id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.00785 | NMM-HRI: Natural Multi-modal Human-Robot Interaction with Voice and
Deictic Posture via Large Language Model | [
"cs.RO"
] | Translating human intent into robot commands is crucial for the future of service robots in an aging society. Existing Human-Robot Interaction (HRI) systems relying on gestures or verbal commands are impractical for the elderly due to difficulties with complex syntax or sign language. To address the challenge, this pap... | {
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2501.00790 | LENS-XAI: Redefining Lightweight and Explainable Network Security
through Knowledge Distillation and Variational Autoencoders for Scalable
Intrusion Detection in Cybersecurity | [
"cs.CR",
"cs.AI",
"cs.CY",
"cs.ET"
] | The rapid proliferation of Industrial Internet of Things (IIoT) systems necessitates advanced, interpretable, and scalable intrusion detection systems (IDS) to combat emerging cyber threats. Traditional IDS face challenges such as high computational demands, limited explainability, and inflexibility against evolving at... | {
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2501.00795 | Multimodal Large Models Are Effective Action Anticipators | [
"cs.CV"
] | The task of long-term action anticipation demands solutions that can effectively model temporal dynamics over extended periods while deeply understanding the inherent semantics of actions. Traditional approaches, which primarily rely on recurrent units or Transformer layers to capture long-term dependencies, often fall... | {
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2501.00798 | Make Shuffling Great Again: A Side-Channel Resistant Fisher-Yates
Algorithm for Protecting Neural Networks | [
"cs.CR",
"cs.AI"
] | Neural network models implemented in embedded devices have been shown to be susceptible to side-channel attacks (SCAs), allowing recovery of proprietary model parameters, such as weights and biases. There are already available countermeasure methods currently used for protecting cryptographic implementations that can b... | {
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2501.00799 | Follow The Approximate Sparse Leader for No-Regret Online Sparse Linear
Approximation | [
"cs.LG",
"math.OC"
] | We consider the problem of \textit{online sparse linear approximation}, where one predicts the best sparse approximation of a sequence of measurements in terms of linear combination of columns of a given measurement matrix. Such online prediction problems are ubiquitous, ranging from medical trials to web caching to re... | {
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2501.00803 | Reasoning-Oriented and Analogy-Based Methods for Locating and Editing in
Zero-Shot Event-Relational Reasoning | [
"cs.CL",
"cs.AI"
] | Zero-shot event-relational reasoning is an important task in natural language processing, and existing methods jointly learn a variety of event-relational prefixes and inference-form prefixes to achieve such tasks. However, training prefixes consumes large computational resources and lacks interpretability. Additionall... | {
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2501.00804 | Automatic Text Pronunciation Correlation Generation and Application for
Contextual Biasing | [
"eess.AS",
"cs.CL"
] | Effectively distinguishing the pronunciation correlations between different written texts is a significant issue in linguistic acoustics. Traditionally, such pronunciation correlations are obtained through manually designed pronunciation lexicons. In this paper, we propose a data-driven method to automatically acquire ... | {
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2501.00805 | SLIDE: Integrating Speech Language Model with LLM for Spontaneous Spoken
Dialogue Generation | [
"eess.AS",
"cs.CL",
"cs.SD"
] | Recently, ``textless" speech language models (SLMs) based on speech units have made huge progress in generating naturalistic speech, including non-verbal vocalizations. However, the generated speech samples often lack semantic coherence. In this paper, we propose SLM and LLM Integration for spontaneous spoken Dialogue ... | {
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2501.00811 | Regression Guided Strategy to Automated Facial Beauty Optimization
through Image Synthesis | [
"cs.CV",
"cs.LG"
] | The use of beauty filters on social media, which enhance the appearance of individuals in images, is a well-researched area, with existing methods proving to be highly effective. Traditionally, such enhancements are performed using rule-based approaches that leverage domain knowledge of facial features associated with ... | {
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2501.00816 | MixSA: Training-free Reference-based Sketch Extraction via
Mixture-of-Self-Attention | [
"cs.CV"
] | Current sketch extraction methods either require extensive training or fail to capture a wide range of artistic styles, limiting their practical applicability and versatility. We introduce Mixture-of-Self-Attention (MixSA), a training-free sketch extraction method that leverages strong diffusion priors for enhanced ske... | {
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2501.00817 | Hardness of Learning Fixed Parities with Neural Networks | [
"cs.LG",
"stat.ML"
] | Learning parity functions is a canonical problem in learning theory, which although computationally tractable, is not amenable to standard learning algorithms such as gradient-based methods. This hardness is usually explained via statistical query lower bounds [Kearns, 1998]. However, these bounds only imply that for a... | {
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2501.00818 | SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy
and Anti-Forgetting Regularization | [
"cs.CV"
] | Test-time Adaptation (TTA) aims to improve model performance when the model encounters domain changes after deployment. The standard TTA mainly considers the case where the target domain is static, while the continual TTA needs to undergo a sequence of domain changes. This encounters a significant challenge as the mode... | {
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2501.00823 | Decoupling Knowledge and Reasoning in Transformers: A Modular
