id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.01761 | Adverse Weather Conditions Augmentation of LiDAR Scenes with Latent
Diffusion Models | [
"cs.CV"
] | LiDAR scenes constitute a fundamental source for several autonomous driving applications. Despite the existence of several datasets, scenes from adverse weather conditions are rarely available. This limits the robustness of downstream machine learning models, and restrains the reliability of autonomous driving systems ... | {
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2501.01763 | Quantifying A Firm's AI Engagement: Constructing Objective, Data-Driven,
AI Stock Indices Using 10-K Filings | [
"q-fin.GN",
"cs.AI",
"econ.EM",
"q-fin.PM",
"q-fin.RM"
] | Following an analysis of existing AI-related exchange-traded funds (ETFs), we reveal the selection criteria for determining which stocks qualify as AI-related are often opaque and rely on vague phrases and subjective judgments. This paper proposes a new, objective, data-driven approach using natural language processing... | {
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2501.01765 | SaLoRA: Safety-Alignment Preserved Low-Rank Adaptation | [
"cs.LG"
] | As advancements in large language models (LLMs) continue and the demand for personalized models increases, parameter-efficient fine-tuning (PEFT) methods (e.g., LoRA) will become essential due to their efficiency in reducing computation costs. However, recent studies have raised alarming concerns that LoRA fine-tuning ... | {
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2501.01767 | LogicAD: Explainable Anomaly Detection via VLM-based Text Feature
Extraction | [
"cs.CV"
] | Logical image understanding involves interpreting and reasoning about the relationships and consistency within an image's visual content. This capability is essential in applications such as industrial inspection, where logical anomaly detection is critical for maintaining high-quality standards and minimizing costly r... | {
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2501.01770 | TCPFormer: Learning Temporal Correlation with Implicit Pose Proxy for 3D
Human Pose Estimation | [
"cs.CV"
] | Recent multi-frame lifting methods have dominated the 3D human pose estimation. However, previous methods ignore the intricate dependence within the 2D pose sequence and learn single temporal correlation. To alleviate this limitation, we propose TCPFormer, which leverages an implicit pose proxy as an intermediate repre... | {
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2501.01773 | Compressed Domain Prior-Guided Video Super-Resolution for Cloud Gaming
Content | [
"eess.IV",
"cs.CV"
] | Cloud gaming is an advanced form of Internet service that necessitates local terminals to decode within limited resources and time latency. Super-Resolution (SR) techniques are often employed on these terminals as an efficient way to reduce the required bit-rate bandwidth for cloud gaming. However, insufficient attenti... | {
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2501.01774 | A Unifying View of Linear Function Approximation in Off-Policy RL
Through Matrix Splitting and Preconditioning | [
"cs.LG"
] | Traditionally, TD and FQI are viewed as differing in the number of updates toward the target value function: TD makes one update, FQI makes an infinite number, and Partial Fitted Q-Iteration (PFQI) performs a finite number, such as the use of a target network in Deep Q-Networks (DQN) in the OPE setting. This perspectiv... | {
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2501.01776 | Smooth Rate Limiter Model for Power System Stability Analysis and
Control | [
"eess.SY",
"cs.SY"
] | The letter proposes a smooth Rate Limiter (RL) model for power system stability analysis and control. The proposed model enables the effects of derivative bounds to be incorporated into system eigenvalue analysis, while replicating the behavior of conventional non-smooth RLs with high fidelity. In addition, it can be d... | {
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2501.01779 | From Occasional to Steady: Habit Formation Insights From a Comprehensive
Fitness Study | [
"cs.CY",
"cs.CE",
"cs.SI"
] | Exercising regularly is widely recognized as a cornerstone of health, yet the challenge of sustaining consistent exercise habits persists. Understanding the factors that influence the formation of these habits is crucial for developing effective interventions. This study utilizes data from Mars Athletic Club, T\"urkiye... | {
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2501.01785 | Can Synthetic Data be Fair and Private? A Comparative Study of Synthetic
Data Generation and Fairness Algorithms | [
"cs.LG",
"cs.AI",
"cs.CY"
] | The increasing use of machine learning in learning analytics (LA) has raised significant concerns around algorithmic fairness and privacy. Synthetic data has emerged as a dual-purpose tool, enhancing privacy and improving fairness in LA models. However, prior research suggests an inverse relationship between fairness a... | {
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2501.01788 | Universal Online Temporal Calibration for Optimization-based
Visual-Inertial Navigation Systems | [
"cs.RO",
"cs.CV"
] | 6-Degree of Freedom (6DoF) motion estimation with a combination of visual and inertial sensors is a growing area with numerous real-world applications. However, precise calibration of the time offset between these two sensor types is a prerequisite for accurate and robust tracking. To address this, we propose a univers... | {
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2501.01790 | Ingredients: Blending Custom Photos with Video Diffusion Transformers | [
"cs.CV"
] | This paper presents a powerful framework to customize video creations by incorporating multiple specific identity (ID) photos, with video diffusion Transformers, referred to as \texttt{Ingredients}. Generally, our method consists of three primary modules: (\textbf{i}) a facial extractor that captures versatile and prec... | {
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2501.01791 | A Minimal Subset Approach for Efficient and Scalable Loop Closure | [
"cs.CV",
"cs.RO"
