id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.01000 | Characterizing and Modeling AI-Driven Animal Ecology Studies at the Edge | [
"eess.SY",
"cs.SY"
] | Platforms that run artificial intelligence (AI) pipelines on edge computing resources are transforming the fields of animal ecology and biodiversity, enabling novel wildlife studies in animals' natural habitats. With emerging remote sensing hardware, e.g., camera traps and drones, and sophisticated AI models in situ, e... | {
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2412.01002 | Mutual Coupling in Dynamic Metasurface Antennas: Foe, but also Friend | [
"eess.SP",
"cs.IT",
"math.IT",
"physics.app-ph"
] | Dynamic metasurface antennas (DMAs), surfaces patterned with reconfigurable metamaterial elements (meta-atoms) that couple waves from waveguides or cavities to free space, are a promising technology to realize 6G wireless base stations and access points with low cost and power consumption. Mutual coupling between the D... | {
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2412.01003 | Competition Dynamics Shape Algorithmic Phases of In-Context Learning | [
"cs.LG",
"cs.CL"
] | In-Context Learning (ICL) has significantly expanded the general-purpose nature of large language models, allowing them to adapt to novel tasks using merely the inputted context. This has motivated a series of papers that analyze tractable synthetic domains and postulate precise mechanisms that may underlie ICL. Howeve... | {
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2412.01004 | Adaptive Rank, Reduced Forgetting: Knowledge Retention in Continual
Learning Vision-Language Models with Dynamic Rank-Selective LoRA | [
"cs.CV"
] | We investigate whether the pre-trained knowledge of vision-language models (VLMs), such as CLIP, can be retained or even enhanced during continual learning (CL) while absorbing knowledge from a data stream. Existing methods often rely on additional reference data, isolated components for distribution or domain predicti... | {
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2412.01007 | CoRNStack: High-Quality Contrastive Data for Better Code Ranking | [
"cs.CL",
"cs.IR"
] | Effective code retrieval plays a crucial role in advancing code generation, bug fixing, and software maintenance, particularly as software systems increase in complexity. While current code embedding models have demonstrated promise in retrieving code snippets for small-scale, well-defined tasks, they often underperfor... | {
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2412.01010 | A Note on Estimation Error Bound and Grouping Effect of Transfer Elastic
Net | [
"stat.ML",
"cs.LG"
] | The Transfer Elastic Net is an estimation method for linear regression models that combines $\ell_1$ and $\ell_2$ norm penalties to facilitate knowledge transfer. In this study, we derive a non-asymptotic $\ell_2$ norm estimation error bound for the estimator and discuss scenarios where the Transfer Elastic Net effecti... | {
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2412.01011 | e-Fold Cross-Validation for Recommender-System Evaluation | [
"cs.LG",
"cs.IR"
] | To combat the rising energy consumption of recommender systems we implement a novel alternative for k-fold cross validation. This alternative, named e-fold cross validation, aims to minimize the number of folds to achieve a reduction in power usage while keeping the reliability and robustness of the test results high. ... | {
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2412.01013 | Jacobian-Enforced Neural Networks (JENN) for Improved Data Assimilation
Consistency in Dynamical Models | [
"cs.LG",
"physics.ao-ph"
] | Machine learning-based weather models have shown great promise in producing accurate forecasts but have struggled when applied to data assimilation tasks, unlike traditional numerical weather prediction (NWP) models. This study introduces the Jacobian-Enforced Neural Network (JENN) framework, designed to enhance DA con... | {
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2412.01014 | Detecting Memorization in Large Language Models | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Large language models (LLMs) have achieved impressive results in natural language processing but are prone to memorizing portions of their training data, which can compromise evaluation metrics, raise privacy concerns, and limit generalization. Traditional methods for detecting memorization rely on output probabilities... | {
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2412.01017 | Inferring Short-Sightedness in Dynamic Noncooperative Games | [
"cs.RO",
"cs.GT",
"cs.MA",
"cs.SY",
"eess.SY"
] | Dynamic game theory is an increasingly popular tool for modeling multi-agent, e.g. human-robot, interactions. Game-theoretic models presume that each agent wishes to minimize a private cost function that depends on others' actions. These games typically evolve over a fixed time horizon, which specifies the degree to wh... | {
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2412.01019 | Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on
Structured Spaces | [
"stat.ML",
"cs.LG"
] | Energy-based models (EBMs) offer a flexible framework for probabilistic modelling across various data domains. However, training EBMs on data in discrete or mixed state spaces poses significant challenges due to the lack of robust and fast sampling methods. In this work, we propose to train discrete EBMs with Energy Di... | {
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2412.01020 | AI Benchmarks and Datasets for LLM Evaluation | [
"cs.DC",
"cs.AI"
] | LLMs demand significant computational resources for both pre-training and fine-tuning, requiring distributed computing capabilities due to their large model sizes \cite{sastry2024computing}. Their complex architecture poses challenges throughout the entire AI lifecycle, from data collection to deployment and monitoring... | {
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2412.01021 | On the Feature Learning in Diffusion Models | [
"stat.ML",
"cs.LG"
] | The predominant success of diffusion models in generative modeling has spurred significant interest in understanding their theoretical foundations. In this work, we propose a feature learning framework aimed at analyzing and comparing the training dynamics of diffusion models with those of traditional classification mo... | {
