id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.08035 | READ: Reinforcement-based Adversarial Learning for Text Classification
with Limited Labeled Data | [
"cs.CL",
"cs.AI"
] | Pre-trained transformer models such as BERT have shown massive gains across many text classification tasks. However, these models usually need enormous labeled data to achieve impressive performances. Obtaining labeled data is often expensive and time-consuming, whereas collecting unlabeled data using some heuristics i... | {
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2501.08036 | Decoding Quantum LDPC Codes using Collaborative Check Node Removal | [
"quant-ph",
"cs.IT",
"math.IT"
] | The fault tolerance of quantum devices requires on-par contributions from error-correcting codes and suitable decoders. One of the most explored error-correcting codes is the family of Quantum Low-Density Parity Check (QLDPC) codes. Although faster than many of the reported decoders for QLDPC codes, iterative decod... | {
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2501.08037 | Enhanced SPS Velocity-adaptive Scheme: Access Fairness in 5G NR V2I
Networks | [
"cs.LG",
"cs.NI"
] | Vehicle-to-Infrastructure (V2I) technology enables information exchange between vehicles and road infrastructure. Specifically, when a vehicle approaches a roadside unit (RSU), it can exchange information with the RSU to obtain accurate data that assists in driving. With the release of the 3rd Generation Partnership Pr... | {
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2501.08038 | Robust Low-Light Human Pose Estimation through Illumination-Texture
Modulation | [
"cs.CV"
] | As critical visual details become obscured, the low visibility and high ISO noise in extremely low-light images pose a significant challenge to human pose estimation. Current methods fail to provide high-quality representations due to reliance on pixel-level enhancements that compromise semantics and the inability to e... | {
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2501.08040 | Convergence Analysis of Real-time Recurrent Learning (RTRL) for a class
of Recurrent Neural Networks | [
"cs.LG",
"math.PR",
"stat.ML"
] | Recurrent neural networks (RNNs) are commonly trained with the truncated backpropagation-through-time (TBPTT) algorithm. For the purposes of computational tractability, the TBPTT algorithm truncates the chain rule and calculates the gradient on a finite block of the overall data sequence. Such approximation could lead ... | {
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2501.08042 | Exploring visual language models as a powerful tool in the diagnosis of
Ewing Sarcoma | [
"cs.CV",
"cs.AI"
] | Ewing's sarcoma (ES), characterized by a high density of small round blue cells without structural organization, presents a significant health concern, particularly among adolescents aged 10 to 19. Artificial intelligence-based systems for automated analysis of histopathological images are promising to contribute to an... | {
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2501.08043 | PolyLUT: Ultra-low Latency Polynomial Inference with Hardware-Aware
Structured Pruning | [
"cs.LG",
"cs.AR"
] | Standard deep neural network inference involves the computation of interleaved linear maps and nonlinear activation functions. Prior work for ultra-low latency implementations has hardcoded these operations inside FPGA lookup tables (LUTs). However, FPGA LUTs can implement a much greater variety of functions. In this p... | {
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2501.08044 | UFGraphFR: An attempt at a federated recommendation system based on user
text characteristics | [
"cs.LG"
] | Federated learning has become an important research area in 'private computing' due to the 'useable invisibility' of data during training. Inspired by Federated learning, the federated recommendation system has gradually become a new recommendation service architecture that can protect users' privacy. The use of user d... | {
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2501.08046 | Building Symbiotic AI: Reviewing the AI Act for a Human-Centred,
Principle-Based Framework | [
"cs.HC",
"cs.AI"
] | Artificial Intelligence (AI) spreads quickly as new technologies and services take over modern society. The need to regulate AI design, development, and use is strictly necessary to avoid unethical and potentially dangerous consequences to humans. The European Union (EU) has released a new legal framework, the AI Act, ... | {
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2501.08047 | Gen-A: Generalizing Ambisonics Neural Encoding to Unseen Microphone
Arrays | [
"eess.AS",
"cs.LG",
"cs.SD"
] | Using deep neural networks (DNNs) for encoding of microphone array (MA) signals to the Ambisonics spatial audio format can surpass certain limitations of established conventional methods, but existing DNN-based methods need to be trained separately for each MA. This paper proposes a DNN-based method for Ambisonics enco... | {
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2501.08049 | Self-Attentive Spatio-Temporal Calibration for Precise Intermediate
Layer Matching in ANN-to-SNN Distillation | [
"cs.AI",
"cs.CV",
"cs.LG"
] | Spiking Neural Networks (SNNs) are promising for low-power computation due to their event-driven mechanism but often suffer from lower accuracy compared to Artificial Neural Networks (ANNs). ANN-to-SNN knowledge distillation can improve SNN performance, but previous methods either focus solely on label information, mis... | {
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2501.08050 | On the use of Statistical Learning Theory for model selection in
Structural Health Monitoring | [
"stat.ML",
"cs.LG"
] | Whenever data-based systems are employed in engineering applications, defining an optimal statistical representation is subject to the problem of model selection. This paper focusses on how well models can generalise in Structural Health Monitoring (SHM). Although statistical model validation in this field is often per... | {
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2501.08053 | Exploring Narrative Clustering in Large Language Models: A Layerwise
Analysis of BERT | [
"cs.CL",
"cs.AI"