] | Loop closure detection in large-scale and long-term missions can be computationally demanding due to the need to identify, verify, and process numerous candidate pairs to establish edge connections for the pose graph optimization. Keyframe sampling mitigates this by reducing the number of frames stored and processed in... | {
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2501.01793 | Creating Artificial Students that Never Existed: Leveraging Large
Language Models and CTGANs for Synthetic Data Generation | [
"cs.LG",
"cs.AI"
] | In this study, we explore the growing potential of AI and deep learning technologies, particularly Generative Adversarial Networks (GANs) and Large Language Models (LLMs), for generating synthetic tabular data. Access to quality students data is critical for advancing learning analytics, but privacy concerns and strict... | {
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2501.01796 | Reading Between the Lines: A dataset and a study on why some texts are
tougher than others | [
"cs.CL"
] | Our research aims at better understanding what makes a text difficult to read for specific audiences with intellectual disabilities, more specifically, people who have limitations in cognitive functioning, such as reading and understanding skills, an IQ below 70, and challenges in conceptual domains. We introduce a sch... | {
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2501.01798 | JoyGen: Audio-Driven 3D Depth-Aware Talking-Face Video Editing | [
"cs.CV"
] | Significant progress has been made in talking-face video generation research; however, precise lip-audio synchronization and high visual quality remain challenging in editing lip shapes based on input audio. This paper introduces JoyGen, a novel two-stage framework for talking-face generation, comprising audio-driven l... | {
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2501.01799 | Grasping in Uncertain Environments: A Case Study For Industrial Robotic
Recycling | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Autonomous robotic grasping of uncertain objects in uncertain environments is an impactful open challenge for the industries of the future. One such industry is the recycling of Waste Electrical and Electronic Equipment (WEEE) materials, in which electric devices are disassembled and readied for the recovery of raw mat... | {
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2501.01801 | John Ellipsoids via Lazy Updates | [
"cs.DS",
"cs.LG"
] | We give a faster algorithm for computing an approximate John ellipsoid around $n$ points in $d$ dimensions. The best known prior algorithms are based on repeatedly computing the leverage scores of the points and reweighting them by these scores [CCLY19]. We show that this algorithm can be substantially sped up by delay... | {
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2501.01802 | BERT4MIMO: A Foundation Model using BERT Architecture for Massive MIMO
Channel State Information Prediction | [
"cs.IT",
"cs.AI",
"eess.SP",
"math.IT"
] | Massive MIMO (Multiple-Input Multiple-Output) is an advanced wireless communication technology, using a large number of antennas to improve the overall performance of the communication system in terms of capacity, spectral, and energy efficiency. The performance of MIMO systems is highly dependent on the quality of cha... | {
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2501.01805 | End-to-End Long Document Summarization using Gradient Caching | [
"cs.CL",
"cs.AI"
] | Training transformer-based encoder-decoder models for long document summarization poses a significant challenge due to the quadratic memory consumption during training. Several approaches have been proposed to extend the input length at test time, but training with these approaches is still difficult, requiring truncat... | {
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2501.01806 | TRG-planner: Traversal Risk Graph-Based Path Planning in Unstructured
Environments for Safe and Efficient Navigation | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Unstructured environments such as mountains, caves, construction sites, or disaster areas are challenging for autonomous navigation because of terrain irregularities. In particular, it is crucial to plan a path to avoid risky terrain and reach the goal quickly and safely. In this paper, we propose a method for safe and... | {
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2501.01808 | MoEE: Mixture of Emotion Experts for Audio-Driven Portrait Animation | [
"cs.CV"
] | The generation of talking avatars has achieved significant advancements in precise audio synchronization. However, crafting lifelike talking head videos requires capturing a broad spectrum of emotions and subtle facial expressions. Current methods face fundamental challenges: a) the absence of frameworks for modeling s... | {
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2501.01811 | QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations
Using Neural Network Potentials | [
"physics.chem-ph",
"cs.LG",
"physics.comp-ph"
] | Accurate prediction of protein-ligand binding affinities is crucial in drug discovery, particularly during hit-to-lead and lead optimization phases, however, limitations in ligand force fields continue to impact prediction accuracy. In this work, we validate relative binding free energy (RBFE) accuracy using neural net... | {
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2501.01813 | Eliciting Understandable Architectonic Gestures for Robotic Furniture
through Co-Design Improvisation | [
"cs.HC",
"cs.RO"
] | The vision of adaptive architecture proposes that robotic technologies could enable interior spaces to physically transform in a bidirectional interaction with occupants. Yet, it is still unknown how this interaction could unfold in an understandable way. Inspired by HRI studies where robotic furniture gestured intents... | {
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2501.01816 | Uncertainty-Aware Label Refinement on Hypergraphs for Personalized
Federated Facial Expression Recognition | [
"cs.CV"
] | Most facial expression recognition (FER) models are trained on large-scale expression data with centralized learning. Unfortunately, collecting a large amount of centralized expression data is difficult in practice due to privacy concerns of facial images. In this paper, we investigate FER under the framework of person... | {
