id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.00542 | Rethinking Generalizability and Discriminability of Self-Supervised
Learning from Evolutionary Game Theory Perspective | [
"cs.AI",
"cs.CV"
] | Representations learned by self-supervised approaches are generally considered to possess sufficient generalizability and discriminability. However, we disclose a nontrivial mutual-exclusion relationship between these critical representation properties through an exploratory demonstration on self-supervised learning. S... | {
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2412.00543 | Evaluating the Consistency of LLM Evaluators | [
"cs.CL"
] | Large language models (LLMs) have shown potential as general evaluators along with the evident benefits of speed and cost. While their correlation against human annotators has been widely studied, consistency as evaluators is still understudied, raising concerns about the reliability of LLM evaluators. In this paper, w... | {
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2412.00544 | RoBo6: Standardized MMT Light Curve Dataset for Rocket Body
Classification | [
"cs.CV",
"astro-ph.IM",
"cs.LG",
"eess.IV"
] | Space debris presents a critical challenge for the sustainability of future space missions, emphasizing the need for robust and standardized identification methods. However, a comprehensive benchmark for rocket body classification remains absent. This paper addresses this gap by introducing the RoBo6 dataset for rocket... | {
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2412.00545 | Optimal Particle-based Approximation of Discrete Distributions (OPAD) | [
"stat.ML",
"cs.LG"
] | Particle-based methods include a variety of techniques, such as Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC), for approximating a probabilistic target distribution with a set of weighted particles. In this paper, we prove that for any set of particles, there is a unique weighting mechanism that mini... | {
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2412.00546 | Rank It, Then Ask It: Input Reranking for Maximizing the Performance of
LLMs on Symmetric Tasks | [
"cs.LG",
"cs.DB",
"cs.IR"
] | Large language models (LLMs) have quickly emerged as practical and versatile tools that provide new solutions for a wide range of domains. In this paper, we consider the application of LLMs on symmetric tasks where a query is asked on an (unordered) bag of elements. Examples of such tasks include answering aggregate qu... | {
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2412.00547 | Motion Dreamer: Realizing Physically Coherent Video Generation through
Scene-Aware Motion Reasoning | [
"cs.CV",
"cs.AI"
] | Recent numerous video generation models, also known as world models, have demonstrated the ability to generate plausible real-world videos. However, many studies have shown that these models often produce motion results lacking logical or physical coherence. In this paper, we revisit video generation models and find th... | {
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2412.00548 | Neural Power-Optimal Magnetorquer Solution for Multi-Agent Formation and
Attitude Control | [
"cs.MA"
] | This paper presents an efficient algorithm for finding the power-optimal currents of magnetorquer, a satellite attitude actuator in Earth orbit, for multi-agent formation and attitude control. Specifically, this study demonstrates that a set of power-optimal solutions can be derived through sequential convex programmin... | {
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2412.00549 | SeQwen at the Financial Misinformation Detection Challenge Task:
Sequential Learning for Claim Verification and Explanation Generation in
Financial Domains | [
"cs.CL",
"cs.CE",
"cs.LG",
"q-fin.CP"
] | This paper presents the system description of our entry for the COLING 2025 FMD challenge, focusing on misinformation detection in financial domains. We experimented with a combination of large language models, including Qwen, Mistral, and Gemma-2, and leveraged pre-processing and sequential learning for not only ident... | {
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2412.00554 | Unveiling Performance Challenges of Large Language Models in
Low-Resource Healthcare: A Demographic Fairness Perspective | [
"cs.CL",
"cs.AI"
] | This paper studies the performance of large language models (LLMs), particularly regarding demographic fairness, in solving real-world healthcare tasks. We evaluate state-of-the-art LLMs with three prevalent learning frameworks across six diverse healthcare tasks and find significant challenges in applying LLMs to real... | {
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2412.00555 | Learning Dynamic Weight Adjustment for Spatial-Temporal Trajectory
Planning in Crowd Navigation | [
"cs.RO"
] | Robot navigation in dense human crowds poses a significant challenge due to the complexity of human behavior in dynamic and obstacle-rich environments. In this work, we propose a dynamic weight adjustment scheme using a neural network to predict the optimal weights of objectives in an optimization-based motion planner.... | {
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2412.00556 | Accelerating Multimodal Large Language Models by Searching Optimal
Vision Token Reduction | [
"cs.CV"
] | Prevailing Multimodal Large Language Models (MLLMs) encode the input image(s) as vision tokens and feed them into the language backbone, similar to how Large Language Models (LLMs) process the text tokens. However, the number of vision tokens increases quadratically as the image resolutions, leading to huge computation... | {
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2412.00557 | Blind Inverse Problem Solving Made Easy by Text-to-Image Latent
Diffusion | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Blind inverse problems, where both the target data and forward operator are unknown, are crucial to many computer vision applications. Existing methods often depend on restrictive assumptions such as additional training, operator linearity, or narrow image distributions, thus limiting their generalizability. In this wo... | {
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2412.00559 | Polish Medical Exams: A new dataset for cross-lingual medical knowledge