] | This study investigates the internal mechanisms of BERT, a transformer-based large language model, with a focus on its ability to cluster narrative content and authorial style across its layers. Using a dataset of narratives developed via GPT-4, featuring diverse semantic content and stylistic variations, we analyze BE... | {
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2501.08057 | Optimizing Speech Multi-View Feature Fusion through Conditional
Computation | [
"eess.AS",
"cs.AI",
"cs.CL",
"cs.SD"
] | Recent advancements have highlighted the efficacy of self-supervised learning (SSL) features in various speech-related tasks, providing lightweight and versatile multi-view speech representations. However, our study reveals that while SSL features expedite model convergence, they conflict with traditional spectral feat... | {
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2501.08058 | Range-Only Dynamic Output Feedback Controller for Safe and Secure Target
Circumnavigation | [
"eess.SY",
"cs.SY"
] | The safety and security of robotic systems are paramount when navigating around a hostile target. This paper addresses the problem of circumnavigating an unknown target by a unicycle robot while ensuring it maintains a desired safe distance and remains within the sensing region around the target throughout its motion. ... | {
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2501.08062 | Skeleton and Font Generation Network for Zero-shot Chinese Character
Generation | [
"cs.CV"
] | Automatic font generation remains a challenging research issue, primarily due to the vast number of Chinese characters, each with unique and intricate structures. Our investigation of previous studies reveals inherent bias capable of causing structural changes in characters. Specifically, when generating a Chinese char... | {
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2501.08067 | Optimal Policy Adaptation under Covariate Shift | [
"cs.LG"
] | Transfer learning of prediction models has been extensively studied, while the corresponding policy learning approaches are rarely discussed. In this paper, we propose principled approaches for learning the optimal policy in the target domain by leveraging two datasets: one with full information from the source domain ... | {
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2501.08068 | A Roadmap to Guide the Integration of LLMs in Hierarchical Planning | [
"cs.AI"
] | Recent advances in Large Language Models (LLMs) are fostering their integration into several reasoning-related fields, including Automated Planning (AP). However, their integration into Hierarchical Planning (HP), a subfield of AP that leverages hierarchical knowledge to enhance planning performance, remains largely un... | {
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2501.08071 | CuAsmRL: Optimizing GPU SASS Schedules via Deep Reinforcement Learning | [
"cs.AR",
"cs.LG"
] | Large language models (LLMs) are remarked by their substantial computational requirements. To mitigate the cost, researchers develop specialized CUDA kernels, which often fuse several tensor operations to maximize the utilization of GPUs as much as possible. However, those specialized kernels may still leave performanc... | {
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2501.08072 | Evaluating Human Perception of Novel View Synthesis: Subjective Quality
Assessment of Gaussian Splatting and NeRF in Dynamic Scenes | [
"cs.CV",
"eess.IV"
] | Gaussian Splatting (GS) and Neural Radiance Fields (NeRF) are two groundbreaking technologies that have revolutionized the field of Novel View Synthesis (NVS), enabling immersive photorealistic rendering and user experiences by synthesizing multiple viewpoints from a set of images of sparse views. The potential applica... | {
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2501.08074 | Artificial Liver Classifier: A New Alternative to Conventional Machine
Learning Models | [
"cs.AI"
] | Supervised machine learning classifiers often encounter challenges related to performance, accuracy, and overfitting. This paper introduces the Artificial Liver Classifier (ALC), a novel supervised learning classifier inspired by the human liver's detoxification function. The ALC is characterized by its simplicity, spe... | {
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2501.08077 | HydroelasticTouch: Simulation of Tactile Sensors with Hydroelastic
Contact Surfaces | [
"cs.RO"
] | Thanks to recent advancements in the development of inexpensive, high-resolution tactile sensors, touch sensing has become popular in contact-rich robotic manipulation tasks. With the surge of data-driven methods and their requirement for substantial datasets, several methods of simulating tactile sensors have emerged ... | {
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2501.08083 | Benchmarking Vision Foundation Models for Input Monitoring in Autonomous
Driving | [
"cs.CV"
] | Deep neural networks (DNNs) remain challenged by distribution shifts in complex open-world domains like automated driving (AD): Absolute robustness against yet unknown novel objects (semantic shift) or styles like lighting conditions (covariate shift) cannot be guaranteed. Hence, reliable operation-time monitors for id... | {
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2501.08085 | Dynamic Multimodal Sentiment Analysis: Leveraging Cross-Modal Attention
for Enabled Classification | [
"cs.CL",
"cs.LG"
] | This paper explores the development of a multimodal sentiment analysis model that integrates text, audio, and visual data to enhance sentiment classification. The goal is to improve emotion detection by capturing the complex interactions between these modalities, thereby enabling more accurate and nuanced sentiment int... | {
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2501.08086 | NOMTO: Neural Operator-based symbolic Model approximaTion and discOvery | [
"cs.AI",
"cs.SC"
] | While many physical and engineering processes are most effectively described by non-linear symbolic models, existing non-linear symbolic regression (SR) methods are restricted to a limited set of continuous algebraic functions, thereby limiting their applicability to discover higher order non-linear differential relati... | {