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2501.01817 | Distributed Framework Construction for Affine Formation Control | [
"eess.SY",
"cs.SY"
] | In affine formation control problems, the construction of the framework with universal rigidity and affine localizability is a critical prerequisite, but it has not yet been well addressed, especially when additional agents join the formation or link/agent failures emerge. Motivated by this observation, we investigate ... | {
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2501.01818 | Rerouting LLM Routers | [
"cs.CR",
"cs.LG"
] | LLM routers aim to balance quality and cost of generation by classifying queries and routing them to a cheaper or more expensive LLM depending on their complexity. Routers represent one type of what we call LLM control planes: systems that orchestrate use of one or more LLMs. In this paper, we investigate routers' adve... | {
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2501.01821 | SDPO: Segment-Level Direct Preference Optimization for Social Agents | [
"cs.AI",
"cs.CL"
] | Social agents powered by large language models (LLMs) can simulate human social behaviors but fall short in handling complex goal-oriented social dialogues. Direct Preference Optimization (DPO) has proven effective in aligning LLM behavior with human preferences across a variety of agent tasks. Existing DPO-based appro... | {
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2501.01825 | Unified Native Spaces in Kernel Methods | [
"stat.ML",
"cs.LG"
] | There exists a plethora of parametric models for positive definite kernels, and their use is ubiquitous in disciplines as diverse as statistics, machine learning, numerical analysis, and approximation theory. Usually, the kernel parameters index certain features of an associated process. Amongst those features, smoothn... | {
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2501.01827 | The Proof is in the Almond Cookies | [
"cs.CL",
"cs.AI"
] | This paper presents a case study on how to process cooking recipes (and more generally, how-to instructions) in a way that makes it possible for a robot or artificial cooking assistant to support human chefs in the kitchen. Such AI assistants would be of great benefit to society, as they can help to sustain the autonom... | {
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2501.01828 | Age-Based Device Selection and Transmit Power Optimization in
Over-the-Air Federated Learning | [
"cs.NI",
"cs.LG"
] | Recently, over-the-air federated learning (FL) has attracted significant attention for its ability to enhance communication efficiency. However, the performance of over-the-air FL is often constrained by device selection strategies and signal aggregation errors. In particular, neglecting straggler devices in FL can lea... | {
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2501.01830 | Auto-RT: Automatic Jailbreak Strategy Exploration for Red-Teaming Large
Language Models | [
"cs.CR",
"cs.AI",
"cs.CL"
] | Automated red-teaming has become a crucial approach for uncovering vulnerabilities in large language models (LLMs). However, most existing methods focus on isolated safety flaws, limiting their ability to adapt to dynamic defenses and uncover complex vulnerabilities efficiently. To address this challenge, we propose Au... | {
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2501.01831 | Online Fault Tolerance Strategy for Abrupt Reachability Constraint
Changes | [
"eess.SY",
"cs.RO",
"cs.SY"
] | When a system's constraints change abruptly, the system's reachability safety does no longer sustain. Thus, the system can reach a forbidden/dangerous value. Conventional remedy practically involves online controller redesign (OCR) to re-establish the reachability's compliance with the new constraints, which, however, ... | {
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2501.01832 | Time Series Language Model for Descriptive Caption Generation | [
"cs.CL",
"cs.LG"
] | The automatic generation of representative natural language descriptions for observable patterns in time series data enhances interpretability, simplifies analysis and increases cross-domain utility of temporal data. While pre-trained foundation models have made considerable progress in natural language processing (NLP... | {
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2501.01834 | MoColl: Agent-Based Specific and General Model Collaboration for Image
Captioning | [
"cs.CV",
"cs.AI"
] | Image captioning is a critical task at the intersection of computer vision and natural language processing, with wide-ranging applications across various domains. For complex tasks such as diagnostic report generation, deep learning models require not only domain-specific image-caption datasets but also the incorporati... | {
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2501.01835 | ASKCOS: an open source software suite for synthesis planning | [
"cs.AI"
] | The advancement of machine learning and the availability of large-scale reaction datasets have accelerated the development of data-driven models for computer-aided synthesis planning (CASP) in the past decade. Here, we detail the newest version of ASKCOS, an open source software suite for synthesis planning that makes ... | {
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2501.01836 | Practical machine learning is learning on small samples | [
"cs.LG",
"cs.AI"
] | Based on limited observations, machine learning discerns a dependence which is expected to hold in the future. What makes it possible? Statistical learning theory imagines indefinitely increasing training sample to justify its approach. In reality, there is no infinite time or even infinite general population for learn... | {
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2501.01840 | Signal Recovery Using a Spiked Mixture Model | [
"stat.ML",
"cs.LG"
] | We introduce the spiked mixture model (SMM) to address the problem of estimating a set of signals from many randomly scaled and noisy observations. Subsequently, we design a novel expectation-maximization (EM) algorithm to recover all parameters of the SMM. Numerical experiments show that in low signal-to-noise ratio r... | {