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2412.01023 | Learning Structured Representations with Hyperbolic Embeddings | [
"cs.LG",
"cs.CV"
] | Most real-world datasets consist of a natural hierarchy between classes or an inherent label structure that is either already available or can be constructed cheaply. However, most existing representation learning methods ignore this hierarchy, treating labels as permutation invariant. Recent work [Zeng et al., 2022] p... | {
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2412.01027 | Unleashing In-context Learning of Autoregressive Models for Few-shot
Image Manipulation | [
"cs.CV"
] | Text-guided image manipulation has experienced notable advancement in recent years. In order to mitigate linguistic ambiguity, few-shot learning with visual examples has been applied for instructions that are underrepresented in the training set, or difficult to describe purely in language. However, learning from visua... | {
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2412.01029 | Deep Learning Based Near-Field User Localization with Beam Squint in
Wideband XL-MIMO Systems | [
"eess.SP",
"cs.IT",
"math.IT"
] | Extremely large-scale multiple-input multiple-output (XL-MIMO) is gaining attention as a prominent technology for enabling the sixth-generation (6G) wireless networks. However, the vast antenna array and the huge bandwidth introduce a non-negligible beam squint effect, causing beams of different frequencies to focus at... | {
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2412.01031 | Evaluating Automated Radiology Report Quality through Fine-Grained
Phrasal Grounding of Clinical Findings | [
"cs.CL",
"cs.AI",
"cs.CV"
] | Several evaluation metrics have been developed recently to automatically assess the quality of generative AI reports for chest radiographs based only on textual information using lexical, semantic, or clinical named entity recognition methods. In this paper, we develop a new method of report quality evaluation by first... | {
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2412.01033 | SAUP: Situation Awareness Uncertainty Propagation on LLM Agent | [
"cs.CL",
"cs.LG"
] | Large language models (LLMs) integrated into multistep agent systems enable complex decision-making processes across various applications. However, their outputs often lack reliability, making uncertainty estimation crucial. Existing uncertainty estimation methods primarily focus on final-step outputs, which fail to ac... | {
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2412.01034 | Quantization-Aware Imitation-Learning for Resource-Efficient Robotic
Control | [
"cs.RO",
"cs.CV",
"cs.LG"
] | Deep neural network (DNN)-based policy models like vision-language-action (VLA) models are transformative in automating complex decision-making across applications by interpreting multi-modal data. However, scaling these models greatly increases computational costs, which presents challenges in fields like robot manipu... | {
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2412.01035 | Adaptive Traffic Element-Based Streetlight Control Using Neighbor
Discovery Algorithm Based on IoT Events | [
"cs.LG",
"cs.SY",
"eess.SY"
] | Intelligent streetlight systems divide the streetlight network into multiple sectors, activating only the streetlights in the corresponding sectors when traffic elements pass by, rather than all streetlights, effectively reducing energy waste. This strategy requires streetlights to understand their neighbor relationshi... | {
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2412.01036 | Generating Freeform Endoskeletal Robots | [
"cs.RO"
] | The automatic design of embodied agents (e.g. robots) has existed for 31 years and is experiencing a renaissance of interest in the literature. To date however, the field has remained narrowly focused on two kinds of anatomically simple robots: (1) fully rigid, jointed bodies; and (2) fully soft, jointless bodies. Here... | {
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2412.01039 | Reducing Inference Energy Consumption Using Dual Complementary CNNs | [
"cs.AI"
] | Energy efficiency of Convolutional Neural Networks (CNNs) has become an important area of research, with various strategies being developed to minimize the power consumption of these models. Previous efforts, including techniques like model pruning, quantization, and hardware optimization, have made significant strides... | {
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2412.01041 | LiDAR SLAMMOT based on Confidence-guided Data Association | [
"cs.RO"
] | In the field of autonomous driving or robotics, simultaneous localization and mapping (SLAM) and multi-object tracking (MOT) are two fundamental problems and are generally applied separately. Solutions to SLAM and MOT usually rely on certain assumptions, such as the static environment assumption for SLAM and the accura... | {
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2412.01042 | TruncFormer: Private LLM Inference Using Only Truncations | [
"cs.CR",
"cs.LG"
] | Private inference (PI) serves an important role in guaranteeing the privacy of user data when interfacing with proprietary machine learning models such as LLMs. However, PI remains practically intractable due to the massive latency costs associated with nonlinear functions present in LLMs. Existing works have focused o... | {
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2412.01046 | Improving Detail in Pluralistic Image Inpainting with Feature
Dequantization | [
"cs.CV"
] | Pluralistic Image Inpainting (PII) offers multiple plausible solutions for restoring missing parts of images and has been successfully applied to various applications including image editing and object removal. Recently, VQGAN-based methods have been proposed and have shown that they significantly improve the structura... | {
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2412.01048 | Cerberus: Attribute-based person re-identification using semantic IDs | [
"cs.CV"
