Using a Hybrid Indexing Approach with
Advanced Filtering | [
"cs.IR",
"cs.DB",
"cs.DC",
"cs.LG"
] | This paper presents a novel approach for similarity search with complex filtering capabilities on billion-scale datasets, optimized for CPU inference. Our method extends the classical IVF-Flat index structure to integrate multi-dimensional filters. The proposed algorithm combines dense embeddings with discrete filterin... | {
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} |
2501.13444 | Explicit Construction of Classical and Quantum Quasi-Cyclic Low-Density
Parity-Check Codes with Column Weight 2 and Girth 12 | [
"cs.IT",
"math.IT",
"quant-ph"
] | This study proposes an explicit construction method for classical and quantum quasi-cyclic low-density parity-check (QC-LDPC) codes with a girth of 12. The proposed method designs parity-check matrices that maximize the girth while maintaining an orthogonal structure suitable for quantum error correction. By utilizing ... | {
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} |
2501.13448 | BMG-Q: Localized Bipartite Match Graph Attention Q-Learning for
Ride-Pooling Order Dispatch | [
"cs.MA",
"cs.AI",
"cs.ET",
"cs.LG"
] | This paper introduces Localized Bipartite Match Graph Attention Q-Learning (BMG-Q), a novel Multi-Agent Reinforcement Learning (MARL) algorithm framework tailored for ride-pooling order dispatch. BMG-Q advances ride-pooling decision-making process with the localized bipartite match graph underlying the Markov Decision ... | {
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} |
2501.13449 | MultiDreamer3D: Multi-concept 3D Customization with Concept-Aware
Diffusion Guidance | [
"cs.CV"
] | While single-concept customization has been studied in 3D, multi-concept customization remains largely unexplored. To address this, we propose MultiDreamer3D that can generate coherent multi-concept 3D content in a divide-and-conquer manner. First, we generate 3D bounding boxes using an LLM-based layout controller. Nex... | {
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"cs.SY": 0
} |
2501.13451 | Deep Modularity Networks with Diversity--Preserving Regularization | [
"cs.LG"
] | Graph clustering plays a crucial role in graph representation learning but often faces challenges in achieving feature-space diversity. While Deep Modularity Networks (DMoN) leverage modularity maximization and collapse regularization to ensure structural separation, they do not explicitly encourage diversity in the fe... | {
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} |
2501.13452 | EchoVideo: Identity-Preserving Human Video Generation by Multimodal
Feature Fusion | [
"cs.CV"
] | Recent advancements in video generation have significantly impacted various downstream applications, particularly in identity-preserving video generation (IPT2V). However, existing methods struggle with "copy-paste" artifacts and low similarity issues, primarily due to their reliance on low-level facial image informati... | {
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} |
2501.13453 | Spurious Forgetting in Continual Learning of Language Models | [
"cs.LG"
] | Recent advancements in large language models (LLMs) reveal a perplexing phenomenon in continual learning: despite extensive training, models experience significant performance declines, raising questions about task alignment and underlying knowledge retention. This study first explores the concept of "spurious forgetti... | {
"Other": 0,
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} |
2501.13456 | KAA: Kolmogorov-Arnold Attention for Enhancing Attentive Graph Neural
Networks | [
"cs.LG",
"cs.AI"
] | Graph neural networks (GNNs) with attention mechanisms, often referred to as attentive GNNs, have emerged as a prominent paradigm in advanced GNN models in recent years. However, our understanding of the critical process of scoring neighbor nodes remains limited, leading to the underperformance of many existing attenti... | {
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} |
2501.13457 | Zero-Shot Trajectory Planning for Signal Temporal Logic Tasks | [
"cs.RO",
"cs.AI",
"cs.LG",
"cs.SY",
"eess.SY"
] | Signal Temporal Logic (STL) is a powerful specification language for describing complex temporal behaviors of continuous signals, making it well-suited for high-level robotic task descriptions. However, generating executable plans for STL tasks is challenging, as it requires consideration of the coupling between the ta... | {
"Other": 0,
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
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