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2501.08088 | AgentPose: Progressive Distribution Alignment via Feature Agent for
Human Pose Distillation | [
"cs.CV"
] | Pose distillation is widely adopted to reduce model size in human pose estimation. However, existing methods primarily emphasize the transfer of teacher knowledge while often neglecting the performance degradation resulted from the curse of capacity gap between teacher and student. To address this issue, we propose Age... | {
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2501.08090 | Hierarchical Autoscaling for Large Language Model Serving with Chiron | [
"cs.DC",
"cs.AI"
] | Large language model (LLM) serving is becoming an increasingly important workload for cloud providers. Based on performance SLO requirements, LLM inference requests can be divided into (a) interactive requests that have tight SLOs in the order of seconds, and (b) batch requests that have relaxed SLO in the order of min... | {
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2501.08094 | CellOMaps: A Compact Representation for Robust Classification of Lung
Adenocarcinoma Growth Patterns | [
"eess.IV",
"cs.CV"
] | Lung adenocarcinoma (LUAD) is a morphologically heterogeneous disease, characterized by five primary histological growth patterns. The classification of such patterns is crucial due to their direct relation to prognosis but the high subjectivity and observer variability pose a major challenge. Although several studies ... | {
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2501.08096 | Hybrid Action Based Reinforcement Learning for Multi-Objective
Compatible Autonomous Driving | [
"cs.RO",
"cs.AI",
"cs.ET",
"cs.LG"
] | Reinforcement Learning (RL) has shown excellent performance in solving decision-making and control problems of autonomous driving, which is increasingly applied in diverse driving scenarios. However, driving is a multi-attribute problem, leading to challenges in achieving multi-objective compatibility for current RL me... | {
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2501.08097 | Guiding the classification of hepatocellular carcinoma on 3D CT-scans
using deep and handcrafted radiological features | [
"cs.CV",
"cs.AI"
] | Hepatocellular carcinoma is the most spread primary liver cancer across the world ($\sim$80\% of the liver tumors). The gold standard for HCC diagnosis is liver biopsy. However, in the clinical routine, expert radiologists provide a visual diagnosis by interpreting hepatic CT-scans according to a standardized protocol,... | {
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2501.08099 | Smooth Handovers via Smoothed Online Learning | [
"cs.NI",
"cs.LG"
] | With users demanding seamless connectivity, handovers (HOs) have become a fundamental element of cellular networks. However, optimizing HOs is a challenging problem, further exacerbated by the growing complexity of mobile networks. This paper presents the first countrywide study of HO optimization, through the prism of... | {
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2501.08102 | Consistency of Responses and Continuations Generated by Large Language
Models on Social Media | [
"cs.CL",
"cs.AI",
"cs.HC"
] | Large Language Models (LLMs) demonstrate remarkable capabilities in text generation, yet their emotional consistency and semantic coherence in social media contexts remain insufficiently understood. This study investigates how LLMs handle emotional content and maintain semantic relationships through continuation and re... | {
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2501.08103 | A Comparative Analysis of Transformer-less Inverter Topologies for
Grid-Connected PV Systems: Minimizing Leakage Current and THD | [
"eess.SY",
"cs.SY"
] | The integration of distributed energy resources (DERs), particularly photovoltaic (PV) systems, into power grids has gained major attention due to their environmental and economic benefits. Although traditional transformer-based grid-connected PV inverters provide galvanic isolation for leakage current, they suffer fro... | {
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2501.08105 | About the Rankin and Berg\'e-Martinet Constants from a Coding Theory
View Point | [
"cs.IT",
"math.IT"
] | The Rankin constant $\gamma_{n,l}$ measures the largest volume of the densest sublattice of rank $l$ of a lattice $\Lambda\in \RR^n$ over all such lattices of rank $n$. The Berg\'e-Martinet constant $\gamma'_{n,l}$ is a variation that takes into account the dual lattice. Exact values and bounds for both constants are m... | {
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2501.08109 | Data-driven inventory management for new products: A warm-start and
adjusted Dyna-$Q$ approach | [
"cs.LG",
"cs.AI",
"cs.CE"
] | In this paper, we propose a novel reinforcement learning algorithm for inventory management of newly launched products with no or limited historical demand information. The algorithm follows the classic Dyna-$Q$ structure, balancing the model-based and model-free approaches, while accelerating the training process of D... | {
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2501.08111 | EarthView: A Large Scale Remote Sensing Dataset for Self-Supervision | [
"cs.CV"
] | This paper presents EarthView, a comprehensive dataset specifically designed for self-supervision on remote sensing data, intended to enhance deep learning applications on Earth monitoring tasks. The dataset spans 15 tera pixels of global remote-sensing data, combining imagery from a diverse range of sources, including... | {
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2501.08114 | Change Captioning in Remote Sensing: Evolution to SAT-Cap -- A
Single-Stage Transformer Approach | [
"cs.CV"
] | Change captioning has become essential for accurately describing changes in multi-temporal remote sensing data, providing an intuitive way to monitor Earth's dynamics through natural language. However, existing change captioning methods face two key challenges: high computational demands due to multistage fusion strate... | {
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2501.08115 | RoHan: Robust Hand Detection in Operation Room | [
"cs.CV",
"cs.LG"