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2501.01841 | Dedicated Inference Engine and Binary-Weight Neural Networks for
Lightweight Instance Segmentation | [
"cs.CV",
"cs.AR"
] | Reducing computational costs is an important issue for development of embedded systems. Binary-weight Neural Networks (BNNs), in which weights are binarized and activations are quantized, are employed to reduce computational costs of various kinds of applications. In this paper, a design methodology of hardware archite... | {
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2501.01844 | Learning from Ambiguous Data with Hard Labels | [
"cs.LG"
] | Real-world data often contains intrinsic ambiguity that the common single-hard-label annotation paradigm ignores. Standard training using ambiguous data with these hard labels may produce overly confident models and thus leading to poor generalization. In this paper, we propose a novel framework called Quantized Label ... | {
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2501.01845 | Semantic Segmentation for Sequential Historical Maps by Learning from
Only One Map | [
"cs.CV"
] | Historical maps are valuable resources that capture detailed geographical information from the past. However, these maps are typically available in printed formats, which are not conducive to modern computer-based analyses. Digitizing these maps into a machine-readable format enables efficient computational analysis. I... | {
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2501.01849 | Multi-Agent Conversational Online Learning for Adaptive LLM Response
Identification | [
"cs.HC",
"cs.AI"
] | The remarkable generative capability of large language models (LLMs) has sparked a growing interest in automatically generating responses for different applications. Given the dynamic nature of user preferences and the uncertainty of LLM response performance, it is crucial to design efficient online learning algorithms... | {
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2501.01850 | LCFed: An Efficient Clustered Federated Learning Framework for
Heterogeneous Data | [
"cs.LG",
"cs.AI",
"cs.DC"
] | Clustered federated learning (CFL) addresses the performance challenges posed by data heterogeneity in federated learning (FL) by organizing edge devices with similar data distributions into clusters, enabling collaborative model training tailored to each group. However, existing CFL approaches strictly limit knowledge... | {
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2501.01855 | UAV-DETR: Efficient End-to-End Object Detection for Unmanned Aerial
Vehicle Imagery | [
"cs.CV"
] | Unmanned aerial vehicle object detection (UAV-OD) has been widely used in various scenarios. However, most existing UAV-OD algorithms rely on manually designed components, which require extensive tuning. End-to-end models that do not depend on such manually designed components are mainly designed for natural images, wh... | {
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2501.01859 | Deposition Rates in Thermal Laser Epitaxy: Simulation and Experiment | [
"cs.CE",
"physics.comp-ph"
] | The modeling of deposition rates in Thermal Laser Epitaxy (TLE) is essential for the accurate prediction of the evaporation process and for improved dynamic process control. We demonstrate excellent agreement between experimental data and a model based on a finite element simulation that describes the temperature distr... | {
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2501.01864 | Towards Hard and Soft Shadow Removal via Dual-Branch Separation Network
and Vision Transformer | [
"cs.CV"
] | Image shadow removal is a crucial task in computer vision. In real-world scenes, shadows alter image color and brightness, posing challenges for perception and texture recognition. Traditional and deep learning methods often overlook the distinct needs for handling hard and soft shadows, thereby lacking detailed proces... | {
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2501.01872 | Turning Logic Against Itself : Probing Model Defenses Through
Contrastive Questions | [
"cs.CL"
] | Large language models, despite extensive alignment with human values and ethical principles, remain vulnerable to sophisticated jailbreak attacks that exploit their reasoning abilities. Existing safety measures often detect overt malicious intent but fail to address subtle, reasoning-driven vulnerabilities. In this wor... | {
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2501.01874 | DFF: Decision-Focused Fine-tuning for Smarter Predict-then-Optimize with
Limited Data | [
"cs.LG"
] | Decision-focused learning (DFL) offers an end-to-end approach to the predict-then-optimize (PO) framework by training predictive models directly on decision loss (DL), enhancing decision-making performance within PO contexts. However, the implementation of DFL poses distinct challenges. Primarily, DL can result in devi... | {
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2501.01876 | Accuracy Can Lie: On the Impact of Surrogate Model in Configuration
Tuning | [
"cs.SE",
"cs.AI"
] | To ease the expensive measurements during configuration tuning, it is natural to build a surrogate model as the replacement of the system, and thereby the configuration performance can be cheaply evaluated. Yet, a stereotype therein is that the higher the model accuracy, the better the tuning result would be. This "acc... | {
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2501.01877 | ANTHROPOS-V: benchmarking the novel task of Crowd Volume Estimation | [
"cs.CV"
] | We introduce the novel task of Crowd Volume Estimation (CVE), defined as the process of estimating the collective body volume of crowds using only RGB images. Besides event management and public safety, CVE can be instrumental in approximating body weight, unlocking weight sensitive applications such as infrastructure ... | {
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2501.01880 | Long Context vs. RAG for LLMs: An Evaluation and Revisits | [
"cs.CL"
] | Extending context windows (i.e., Long Context, LC) and using retrievers to selectively access relevant information (i.e., Retrieval-Augmented Generation, RAG) are the two main strategies to enable LLMs to incorporate extremely long external contexts. This paper revisits recent studies on this topic, highlighting their ... | {