Architecture with Generalized Cross-Attention | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Transformers have achieved remarkable success across diverse domains, but their monolithic architecture presents challenges in interpretability, adaptability, and scalability. This paper introduces a novel modular Transformer architecture that explicitly decouples knowledge and reasoning through a generalized cross-att... | {
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2501.00824 | Information Sifting Funnel: Privacy-preserving Collaborative Inference
Against Model Inversion Attacks | [
"cs.CR",
"cs.IT",
"math.IT"
] | The complexity of neural networks and inference tasks, coupled with demands for computational efficiency and real-time feedback, poses significant challenges for resource-constrained edge devices. Collaborative inference mitigates this by assigning shallow feature extraction to edge devices and offloading features to t... | {
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2501.00826 | LLM-Powered Multi-Agent System for Automated Crypto Portfolio Management | [
"q-fin.TR",
"cs.AI"
] | Cryptocurrency investment is inherently difficult due to its shorter history compared to traditional assets, the need to integrate vast amounts of data from various modalities, and the requirement for complex reasoning. While deep learning approaches have been applied to address these challenges, their black-box nature... | {
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2501.00828 | Embedding Style Beyond Topics: Analyzing Dispersion Effects Across
Different Language Models | [
"cs.CL",
"cs.AI"
] | This paper analyzes how writing style affects the dispersion of embedding vectors across multiple, state-of-the-art language models. While early transformer models primarily aligned with topic modeling, this study examines the role of writing style in shaping embedding spaces. Using a literary corpus that alternates be... | {
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2501.00829 | An LLM-Empowered Adaptive Evolutionary Algorithm For Multi-Component
Deep Learning Systems | [
"cs.NE",
"cs.AI"
] | Multi-objective evolutionary algorithms (MOEAs) are widely used for searching optimal solutions in complex multi-component applications. Traditional MOEAs for multi-component deep learning (MCDL) systems face challenges in enhancing the search efficiency while maintaining the diversity. To combat these, this paper prop... | {
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2501.00830 | LLM+AL: Bridging Large Language Models and Action Languages for Complex
Reasoning about Actions | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) have made significant strides in various intelligent tasks but still struggle with complex action reasoning tasks that require systematic search. To address this limitation, we propose a method that bridges the natural language understanding capabilities of LLMs with the symbolic reasoning ... | {
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2501.00836 | Recognizing Artistic Style of Archaeological Image Fragments Using Deep
Style Extrapolation | [
"cs.CV"
] | Ancient artworks obtained in archaeological excavations usually suffer from a certain degree of fragmentation and physical degradation. Often, fragments of multiple artifacts from different periods or artistic styles could be found on the same site. With each fragment containing only partial information about its sourc... | {
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2501.00838 | Spatially-guided Temporal Aggregation for Robust Event-RGB Optical Flow
Estimation | [
"cs.CV",
"cs.LG"
] | Current optical flow methods exploit the stable appearance of frame (or RGB) data to establish robust correspondences across time. Event cameras, on the other hand, provide high-temporal-resolution motion cues and excel in challenging scenarios. These complementary characteristics underscore the potential of integratin... | {
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2501.00840 | Distilled Lifelong Self-Adaptation for Configurable Systems | [
"cs.SE",
"cs.AI"
] | Modern configurable systems provide tremendous opportunities for engineering future intelligent software systems. A key difficulty thereof is how to effectively self-adapt the configuration of a running system such that its performance (e.g., runtime and throughput) can be optimized under time-varying workloads. This u... | {
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2501.00843 | FusionSORT: Fusion Methods for Online Multi-object Visual Tracking | [
"cs.CV"
] | In this work, we investigate four different fusion methods for associating detections to tracklets in multi-object visual tracking. In addition to considering strong cues such as motion and appearance information, we also consider weak cues such as height intersection-over-union (height-IoU) and tracklet confidence inf... | {
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2501.00848 | IllusionBench: A Large-scale and Comprehensive Benchmark for Visual
Illusion Understanding in Vision-Language Models | [
"cs.CV"
] | Current Visual Language Models (VLMs) show impressive image understanding but struggle with visual illusions, especially in real-world scenarios. Existing benchmarks focus on classical cognitive illusions, which have been learned by state-of-the-art (SOTA) VLMs, revealing issues such as hallucinations and limited perce... | {
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2501.00851 | Scale-wise Bidirectional Alignment Network for Referring Remote Sensing
Image Segmentation | [
"cs.CV"
] | The goal of referring remote sensing image segmentation (RRSIS) is to extract specific pixel-level regions within an aerial image via a natural language expression. Recent advancements, particularly Transformer-based fusion designs, have demonstrated remarkable progress in this domain. However, existing methods primari... | {
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2501.00852 | Hybridising Reinforcement Learning and Heuristics for Hierarchical
Directed Arc Routing Problems | [
"cs.LG"
] | The Hierarchical Directed Capacitated Arc Routing Problem (HDCARP) is an extension of the Capacitated Arc Routing Problem (CARP), where the arcs of a graph are divided into classes based on their priority. The traversal of these classes is determined by either precedence constraints or a hierarchical objective, resulti... | {