] | Hand-specific localization has garnered significant interest within the computer vision community. Although there are numerous datasets with hand annotations from various angles and settings, domain transfer techniques frequently struggle in surgical environments. This is mainly due to the limited availability of glove... | {
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2501.08118 | Revisiting Birds Eye View Perception Models with Frozen Foundation
Models: DINOv2 and Metric3Dv2 | [
"cs.CV"
] | Birds Eye View perception models require extensive data to perform and generalize effectively. While traditional datasets often provide abundant driving scenes from diverse locations, this is not always the case. It is crucial to maximize the utility of the available training data. With the advent of large foundation m... | {
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2501.08120 | In-situ graph reasoning and knowledge expansion using Graph-PReFLexOR | [
"cs.AI",
"cond-mat.dis-nn",
"cond-mat.mtrl-sci",
"cs.CL"
] | The pursuit of automated scientific discovery has fueled progress from symbolic logic to modern AI, forging new frontiers in reasoning and pattern recognition. Transformers function as potential systems, where every possible relationship remains latent potentiality until tasks impose constraints, akin to measurement. Y... | {
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2501.08131 | SAR Strikes Back: A New Hope for RSVQA | [
"cs.CV"
] | Remote sensing visual question answering (RSVQA) is a task that automatically extracts information from satellite images and processes a question to predict the answer from the images in textual form, helping with the interpretation of the image. While different methods have been proposed to extract information from op... | {
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2501.08134 | An Empirical Wall-Pressure Spectrum Model for Aeroacoustic Predictions
Based on Symbolic Regression | [
"physics.flu-dyn",
"cs.AI",
"cs.LG"
] | Fast-turn around methods to predict airfoil trailing-edge noise are crucial for incorporating noise limitations into design optimization loops of several applications. Among these aeroacoustic predictive models, Amiet's theory offers the best balance between accuracy and simplicity. The accuracy of the model relies hea... | {
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2501.08137 | Audio-Visual Deepfake Detection With Local Temporal Inconsistencies | [
"cs.CV",
"cs.CR",
"cs.MM",
"cs.SD",
"eess.AS"
] | This paper proposes an audio-visual deepfake detection approach that aims to capture fine-grained temporal inconsistencies between audio and visual modalities. To achieve this, both architectural and data synthesis strategies are introduced. From an architectural perspective, a temporal distance map, coupled with an at... | {
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2501.08139 | EEG-ReMinD: Enhancing Neurodegenerative EEG Decoding through
Self-Supervised State Reconstruction-Primed Riemannian Dynamics | [
"eess.SP",
"cs.AI",
"cs.LG"
] | The development of EEG decoding algorithms confronts challenges such as data sparsity, subject variability, and the need for precise annotations, all of which are vital for advancing brain-computer interfaces and enhancing the diagnosis of diseases. To address these issues, we propose a novel two-stage approach named S... | {
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2501.08142 | Bootstrapping Corner Cases: High-Resolution Inpainting for Safety
Critical Detect and Avoid for Automated Flying | [
"cs.CV",
"cs.LG"
] | Modern machine learning techniques have shown tremendous potential, especially for object detection on camera images. For this reason, they are also used to enable safety-critical automated processes such as autonomous drone flights. We present a study on object detection for Detect and Avoid, a safety critical functio... | {
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2501.08145 | Refusal Behavior in Large Language Models: A Nonlinear Perspective | [
"cs.CL",
"cs.AI"
] | Refusal behavior in large language models (LLMs) enables them to decline responding to harmful, unethical, or inappropriate prompts, ensuring alignment with ethical standards. This paper investigates refusal behavior across six LLMs from three architectural families. We challenge the assumption of refusal as a linear p... | {
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2501.08149 | Multiple-Input Variational Auto-Encoder for Anomaly Detection in
Heterogeneous Data | [
"cs.AI",
"cs.LG",
"stat.ML"
] | Anomaly detection (AD) plays a pivotal role in AI applications, e.g., in classification, and intrusion/threat detection in cybersecurity. However, most existing methods face challenges of heterogeneity amongst feature subsets posed by non-independent and identically distributed (non-IID) data. We propose a novel neural... | {
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2501.08150 | Evaluating Policy Effects through Network Dynamics and Sampling | [
"cs.SI",
"stat.AP"
] | In the process of enacting or introducing a new policy, policymakers frequently consider the population's responses. These considerations are critical for effective governance. There are numerous methods to gauge the ground sentiment from a subset of the population; examples include surveys or listening to various feed... | {
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2501.08152 | Energy Backdoor Attack to Deep Neural Networks | [
"cs.CV"
] | The rise of deep learning (DL) has increased computing complexity and energy use, prompting the adoption of application specific integrated circuits (ASICs) for energy-efficient edge and mobile deployment. However, recent studies have demonstrated the vulnerability of these accelerators to energy attacks. Despite the d... | {
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2501.08155 | FairTTTS: A Tree Test Time Simulation Method for Fairness-Aware
Classification | [
"cs.LG",
"cs.AI"
] | Algorithmic decision-making has become deeply ingrained in many domains, yet biases in machine learning models can still produce discriminatory outcomes, often harming unprivileged groups. Achieving fair classification is inherently challenging, requiring a careful balance between predictive performance and ethical con... | {