transfer assessment | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) have demonstrated significant potential in handling specialized tasks, including medical problem-solving. However, most studies predominantly focus on English-language contexts. This study introduces a novel benchmark dataset based on Polish medical licensing and specialization exams (LEK, ... | {
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2412.00560 | Friend or Foe? Harnessing Controllable Overfitting for Anomaly Detection | [
"cs.LG",
"cs.AI"
] | Overfitting has long been stigmatized as detrimental to model performance, especially in the context of anomaly detection. Our work challenges this conventional view by introducing a paradigm shift, recasting overfitting as a controllable and strategic mechanism for enhancing model discrimination capabilities. In this ... | {
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2412.00568 | The Well: a Large-Scale Collection of Diverse Physics Simulations for
Machine Learning | [
"cs.LG",
"physics.flu-dyn"
] | Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small classes of physical behavior, it can be difficult to evaluate the efficacy of new approaches. To address this gap, we introduce the Well: a ... | {
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2412.00569 | Contextual Bandits in Payment Processing: Non-uniform Exploration and
Supervised Learning at Adyen | [
"cs.LG",
"cs.IR"
] | Uniform random exploration in decision-making systems supports off-policy learning via supervision but incurs high regret, making it impractical for many applications. Conversely, non-uniform exploration offers better immediate performance but lacks support for off-policy learning. Recent research suggests that regress... | {
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2412.00573 | Opus: A Large Work Model for Complex Workflow Generation | [
"cs.AI"
] | This paper introduces Opus, a novel framework for generating and optimizing Workflows tailored to complex Business Process Outsourcing (BPO) use cases, focusing on cost reduction and quality enhancement while adhering to established industry processes and operational constraints. Our approach generates executable Workf... | {
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2412.00575 | Multi-resolution Guided 3D GANs for Medical Image Translation | [
"eess.IV",
"cs.CV"
] | Medical image translation is the process of converting from one imaging modality to another, in order to reduce the need for multiple image acquisitions from the same patient. This can enhance the efficiency of treatment by reducing the time, equipment, and labor needed. In this paper, we introduce a multi-resolution g... | {
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2412.00577 | Turing Representational Similarity Analysis (RSA): A Flexible Method for
Measuring Alignment Between Human and Artificial Intelligence | [
"cs.AI"
] | As we consider entrusting Large Language Models (LLMs) with key societal and decision-making roles, measuring their alignment with human cognition becomes critical. This requires methods that can assess how these systems represent information and facilitate comparisons to human understanding across diverse tasks. To me... | {
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2412.00578 | Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse
Primitives | [
"cs.CV",
"cs.GR"
] | 3D Gaussian Splatting (3D-GS) is a recent 3D scene reconstruction technique that enables real-time rendering of novel views by modeling scenes as parametric point clouds of differentiable 3D Gaussians. However, its rendering speed and model size still present bottlenecks, especially in resource-constrained settings. In... | {
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2412.00579 | Operator learning regularization for macroscopic permeability prediction
in dual-scale flow problem | [
"physics.flu-dyn",
"cs.LG",
"cs.NA",
"math.NA",
"physics.comp-ph"
] | Liquid composites moulding is an important manufacturing technology for fibre reinforced composites, due to its cost-effectiveness. Challenges lie in the optimisation of the process due to the lack of understanding of key characteristic of textile fabrics - permeability. The problem of computing the permeability coeffi... | {
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2412.00580 | Continuous Concepts Removal in Text-to-image Diffusion Models | [
"cs.CV"
] | Text-to-image diffusion models have shown an impressive ability to generate high-quality images from input textual descriptions. However, concerns have been raised about the potential for these models to create content that infringes on copyrights or depicts disturbing subject matter. Removing specific concepts from th... | {
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2412.00581 | Dynamics Modeling using Visual Terrain Features for High-Speed
Autonomous Off-Road Driving | [
"cs.RO"
] | Rapid autonomous traversal of unstructured terrain is essential for scenarios such as disaster response, search and rescue, or planetary exploration. As a vehicle navigates at the limit of its capabilities over extreme terrain, its dynamics can change suddenly and dramatically. For example, high-speed and varying terra... | {
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2412.00589 | Invariant Measures in Time-Delay Coordinates for Unique Dynamical System
Identification | [
"math.DS",
"cs.LG",
"nlin.CD",
"physics.comp-ph"
] | Invariant measures are widely used to compare chaotic dynamical systems, as they offer robustness to noisy data, uncertain initial conditions, and irregular sampling. However, large classes of systems with distinct transient dynamics can still exhibit the same asymptotic statistical behavior, which poses challenges whe... | {
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2412.00591 | Audio Atlas: Visualizing and Exploring Audio Datasets | [
"cs.SD",
"cs.AI",
"eess.AS"
] | We introduce Audio Atlas, an interactive web application for visualizing audio data using text-audio embeddings. Audio Atlas is designed to facilitate the exploration and analysis of audio datasets using a contrastive embedding model and a vector database for efficient data management and semantic search. The system ma... | {
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2412.00592 | Generative LiDAR Editing with Controllable Novel Object Layouts | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.RO"