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2501.00854 | A Graphical Approach to State Variable Selection in Off-policy Learning | [
"stat.ME",
"cs.LG"
] | Sequential decision problems are widely studied across many areas of science. A key challenge when learning policies from historical data - a practice commonly referred to as off-policy learning - is how to ``identify'' the impact of a policy of interest when the observed data are not randomized. Off-policy learning ha... | {
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2501.00855 | What is a Social Media Bot? A Global Comparison of Bot and Human
Characteristics | [
"cs.CY",
"cs.AI",
"cs.SI"
] | Chatter on social media is 20% bots and 80% humans. Chatter by bots and humans is consistently different: bots tend to use linguistic cues that can be easily automated while humans use cues that require dialogue understanding. Bots use words that match the identities they choose to present, while humans may send messag... | {
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2501.00856 | Advances in UAV Avionics Systems Architecture, Classification and
Integration: A Comprehensive Review and Future Perspectives | [
"eess.SY",
"cs.SY"
] | Avionics systems of an Unmanned Aerial Vehicle (UAV) or drone are the critical electronic components found onboard that regulate, navigate, and control UAV travel while ensuring public safety. Contemporary UAV avionics work together to facilitate success of UAV missions by enabling stable communication, secure identifi... | {
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2501.00862 | DiffETM: Diffusion Process Enhanced Embedded Topic Model | [
"cs.CL",
"cs.AI",
"cs.IR",
"cs.LG"
] | The embedded topic model (ETM) is a widely used approach that assumes the sampled document-topic distribution conforms to the logistic normal distribution for easier optimization. However, this assumption oversimplifies the real document-topic distribution, limiting the model's performance. In response, we propose a no... | {
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2501.00865 | Negative to Positive Co-learning with Aggressive Modality Dropout | [
"cs.CL",
"cs.LG"
] | This paper aims to document an effective way to improve multimodal co-learning by using aggressive modality dropout. We find that by using aggressive modality dropout we are able to reverse negative co-learning (NCL) to positive co-learning (PCL). Aggressive modality dropout can be used to "prep" a multimodal model for... | {
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2501.00867 | Interactionalism: Re-Designing Higher Learning for the Large Language
Agent Era | [
"cs.HC",
"cs.MA"
] | We introduce Interactionalism as a new set of guiding principles and heuristics for the design and architecture of learning now available due to Generative AI (GenAI) platforms. Specifically, we articulate interactional intelligence as a net new skill set that is increasingly important when core cognitive tasks are aut... | {
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2501.00868 | Large Language Models Are Read/Write Policy-Makers for Simultaneous
Generation | [
"cs.CL"
] | Simultaneous generation models write generation results while reading streaming inputs, necessitating a policy-maker to determine the appropriate output timing. Existing simultaneous generation methods generally adopt the traditional encoder-decoder architecture and learn the generation and policy-making capabilities t... | {
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2501.00872 | Observer-Based Data-Driven Consensus Control for Nonlinear Multi-Agent
Systems against DoS and FDI attacks | [
"eess.SY",
"cs.SY"
] | Existing data-driven control methods generally do not address False Data Injection (FDI) and Denial-of-Service (DoS) attacks simultaneously. This letter introduces a distributed data-driven attack-resilient consensus problem under both FDI and DoS attacks and proposes a data-driven consensus control framework, consisti... | {
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2501.00873 | Exploring Structured Semantic Priors Underlying Diffusion Score for
Test-time Adaptation | [
"cs.CV",
"cs.LG"
] | Capitalizing on the complementary advantages of generative and discriminative models has always been a compelling vision in machine learning, backed by a growing body of research. This work discloses the hidden semantic structure within score-based generative models, unveiling their potential as effective discriminativ... | {
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2501.00874 | LUSIFER: Language Universal Space Integration for Enhanced Multilingual
Embeddings with Large Language Models | [
"cs.CL",
"cs.IR"
] | Recent advancements in large language models (LLMs) based embedding models have established new state-of-the-art benchmarks for text embedding tasks, particularly in dense vector-based retrieval. However, these models predominantly focus on English, leaving multilingual embedding capabilities largely unexplored. To add... | {
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2501.00876 | A Novel Approach using CapsNet and Deep Belief Network for Detection and
Identification of Oral Leukopenia | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Oral cancer constitutes a significant global health concern, resulting in 277,484 fatalities in 2023, with the highest prevalence observed in low- and middle-income nations. Facilitating automation in the detection of possibly malignant and malignant lesions in the oral cavity could result in cost-effective and early d... | {
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2501.00877 | FGAseg: Fine-Grained Pixel-Text Alignment for Open-Vocabulary Semantic
Segmentation | [
"cs.CV"
] | Open-vocabulary segmentation aims to identify and segment specific regions and objects based on text-based descriptions. A common solution is to leverage powerful vision-language models (VLMs), such as CLIP, to bridge the gap between vision and text information. However, VLMs are typically pretrained for image-level vi... | {
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2501.00879 | TrustRAG: Enhancing Robustness and Trustworthiness in RAG | [