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2501.08156 | Are DeepSeek R1 And Other Reasoning Models More Faithful? | [
"cs.LG"
] | Language models trained to solve reasoning tasks via reinforcement learning have achieved striking results. We refer to these models as reasoning models. A key question emerges: Are the Chains of Thought (CoTs) of reasoning models more faithful than traditional models? To investigate this, we evaluate three reasoning m... | {
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2501.08163 | DM-Mamba: Dual-domain Multi-scale Mamba for MRI reconstruction | [
"eess.IV",
"cs.CV"
] | The accelerated MRI reconstruction poses a challenging ill-posed inverse problem due to the significant undersampling in k-space. Deep neural networks, such as CNNs and ViT, have shown substantial performance improvements for this task while encountering the dilemma between global receptive fields and efficient computa... | {
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2501.08165 | I Can Find You in Seconds! Leveraging Large Language Models for Code
Authorship Attribution | [
"cs.SE",
"cs.AI"
] | Source code authorship attribution is important in software forensics, plagiarism detection, and protecting software patch integrity. Existing techniques often rely on supervised machine learning, which struggles with generalization across different programming languages and coding styles due to the need for large labe... | {
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2501.08167 | Potential and Perils of Large Language Models as Judges of Unstructured
Textual Data | [
"cs.CL",
"cs.AI",
"cs.CY"
] | Rapid advancements in large language models have unlocked remarkable capabilities when it comes to processing and summarizing unstructured text data. This has implications for the analysis of rich, open-ended datasets, such as survey responses, where LLMs hold the promise of efficiently distilling key themes and sentim... | {
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2501.08168 | LeapVAD: A Leap in Autonomous Driving via Cognitive Perception and
Dual-Process Thinking | [
"cs.AI"
] | While autonomous driving technology has made remarkable strides, data-driven approaches still struggle with complex scenarios due to their limited reasoning capabilities. Meanwhile, knowledge-driven autonomous driving systems have evolved considerably with the popularization of visual language models. In this paper, we... | {
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2501.08169 | Revolutionizing Communication with Deep Learning and XAI for Enhanced
Arabic Sign Language Recognition | [
"cs.CV",
"cs.AI",
"cs.CY",
"cs.LG"
] | This study introduces an integrated approach to recognizing Arabic Sign Language (ArSL) using state-of-the-art deep learning models such as MobileNetV3, ResNet50, and EfficientNet-B2. These models are further enhanced by explainable AI (XAI) techniques to boost interpretability. The ArSL2018 and RGB Arabic Alphabets Si... | {
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2501.08170 | Benchmarking Multimodal Models for Fine-Grained Image Analysis: A
Comparative Study Across Diverse Visual Features | [
"cs.CV"
] | This article introduces a benchmark designed to evaluate the capabilities of multimodal models in analyzing and interpreting images. The benchmark focuses on seven key visual aspects: main object, additional objects, background, detail, dominant colors, style, and viewpoint. A dataset of 14,580 images, generated from d... | {
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2501.08174 | Object-Centric 2D Gaussian Splatting: Background Removal and
Occlusion-Aware Pruning for Compact Object Models | [
"cs.CV"
] | Current Gaussian Splatting approaches are effective for reconstructing entire scenes but lack the option to target specific objects, making them computationally expensive and unsuitable for object-specific applications. We propose a novel approach that leverages object masks to enable targeted reconstruction, resulting... | {
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2501.08180 | D$^2$-DPM: Dual Denoising for Quantized Diffusion Probabilistic Models | [
"cs.CV",
"cs.LG"
] | Diffusion models have achieved cutting-edge performance in image generation. However, their lengthy denoising process and computationally intensive score estimation network impede their scalability in low-latency and resource-constrained scenarios. Post-training quantization (PTQ) compresses and accelerates diffusion m... | {
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2501.08181 | Economic Model Predictive Control for Periodic Operation: A Quadratic
Programming Approach | [
"eess.SY",
"cs.SY",
"math.OC"
] | Periodic dynamical systems, distinguished by their repetitive behavior over time, are prevalent across various engineering disciplines. In numerous applications, particularly within industrial contexts, the implementation of model predictive control (MPC) schemes tailored to optimize specific economic criteria was show... | {
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2501.08182 | CG-MER: A Card Game-based Multimodal dataset for Emotion Recognition | [
"cs.AI",
"cs.CV",
"cs.HC"
] | The field of affective computing has seen significant advancements in exploring the relationship between emotions and emerging technologies. This paper presents a novel and valuable contribution to this field with the introduction of a comprehensive French multimodal dataset designed specifically for emotion recognitio... | {
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2501.08184 | Assessing AI Adoption and Digitalization in SMEs: A Framework for
Implementation | [
"cs.AI"
] | The primary objective of this research is to examine the current state of digitalization and the integration of artificial intelligence (AI) within small and medium-sized enterprises (SMEs) in Italy. There is a significant gap between SMEs and large corporations in their use of AI, with SMEs facing numerous barriers to... | {
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2501.08187 | A Multi-Modal AI Copilot for Single-Cell Analysis with Instruction
Following | [
"cs.CL",
"cs.AI",
"cs.CE",
"cs.HC",
"cs.LG",
"q-bio.CB"