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2501.01884 | Telegram as a Battlefield: Kremlin-related Communications during the
Russia-Ukraine Conflict | [
"cs.SI",
"cs.CY",
"cs.HC"
] | Telegram emerged as a crucial platform for both parties during the conflict between Russia and Ukraine. Per its minimal policies for content moderation, Pro-Kremlin narratives and potential misinformation were spread on Telegram, while anti-Kremlin narratives with related content were also propagated, such as war foota... | {
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2501.01886 | Evaluating Scenario-based Decision-making for Interactive Autonomous
Driving Using Rational Criteria: A Survey | [
"cs.RO",
"cs.AI",
"cs.SY",
"eess.SY"
] | Autonomous vehicles (AVs) can significantly promote the advances in road transport mobility in terms of safety, reliability, and decarbonization. However, ensuring safety and efficiency in interactive during within dynamic and diverse environments is still a primary barrier to large-scale AV adoption. In recent years, ... | {
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2501.01889 | Exploring Equality: An Investigation into Custom Loss Functions for
Fairness Definitions | [
"cs.LG",
"cs.CY"
] | This paper explores the complex tradeoffs between various fairness metrics such as equalized odds, disparate impact, and equal opportunity and predictive accuracy within COMPAS by building neural networks trained with custom loss functions optimized to specific fairness criteria. This paper creates the first fairness-d... | {
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2501.01892 | QuArch: A Question-Answering Dataset for AI Agents in Computer
Architecture | [
"cs.AR",
"cs.AI",
"cs.LG"
] | We introduce QuArch, a dataset of 1500 human-validated question-answer pairs designed to evaluate and enhance language models' understanding of computer architecture. The dataset covers areas including processor design, memory systems, and performance optimization. Our analysis highlights a significant performance gap:... | {
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2501.01895 | EnerVerse: Envisioning Embodied Future Space for Robotics Manipulation | [
"cs.RO",
"cs.CV",
"cs.LG"
] | We introduce EnerVerse, a generative robotics foundation model that constructs and interprets embodied spaces. EnerVerse employs an autoregressive video diffusion framework to predict future embodied spaces from instructions, enhanced by a sparse context memory for long-term reasoning. To model the 3D robotics world, w... | {
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2501.01904 | Virgo: A Preliminary Exploration on Reproducing o1-like MLLM | [
"cs.CV",
"cs.AI"
] | Recently, slow-thinking reasoning systems, built upon large language models (LLMs), have garnered widespread attention by scaling the thinking time during inference. There is also growing interest in adapting this capability to multimodal large language models (MLLMs). Given that MLLMs handle more complex data semantic... | {
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2501.01905 | Alleviating Overfitting in Transformation-Interaction-Rational Symbolic
Regression with Multi-Objective Optimization | [
"cs.LG"
] | The Transformation-Interaction-Rational is a representation for symbolic regression that limits the search space of functions to the ratio of two nonlinear functions each one defined as the linear regression of transformed variables. This representation has the main objective to bias the search towards simpler expressi... | {
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2501.01908 | Detecting and Mitigating Adversarial Attacks on Deep Learning-Based MRI
Reconstruction Without Any Retraining | [
"cs.CV",
"cs.LG",
"eess.IV",
"physics.med-ph"
] | Deep learning (DL) methods, especially those based on physics-driven DL, have become the state-of-the-art for reconstructing sub-sampled magnetic resonance imaging (MRI) data. However, studies have shown that these methods are susceptible to small adversarial input perturbations, or attacks, resulting in major distorti... | {
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} |
2501.01912 | Exoplanet Detection via Differentiable Rendering | [
"astro-ph.EP",
"astro-ph.IM",
"cs.CV",
"eess.IV"
] | Direct imaging of exoplanets is crucial for advancing our understanding of planetary systems beyond our solar system, but it faces significant challenges due to the high contrast between host stars and their planets. Wavefront aberrations introduce speckles in the telescope science images, which are patterns of diffrac... | {
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2501.01913 | Mingling with the Good to Backdoor Federated Learning | [
"cs.CR",
"cs.AI",
"cs.DC"
] | Federated learning (FL) is a decentralized machine learning technique that allows multiple entities to jointly train a model while preserving dataset privacy. However, its distributed nature has raised various security concerns, which have been addressed by increasingly sophisticated defenses. These protections utilize... | {
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2501.01915 | Social Processes: Probabilistic Meta-learning for Adaptive Multiparty
Interaction Forecasting | [
"cs.LG"
] | Adaptively forecasting human behavior in social settings is an important step toward achieving Artificial General Intelligence. Most existing research in social forecasting has focused either on unfocused interactions, such as pedestrian trajectory prediction, or on monadic and dyadic behavior forecasting. In contrast,... | {
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2501.01924 | Transformer-Driven Inverse Problem Transform for Fast Blind
Hyperspectral Image Dehazing | [
"cs.CV",
"eess.IV"
] | Hyperspectral dehazing (HyDHZ) has become a crucial signal processing technology to facilitate the subsequent identification and classification tasks, as the airborne visible/infrared imaging spectrometer (AVIRIS) data portal reports a massive portion of haze-corrupted areas in typical hyperspectral remote sensing imag... | {
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2501.01926 | Mitigating Hallucination for Large Vision Language Model by