"cs.CL"
] | Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge sources, enabling more accurate and contextually relevant responses tailored to user queries. However, these systems remain vulnerable to corpus poisoning attacks that can significantly degrade LLM perfor... | {
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2501.00880 | Improving Autoregressive Visual Generation with Cluster-Oriented Token
Prediction | [
"cs.CV"
] | Employing LLMs for visual generation has recently become a research focus. However, the existing methods primarily transfer the LLM architecture to visual generation but rarely investigate the fundamental differences between language and vision. This oversight may lead to suboptimal utilization of visual generation cap... | {
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2501.00881 | Agentic Systems: A Guide to Transforming Industries with Vertical AI
Agents | [
"cs.MA"
] | The evolution of agentic systems represents a significant milestone in artificial intelligence and modern software systems, driven by the demand for vertical intelligence tailored to diverse industries. These systems enhance business outcomes through adaptability, learning, and interaction with dynamic environments. At... | {
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2501.00882 | FullTransNet: Full Transformer with Local-Global Attention for Video
Summarization | [
"cs.CV"
] | Video summarization mainly aims to produce a compact, short, informative, and representative synopsis of raw videos, which is of great importance for browsing, analyzing, and understanding video content. Dominant video summarization approaches are generally based on recurrent or convolutional neural networks, even rece... | {
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2501.00884 | Diversity Optimization for Travelling Salesman Problem via Deep
Reinforcement Learning | [
"cs.LG",
"cs.AI"
] | Existing neural methods for the Travelling Salesman Problem (TSP) mostly aim at finding a single optimal solution. To discover diverse yet high-quality solutions for Multi-Solution TSP (MSTSP), we propose a novel deep reinforcement learning based neural solver, which is primarily featured by an encoder-decoder structur... | {
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2501.00885 | Representation in large language models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | The extraordinary success of recent Large Language Models (LLMs) on a diverse array of tasks has led to an explosion of scientific and philosophical theorizing aimed at explaining how they do what they do. Unfortunately, disagreement over fundamental theoretical issues has led to stalemate, with entrenched camps of LLM... | {
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2501.00888 | Unfolding the Headline: Iterative Self-Questioning for News Retrieval
and Timeline Summarization | [
"cs.CL"
] | In the fast-changing realm of information, the capacity to construct coherent timelines from extensive event-related content has become increasingly significant and challenging. The complexity arises in aggregating related documents to build a meaningful event graph around a central topic. This paper proposes CHRONOS -... | {
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2501.00889 | Evaluating Time Series Foundation Models on Noisy Periodic Time Series | [
"cs.LG"
] | While recent advancements in foundation models have significantly impacted machine learning, rigorous tests on the performance of time series foundation models (TSFMs) remain largely underexplored. This paper presents an empirical study evaluating the zero-shot, long-horizon forecasting abilities of several leading TSF... | {
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2501.00890 | Spatial Temporal Attention based Target Vehicle Trajectory Prediction
for Internet of Vehicles | [
"cs.RO",
"cs.LG"
] | Forecasting vehicle behavior within complex traffic environments is pivotal within Intelligent Transportation Systems (ITS). Though this technology plays a significant role in alleviating the prevalent operational difficulties in logistics and transportation systems, the precise prediction of vehicle trajectories still... | {
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2501.00891 | Demystifying Online Clustering of Bandits: Enhanced Exploration Under
Stochastic and Smoothed Adversarial Contexts | [
"cs.LG",
"cs.AI",
"stat.ML"
] | The contextual multi-armed bandit (MAB) problem is crucial in sequential decision-making. A line of research, known as online clustering of bandits, extends contextual MAB by grouping similar users into clusters, utilizing shared features to improve learning efficiency. However, existing algorithms, which rely on the u... | {
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2501.00895 | Text2Earth: Unlocking Text-driven Remote Sensing Image Generation with a
Global-Scale Dataset and a Foundation Model | [
"cs.CV"
] | Generative foundation models have advanced large-scale text-driven natural image generation, becoming a prominent research trend across various vertical domains. However, in the remote sensing field, there is still a lack of research on large-scale text-to-image (text2image) generation technology. Existing remote sensi... | {
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2501.00906 | Large Language Model Based Multi-Agent System Augmented Complex Event
Processing Pipeline for Internet of Multimedia Things | [
"cs.MA",
"cs.AI",
"cs.MM"
] | This paper presents the development and evaluation of a Large Language Model (LLM), also known as foundation models, based multi-agent system framework for complex event processing (CEP) with a focus on video query processing use cases. The primary goal is to create a proof-of-concept (POC) that integrates state-of-the... | {
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2501.00907 | U-GIFT: Uncertainty-Guided Firewall for Toxic Speech in Few-Shot
Scenario | [
"cs.SD",
"cs.CL",
"eess.AS"
] | With the widespread use of social media, user-generated content has surged on online platforms. When such content includes hateful, abusive, offensive, or cyberbullying behavior, it is classified as toxic speech, posing a significant threat to the online ecosystem's integrity and safety. While manual content moderation... | {