] | We introduce a new framework, dubbed Cerberus, for attribute-based person re-identification (reID). Our approach leverages person attribute labels to learn local and global person representations that encode specific traits, such as gender and clothing style. To achieve this, we define semantic IDs (SIDs) by combining ... | {
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2412.01050 | Resilience-oriented Planning and Cost Allocation of Energy Storage
Integrated with Soft Open Point Based on Resilience Insurance | [
"eess.SY",
"cs.SY"
] | In recent years, frequent extreme events have put forward higher requirements for improving the resilience of distribution networks (DNs). Introducing energy storage integrated with soft open point (E-SOP) is one of the effective ways to improve resilience. However, the widespread application of E-SOP is limited by its... | {
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2412.01051 | An Efficient Unsupervised Framework for Convex Quadratic Programs via
Deep Unrolling | [
"math.OC",
"cs.LG"
] | Quadratic programs (QPs) arise in various domains such as machine learning, finance, and control. Recently, learning-enhanced primal-dual hybrid gradient (PDHG) methods have shown great potential in addressing large-scale linear programs; however, this approach has not been extended to QPs. In this work, we focus on un... | {
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2412.01052 | CRISP: Object Pose and Shape Estimation with Test-Time Adaptation | [
"cs.CV",
"cs.RO"
] | We consider the problem of estimating object pose and shape from an RGB-D image. Our first contribution is to introduce CRISP, a category-agnostic object pose and shape estimation pipeline. The pipeline implements an encoder-decoder model for shape estimation. It uses FiLM-conditioning for implicit shape reconstruction... | {
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2412.01054 | Embedded Machine Learning for Solar PV Power Regulation in a Remote
Microgrid | [
"eess.SY",
"cs.LG",
"cs.SY"
] | This paper presents a machine-learning study for solar inverter power regulation in a remote microgrid. Machine learning models for active and reactive power control are respectively trained using an ensemble learning method. Then, unlike conventional schemes that make inferences on a central server in the far-end cont... | {
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2412.01056 | Classifying Simulated Gait Impairments using Privacy-preserving
Explainable Artificial Intelligence and Mobile Phone Videos | [
"cs.CV"
] | Accurate diagnosis of gait impairments is often hindered by subjective or costly assessment methods, with current solutions requiring either expensive multi-camera equipment or relying on subjective clinical observation. There is a critical need for accessible, objective tools that can aid in gait assessment while pres... | {
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2412.01062 | Research on Optimizing Real-Time Data Processing in High-Frequency
Trading Algorithms using Machine Learning | [
"cs.LG",
"q-fin.CP"
] | High-frequency trading (HFT) represents a pivotal and intensely competitive domain within the financial markets. The velocity and accuracy of data processing exert a direct influence on profitability, underscoring the significance of this field. The objective of this work is to optimise the real-time processing of data... | {
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2412.01063 | MuSiCNet: A Gradual Coarse-to-Fine Framework for Irregularly Sampled
Multivariate Time Series Analysis | [
"cs.LG",
"stat.ML"
] | Irregularly sampled multivariate time series (ISMTS) are prevalent in reality. Most existing methods treat ISMTS as synchronized regularly sampled time series with missing values, neglecting that the irregularities are primarily attributed to variations in sampling rates. In this paper, we introduce a novel perspective... | {
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2412.01064 | FLOAT: Generative Motion Latent Flow Matching for Audio-driven Talking
Portrait | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.MM",
"eess.IV"
] | With the rapid advancement of diffusion-based generative models, portrait image animation has achieved remarkable results. However, it still faces challenges in temporally consistent video generation and fast sampling due to its iterative sampling nature. This paper presents FLOAT, an audio-driven talking portrait vide... | {
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2412.01065 | Lookahead Counterfactual Fairness | [
"cs.LG",
"cs.AI",
"stat.ML"
] | As machine learning (ML) algorithms are used in applications that involve humans, concerns have arisen that these algorithms may be biased against certain social groups. \textit{Counterfactual fairness} (CF) is a fairness notion proposed in Kusner et al. (2017) that measures the unfairness of ML predictions; it require... | {
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2412.01075 | Multi-Agent Deep Reinforcement Learning for Distributed and Autonomous
Platoon Coordination via Speed-regulation over Large-scale Transportation
Networks | [
"cs.LG",
"cs.AI"
] | Truck platooning technology enables a group of trucks to travel closely together, with which the platoon can save fuel, improve traffic flow efficiency, and improve safety. In this paper, we consider the platoon coordination problem in a large-scale transportation network, to promote cooperation among trucks and optimi... | {
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2412.01077 | A Memory-Based Reinforcement Learning Approach to Integrated Sensing and
Communication | [
"cs.IT",
"cs.LG",
"math.IT"
] | In this paper, we consider a point-to-point integrated sensing and communication (ISAC) system, where a transmitter conveys a message to a receiver over a channel with memory and simultaneously estimates the state of the channel through the backscattered signals from the emitted waveform. Using Massey's concept of dire... | {
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2412.01078 | Advancing Speech Language Models by Scaling Supervised Fine-Tuning with
Over 60,000 Hours of Synthetic Speech Dialogue Data | [
"cs.CL",
"cs.AI",
"cs.HC"