] | We propose a framework to edit real-world Lidar scans with novel object layouts while preserving a realistic background environment. Compared to the synthetic data generation frameworks where Lidar point clouds are generated from scratch, our framework focuses on new scenario generation in a given background environmen... | {
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2412.00596 | PhyT2V: LLM-Guided Iterative Self-Refinement for Physics-Grounded
Text-to-Video Generation | [
"cs.CV",
"cs.AI"
] | Text-to-video (T2V) generation has been recently enabled by transformer-based diffusion models, but current T2V models lack capabilities in adhering to the real-world common knowledge and physical rules, due to their limited understanding of physical realism and deficiency in temporal modeling. Existing solutions are e... | {
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2412.00597 | Spline-FRIDA: Towards Diverse, Humanlike Robot Painting Styles with a
Sample-Efficient, Differentiable Brush Stroke Model | [
"cs.RO"
] | A painting is more than just a picture on a wall; a painting is a process comprised of many intentional brush strokes, the shapes of which are an important component of a painting's overall style and message. Prior work in modeling brush stroke trajectories either does not work with real-world robotics or is not flexib... | {
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2412.00600 | DynRank: Improving Passage Retrieval with Dynamic Zero-Shot Prompting
Based on Question Classification | [
"cs.CL"
] | This paper presents DynRank, a novel framework for enhancing passage retrieval in open-domain question-answering systems through dynamic zero-shot question classification. Traditional approaches rely on static prompts and pre-defined templates, which may limit model adaptability across different questions and contexts.... | {
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2412.00603 | CAT-ORA: Collision-Aware Time-Optimal Formation Reshaping for Efficient
Robot Coordination in 3D Environments | [
"cs.RO",
"cs.SY",
"eess.SY"
] | In this paper, we introduce an algorithm designed to address the problem of time-optimal formation reshaping in three-dimensional environments while preventing collisions between agents. The utility of the proposed approach is particularly evident in mobile robotics, where agents benefit from being organized and naviga... | {
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2412.00605 | The Impact of Generative AI on Student Churn and the Future of Formal
Education | [
"cs.IR"
] | In the contemporary educational landscape, the advent of Generative Artificial Intelligence (AI) presents unprecedented opportunities for personalised learning, fundamentally challenging the traditional paradigms of education. This research explores the emerging trend where high school students, empowered by tailored e... | {
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2412.00606 | Fairness at Every Intersection: Uncovering and Mitigating Intersectional
Biases in Multimodal Clinical Predictions | [
"cs.AI"
] | Biases in automated clinical decision-making using Electronic Healthcare Records (EHR) impose significant disparities in patient care and treatment outcomes. Conventional approaches have primarily focused on bias mitigation strategies stemming from single attributes, overlooking intersectional subgroups -- groups forme... | {
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2412.00608 | Leveraging LLM for Automated Ontology Extraction and Knowledge Graph
Generation | [
"cs.AI"
] | Extracting relevant and structured knowledge from large, complex technical documents within the Reliability and Maintainability (RAM) domain is labor-intensive and prone to errors. Our work addresses this challenge by presenting OntoKGen, a genuine pipeline for ontology extraction and Knowledge Graph (KG) generation. O... | {
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2412.00609 | Exploration and Evaluation of Bias in Cyberbullying Detection with
Machine Learning | [
"cs.LG"
] | It is well known that the usefulness of a machine learning model is due to its ability to generalize to unseen data. This study uses three popular cyberbullying datasets to explore the effects of data, how it's collected, and how it's labeled, on the resulting machine learning models. The bias introduced from differing... | {
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2412.00613 | Revisit Non-parametric Two-sample Testing as a Semi-supervised Learning
Problem | [
"cs.LG",
"stat.ML"
] | Learning effective data representations is crucial in answering if two samples X and Y are from the same distribution (a.k.a. the non-parametric two-sample testing problem), which can be categorized into: i) learning discriminative representations (DRs) that distinguish between two samples in a supervised-learning para... | {
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2412.00621 | Exposing LLM Vulnerabilities: Adversarial Scam Detection and Performance | [
"cs.CR",
"cs.AI",
"cs.CY"
] | Can we trust Large Language Models (LLMs) to accurately predict scam? This paper investigates the vulnerabilities of LLMs when facing adversarial scam messages for the task of scam detection. We addressed this issue by creating a comprehensive dataset with fine-grained labels of scam messages, including both original a... | {
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2412.00622 | Visual Modality Prompt for Adapting Vision-Language Object Detectors | [
"cs.CV",
"cs.AI",
"cs.LG"
] | The zero-shot performance of object detectors degrades when tested on different modalities, such as infrared and depth. While recent work has explored image translation techniques to adapt detectors to new modalities, these methods are limited to a single modality and apply only to traditional detectors. Recently, visi... | {
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2412.00623 | A Lesson in Splats: Teacher-Guided Diffusion for 3D Gaussian Splats
Generation with 2D Supervision | [
"cs.CV"
] | We introduce a diffusion model for Gaussian Splats, SplatDiffusion, to enable generation of three-dimensional structures from single images, addressing the ill-posed nature of lifting 2D inputs to 3D. Existing methods rely on deterministic, feed-forward predictions, which limit their ability to handle the inherent ambi... | {