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2501.00909 | RIS-Aided Integrated Sensing and Communication Systems under
Dual-polarized Channels | [
"cs.IT",
"eess.SP",
"math.IT"
] | This paper considers reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) systems under dual-polarized (DP) channels. Unlike the existing ISAC systems, which ignored polarization of electromagnetic waves, this study adopts DP base station (BS) and DP RIS to serve users with a pai... | {
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2501.00910 | Population Aware Diffusion for Time Series Generation | [
"cs.LG",
"cs.AI"
] | Diffusion models have shown promising ability in generating high-quality time series (TS) data. Despite the initial success, existing works mostly focus on the authenticity of data at the individual level, but pay less attention to preserving the population-level properties on the entire dataset. Such population-level ... | {
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2501.00911 | Aligning LLMs with Domain Invariant Reward Models | [
"cs.LG"
] | Aligning large language models (LLMs) to human preferences is challenging in domains where preference data is unavailable. We address the problem of learning reward models for such target domains by leveraging feedback collected from simpler source domains, where human preferences are easier to obtain. Our key insight ... | {
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2501.00912 | AutoPresent: Designing Structured Visuals from Scratch | [
"cs.CV",
"cs.CL"
] | Designing structured visuals such as presentation slides is essential for communicative needs, necessitating both content creation and visual planning skills. In this work, we tackle the challenge of automated slide generation, where models produce slide presentations from natural language (NL) instructions. We first i... | {
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2501.00913 | $\beta$-DQN: Improving Deep Q-Learning By Evolving the Behavior | [
"cs.LG",
"cs.AI"
] | While many sophisticated exploration methods have been proposed, their lack of generality and high computational cost often lead researchers to favor simpler methods like $\epsilon$-greedy. Motivated by this, we introduce $\beta$-DQN, a simple and efficient exploration method that augments the standard DQN with a behav... | {
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2501.00915 | Diffusion Policies for Generative Modeling of Spacecraft Trajectories | [
"cs.RO",
"cs.LG",
"cs.SY",
"eess.SY",
"math.OC"
] | Machine learning has demonstrated remarkable promise for solving the trajectory generation problem and in paving the way for online use of trajectory optimization for resource-constrained spacecraft. However, a key shortcoming in current machine learning-based methods for trajectory generation is that they require larg... | {
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2501.00917 | Hierarchical Vision-Language Alignment for Text-to-Image Generation via
Diffusion Models | [
"cs.CV"
] | Text-to-image generation has witnessed significant advancements with the integration of Large Vision-Language Models (LVLMs), yet challenges remain in aligning complex textual descriptions with high-quality, visually coherent images. This paper introduces the Vision-Language Aligned Diffusion (VLAD) model, a generative... | {
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2501.00919 | Exploring Geometric Representational Alignment through Ollivier-Ricci
Curvature and Ricci Flow | [
"cs.LG"
] | Representational analysis explores how input data of a neural system are encoded in high dimensional spaces of its distributed neural activations, and how we can compare different systems, for instance, artificial neural networks and brains, on those grounds. While existing methods offer important insights, they typica... | {
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2501.00921 | Aligning Netlist to Source Code using SynAlign | [
"cs.AR",
"cs.CL"
] | In current chip design processes, using multiple tools to obtain a gate-level netlist often results in the loss of source code correlation. SynAlign addresses this challenge by automating the alignment process, simplifying iterative design, reducing overhead, and maintaining correlation across various tools. This enhan... | {
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2501.00924 | On the Low-Complexity of Fair Learning for Combinatorial Multi-Armed
Bandit | [
"cs.LG"
] | Combinatorial Multi-Armed Bandit with fairness constraints is a framework where multiple arms form a super arm and can be pulled in each round under uncertainty to maximize cumulative rewards while ensuring the minimum average reward required by each arm. The existing pessimistic-optimistic algorithm linearly combines ... | {
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2501.00930 | Tight Constraint Prediction of Six-Degree-of-Freedom Transformer-based
Powered Descent Guidance | [
"math.OC",
"cs.LG",
"cs.RO",
"cs.SY",
"eess.SY"
] | This work introduces Transformer-based Successive Convexification (T-SCvx), an extension of Transformer-based Powered Descent Guidance (T-PDG), generalizable for efficient six-degree-of-freedom (DoF) fuel-optimal powered descent trajectory generation. Our approach significantly enhances the sample efficiency and soluti... | {
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2501.00935 | Multiscaled Multi-Head Attention-based Video Transformer Network for
Hand Gesture Recognition | [
"cs.CV",
"cs.HC"
] | Dynamic gesture recognition is one of the challenging research areas due to variations in pose, size, and shape of the signer's hand. In this letter, Multiscaled Multi-Head Attention Video Transformer Network (MsMHA-VTN) for dynamic hand gesture recognition is proposed. A pyramidal hierarchy of multiscale features is e... | {
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2501.00941 | A Novel Diffusion Model for Pairwise Geoscience Data Generation with
Unbalanced Training Dataset | [
"cs.LG",
"cs.CV",
"physics.geo-ph"