] | The GPT-4o represents a significant milestone in enabling real-time interaction with large language models (LLMs) through speech, its remarkable low latency and high fluency not only capture attention but also stimulate research interest in the field. This real-time speech interaction is particularly valuable in scenar... | {
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2412.01079 | Federated Motor Imagery Classification for Privacy-Preserving
Brain-Computer Interfaces | [
"cs.LG",
"cs.HC"
] | Training an accurate classifier for EEG-based brain-computer interface (BCI) requires EEG data from a large number of users, whereas protecting their data privacy is a critical consideration. Federated learning (FL) is a promising solution to this challenge. This paper proposes Federated classification with local Batch... | {
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2412.01080 | Edge Computing for Microgrid via MATLAB Embedded Coder and Low-Cost
Smart Meters | [
"eess.SY",
"cs.SY"
] | In this paper, an edge computing-based machine-learning study is conducted for solar inverter power forecasting and droop control in a remote microgrid. The machine learning models and control algorithms are directly deployed on an edge-computing device (a smart meter-concentrator) in the microgrid rather than on a clo... | {
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2412.01082 | A Hybrid Evolutionary Approach for Multi Robot Coordinated Planning at
Intersections | [
"cs.RO",
"cs.AI",
"cs.NE",
"math.OC",
"stat.CO"
] | Coordinated multi-robot motion planning at intersections is key for safe mobility in roads, factories and warehouses. The rapidly exploring random tree (RRT) algorithms are popular in multi-robot motion planning. However, generating the graph configuration space and searching in the composite tensor configuration space... | {
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2412.01083 | RoboHanger: Learning Generalizable Robotic Hanger Insertion for Diverse
Garments | [
"cs.RO"
] | For the task of hanging clothes, learning how to insert a hanger into a garment is crucial but has been seldom explored in robotics. In this work, we address the problem of inserting a hanger into various unseen garments that are initially laid out flat on a table. This task is challenging due to its long-horizon natur... | {
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2412.01085 | Data-driven optimal control of unknown nonlinear dynamical systems using
the Koopman operator | [
"eess.SY",
"cs.SY"
] | Nonlinear optimal control is vital for numerous applications but remains challenging for unknown systems due to the difficulties in accurately modelling dynamics and handling computational demands, particularly in high-dimensional settings. This work develops a theoretically certifiable framework that integrates a modi... | {
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2412.01087 | Gated Parametric Neuron for Spike-based Audio Recognition | [
"cs.LG"
] | Spiking neural networks (SNNs) aim to simulate real neural networks in the human brain with biologically plausible neurons. The leaky integrate-and-fire (LIF) neuron is one of the most widely studied SNN architectures. However, it has the vanishing gradient problem when trained with backpropagation. Additionally, its n... | {
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2412.01090 | STATIC : Surface Temporal Affine for TIme Consistency in Video Monocular
Depth Estimation | [
"cs.CV"
] | Video monocular depth estimation is essential for applications such as autonomous driving, AR/VR, and robotics. Recent transformer-based single-image monocular depth estimation models perform well on single images but struggle with depth consistency across video frames. Traditional methods aim to improve temporal consi... | {
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2412.01091 | DuoCast: Duo-Probabilistic Meteorology-Aware Model for Extended
Precipitation Nowcasting | [
"cs.CV"
] | Recently, extended short-term precipitation nowcasting struggles with decreasing precision because of insufficient consideration of meteorological knowledge, such as weather fronts which significantly influence precipitation intensity, duration, and spatial distribution. Therefore, in this paper, we present DuoCast, a ... | {
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2412.01092 | Deep Learning-Based Approach for Identification and Compensation of
Nonlinear Distortions in Parametric Array Loudspeakers | [
"eess.AS",
"cs.SD",
"cs.SY",
"eess.SY"
] | Compared to traditional electrodynamic loudspeakers, the parametric array loudspeaker (PAL) offers exceptional directivity for audio applications but suffers from significant nonlinear distortions due to its inherent intricate demodulation process. The Volterra filter-based approaches have been widely used to reduce th... | {
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2412.01093 | Automated Extraction of Acronym-Expansion Pairs from Scientific Papers | [
"cs.CL",
"cs.IR"
] | This project addresses challenges posed by the widespread use of abbreviations and acronyms in digital texts. We propose a novel method that combines document preprocessing, regular expressions, and a large language model to identify abbreviations and map them to their corresponding expansions. The regular expressions ... | {
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2412.01094 | A Hierarchical Heuristic for Clustered Steiner Trees in the Plane with
Obstacles | [
"cs.AI",
"cs.CG",
"cs.NE",
"cs.RO",
"math.OC"
] | Euclidean Steiner trees are relevant to model minimal networks in real-world applications ubiquitously. In this paper, we study the feasibility of a hierarchical approach embedded with bundling operations to compute multiple and mutually disjoint Euclidean Steiner trees that avoid clutter and overlapping with obstacles... | {
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2412.01095 | VERA: Explainable Video Anomaly Detection via Verbalized Learning of
Vision-Language Models | [
"cs.AI",
"cs.CV",
"cs.LG"
] | The rapid advancement of vision-language models (VLMs) has established a new paradigm in video anomaly detection (VAD): leveraging VLMs to simultaneously detect anomalies and provide comprehendible explanations for the decisions. Existing work in this direction often assumes the complex reasoning required for VAD excee... | {