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2412.00624 | VideoSAVi: Self-Aligned Video Language Models without Human Supervision | [
"cs.CV"
] | Recent advances in vision-language models (VLMs) have significantly enhanced video understanding tasks. Instruction tuning (i.e., fine-tuning models on datasets of instructions paired with desired outputs) has been key to improving model performance. However, creating diverse instruction-tuning datasets is challenging ... | {
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2412.00626 | MambaNUT: Nighttime UAV Tracking via Mamba and Adaptive Curriculum
Learning | [
"cs.CV"
] | Harnessing low-light enhancement and domain adaptation, nighttime UAV tracking has made substantial strides. However, over-reliance on image enhancement, scarcity of high-quality nighttime data, and neglecting the relationship between daytime and nighttime trackers, which hinders the development of an end-to-end traina... | {
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2412.00627 | ARChef: An iOS-Based Augmented Reality Cooking Assistant Powered by
Multimodal Gemini LLM | [
"cs.HC",
"cs.AI"
] | Cooking meals can be difficult, causing many to resort to cookbooks and online recipes. However, relying on these traditional methods of cooking often results in missing ingredients, nutritional hazards, and unsatisfactory meals. Using Augmented Reality (AR) can address these issues; however, current AR cooking applica... | {
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2412.00629 | Online Voltage Regulation of Distribution Systems with
Disturbance-Action Controllers | [
"eess.SY",
"cs.SY"
] | Inverter-based distributed energy resources facilitate the advanced voltage control algorithms in the online setting with the flexibility in both active and reactive power injections. A key challenge is to continuously track the time-varying global optima with the robustness against dynamics inaccuracy and communicatio... | {
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2412.00631 | ROSE: A Reward-Oriented Data Selection Framework for LLM Task-Specific
Instruction Tuning | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Instruction tuning has underscored the significant potential of large language models (LLMs) in producing more human-controllable and effective outputs in various domains. In this work, we focus on the data selection problem for task-specific instruction tuning of LLMs. Prevailing methods primarily rely on the crafted ... | {
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2412.00632 | Flexible Rate-Splitting Multiple Access for Near-Field Integrated
Sensing and Communications | [
"cs.IT",
"eess.SP",
"math.IT"
] | This letter presents a flexible rate-splitting multiple access (RSMA) framework for near-field (NF) integrated sensing and communications (ISAC). The spatial beams configured to meet the communication rate requirements of NF users are simultaneously leveraged to sense an additional NF target. A key innovation lies in i... | {
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2412.00638 | Sketch-Guided Motion Diffusion for Stylized Cinemagraph Synthesis | [
"cs.CV",
"cs.GR"
] | Designing stylized cinemagraphs is challenging due to the difficulty in customizing complex and expressive flow motions. To achieve intuitive and detailed control of the generated cinemagraphs, freehand sketches can provide a better solution to convey personalized design requirements than only text inputs. In this pape... | {
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2412.00639 | Needle: A Generative-AI Powered Monte Carlo Method for Answering Complex
Natural Language Queries on Multi-modal Data | [
"cs.IR",
"cs.DB"
] | Multi-modal data, such as image data sets, often miss the detailed descriptions that properly capture the rich information encoded in them. This makes answering complex natural language queries a major challenge in these domains. In particular, unlike the traditional nearest-neighbor search, where the tuples and the qu... | {
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2412.00642 | Pessimistic Cardinality Estimation | [
"cs.DB",
"cs.IT",
"math.IT"
] | Cardinality Estimation is to estimate the size of the output of a query without computing it, by using only statistics on the input relations. Existing estimators try to return an unbiased estimate of the cardinality: this is notoriously difficult. A new class of estimators have been proposed recently, called "pessimis... | {
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2412.00648 | DFRot: Achieving Outlier-Free and Massive Activation-Free for Rotated
LLMs with Refined Rotation | [
"cs.LG",
"stat.ML"
] | Rotating the activation and weight matrices to reduce the influence of outliers in large language models (LLMs) has recently attracted significant attention, particularly in the context of model quantization. Prior studies have shown that in low-precision quantization scenarios, such as 4-bit weights and 4-bit activati... | {
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2412.00651 | Towards Unified Molecule-Enhanced Pathology Image Representation
Learning via Integrating Spatial Transcriptomics | [
"cs.CV",
"q-bio.GN"
] | Recent advancements in multimodal pre-training models have significantly advanced computational pathology. However, current approaches predominantly rely on visual-language models, which may impose limitations from a molecular perspective and lead to performance bottlenecks. Here, we introduce a Unified Molecule-enhanc... | {
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2412.00652 | Multi-Agent Collaboration in Incident Response with Large Language
Models | [
"cs.CL",
"cs.CR"
] | Incident response (IR) is a critical aspect of cybersecurity, requiring rapid decision-making and coordinated efforts to address cyberattacks effectively. Leveraging large language models (LLMs) as intelligent agents offers a novel approach to enhancing collaboration and efficiency in IR scenarios. This paper explores ... | {