Inter-Modality Correlation Calibration Decoding | [
"cs.CV",
"cs.AI"
] | Large vision-language models (LVLMs) have shown remarkable capabilities in visual-language understanding for downstream multi-modal tasks. Despite their success, LVLMs still suffer from generating hallucinations in complex generation tasks, leading to inconsistencies between visual inputs and generated content. To addr... | {
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2501.01929 | Compressed sensing for inverse problems II: applications to
deconvolution, source recovery, and MRI | [
"math.FA",
"cs.IT",
"math.IT",
"math.OC"
] | This paper extends the sample complexity theory for ill-posed inverse problems developed in a recent work by the authors [`Compressed sensing for inverse problems and the sample complexity of the sparse Radon transform', J. Eur. Math. Soc., to appear], which was originally focused on the sparse Radon transform. We demo... | {
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2501.01930 | GoBERT: Gene Ontology Graph Informed BERT for Universal Gene Function
Prediction | [
"cs.LG"
] | Exploring the functions of genes and gene products is crucial to a wide range of fields, including medical research, evolutionary biology, and environmental science. However, discovering new functions largely relies on expensive and exhaustive wet lab experiments. Existing methods of automatic function annotation or pr... | {
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2501.01932 | Bridging Classification and Segmentation in Osteosarcoma Assessment via
Foundation and Discrete Diffusion Models | [
"cs.CV"
] | Osteosarcoma, the most common primary bone cancer, often requires accurate necrosis assessment from whole slide images (WSIs) for effective treatment planning and prognosis. However, manual assessments are subjective and prone to variability. In response, we introduce FDDM, a novel framework bridging the gap between pa... | {
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2501.01933 | Abstractive Text Summarization for Contemporary Sanskrit Prose: Issues
and Challenges | [
"cs.CL",
"cs.AI"
] | This thesis presents Abstractive Text Summarization models for contemporary Sanskrit prose. The first chapter, titled Introduction, presents the motivation behind this work, the research questions, and the conceptual framework. Sanskrit is a low-resource inflectional language. The key research question that this thesis... | {
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2501.01934 | Fusion DeepONet: A Data-Efficient Neural Operator for Geometry-Dependent
Hypersonic Flows on Arbitrary Grids | [
"cs.LG",
"physics.flu-dyn"
] | Designing re-entry vehicles requires accurate predictions of hypersonic flow around their geometry. Rapid prediction of such flows can revolutionize vehicle design, particularly for morphing geometries. We evaluate advanced neural operator models such as Deep Operator Networks (DeepONet), parameter-conditioned U-Net, F... | {
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2501.01936 | Improving Transducer-Based Spoken Language Understanding with
Self-Conditioned CTC and Knowledge Transfer | [
"cs.LG"
] | In this paper, we propose to improve end-to-end (E2E) spoken language understand (SLU) in an RNN transducer model (RNN-T) by incorporating a joint self-conditioned CTC automatic speech recognition (ASR) objective. Our proposed model is akin to an E2E differentiable cascaded model which performs ASR and SLU sequentially... | {
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2501.01945 | Cold-Start Recommendation towards the Era of Large Language Models
(LLMs): A Comprehensive Survey and Roadmap | [
"cs.IR",
"cs.AI"
] | Cold-start problem is one of the long-standing challenges in recommender systems, focusing on accurately modeling new or interaction-limited users or items to provide better recommendations. Due to the diversification of internet platforms and the exponential growth of users and items, the importance of cold-start reco... | {
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2501.01949 | VideoLifter: Lifting Videos to 3D with Fast Hierarchical Stereo
Alignment | [
"cs.CV"
] | Efficiently reconstructing accurate 3D models from monocular video is a key challenge in computer vision, critical for advancing applications in virtual reality, robotics, and scene understanding. Existing approaches typically require pre-computed camera parameters and frame-by-frame reconstruction pipelines, which are... | {
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2501.01950 | MADGEN: Mass-Spec attends to De Novo Molecular generation | [
"cs.LG",
"cs.AI"
] | The annotation (assigning structural chemical identities) of MS/MS spectra remains a significant challenge due to the enormous molecular diversity in biological samples and the limited scope of reference databases. Currently, the vast majority of spectral measurements remain in the "dark chemical space" without structu... | {
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2501.01951 | MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of
Accelerators | [
"cs.LG",
"cs.AI"
] | Graph convolutional networks (GCNs) have demonstrated superiority in graph-based learning tasks. However, training GCNs on full graphs is particularly challenging, due to the following two challenges: (1) the associated feature tensors can easily explode the memory and block the communication bandwidth of modern accele... | {
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2501.01956 | Metadata Conditioning Accelerates Language Model Pre-training | [
"cs.CL"
] | The vast diversity of styles, domains, and quality levels present in language model pre-training corpora is essential in developing general model capabilities, but efficiently learning and deploying the correct behaviors exemplified in each of these heterogeneous data sources is challenging. To address this, we propose... | {
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2501.01957 | VITA-1.5: Towards GPT-4o Level Real-Time Vision and Speech Interaction | [
"cs.CV",
"cs.SD",
"eess.AS"
] | Recent Multimodal Large Language Models (MLLMs) have typically focused on integrating visual and textual modalities, with less emphasis placed on the role of speech in enhancing interaction. However, speech plays a crucial role in multimodal dialogue systems, and implementing high-performance in both vision and speech ... | {