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2412.01096 | How the use of feature selection methods influences the efficiency and
accuracy of complex network simulations | [
"cs.AI",
"physics.soc-ph"
] | Complex network systems' models are designed to perfectly emulate real-world networks through the use of simulation and link prediction. Complex network systems are defined by nodes and their connections where both have real-world features that result in a heterogeneous network in which each of the nodes has distinct c... | {
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2412.01098 | Spatial Conformal Inference through Localized Quantile Regression | [
"stat.ML",
"cs.LG"
] | Reliable uncertainty quantification at unobserved spatial locations, especially in the presence of complex and heterogeneous datasets, remains a core challenge in spatial statistics. Traditional approaches like Kriging rely heavily on assumptions such as normality, which often break down in large-scale, diverse dataset... | {
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2412.01101 | Hiding Faces in Plain Sight: Defending DeepFakes by Disrupting Face
Detection | [
"cs.CV",
"cs.CR"
] | This paper investigates the feasibility of a proactive DeepFake defense framework, {\em FacePosion}, to prevent individuals from becoming victims of DeepFake videos by sabotaging face detection. The motivation stems from the reliance of most DeepFake methods on face detectors to automatically extract victim faces from ... | {
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2412.01102 | Personalized Coupled Tensor Decomposition for Multimodal Data Fusion:
Uniqueness and Algorithms | [
"cs.LG",
"eess.SP"
] | Coupled tensor decompositions (CTDs) perform data fusion by linking factors from different datasets. Although many CTDs have been already proposed, current works do not address important challenges of data fusion, where: 1) the datasets are often heterogeneous, constituting different "views" of a given phenomena (multi... | {
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2412.01103 | FRIDAY: Real-time Learning DNN-based Stable LQR controller for Nonlinear
Systems under Uncertain Disturbances | [
"eess.SY",
"cs.SY"
] | Linear Quadratic Regulator (LQR) is often combined with feedback linearization (FBL) for nonlinear systems that have the nonlinearity additive to the input. Conventional approaches estimate and cancel the nonlinearity based on the first principle or data-driven methods such as Gaussian Processes (GPs). However, the for... | {
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2412.01106 | One Shot, One Talk: Whole-body Talking Avatar from a Single Image | [
"cs.CV",
"cs.GR"
] | Building realistic and animatable avatars still requires minutes of multi-view or monocular self-rotating videos, and most methods lack precise control over gestures and expressions. To push this boundary, we address the challenge of constructing a whole-body talking avatar from a single image. We propose a novel pipel... | {
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2412.01108 | Multi-Scale Representation Learning for Protein Fitness Prediction | [
"cs.LG",
"q-bio.BM"
] | Designing novel functional proteins crucially depends on accurately modeling their fitness landscape. Given the limited availability of functional annotations from wet-lab experiments, previous methods have primarily relied on self-supervised models trained on vast, unlabeled protein sequence or structure datasets. Whi... | {
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2412.01109 | Revisiting Absence withSymptoms that *T* Show up Decades Later to
Recover Empty Categories | [
"cs.CL"
] | This paper explores null elements in English, Chinese, and Korean Penn treebanks. Null elements contain important syntactic and semantic information, yet they have typically been treated as entities to be removed during language processing tasks, particularly in constituency parsing. Thus, we work towards the removal a... | {
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2412.01110 | Simplified derivations for high-dimensional convex learning problems | [
"cond-mat.dis-nn",
"cs.NE",
"q-bio.NC"
] | Statistical-physics calculations in machine learning and theoretical neuroscience often involve lengthy derivations that obscure physical interpretation. We present concise, non-replica derivations of key results and highlight their underlying similarities. Using a cavity approach, we analyze high-dimensional learning ... | {
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2412.01111 | Exploring Climate Change Discourse: Measurements and Analysis of Reddit
Data | [
"cs.SI"
] | Social media is very popular for facilitating conversations about important topics and bringing forth insights and issues related to these topics. Reddit serves as a platform that fosters social interactions and hosts engaging discussions on a wide array of topics, thus forming narratives around these topics. One such ... | {
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2412.01113 | Think-to-Talk or Talk-to-Think? When LLMs Come Up with an Answer in
Multi-Step Reasoning | [
"cs.CL"
] | This study investigates the internal reasoning mechanism of language models during symbolic multi-step reasoning, motivated by the question of whether chain-of-thought (CoT) outputs are faithful to the model's internals. Specifically, we inspect when they internally determine their answers, particularly before or after... | {
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2412.01114 | Dense Dynamics-Aware Reward Synthesis: Integrating Prior Experience with
Demonstrations | [
"cs.LG"
] | Many continuous control problems can be formulated as sparse-reward reinforcement learning (RL) tasks. In principle, online RL methods can automatically explore the state space to solve each new task. However, discovering sequences of actions that lead to a non-zero reward becomes exponentially more difficult as the ta... | {
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2412.01115 | DIR: Retrieval-Augmented Image Captioning with Comprehensive
Understanding | [
"cs.CV"