] | Large language models excel at interpreting complex natural language instructions, enabling them to perform a wide range of tasks. In the life sciences, single-cell RNA sequencing (scRNA-seq) data serves as the "language of cellular biology", capturing intricate gene expression patterns at the single-cell level. Howeve... | {
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2501.08188 | A Critical Synthesis of Uncertainty Quantification and Foundation Models
in Monocular Depth Estimation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | While recent foundation models have enabled significant breakthroughs in monocular depth estimation, a clear path towards safe and reliable deployment in the real-world remains elusive. Metric depth estimation, which involves predicting absolute distances, poses particular challenges, as even the most advanced foundati... | {
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2501.08192 | PRESERVE: Prefetching Model Weights and KV-Cache in Distributed LLM
Serving | [
"cs.AI",
"cs.AR",
"cs.DC"
] | Large language models (LLMs) are widely used across various applications, but their substantial computational requirements pose significant challenges, particularly in terms of HBM bandwidth bottlenecks and inter-device communication overhead. In this paper, we present PRESERVE, a novel prefetching framework designed t... | {
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2501.08193 | Modeling Quantum Machine Learning for Genomic Data Analysis | [
"cs.LG"
] | Quantum Machine Learning (QML) continues to evolve, unlocking new opportunities for diverse applications. In this study, we investigate and evaluate the applicability of QML models for binary classification of genome sequence data by employing various feature mapping techniques. We present an open-source, independent Q... | {
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2501.08195 | Self-supervised Deep Hyperspectral Inpainting with the Plug and Play and
Deep Image Prior Models | [
"cs.CV",
"cs.LG"
] | Hyperspectral images are typically composed of hundreds of narrow and contiguous spectral bands, each containing information regarding the material composition of the imaged scene. However, these images can be affected by various sources of noise, distortions, or data loss, which can significantly degrade their quality... | {
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2501.08197 | OpenCSG Chinese Corpus: A Series of High-quality Chinese Datasets for
LLM Training | [
"cs.CL"
] | Large language models (LLMs) have demonstrated remarkable capabilities, but their success heavily relies on the quality of pretraining corpora. For Chinese LLMs, the scarcity of high-quality Chinese datasets presents a significant challenge, often limiting their performance. To address this issue, we propose the OpenCS... | {
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2501.08199 | EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition | [
"cs.CV",
"cs.AI"
] | Facial expressions play a crucial role in human communication serving as a powerful and impactful means to express a wide range of emotions. With advancements in artificial intelligence and computer vision, deep neural networks have emerged as effective tools for facial emotion recognition. In this paper, we propose Em... | {
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2501.08200 | CWEval: Outcome-driven Evaluation on Functionality and Security of LLM
Code Generation | [
"cs.SE",
"cs.CL",
"cs.LG"
] | Large Language Models (LLMs) have significantly aided developers by generating or assisting in code writing, enhancing productivity across various tasks. While identifying incorrect code is often straightforward, detecting vulnerabilities in functionally correct code is more challenging, especially for developers with ... | {
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2501.08201 | Globally Convergent Variational Inference | [
"stat.ML",
"cs.LG"
] | In variational inference (VI), an approximation of the posterior distribution is selected from a family of distributions through numerical optimization. With the most common variational objective function, known as the evidence lower bound (ELBO), only convergence to a local optimum can be guaranteed. In this work, we ... | {
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2501.08202 | Data-driven system identification using quadratic embeddings of
nonlinear dynamics | [
"math.DS",
"cs.LG",
"stat.ML"
] | We propose a novel data-driven method called QENDy (Quadratic Embedding of Nonlinear Dynamics) that not only allows us to learn quadratic representations of highly nonlinear dynamical systems, but also to identify the governing equations. The approach is based on an embedding of the system into a higher-dimensional fea... | {
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2501.08203 | ArithmAttack: Evaluating Robustness of LLMs to Noisy Context in Math
Problem Solving | [
"cs.CL"
] | While Large Language Models (LLMs) have shown impressive capabilities in math problem-solving tasks, their robustness to noisy inputs is not well-studied. In this work, we propose ArithmAttack to examine how robust the LLMs are when they encounter noisy prompts that contain extra noise in the form of punctuation marks.... | {
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2501.08205 | Modeling Feature Maps for Quantum Machine Learning | [
"cs.LG",
"cs.AI"
] | Quantum Machine Learning (QML) offers significant potential for complex tasks like genome sequence classification, but quantum noise on Noisy Intermediate-Scale Quantum (NISQ) devices poses practical challenges. This study systematically evaluates how various quantum noise models including dephasing, amplitude damping,... | {
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2501.08207 | Efficient Dataframe Systems: Lazy Fat Pandas on a Diet | [
"cs.DB"
] | Pandas is widely used for data science applications, but users often run into problems when datasets are larger than memory. There are several frameworks based on lazy evaluation that handle large datasets, but the programs have to be rewritten to suit the framework, and the presence of multiple frameworks complicates ... | {
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2501.08208 | ASTRID -- An Automated and Scalable TRIaD for the Evaluation of