] | Recently, the advent of generative AI technologies has made transformational impacts on our daily lives, yet its application in scientific applications remains in its early stages. Data scarcity is a major, well-known barrier in data-driven scientific computing, so physics-guided generative AI holds significant promise... | {
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2501.00942 | Efficient Unsupervised Shortcut Learning Detection and Mitigation in
Transformers | [
"cs.LG",
"cs.CV"
] | Shortcut learning, i.e., a model's reliance on undesired features not directly relevant to the task, is a major challenge that severely limits the applications of machine learning algorithms, particularly when deploying them to assist in making sensitive decisions, such as in medical diagnostics. In this work, we lever... | {
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2501.00944 | Diffusion Prism: Enhancing Diversity and Morphology Consistency in
Mask-to-Image Diffusion | [
"cs.CV",
"eess.IV"
] | The emergence of generative AI and controllable diffusion has made image-to-image synthesis increasingly practical and efficient. However, when input images exhibit low entropy and sparse, the inherent characteristics of diffusion models often result in limited diversity. This constraint significantly interferes with d... | {
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2501.00946 | Cached Adaptive Token Merging: Dynamic Token Reduction and Redundant
Computation Elimination in Diffusion Model | [
"cs.CV"
] | Diffusion models have emerged as a promising approach for generating high-quality, high-dimensional images. Nevertheless, these models are hindered by their high computational cost and slow inference, partly due to the quadratic computational complexity of the self-attention mechanisms with respect to input size. Vario... | {
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2501.00953 | Incremental Dialogue Management: Survey, Discussion, and Implications
for HRI | [
"cs.CL",
"cs.AI"
] | Efforts towards endowing robots with the ability to speak have benefited from recent advancements in NLP, in particular large language models. However, as powerful as current models have become, they still operate on sentence or multi-sentence level input, not on the word-by-word input that humans operate on, affecting... | {
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2501.00954 | Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1
Image Generation: A StyleGAN3 Approach | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Diabetic Retinopathy (DR) is a leading cause of preventable blindness. Early detection at the DR1 stage is critical but is hindered by a scarcity of high-quality fundus images. This study uses StyleGAN3 to generate synthetic DR1 images characterized by microaneurysms with high fidelity and diversity. The aim is to addr... | {
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2501.00958 | 2.5 Years in Class: A Multimodal Textbook for Vision-Language
Pretraining | [
"cs.CV",
"cs.CL",
"cs.LG"
] | Compared to image-text pair data, interleaved corpora enable Vision-Language Models (VLMs) to understand the world more naturally like humans. However, such existing datasets are crawled from webpage, facing challenges like low knowledge density, loose image-text relations, and poor logical coherence between images. On... | {
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2501.00961 | The Silent Majority: Demystifying Memorization Effect in the Presence of
Spurious Correlations | [
"cs.LG",
"cs.AI",
"cs.CV",
"eess.IV"
] | Machine learning models often rely on simple spurious features -- patterns in training data that correlate with targets but are not causally related to them, like image backgrounds in foreground classification. This reliance typically leads to imbalanced test performance across minority and majority groups. In this wor... | {
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2501.00962 | OASIS Uncovers: High-Quality T2I Models, Same Old Stereotypes | [
"cs.CV",
"cs.CY",
"cs.LG"
] | Images generated by text-to-image (T2I) models often exhibit visual biases and stereotypes of concepts such as culture and profession. Existing quantitative measures of stereotypes are based on statistical parity that does not align with the sociological definition of stereotypes and, therefore, incorrectly categorizes... | {
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2501.00967 | On the Implementation of a Bayesian Optimization Framework for
Interconnected Systems | [
"stat.ML",
"cs.LG"
] | Bayesian optimization (BO) is an effective paradigm for the optimization of expensive-to-sample systems. Standard BO learns the performance of a system $f(x)$ by using a Gaussian Process (GP) model; this treats the system as a black-box and limits its ability to exploit available structural knowledge (e.g., physics and... | {
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2501.00973 | Defense Strategies for Autonomous Multi-agent Systems: Ensuring Safety
and Resilience Under Exponentially Unbounded FDI Attacks | [
"eess.SY",
"cs.SY"
] | False data injection (FDI) attacks pose a significant threat to autonomous multi-agent systems (MASs). While resilient control strategies address FDI attacks, they typically have strict assumptions on the attack signals and overlook safety constraints, such as collision avoidance. In practical applications, leader agen... | {
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2501.00975 | CoordFlow: Coordinate Flow for Pixel-wise Neural Video Representation | [
"cs.CV",
"cs.LG"
] | In the field of video compression, the pursuit for better quality at lower bit rates remains a long-lasting goal. Recent developments have demonstrated the potential of Implicit Neural Representation (INR) as a promising alternative to traditional transform-based methodologies. Video INRs can be roughly divided into fr... | {
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2501.00982 | Are LLMs effective psychological assessors? Leveraging adaptive RAG for
interpretable mental health screening through psychometric practice | [
"cs.CL",
"cs.AI"
] | In psychological practice, standardized questionnaires serve as essential tools for assessing mental constructs (e.g., attitudes, traits, and emotions) through structured questions (aka items). With the increasing prevalence of social media platforms where users share personal experiences and emotions, researchers are ... | {