] | Image captioning models often suffer from performance degradation when applied to novel datasets, as they are typically trained on domain-specific data. To enhance generalization in out-of-domain scenarios, retrieval-augmented approaches have garnered increasing attention. However, current methods face two key challeng... | {
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2412.01116 | Look Ma, No Ground Truth! Ground-Truth-Free Tuning of Structure from
Motion and Visual SLAM | [
"cs.CV",
"cs.RO"
] | Evaluation is critical to both developing and tuning Structure from Motion (SfM) and Visual SLAM (VSLAM) systems, but is universally reliant on high-quality geometric ground truth -- a resource that is not only costly and time-intensive but, in many cases, entirely unobtainable. This dependency on ground truth restrict... | {
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2412.01118 | LoyalDiffusion: A Diffusion Model Guarding Against Data Replication | [
"cs.CV",
"cs.CR"
] | Diffusion models have demonstrated significant potential in image generation. However, their ability to replicate training data presents a privacy risk, particularly when the training data includes confidential information. Existing mitigation strategies primarily focus on augmenting the training dataset, leaving the i... | {
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2412.01119 | Object Tracking in a $360^o$ View: A Novel Perspective on Bridging the
Gap to Biomedical Advancements | [
"cs.CV",
"cs.AI"
] | Object tracking is a fundamental tool in modern innovation, with applications in defense systems, autonomous vehicles, and biomedical research. It enables precise identification, monitoring, and spatiotemporal analysis of objects across sequential frames, providing insights into dynamic behaviors. In cell biology, obje... | {
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2412.01120 | Reliable and scalable variable importance estimation via warm-start and
early stopping | [
"stat.ML",
"cs.LG"
] | As opaque black-box predictive models become more prevalent, the need to develop interpretations for these models is of great interest. The concept of variable importance and Shapley values are interpretability measures that applies to any predictive model and assesses how much a variable or set of variables improves p... | {
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2412.01122 | TAS-TsC: A Data-Driven Framework for Estimating Time of Arrival Using
Temporal-Attribute-Spatial Tri-space Coordination of Truck Trajectories | [
"cs.AI"
] | Accurately estimating time of arrival (ETA) for trucks is crucial for optimizing transportation efficiency in logistics. GPS trajectory data offers valuable information for ETA, but challenges arise due to temporal sparsity, variable sequence lengths, and the interdependencies among multiple trucks. To address these is... | {
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2412.01124 | SUICA: Learning Super-high Dimensional Sparse Implicit Neural
Representations for Spatial Transcriptomics | [
"cs.LG",
"q-bio.GN"
] | Spatial Transcriptomics (ST) is a method that captures spatial gene expression profiles within histological sections. The discrete spatial distribution and the super-high dimensional sequencing results make ST data challenging to be modeled effectively. In this paper, we manage to model ST in a continuous and compact m... | {
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2412.01127 | Precision Profile Pollution Attack on Sequential Recommenders via
Influence Function | [
"cs.IR"
] | Sequential recommendation approaches have demonstrated remarkable proficiency in modeling user preferences. Nevertheless, they are susceptible to profile pollution attacks (PPA), wherein items are introduced into a user's interaction history deliberately to influence the recommendation list. Since retraining the model ... | {
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2412.01129 | RILQ: Rank-Insensitive LoRA-based Quantization Error Compensation for
Boosting 2-bit Large Language Model Accuracy | [
"cs.LG",
"cs.AI"
] | Low-rank adaptation (LoRA) has become the dominant method for parameter-efficient LLM fine-tuning, with LoRA-based quantization error compensation (LQEC) emerging as a powerful tool for recovering accuracy in compressed LLMs. However, LQEC has underperformed in sub-4-bit scenarios, with no prior investigation into unde... | {
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2412.01130 | Enhancing Function-Calling Capabilities in LLMs: Strategies for Prompt
Formats, Data Integration, and Multilingual Translation | [
"cs.CL"
] | Large language models (LLMs) have significantly advanced autonomous agents, particularly in zero-shot tool usage, also known as function calling. This research delves into enhancing the function-calling capabilities of LLMs by exploring different approaches, including prompt formats for integrating function description... | {
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2412.01131 | A Comprehensive Evaluation of Semantic Relation Knowledge of Pretrained
Language Models and Humans | [
"cs.CL"
] | Recently, much work has concerned itself with the enigma of what exactly PLMs (pretrained language models) learn about different aspects of language, and how they learn it. One stream of this type of research investigates the knowledge that PLMs have about semantic relations. However, many aspects of semantic relations... | {
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2412.01132 | Eyes on the Road: State-of-the-Art Video Question Answering Models
Assessment for Traffic Monitoring Tasks | [
"cs.CV"
] | Recent advances in video question answering (VideoQA) offer promising applications, especially in traffic monitoring, where efficient video interpretation is critical. Within ITS, answering complex, real-time queries like "How many red cars passed in the last 10 minutes?" or "Was there an incident between 3:00 PM and 3... | {
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2412.01136 | Referring Video Object Segmentation via Language-aligned Track Selection | [
"cs.CV"
] | Referring Video Object Segmentation (RVOS) seeks to segment objects throughout a video based on natural language expressions. While existing methods have made strides in vision-language alignment, they often overlook the importance of robust video object tracking, where inconsistent mask tracks can disrupt vision-langu... | {