RAG-based Clinical Question Answering Systems | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) have shown impressive potential in clinical question answering (QA), with Retrieval Augmented Generation (RAG) emerging as a leading approach for ensuring the factual accuracy of model responses. However, current automated RAG metrics perform poorly in clinical and conversational use cases.... | {
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2501.08219 | Investigating Energy Efficiency and Performance Trade-offs in LLM
Inference Across Tasks and DVFS Settings | [
"cs.LG"
] | Large language models (LLMs) have shown significant improvements in many natural language processing (NLP) tasks, accelerating their rapid adoption across many industries. These models are resource-intensive, requiring extensive computational resources both during training and inference, leading to increased energy con... | {
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2501.08220 | Optimization of Link Configuration for Satellite Communication Using
Reinforcement Learning | [
"cs.AI"
] | Satellite communication is a key technology in our modern connected world. With increasingly complex hardware, one challenge is to efficiently configure links (connections) on a satellite transponder. Planning an optimal link configuration is extremely complex and depends on many parameters and metrics. The optimal use... | {
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2501.08222 | Data-driven Spatial Classification using Multi-Arm Bandits for
Monitoring with Energy-Constrained Mobile Robots | [
"cs.RO"
] | We consider the spatial classification problem for monitoring using data collected by a coordinated team of mobile robots. Such classification problems arise in several applications including search-and-rescue and precision agriculture. Specifically, we want to classify the regions of a search environment into interest... | {
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2501.08223 | Big Batch Bayesian Active Learning by Considering Predictive
Probabilities | [
"cs.LG",
"stat.ML"
] | We observe that BatchBALD, a popular acquisition function for batch Bayesian active learning for classification, can conflate epistemic and aleatoric uncertainty, leading to suboptimal performance. Motivated by this observation, we propose to focus on the predictive probabilities, which only exhibit epistemic uncertain... | {
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2501.08225 | FramePainter: Endowing Interactive Image Editing with Video Diffusion
Priors | [
"cs.CV"
] | Interactive image editing allows users to modify images through visual interaction operations such as drawing, clicking, and dragging. Existing methods construct such supervision signals from videos, as they capture how objects change with various physical interactions. However, these models are usually built upon text... | {
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2501.08226 | Efficient Deep Learning-based Forward Solvers for Brain Tumor Growth
Models | [
"cs.CV",
"cs.LG"
] | Glioblastoma, a highly aggressive brain tumor, poses major challenges due to its poor prognosis and high morbidity rates. Partial differential equation-based models offer promising potential to enhance therapeutic outcomes by simulating patient-specific tumor behavior for improved radiotherapy planning. However, model ... | {
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2501.08227 | Nonlinear Cruise Controllers with Bidirectional Sensing for a String of
Vehicles | [
"math.OC",
"cs.SY",
"eess.SY"
] | We introduce a nonlinear cruise controller that is fully decentralized (by vehicle) and uses spacing and speed measurements from the preceding and following vehicles to decide on the appropriate control action (acceleration) for each vehicle. The proposed cruise controller is studied on both a ring-road and an open roa... | {
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2501.08234 | Dynamic Pricing in High-Speed Railways Using Multi-Agent Reinforcement
Learning | [
"cs.LG",
"cs.AI",
"cs.MA"
] | This paper addresses a critical challenge in the high-speed passenger railway industry: designing effective dynamic pricing strategies in the context of competing and cooperating operators. To address this, a multi-agent reinforcement learning (MARL) framework based on a non-zero-sum Markov game is proposed, incorporat... | {
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2501.08236 | Privacy-Preserving Model and Preprocessing Verification for Machine
Learning | [
"cs.LG"
] | This paper presents a framework for privacy-preserving verification of machine learning models, focusing on models trained on sensitive data. Integrating Local Differential Privacy (LDP) with model explanations from LIME and SHAP, our framework enables robust verification without compromising individual privacy. It add... | {
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2501.08241 | A Feature-Level Ensemble Model for COVID-19 Identification in CXR Images
using Choquet Integral and Differential Evolution Optimization | [
"cs.CV",
"cs.AI",
"cs.LG",
"eess.IV"
] | The COVID-19 pandemic has profoundly impacted billions globally. It challenges public health and healthcare systems due to its rapid spread and severe respiratory effects. An effective strategy to mitigate the COVID-19 pandemic involves integrating testing to identify infected individuals. While RT-PCR is considered th... | {
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2501.08243 | Engineering LLM Powered Multi-agent Framework for Autonomous CloudOps | [
"cs.SE",
"cs.AI",
"cs.LG"
] | Cloud Operations (CloudOps) is a rapidly growing field focused on the automated management and optimization of cloud infrastructure which is essential for organizations navigating increasingly complex cloud environments. MontyCloud Inc. is one of the major companies in the CloudOps domain that leverages autonomous bots... | {
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2501.08245 | Continual Deep Active Learning for Medical Imaging: Replay-Base
Architecture for Context Adaptation | [
"cs.CV",
"cs.LG"