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2501.01959 | STEAM-EEG: Spatiotemporal EEG Analysis with Markov Transfer Fields and
Attentive CNNs | [
"cs.CV",
"cs.AI",
"cs.CE"
] | Electroencephalogram (EEG) signals play a pivotal role in biomedical research and clinical applications, including epilepsy diagnosis, sleep disorder analysis, and brain-computer interfaces. However, the effective analysis and interpretation of these complex signals often present significant challenges. This paper pres... | {
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2501.01960 | GAF-FusionNet: Multimodal ECG Analysis via Gramian Angular Fields and
Split Attention | [
"cs.CV",
"cs.AI",
"cs.GR",
"cs.LG"
] | Electrocardiogram (ECG) analysis plays a crucial role in diagnosing cardiovascular diseases, but accurate interpretation of these complex signals remains challenging. This paper introduces a novel multimodal framework(GAF-FusionNet) for ECG classification that integrates time-series analysis with image-based representa... | {
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2501.01963 | Statistical learning does not always entail knowledge | [
"cs.LG",
"cs.AI",
"cs.IT",
"math.IT",
"math.PR",
"math.ST",
"stat.ML",
"stat.TH"
] | In this paper, we study learning and knowledge acquisition (LKA) of an agent about a proposition that is either true or false. We use a Bayesian approach, where the agent receives data to update his beliefs about the proposition according to a posterior distribution. The LKA is formulated in terms of active information... | {
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} |
2501.01969 | Optimal bounds for dissatisfaction in perpetual voting | [
"cs.GT",
"cs.AI",
"cs.LG"
] | In perpetual voting, multiple decisions are made at different moments in time. Taking the history of previous decisions into account allows us to satisfy properties such as proportionality over periods of time. In this paper, we consider the following question: is there a perpetual approval voting method that guarantee... | {
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} |
2501.01973 | INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models | [
"cs.CV",
"cs.AI",
"cs.CY"
] | The rapid development of large language models (LLMs) and large vision models (LVMs) have propelled the evolution of multi-modal AI systems, which have demonstrated the remarkable potential for industrial applications by emulating human-like cognition. However, they also pose significant ethical challenges, including a... | {
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2501.01974 | Hawkes based Representation Learning for Reasoning over Scale-free
Community-structured Temporal Knowledge Graphs | [
"cs.SI",
"cs.AI",
"cs.LG"
] | Temporal knowledge graph (TKG) reasoning has become a hot topic due to its great value in many practical tasks. The key to TKG reasoning is modeling the structural information and evolutional patterns of the TKGs. While great efforts have been devoted to TKG reasoning, the structural and evolutional characteristics of ... | {
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2501.01980 | Polarimetric BSSRDF Acquisition of Dynamic Faces | [
"cs.CV",
"cs.GR"
] | Acquisition and modeling of polarized light reflection and scattering help reveal the shape, structure, and physical characteristics of an object, which is increasingly important in computer graphics. However, current polarimetric acquisition systems are limited to static and opaque objects. Human faces, on the other h... | {
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2501.01981 | Optical Character Recognition using Convolutional Neural Networks for
Ashokan Brahmi Inscriptions | [
"cs.CV",
"eess.IV"
] | This research paper delves into the development of an Optical Character Recognition (OCR) system for the recognition of Ashokan Brahmi characters using Convolutional Neural Networks. It utilizes a comprehensive dataset of character images to train the models, along with data augmentation techniques to optimize the trai... | {
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2501.01982 | Is Your Image a Good Storyteller? | [
"cs.CV",
"cs.AI",
"cs.CL"
] | Quantifying image complexity at the entity level is straightforward, but the assessment of semantic complexity has been largely overlooked. In fact, there are differences in semantic complexity across images. Images with richer semantics can tell vivid and engaging stories and offer a wide range of application scenario... | {
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2501.01983 | ECG-guided individual identification via PPG | [
"cs.CV",
"cs.AI"
] | Photoplethsmography (PPG)-based individual identification aiming at recognizing humans via intrinsic cardiovascular activities has raised extensive attention due to its high security and resistance to mimicry. However, this kind of technology witnesses unpromising results due to the limitation of low information densit... | {
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2501.01984 | Leveraging AI for Automatic Classification of PCOS Using Ultrasound
Imaging | [
"eess.IV",
"cs.AI",
"cs.CV"
] | The AUTO-PCOS Classification Challenge seeks to advance the diagnostic capabilities of artificial intelligence (AI) in identifying Polycystic Ovary Syndrome (PCOS) through automated classification of healthy and unhealthy ultrasound frames. This report outlines our methodology for building a robust AI pipeline utilizin... | {
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2501.01985 | Fall Detection in Passenger Elevators using Intelligent Surveillance
Camera Systems: An Application with YoloV8 Nano Model | [
"cs.CV",
"cs.AI"
] | Computer vision technology, which involves analyzing images and videos captured by cameras through deep learning algorithms, has significantly advanced the field of human fall detection. This study focuses on the application of the YoloV8 Nano model in identifying fall incidents within passenger elevators, a context th... | {
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2501.01986 | FrameFusion: Combining Similarity and Importance for Video Token