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2412.01137 | TextSSR: Diffusion-based Data Synthesis for Scene Text Recognition | [
"cs.CV"
] | Scene text recognition (STR) suffers from the challenges of either less realistic synthetic training data or the difficulty of collecting sufficient high-quality real-world data, limiting the effectiveness of trained STR models. Meanwhile, despite producing holistically appealing text images, diffusion-based text image... | {
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2412.01140 | Dense Dispersed Structured Light for Hyperspectral 3D Imaging of Dynamic
Scenes | [
"cs.CV",
"eess.IV"
] | Hyperspectral 3D imaging captures both depth maps and hyperspectral images, enabling comprehensive geometric and material analysis. Recent methods achieve high spectral and depth accuracy; however, they require long acquisition times often over several minutes or rely on large, expensive systems, restricting their use ... | {
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2412.01141 | Lossless and Privacy-Preserving Graph Convolution Network for Federated
Item Recommendation | [
"cs.IR"
] | Graph neural network (GNN) has emerged as a state-of-the-art solution for item recommendation. However, existing GNN-based recommendation methods rely on a centralized storage of fragmented user-item interaction sub-graphs and training on an aggregated global graph, which will lead to privacy concerns. As a response, s... | {
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2412.01147 | A2VIS: Amodal-Aware Approach to Video Instance Segmentation | [
"cs.CV"
] | Handling occlusion remains a significant challenge for video instance-level tasks like Multiple Object Tracking (MOT) and Video Instance Segmentation (VIS). In this paper, we propose a novel framework, Amodal-Aware Video Instance Segmentation (A2VIS), which incorporates amodal representations to achieve a reliable and ... | {
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2412.01150 | Representation Learning for Time-Domain High-Energy Astrophysics:
Discovery of Extragalactic Fast X-ray Transient XRT 200515 | [
"astro-ph.HE",
"astro-ph.IM",
"cs.AI",
"cs.LG"
] | We present a novel representation learning method for downstream tasks such as anomaly detection and unsupervised transient classification in high-energy datasets. This approach enabled the discovery of a new fast X-ray transient (FXT) in the Chandra archive, XRT 200515, a needle-in-the-haystack event and the first Cha... | {
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2412.01154 | R.I.P.: A Simple Black-box Attack on Continual Test-time Adaptation | [
"cs.LG",
"cs.AI"
] | Test-time adaptation (TTA) has emerged as a promising solution to tackle the continual domain shift in machine learning by allowing model parameters to change at test time, via self-supervised learning on unlabeled testing data. At the same time, it unfortunately opens the door to unforeseen vulnerabilities for degrada... | {
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2412.01156 | Covariance Matrix Adaptation Evolution Strategy for Low Effective
Dimensionality | [
"cs.NE"
] | Despite the state-of-the-art performance of the covariance matrix adaptation evolution strategy (CMA-ES), high-dimensional black-box optimization problems are challenging tasks. Such problems often involve a property called low effective dimensionality (LED), in which the objective function is formulated with redundant... | {
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2412.01160 | ControlFace: Harnessing Facial Parametric Control for Face Rigging | [
"cs.CV"
] | Manipulation of facial images to meet specific controls such as pose, expression, and lighting, also known as face rigging, is a complex task in computer vision. Existing methods are limited by their reliance on image datasets, which necessitates individual-specific fine-tuning and limits their ability to retain fine-g... | {
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2412.01163 | Graph Community Augmentation with GMM-based Modeling in Latent Space | [
"cs.LG",
"cs.IT",
"math.IT",
"stat.ML"
] | This study addresses the issue of graph generation with generative models. In particular, we are concerned with graph community augmentation problem, which refers to the problem of generating unseen or unfamiliar graphs with a new community out of the probability distribution estimated with a given graph dataset. The g... | {
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2412.01165 | Double-Directional V2V Channel Measurement using ReRoMA at 60 GHz | [
"cs.IT",
"math.IT"
] | The coordination of vehicles is a crucial element of autonomous driving, as it enhances the efficiency, convenience, and safety of road traffic. In order to fully exploit the capabilities of such coordination, communication with high data rate and low latency is required. It can be reasonably argued that millimeter-wav... | {
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2412.01166 | Object Agnostic 3D Lifting in Space and Time | [
"cs.CV",
"cs.AI"
] | We present a spatio-temporal perspective on category-agnostic 3D lifting of 2D keypoints over a temporal sequence. Our approach differs from existing state-of-the-art methods that are either: (i) object-agnostic, but can only operate on individual frames, or (ii) can model space-time dependencies, but are only designed... | {
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2412.01167 | HumekaFL: Automated Detection of Neonatal Asphyxia Using Federated
Learning | [
"cs.LG",
"eess.AS"
] | Birth Apshyxia (BA) is a severe condition characterized by insufficient supply of oxygen to a newborn during the delivery. BA is one of the primary causes of neonatal death in the world. Although there has been a decline in neonatal deaths over the past two decades, the developing world, particularly sub-Saharan Africa... | {
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2412.01168 | On the Surprising Effectiveness of Spectrum Clipping in Learning Stable
Linear Dynamics | [
"cs.RO",
"cs.SY",
"eess.SY"