] | Deep Learning for medical imaging faces challenges in adapting and generalizing to new contexts. Additionally, it often lacks sufficient labeled data for specific tasks requiring significant annotation effort. Continual Learning (CL) tackles adaptability and generalizability by enabling lifelong learning from a data st... | {
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2501.08246 | Text-Diffusion Red-Teaming of Large Language Models: Unveiling Harmful
Behaviors with Proximity Constraints | [
"cs.LG"
] | Recent work has proposed automated red-teaming methods for testing the vulnerabilities of a given target large language model (LLM). These methods use red-teaming LLMs to uncover inputs that induce harmful behavior in a target LLM. In this paper, we study red-teaming strategies that enable a targeted security assessmen... | {
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2501.08248 | Eliciting In-context Retrieval and Reasoning for Long-context Large
Language Models | [
"cs.CL",
"cs.AI",
"cs.IR",
"cs.LG"
] | Recent advancements in long-context language models (LCLMs) promise to transform Retrieval-Augmented Generation (RAG) by simplifying pipelines. With their expanded context windows, LCLMs can process entire knowledge bases and perform retrieval and reasoning directly -- a capability we define as In-Context Retrieval and... | {
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2501.08258 | Towards an End-to-End (E2E) Adversarial Learning and Application in the
Physical World | [
"cs.CV",
"cs.CR"
] | The traditional learning process of patch-based adversarial attacks, conducted in the digital domain and then applied in the physical domain (e.g., via printed stickers), may suffer from reduced performance due to adversarial patches' limited transferability from the digital domain to the physical domain. Given that pr... | {
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} |
2501.08259 | FDPP: Fine-tune Diffusion Policy with Human Preference | [
"cs.RO",
"cs.LG"
] | Imitation learning from human demonstrations enables robots to perform complex manipulation tasks and has recently witnessed huge success. However, these techniques often struggle to adapt behavior to new preferences or changes in the environment. To address these limitations, we propose Fine-tuning Diffusion Policy wi... | {
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} |
2501.08263 | Multiplayer Federated Learning: Reaching Equilibrium with Less
Communication | [
"cs.LG",
"math.OC",
"stat.ML"
] | Traditional Federated Learning (FL) approaches assume collaborative clients with aligned objectives working towards a shared global model. However, in many real-world scenarios, clients act as rational players with individual objectives and strategic behaviors, a concept that existing FL frameworks are not equipped to ... | {
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} |
2501.08266 | AI Driven Water Segmentation with deep learning models for Enhanced
Flood Monitoring | [
"cs.CV",
"cs.AI",
"cs.LG",
"eess.IV"
] | Flooding is a major natural hazard causing significant fatalities and economic losses annually, with increasing frequency due to climate change. Rapid and accurate flood detection and monitoring are crucial for mitigating these impacts. This study compares the performance of three deep learning models UNet, ResNet, and... | {
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} |
2501.08267 | TriMod Fusion for Multimodal Named Entity Recognition in Social Media | [
"cs.IR",
"cs.SI"
] | Social media platforms serve as invaluable sources of user-generated content, offering insights into various aspects of human behavior. Named Entity Recognition (NER) plays a crucial role in analyzing such content by identifying and categorizing named entities into predefined classes. However, traditional NER models of... | {
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} |
2501.08271 | Comparative Analysis of Efficient Adapter-Based Fine-Tuning of
State-of-the-Art Transformer Models | [
"cs.CL",
"cs.AI"
] | In this work, we investigate the efficacy of various adapter architectures on supervised binary classification tasks from the SuperGLUE benchmark as well as a supervised multi-class news category classification task from Kaggle. Specifically, we compare classification performance and time complexity of three transforme... | {
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} |
2501.08276 | Exploring Robustness of LLMs to Sociodemographically-Conditioned
Paraphrasing | [
"cs.CL"
] | Large Language Models (LLMs) have shown impressive performance in various NLP tasks. However, there are concerns about their reliability in different domains of linguistic variations. Many works have proposed robustness evaluation measures for local adversarial attacks, but we need globally robust models unbiased to di... | {
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} |
2501.08279 | SmartEraser: Remove Anything from Images using Masked-Region Guidance | [
"cs.CV"
] | Object removal has so far been dominated by the mask-and-inpaint paradigm, where the masked region is excluded from the input, leaving models relying on unmasked areas to inpaint the missing region. However, this approach lacks contextual information for the masked area, often resulting in unstable performance. In this... | {
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} |
2501.08281 | Decoding Interpretable Logic Rules from Neural Networks | [
"cs.LG"
] | As deep neural networks continue to excel across various domains, their black-box nature has raised concerns about transparency and trust. In particular, interpretability has become increasingly essential for applications that demand high safety and knowledge rigor, such as drug discovery, autonomous driving, and genom... | {
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} |
2501.08282 | LLaVA-ST: A Multimodal Large Language Model for Fine-Grained
Spatial-Temporal Understanding | [
"cs.CV"
] | Recent advancements in multimodal large language models (MLLMs) have shown promising results, yet existing approaches struggle to effectively handle both temporal and spatial localization simultaneously. This challenge stems from two key issues: first, incorporating spatial-temporal localization introduces a vast numbe... | {
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
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