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2501.00987 | Search Plurality | [
"cs.IR",
"cs.CY",
"cs.HC"
] | In light of Phillips' contention regarding the impracticality of Search Neutrality, asserting that non-epistemic factors presently dictate result prioritization, our objective in this study is to confront this constraint by questioning prevailing design practices in search engines. We posit that the concept of prioriti... | {
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2501.00988 | Optimizing Noise Schedules of Generative Models in High Dimensionss | [
"cs.LG"
] | Recent works have shown that diffusion models can undergo phase transitions, the resolution of which is needed for accurately generating samples. This has motivated the use of different noise schedules, the two most common choices being referred to as variance preserving (VP) and variance exploding (VE). Here we revisi... | {
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2501.00989 | Bootstrapped Reward Shaping | [
"cs.LG",
"cs.AI"
] | In reinforcement learning, especially in sparse-reward domains, many environment steps are required to observe reward information. In order to increase the frequency of such observations, "potential-based reward shaping" (PBRS) has been proposed as a method of providing a more dense reward signal while leaving the opti... | {
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2501.00990 | Cyber-physical Defense for Heterogeneous Multi-agent Systems Against
Exponentially Unbounded Attacks on Signed Digraphs | [
"eess.SY",
"cs.SY"
] | Cyber-physical systems (CPSs) are subjected to attacks on both cyber and physical spaces. In reality, the attackers could launch exponentially unbounded false data injection (EU-FDI) attacks, which are more destructive and could lead to the system's collapse or instability. Existing literature generally addresses bound... | {
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2501.00995 | Is It Still Fair? Investigating Gender Fairness in Cross-Corpus Speech
Emotion Recognition | [
"cs.LG"
] | Speech emotion recognition (SER) is a vital component in various everyday applications. Cross-corpus SER models are increasingly recognized for their ability to generalize performance. However, concerns arise regarding fairness across demographics in diverse corpora. Existing fairness research often focuses solely on c... | {
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2501.00999 | Exploring Information Processing in Large Language Models: Insights from
Information Bottleneck Theory | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of tasks by understanding input information and predicting corresponding outputs. However, the internal mechanisms by which LLMs comprehend input and make effective predictions remain poorly understood. In this paper, we explore t... | {
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2501.01000 | Physics-informed Gaussian Processes for Safe Envelope Expansion | [
"cs.LG"
] | Flight test analysis often requires predefined test points with arbitrarily tight tolerances, leading to extensive and resource-intensive experimental campaigns. To address this challenge, we propose a novel approach to flight test analysis using Gaussian processes (GPs) with physics-informed mean functions to estimate... | {
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2501.01002 | Multi-Objective Optimization-Based Anonymization of Structured Data for
Machine Learning | [
"cs.LG",
"math.OC"
] | Data is essential for secondary use, but ensuring its privacy while allowing such use is a critical challenge. Various techniques have been proposed to address privacy concerns in data sharing and publishing. However, these methods often degrade data utility, impacting the performance of machine learning (ML) models. O... | {
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2501.01003 | EasySplat: View-Adaptive Learning makes 3D Gaussian Splatting Easy | [
"cs.CV"
] | 3D Gaussian Splatting (3DGS) techniques have achieved satisfactory 3D scene representation. Despite their impressive performance, they confront challenges due to the limitation of structure-from-motion (SfM) methods on acquiring accurate scene initialization, or the inefficiency of densification strategy. In this paper... | {
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2501.01005 | FlashInfer: Efficient and Customizable Attention Engine for LLM
Inference Serving | [
"cs.DC",
"cs.AI",
"cs.LG"
] | Transformers, driven by attention mechanisms, form the foundation of large language models (LLMs). As these models scale up, efficient GPU attention kernels become essential for high-throughput and low-latency inference. Diverse LLM applications demand flexible and high-performance attention solutions. We present Flash... | {
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2501.01007 | Deep Reinforcement Learning for Job Scheduling and Resource Management
in Cloud Computing: An Algorithm-Level Review | [
"cs.DC",
"cs.AI"
] | Cloud computing has revolutionized the provisioning of computing resources, offering scalable, flexible, and on-demand services to meet the diverse requirements of modern applications. At the heart of efficient cloud operations are job scheduling and resource management, which are critical for optimizing system perform... | {
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2501.01010 | CryptoMamba: Leveraging State Space Models for Accurate Bitcoin Price
Prediction | [
"cs.LG",
"cs.AI",
"cs.CE"
] | Predicting Bitcoin price remains a challenging problem due to the high volatility and complex non-linear dynamics of cryptocurrency markets. Traditional time-series models, such as ARIMA and GARCH, and recurrent neural networks, like LSTMs, have been widely applied to this task but struggle to capture the regime shifts... | {
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2501.01011 | Prediction of Geoeffective CMEs Using SOHO Images and Deep Learning | [
"cs.LG",
"astro-ph.SR",
"physics.space-ph"
] | The application of machine learning to the study of coronal mass ejections (CMEs) and their impacts on Earth has seen significant growth recently. Understanding and forecasting CME geoeffectiveness is crucial for protecting infrastructure in space and ensuring the resilience of technological systems on Earth. Here we p... | {