] | When learning stable linear dynamical systems from data, three important properties are desirable: i) predictive accuracy, ii) provable stability, and iii) computational efficiency. Unconstrained minimization of reconstruction errors leads to high accuracy and efficiency but cannot guarantee stability. Existing methods... | {
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2412.01169 | OmniFlow: Any-to-Any Generation with Multi-Modal Rectified Flows | [
"cs.MM",
"cs.CV",
"cs.SD",
"eess.AS"
] | We introduce OmniFlow, a novel generative model designed for any-to-any generation tasks such as text-to-image, text-to-audio, and audio-to-image synthesis. OmniFlow advances the rectified flow (RF) framework used in text-to-image models to handle the joint distribution of multiple modalities. It outperforms previous a... | {
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2412.01171 | Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion
Recognition | [
"cs.LG",
"cs.HC"
] | Emotion recognition is a critical component of affective computing. Training accurate machine learning models for emotion recognition typically requires a large amount of labeled data. Due to the subtleness and complexity of emotions, multiple evaluators are usually needed for each affective sample to obtain its ground... | {
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} |
2412.01172 | Coded Distributed (Batch) Matrix Multiplication over Galois Ring via
RMFE | [
"cs.DC",
"cs.IT",
"math.IT"
] | Coded Distributed Matrix Multiplication (CDMM) is a distributed matrix multiplication (DMM) for large-scale matrices through a coding scheme such that any $R$ worker node among all $N$ worker nodes can recover the final product, where $N$ corresponds to the length of the code and $R\leq N$ is called the recovery thresh... | {
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} |
2412.01174 | Rectified Flow For Structure Based Drug Design | [
"cs.LG",
"cs.CE"
] | Deep generative models have achieved tremendous success in structure-based drug design in recent years, especially for generating 3D ligand molecules that bind to specific protein pocket. Notably, diffusion models have transformed ligand generation by providing exceptional quality and creativity. However, traditional d... | {
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} |
2412.01175 | OBI-Bench: Can LMMs Aid in Study of Ancient Script on Oracle Bones? | [
"cs.CV",
"cs.AI"
] | We introduce OBI-Bench, a holistic benchmark crafted to systematically evaluate large multi-modal models (LMMs) on whole-process oracle bone inscriptions (OBI) processing tasks demanding expert-level domain knowledge and deliberate cognition. OBI-Bench includes 5,523 meticulously collected diverse-sourced images, cover... | {
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} |
2412.01176 | Superhypergraph Neural Networks and Plithogenic Graph Neural Networks:
Theoretical Foundations | [
"cs.AI",
"cs.CE",
"cs.LG",
"math.CO",
"math.LO"
] | Hypergraphs extend traditional graphs by allowing edges to connect multiple nodes, while superhypergraphs further generalize this concept to represent even more complex relationships. Neural networks, inspired by biological systems, are widely used for tasks such as pattern recognition, data classification, and predict... | {
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} |
2412.01179 | Dual-Branch Graph Transformer Network for 3D Human Mesh Reconstruction
from Video | [
"cs.CV"
] | Human Mesh Reconstruction (HMR) from monocular video plays an important role in human-robot interaction and collaboration. However, existing video-based human mesh reconstruction methods face a trade-off between accurate reconstruction and smooth motion. These methods design networks based on either RNNs or attention m... | {
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} |
2412.01181 | Training Stiff Neural Ordinary Differential Equations with Explicit
Exponential Integration Methods | [
"math.NA",
"cs.AI",
"cs.LG",
"cs.NA",
"cs.SC"
] | Stiff ordinary differential equations (ODEs) are common in many science and engineering fields, but standard neural ODE approaches struggle to accurately learn these stiff systems, posing a significant barrier to widespread adoption of neural ODEs. In our earlier work, we addressed this challenge by utilizing single-st... | {
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} |
2412.01182 | MeasureNet: Measurement Based Celiac Disease Identification | [
"cs.CV"
] | Celiac disease is an autoimmune disorder triggered by the consumption of gluten. It causes damage to the villi, the finger-like projections in the small intestine that are responsible for nutrient absorption. Additionally, the crypts, which form the base of the villi, are also affected, impairing the regenerative proce... | {
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} |
2412.01186 | SailCompass: Towards Reproducible and Robust Evaluation for Southeast
Asian Languages | [
"cs.CL"
] | In this paper, we introduce SailCompass, a reproducible and robust evaluation benchmark for assessing Large Language Models (LLMs) on Southeast Asian Languages (SEA). SailCompass encompasses three main SEA languages, eight primary tasks including 14 datasets covering three task types (generation, multiple-choice questi... | {
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} |
2412.01189 | MiningGPT -- A Domain-Specific Large Language Model for the Mining
Industry | [
"cs.CL"
] | Recent advancements of generative LLMs (Large Language Models) have exhibited human-like language capabilities but have shown a lack of domain-specific understanding. Therefore, the research community has started the development of domain-specific LLMs for many domains. In this work we focus on discussing how to build ... | {
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} |
2412.01191 | A Semantic Communication System for Real-time 3D Reconstruction Tasks | [
"cs.RO",
"cs.AI"
] | 3D semantic maps have played an increasingly important role in high-precision robot localization and scene understanding. However, real-time construction of semantic maps requires mobile edge devices with extremely high computing power, which are expensive and limit the widespread application of semantic mapping. In or... | {
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} |
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