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2412.00653 | Predictive Inference With Fast Feature Conformal Prediction | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Conformal prediction is widely adopted in uncertainty quantification, due to its post-hoc, distribution-free, and model-agnostic properties. In the realm of modern deep learning, researchers have proposed Feature Conformal Prediction (FCP), which deploys conformal prediction in a feature space, yielding reduced band le... | {
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2412.00656 | Two-Stage Adaptive Robust Optimization Model for Joint Unit Maintenance
and Unit Commitment Considering Source-Load Uncertainty | [
"eess.SY",
"cs.SY"
] | Unit maintenance and unit commitment are two critical and interrelated aspects of electric power system operation, both of which face the challenge of coordinating efforts to enhance reliability and economic performance. This challenge becomes increasingly pronounced in the context of increased integration of renewable... | {
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2412.00657 | Improving Vietnamese Legal Document Retrieval using Synthetic Data | [
"cs.IR",
"cs.AI"
] | In the field of legal information retrieval, effective embedding-based models are essential for accurate question-answering systems. However, the scarcity of large annotated datasets poses a significant challenge, particularly for Vietnamese legal texts. To address this issue, we propose a novel approach that leverages... | {
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2412.00661 | Mean-Field Sampling for Cooperative Multi-Agent Reinforcement Learning | [
"cs.LG",
"cs.AI",
"cs.MA",
"cs.SY",
"eess.SY",
"math.OC"
] | Designing efficient algorithms for multi-agent reinforcement learning (MARL) is fundamentally challenging because the size of the joint state and action spaces grows exponentially in the number of agents. These difficulties are exacerbated when balancing sequential global decision-making with local agent interactions. ... | {
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2412.00663 | Deep Learning for Longitudinal Gross Tumor Volume Segmentation in
MRI-Guided Adaptive Radiotherapy for Head and Neck Cancer | [
"eess.IV",
"cs.AI",
"cs.CV",
"physics.med-ph"
] | Accurate segmentation of gross tumor volume (GTV) is essential for effective MRI-guided adaptive radiotherapy (MRgART) in head and neck cancer. However, manual segmentation of the GTV over the course of therapy is time-consuming and prone to interobserver variability. Deep learning (DL) has the potential to overcome th... | {
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2412.00664 | Improving Decoupled Posterior Sampling for Inverse Problems using Data
Consistency Constraint | [
"cs.LG",
"cs.CV",
"stat.ML"
] | Diffusion models have shown strong performances in solving inverse problems through posterior sampling while they suffer from errors during earlier steps. To mitigate this issue, several Decoupled Posterior Sampling methods have been recently proposed. However, the reverse process in these methods ignores measurement i... | {
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2412.00665 | Learning on Less: Constraining Pre-trained Model Learning for
Generalizable Diffusion-Generated Image Detection | [
"cs.CV"
] | Diffusion Models enable realistic image generation, raising the risk of misinformation and eroding public trust. Currently, detecting images generated by unseen diffusion models remains challenging due to the limited generalization capabilities of existing methods. To address this issue, we rethink the effectiveness of... | {
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2412.00666 | Explaining Object Detectors via Collective Contribution of Pixels | [
"cs.CV"
] | Visual explanations for object detectors are crucial for enhancing their reliability. Since object detectors identify and localize instances by assessing multiple features collectively, generating explanations that capture these collective contributions is critical. However, existing methods focus solely on individual ... | {
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2412.00671 | FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for
Detailed Depth Estimation | [
"cs.CV"
] | Monocular Depth Estimation (MDE) is essential for applications like 3D scene reconstruction, autonomous navigation, and AI content creation. However, robust MDE remains challenging due to noisy real-world data and distribution gaps in synthetic datasets. Existing methods often struggle with low efficiency, reduced accu... | {
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2412.00672 | A Sensor Position Localization Method for Flexible, Non-Uniform
Capacitive Tactile Sensor Arrays | [
"cs.RO"
] | Tactile sensing is used in robotics to obtain real-time feedback during physical interactions. Fine object manipulation is a robotic application that benefits from a high density of sensors to accurately estimate object pose, whereas a low sensing resolution is sufficient for collision detection. Introducing variable s... | {
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2412.00674 | A Generalized Trace Reconstruction Problem: Recovering a String of
Probabilities | [
"cs.DS",
"cs.IT",
"math.IT"
] | We introduce the following natural generalization of trace reconstruction, parameterized by a deletion probability $\delta \in (0,1)$ and length $n$: There is a length $n$ string of probabilities, $S=p_1,\ldots,p_n,$ and each "trace" is obtained by 1) sampling a length $n$ binary string whose $i$th coordinate is indepe... | {
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2412.00678 | 2DMamba: Efficient State Space Model for Image Representation with
Applications on Giga-Pixel Whole Slide Image Classification | [
"cs.CV"
] | Efficiently modeling large 2D contexts is essential for various fields including Giga-Pixel Whole Slide Imaging (WSI) and remote sensing. Transformer-based models offer high parallelism but face challenges due to their quadratic complexity for handling long sequences. Recently, Mamba introduced a selective State Space ... | {
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2412.00679 | Remote Estimation Games with Random Walk Processes: Stackelberg
Equilibrium | [
"cs.IT",
"cs.GT",
"cs.SY",
"eess.SP",
"eess.SY",
"math.IT"