Reduction on Large Visual Language Models | [
"cs.CV",
"cs.AI"
] | The increasing demand to process long and high-resolution videos significantly burdens Large Vision-Language Models (LVLMs) due to the enormous number of visual tokens. Existing token reduction methods primarily focus on importance-based token pruning, which overlooks the redundancy caused by frame resemblance and repe... | {
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2501.01987 | Gender Bias in Text-to-Video Generation Models: A case study of Sora | [
"cs.CV",
"cs.AI",
"cs.CY",
"cs.LG"
] | The advent of text-to-video generation models has revolutionized content creation as it produces high-quality videos from textual prompts. However, concerns regarding inherent biases in such models have prompted scrutiny, particularly regarding gender representation. Our study investigates the presence of gender bias i... | {
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} |
2501.01989 | CRRG-CLIP: Automatic Generation of Chest Radiology Reports and
Classification of Chest Radiographs | [
"cs.CV",
"cs.AI"
] | The complexity of stacked imaging and the massive number of radiographs make writing radiology reports complex and inefficient. Even highly experienced radiologists struggle to maintain accuracy and consistency in interpreting radiographs under prolonged high-intensity work. To address these issues, this work proposes ... | {
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} |
2501.01990 | Towards Sustainable Large Language Model Serving | [
"cs.LG",
"cs.DC"
] | In this work, we study LLMs from a carbon emission perspective, addressing both operational and embodied emissions, and paving the way for sustainable LLM serving. We characterize the performance and energy of LLaMA with 1B, 3B, and 7B parameters using two Nvidia GPU types, a latest-generation RTX6000 Ada and an older-... | {
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} |
2501.01991 | A Hybrid Deep Learning and Model-Checking Framework for Accurate Brain
Tumor Detection and Validation | [
"cs.CV",
"cs.AI"
] | Model checking, a formal verification technique, ensures systems meet predefined requirements, playing a crucial role in minimizing errors and enhancing quality during development. This paper introduces a novel hybrid framework integrating model checking with deep learning for brain tumor detection and validation in me... | {
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} |
2501.01992 | Disagree and Commit: Degrees of Argumentation-based Agreements | [
"cs.AI",
"cs.LO",
"cs.MA"
] | In cooperative human decision-making, agreements are often not total; a partial degree of agreement is sufficient to commit to a decision and move on, as long as one is somewhat confident that the involved parties are likely to stand by their commitment in the future, given no drastic unexpected changes. In this paper,... | {
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} |
2501.01993 | A Novel Convolution and Attention Mechanism-based Model for 6D Object
Pose Estimation | [
"cs.CV",
"cs.LG"
] | Estimating 6D object poses from RGB images is challenging because the lack of depth information requires inferring a three dimensional structure from 2D projections. Traditional methods often rely on deep learning with grid based data structures but struggle to capture complex dependencies among extracted features. To ... | {
"Other": 0,
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} |
2501.01994 | Fuzzy Model Identification and Self Learning with Smooth Compositions | [
"eess.SY",
"cs.AI",
"cs.SY"
] | This paper develops a smooth model identification and self-learning strategy for dynamic systems taking into account possible parameter variations and uncertainties. We have tried to solve the problem such that the model follows the changes and variations in the system on a continuous and smooth surface. Running the mo... | {
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} |
2501.01998 | SmartSpatial: Enhancing the 3D Spatial Arrangement Capabilities of
Stable Diffusion Models and Introducing a Novel 3D Spatial Evaluation
Framework | [
"cs.CV",
"cs.AI"
] | Stable Diffusion models have made remarkable strides in generating photorealistic images from text prompts but often falter when tasked with accurately representing complex spatial arrangements, particularly involving intricate 3D relationships. To address this limitation, we introduce SmartSpatial, an innovative appro... | {
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} |
2501.01999 | On the Utility of Equivariance and Symmetry Breaking in Deep Learning
Architectures on Point Clouds | [
"cs.CV",
"cs.AI",
"cs.LG"
] | This paper explores the key factors that influence the performance of models working with point clouds, across different tasks of varying geometric complexity. In this work, we explore the trade-offs between flexibility and weight-sharing introduced by equivariant layers, assessing when equivariance boosts or detracts ... | {
"Other": 0,
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} |
2501.02000 | Multi-Center Study on Deep Learning-Assisted Detection and
Classification of Fetal Central Nervous System Anomalies Using Ultrasound
Imaging | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Prenatal ultrasound evaluates fetal growth and detects congenital abnormalities during pregnancy, but the examination of ultrasound images by radiologists requires expertise and sophisticated equipment, which would otherwise fail to improve the rate of identifying specific types of fetal central nervous system (CNS) ab... | {
"Other": 0,
"cs.AI": 1,
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} |
2501.02001 | Communication Efficient Cooperative Edge AI via Event-Triggered
Computation Offloading | [
"cs.LG",
"eess.IV",
"eess.SP"
] | Rare events, despite their infrequency, often carry critical information and require immediate attentions in mission-critical applications such as autonomous driving, healthcare, and industrial automation. The data-intensive nature of these tasks and their need for prompt responses, combined with designing edge AI (or ... | {
"Other": 0,
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"cs.SY": 0
} |
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