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2501.01014 | MDSF: Context-Aware Multi-Dimensional Data Storytelling Framework based
on Large language Model | [
"cs.CL",
"cs.AI"
] | The exponential growth of data and advancements in big data technologies have created a demand for more efficient and automated approaches to data analysis and storytelling. However, automated data analysis systems still face challenges in leveraging large language models (LLMs) for data insight discovery, augmented an... | {
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2501.01015 | Boosting Adversarial Transferability with Spatial Adversarial Alignment | [
"cs.CV",
"cs.CR"
] | Deep neural networks are vulnerable to adversarial examples that exhibit transferability across various models. Numerous approaches are proposed to enhance the transferability of adversarial examples, including advanced optimization, data augmentation, and model modifications. However, these methods still show limited ... | {
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} |
2501.01022 | Efficient Connectivity-Preserving Instance Segmentation with
Supervoxel-Based Loss Function | [
"cs.CV",
"q-bio.NC"
] | Reconstructing the intricate local morphology of neurons and their long-range projecting axons can address many connectivity related questions in neuroscience. The main bottleneck in connectomics pipelines is correcting topological errors, as multiple entangled neuronal arbors is a challenging instance segmentation pro... | {
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} |
2501.01023 | Hadamard Attention Recurrent Transformer: A Strong Baseline for Stereo
Matching Transformer | [
"cs.CV"
] | In light of the advancements in transformer technology, extant research posits the construction of stereo transformers as a potential solution to the binocular stereo matching challenge. However, constrained by the low-rank bottleneck and quadratic complexity of attention mechanisms, stereo transformers still fail to d... | {
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} |
2501.01025 | Towards Adversarially Robust Deep Metric Learning | [
"cs.LG",
"cs.AI"
] | Deep Metric Learning (DML) has shown remarkable successes in many domains by taking advantage of powerful deep neural networks. Deep neural networks are prone to adversarial attacks and could be easily fooled by adversarial examples. The current progress on this robustness issue is mainly about deep classification mode... | {
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} |
2501.01028 | KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model | [
"cs.CL"
] | As retrieval-augmented generation prevails in large language models, embedding models are becoming increasingly crucial. Despite the growing number of general embedding models, prior work often overlooks the critical role of training data quality. In this work, we introduce KaLM-Embedding, a general multilingual embedd... | {
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} |
2501.01029 | State-of-the-art AI-based Learning Approaches for Deepfake Generation
and Detection, Analyzing Opportunities, Threading through Pros, Cons, and
Future Prospects | [
"cs.LG"
] | The rapid advancement of deepfake technologies, specifically designed to create incredibly lifelike facial imagery and video content, has ignited a remarkable level of interest and curiosity across many fields, including forensic analysis, cybersecurity and the innovative creation of digital characters. By harnessing t... | {
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} |
2501.01030 | Reasoning based on symbolic and parametric knowledge bases: a survey | [
"cs.CL",
"cs.AI"
] | Reasoning is fundamental to human intelligence, and critical for problem-solving, decision-making, and critical thinking. Reasoning refers to drawing new conclusions based on existing knowledge, which can support various applications like clinical diagnosis, basic education, and financial analysis. Though a good number... | {
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} |
2501.01031 | ValuesRAG: Enhancing Cultural Alignment Through Retrieval-Augmented
Contextual Learning | [
"cs.CL",
"cs.AI",
"cs.SI"
] | Cultural values alignment in Large Language Models (LLMs) is a critical challenge due to their tendency to embed Western-centric biases from training data, leading to misrepresentations and fairness issues in cross-cultural contexts. Recent approaches, such as role-assignment and few-shot learning, often struggle with ... | {
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} |
2501.01032 | DynamicLip: Shape-Independent Continuous Authentication via Lip
Articulator Dynamics | [
"cs.CV",
"cs.CR"
] | Biometrics authentication has become increasingly popular due to its security and convenience; however, traditional biometrics are becoming less desirable in scenarios such as new mobile devices, Virtual Reality, and Smart Vehicles. For example, while face authentication is widely used, it suffers from significant priv... | {
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} |
2501.01034 | Advancing Singlish Understanding: Bridging the Gap with Datasets and
Multimodal Models | [
"cs.CL",
"cs.SD",
"eess.AS"
] | Singlish, a Creole language rooted in English, is a key focus in linguistic research within multilingual and multicultural contexts. However, its spoken form remains underexplored, limiting insights into its linguistic structure and applications. To address this gap, we standardize and annotate the largest spoken Singl... | {
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} |
2501.01037 | MSC-Bench: Benchmarking and Analyzing Multi-Sensor Corruption for
Driving Perception | [
"cs.RO",
"cs.AI",
"cs.CV"
] | Multi-sensor fusion models play a crucial role in autonomous driving perception, particularly in tasks like 3D object detection and HD map construction. These models provide essential and comprehensive static environmental information for autonomous driving systems. While camera-LiDAR fusion methods have shown promisin... | {
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} |
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