] | Remote estimation is a crucial element of real time monitoring of a stochastic process. While most of the existing works have concentrated on obtaining optimal sampling strategies, motivated by malicious attacks on cyber-physical systems, we model sensing under surveillance as a game between an attacker and a defender.... | {
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2412.00681 | MIMIC: Multimodal Islamophobic Meme Identification and Classification | [
"cs.CV"
] | Anti-Muslim hate speech has emerged within memes, characterized by context-dependent and rhetorical messages using text and images that seemingly mimic humor but convey Islamophobic sentiments. This work presents a novel dataset and proposes a classifier based on the Vision-and-Language Transformer (ViLT) specifically ... | {
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2412.00682 | FlashSLAM: Accelerated RGB-D SLAM for Real-Time 3D Scene Reconstruction
with Gaussian Splatting | [
"cs.CV"
] | We present FlashSLAM, a novel SLAM approach that leverages 3D Gaussian Splatting for efficient and robust 3D scene reconstruction. Existing 3DGS-based SLAM methods often fall short in sparse view settings and during large camera movements due to their reliance on gradient descent-based optimization, which is both slow ... | {
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2412.00683 | DMFourLLIE: Dual-Stage and Multi-Branch Fourier Network for Low-Light
Image Enhancement | [
"cs.CV"
] | In the Fourier frequency domain, luminance information is primarily encoded in the amplitude component, while spatial structure information is significantly contained within the phase component. Existing low-light image enhancement techniques using Fourier transform have mainly focused on amplifying the amplitude compo... | {
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2412.00684 | Paint Outside the Box: Synthesizing and Selecting Training Data for
Visual Grounding | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Visual grounding aims to localize the image regions based on a textual query. Given the difficulty of large-scale data curation, we investigate how to effectively learn visual grounding under data-scarce settings in this paper. To address data scarcity, we propose a novel framework, POBF (Paint Outside the Box, then Fi... | {
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2412.00686 | LVLM-COUNT: Enhancing the Counting Ability of Large Vision-Language
Models | [
"cs.CV",
"cs.AI"
] | Counting is a fundamental operation for various visual tasks in real-life applications, requiring both object recognition and robust counting capabilities. Despite their advanced visual perception, large vision-language models (LVLMs) struggle with counting tasks, especially when the number of objects exceeds those com... | {
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2412.00687 | Towards Privacy-Preserving Medical Imaging: Federated Learning with
Differential Privacy and Secure Aggregation Using a Modified ResNet
Architecture | [
"cs.LG",
"cs.CR"
] | With increasing concerns over privacy in healthcare, especially for sensitive medical data, this research introduces a federated learning framework that combines local differential privacy and secure aggregation using Secure Multi-Party Computation for medical image classification. Further, we propose DPResNet, a modif... | {
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2412.00689 | A Machine Learning Approach to Contact Localization in Variable Density
Three-Dimensional Tactile Artificial Skin | [
"cs.RO"
] | Estimating the location of contact is a primary function of artificial tactile sensing apparatuses that perceive the environment through touch. Existing contact localization methods use flat geometry and uniform sensor distributions as a simplifying assumption, limiting their ability to be used on 3D surfaces with vari... | {
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2412.00691 | The Advancement of Personalized Learning Potentially Accelerated by
Generative AI | [
"cs.AI",
"stat.AP"
] | The rapid development of Generative AI (GAI) has sparked revolutionary changes across various aspects of education. Personalized learning, a focal point and challenge in educational research, has also been influenced by the development of GAI. To explore GAI's extensive impact on personalized learning, this study inves... | {
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2412.00692 | BEV-SUSHI: Multi-Target Multi-Camera 3D Detection and Tracking in
Bird's-Eye View | [
"cs.CV"
] | Object perception from multi-view cameras is crucial for intelligent systems, particularly in indoor environments, e.g., warehouses, retail stores, and hospitals. Most traditional multi-target multi-camera (MTMC) detection and tracking methods rely on 2D object detection, single-view multi-object tracking (MOT), and cr... | {
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2412.00696 | Intermediate Outputs Are More Sensitive Than You Think | [
"cs.CV",
"cs.CR",
"cs.LG",
"stat.ML"
] | The increasing reliance on deep computer vision models that process sensitive data has raised significant privacy concerns, particularly regarding the exposure of intermediate results in hidden layers. While traditional privacy risk assessment techniques focus on protecting overall model outputs, they often overlook vu... | {
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2412.00697 | DISCO-Dynamic Interference Suppression for Radar and Communication
Cohabitation | [
"cs.NI",
"cs.IT",
"cs.SI",
"math.IT"
] | We propose the joint dynamic power allocation and multi-relay selection for the cohabitation of high-priority military radar and low-priority commercial 5G communication. To improve the 5G network performance, we design the full-duplex underlay cognitive radio network for the low-priority commercial 5G network, where m... | {
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2412.00702 | Enhancing Skin Lesion Classification Generalization with Active Domain
Adaptation | [
"cs.CV"
] | We propose a method to improve the generalization of skin lesion classification models by combining self-supervised learning (SSL) and active domain adaptation (ADA). The main steps of the approach include selection of an SSL pre-trained model on natural image datasets, subsequent SSL retraining on all available skin-l... | {
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2412.00705 | Photoacoustic Iterative Optimization Algorithm with Shape Prior
Regularization | [
"physics.optics",
"cs.CV"
] | Photoacoustic imaging (PAI) suffers from inherent limitations that can degrade the quality of reconstructed results, such as noise, artifacts and incomplete data acquisition caused by sparse sampling or partial array detection. In this study, we proposed a new optimization method for both two-dimensional (2D) and three... | {
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2412.00707 | Protect Your Secrets: Understanding and Measuring Data Exposure in
VSCode Extensions | [
"cs.CR",
"cs.AI",
"cs.SE"
] | Recent years have witnessed the emerging trend of extensions in modern Integrated Development Environments (IDEs) like Visual Studio Code (VSCode) that significantly enhance developer productivity. Especially, popular AI coding assistants like GitHub Copilot and Tabnine provide conveniences like automated code completi... | {
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2412.00711 | GenTact Toolbox: A Computational Design Pipeline to Procedurally
Generate Context-Driven 3D Printed Whole-Body Tactile Skins | [
"cs.RO"
] | Developing whole-body tactile skins for robots remains a challenging task, as existing solutions often prioritize modular, one-size-fits-all designs, which, while versatile, fail to account for the robot's specific shape and the unique demands of its operational context. In this work, we introduce the GenTact Toolbox, ... | {
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2412.00714 | Scaling New Frontiers: Insights into Large Recommendation Models | [
"cs.IR"
] | Recommendation systems are essential for filtering data and retrieving relevant information across various applications. Recent advancements have seen these systems incorporate increasingly large embedding tables, scaling up to tens of terabytes for industrial use. However, the expansion of network parameters in tradit... | {
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2412.00715 | A Semi-Supervised Approach with Error Reflection for Echocardiography
Segmentation | [
"eess.IV",
"cs.CV"
] | Segmenting internal structure from echocardiography is essential for the diagnosis and treatment of various heart diseases. Semi-supervised learning shows its ability in alleviating annotations scarcity. While existing semi-supervised methods have been successful in image segmentation across various medical imaging mod... | {
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2412.00718 | Well log data generation and imputation using sequence-based generative
adversarial networks | [
"physics.geo-ph",
"cs.AI",
"cs.LG"
] | Well log analysis is crucial for hydrocarbon exploration, providing detailed insights into subsurface geological formations. However, gaps and inaccuracies in well log data, often due to equipment limitations, operational challenges, and harsh subsurface conditions, can introduce significant uncertainties in reservoir ... | {
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2412.00719 | Synergizing Motion and Appearance: Multi-Scale Compensatory Codebooks
for Talking Head Video Generation | [
"cs.CV"
] | Talking head video generation aims to generate a realistic talking head video that preserves the person's identity from a source image and the motion from a driving video. Despite the promising progress made in the field, it remains a challenging and critical problem to generate videos with accurate poses and fine-grai... | {
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2412.00720 | Bridging Fairness Gaps: A (Conditional) Distance Covariance Perspective
in Fairness Learning | [
"cs.LG",
"cs.CY",
"stat.ML"
] | We bridge fairness gaps from a statistical perspective by selectively utilizing either conditional distance covariance or distance covariance statistics as measures to assess the independence between predictions and sensitive attributes. We enhance fairness by incorporating sample (conditional) distance covariance as a... | {
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2412.00721 | A Comparative Study of LLM-based ASR and Whisper in Low Resource and
Code Switching Scenario | [
"cs.AI",
"cs.CL",
"cs.SD",
"eess.AS"
] | Large Language Models (LLMs) have showcased exceptional performance across diverse NLP tasks, and their integration with speech encoder is rapidly emerging as a dominant trend in the Automatic Speech Recognition (ASR) field. Previous works mainly concentrated on leveraging LLMs for speech recognition in English and Chi... | {
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2412.00722 | Towards Adaptive Mechanism Activation in Language Agent | [
"cs.CL",
"cs.AI"
] | Language Agent could be endowed with different mechanisms for autonomous task accomplishment. Current agents typically rely on fixed mechanisms or a set of mechanisms activated in a predefined order, limiting their adaptation to varied potential task solution structures. To this end, this paper proposes \textbf{A}dapti... | {
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2412.00724 | AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation
Loop on Mobile Devices | [
"cs.AI"
] | Deep learning is reshaping mobile applications, with a growing trend of deploying deep neural networks (DNNs) directly to mobile and embedded devices to address real-time performance and privacy. To accommodate local resource limitations, techniques like weight compression, convolution decomposition, and specialized la... | {
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2412.00725 | Decision Transformer vs. Decision Mamba: Analysing the Complexity of
Sequential Decision Making in Atari Games | [
"cs.LG",
"cs.AI"
] | This work analyses the disparity in performance between Decision Transformer (DT) and Decision Mamba (DM) in sequence modelling reinforcement learning tasks for different Atari games. The study first observed that DM generally outperformed DT in the games Breakout and Qbert, while DT performed better in more complicate... | {
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2412.00726 | Free and Customizable Code Documentation with LLMs: A Fine-Tuning
Approach | [
"cs.SE",
"cs.AI",
"cs.LG"
] | Automated documentation of programming source code is a challenging task with significant practical and scientific implications for the developer community. We present a large language model (LLM)-based application that developers can use as a support tool to generate basic documentation for any publicly available repo... | {
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2412.00727 | Perturb and Recover: Fine-tuning for Effective Backdoor Removal from
CLIP | [
"cs.LG",
"cs.CR",
"cs.CV"
] | Vision-Language models like CLIP have been shown to be highly effective at linking visual perception and natural language understanding, enabling sophisticated image-text capabilities, including strong retrieval and zero-shot classification performance. Their widespread use, as well as the fact that CLIP models are tra... | {
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2412.00730 | SEED4D: A Synthetic Ego--Exo Dynamic 4D Data Generator, Driving Dataset
and Benchmark | [
"cs.CV"
] | Models for egocentric 3D and 4D reconstruction, including few-shot interpolation and extrapolation settings, can benefit from having images from exocentric viewpoints as supervision signals. No existing dataset provides the necessary mixture of complex, dynamic, and multi-view data. To facilitate the development of 3D ... | {
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2412.00731 | Refine3DNet: Scaling Precision in 3D Object Reconstruction from
Multi-View RGB Images using Attention | [
"cs.CV"
] | Generating 3D models from multi-view 2D RGB images has gained significant attention, extending the capabilities of technologies like Virtual Reality, Robotic Vision, and human-machine interaction. In this paper, we introduce a hybrid strategy combining CNNs and transformers, featuring a visual auto-encoder with self-at... | {
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2412.00732 | Design of a Five-Fingered Hand with Full-Fingered Tactile Sensors Using
Conductive Filaments and Its Application to Bending after Insertion Motion | [
"cs.RO"
] | The purpose of this study is to construct a contact point estimation system for the both side of a finger, and to realize a motion of bending the finger after inserting the finger into a tool (hereinafter referred to as the bending after insertion motion). In order to know the contact points of the full finger includin... | {
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2412.00733 | Hallo3: Highly Dynamic and Realistic Portrait Image Animation with
Diffusion Transformer Networks | [
"cs.CV",
"cs.GR",
"cs.LG"
] | Existing methodologies for animating portrait images face significant challenges, particularly in handling non-frontal perspectives, rendering dynamic objects around the portrait, and generating immersive, realistic backgrounds. In this paper, we introduce the first application of a pretrained transformer-based video g... | {
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2412.00734 | ChatSplat: 3D Conversational Gaussian Splatting | [
"cs.CV"
] | Humans naturally interact with their 3D surroundings using language, and modeling 3D language fields for scene understanding and interaction has gained growing interest. This paper introduces ChatSplat, a system that constructs a 3D language field, enabling rich chat-based interaction within 3D space. Unlike existing m... | {
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} |
2412.00736 | Bringing Quantum Systems under Control: A Tutorial Invitation to Quantum
Computing and Its Relation to Bilinear Control Systems | [
"eess.SY",
"cs.SY",
"math.OC"
] | Quantum computing comes with the potential to push computational boundaries in various domains including, e.g., cryptography, simulation, optimization, and machine learning. Exploiting the principles of quantum mechanics, new algorithms can be developed with capabilities that are unprecedented by classical computers. H... | {
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2412.00737 | Modification of muscle antagonistic relations and hand trajectory on the
dynamic motion of Musculoskeletal Humanoid | [
"cs.RO"
] | In recent years, some research on musculoskeletal humanoids is in progress. However, there are some challenges such as unmeasurable transformation of body structure and muscle path, and difficulty in measuring own motion because of lack of joint angle sensor. In this study, we suggest two motion acquisition methods. On... | {
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} |
2412.00740 | Precise Facial Landmark Detection by Dynamic Semantic Aggregation
Transformer | [
"cs.CV"
] | At present, deep neural network methods have played a dominant role in face alignment field. However, they generally use predefined network structures to predict landmarks, which tends to learn general features and leads to mediocre performance, e.g., they perform well on neutral samples but struggle with faces exhibit... | {
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} |
2412.00742 | Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral
Clustering Perspective | [
"cs.AI"
] | Self-supervised heterogeneous graph learning (SHGL) has shown promising potential in diverse scenarios. However, while existing SHGL methods share a similar essential with clustering approaches, they encounter two significant limitations: (i) noise in graph structures is often introduced during the message-passing proc... | {
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2412.00744 | A Cross-Scene Benchmark for Open-World Drone Active Tracking | [
"cs.RO",
"cs.AI"
] | Drone Visual Active Tracking aims to autonomously follow a target object by controlling the motion system based on visual observations, providing a more practical solution for effective tracking in dynamic environments. However, accurate Drone Visual Active Tracking using reinforcement learning remains challenging due ... | {
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} |
2412.00748 | Exploring Cognition through Morphological Info-Computational Framework | [
"cs.AI"
] | Traditionally, cognition has been considered a uniquely human capability involving perception, memory, learning, reasoning, and problem-solving. However, recent research shows that cognition is a fundamental ability shared by all living beings, from single cells to complex organisms. This chapter takes an info-computat... | {
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} |
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