id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.09870 | Impact-Aware Control using Time-Invariant Reference Spreading | [
"cs.RO"
] | With the goal of increasing the speed and efficiency in robotic manipulation, a control approach is presented that aims to utilize intentional simultaneous impacts to its advantage. This approach exploits the concept of the time-invariant reference spreading framework, in which partly-overlapping ante- and post-impact ... | {
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2411.09871 | Content-Aware Preserving Image Generation | [
"cs.CV"
] | Remarkable progress has been achieved in image generation with the introduction of generative models. However, precisely controlling the content in generated images remains a challenging task due to their fundamental training objective. This paper addresses this challenge by proposing a novel image generation framework... | {
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2411.09874 | A Hybrid Artificial Intelligence System for Automated EEG Background
Analysis and Report Generation | [
"cs.AI",
"eess.SP"
] | Electroencephalography (EEG) plays a crucial role in the diagnosis of various neurological disorders. However, small hospitals and clinics often lack advanced EEG signal analysis systems and are prone to misinterpretation in manual EEG reading. This study proposes an innovative hybrid artificial intelligence (AI) syste... | {
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2411.09881 | Regulating Stability Margins in Symbiotic Control: A Low-Pass Filter
Approach | [
"eess.SY",
"cs.SY"
] | Symbiotic control synergistically integrates fixed-gain control and adaptive learning architectures to mitigate system uncertainties more predictably than adaptive learning alone and without requiring prior knowledge of uncertainty bounds as compared to fixed-gain control alone. Specifically, increasing the fixed-gain ... | {
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2411.09884 | Research on Domain-Specific Chinese Spelling Correction Method Based on
Plugin Extension Modules | [
"cs.CL"
] | This paper proposes a Chinese spelling correction method based on plugin extension modules, aimed at addressing the limitations of existing models in handling domain-specific texts. Traditional Chinese spelling correction models are typically trained on general-domain datasets, resulting in poor performance when encoun... | {
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2411.09887 | Planning by Simulation: Motion Planning with Learning-based Parallel
Scenario Prediction for Autonomous Driving | [
"cs.RO"
] | Planning safe trajectories for autonomous vehicles is essential for operational safety but remains extremely challenging due to the complex interactions among traffic participants. Recent autonomous driving frameworks have focused on improving prediction accuracy to explicitly model these interactions. However, some me... | {
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2411.09891 | Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward
Augmented Imitation | [
"cs.LG",
"cs.AI",
"cs.RO"
] | Training a policy in a source domain for deployment in the target domain under a dynamics shift can be challenging, often resulting in performance degradation. Previous work tackles this challenge by training on the source domain with modified rewards derived by matching distributions between the source and the target ... | {
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2411.09892 | A Self-Supervised Robotic System for Autonomous Contact-Based Spatial
Mapping of Semiconductor Properties | [
"cs.RO",
"cond-mat.mtrl-sci",
"cs.LG"
] | Integrating robotically driven contact-based material characterization techniques into self-driving laboratories can enhance measurement quality, reliability, and throughput. While deep learning models support robust autonomy, current methods lack reliable pixel-precision positioning and require extensive labeled data.... | {
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2411.09893 | Memory Proxy Maps for Visual Navigation | [
"cs.CV"
] | Visual navigation takes inspiration from humans, who navigate in previously unseen environments using vision without detailed environment maps. Inspired by this, we introduce a novel no-RL, no-graph, no-odometry approach to visual navigation using feudal learning to build a three tiered agent. Key to our approach is a ... | {
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2411.09894 | Free Lunch in Pathology Foundation Model: Task-specific Model Adaptation
with Concept-Guided Feature Enhancement | [
"cs.CV"
] | Whole slide image (WSI) analysis is gaining prominence within the medical imaging field. Recent advances in pathology foundation models have shown the potential to extract powerful feature representations from WSIs for downstream tasks. However, these foundation models are usually designed for general-purpose pathology... | {
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2411.09896 | Revealing the Evolution of Order in Materials Microstructures Using
Multi-Modal Computer Vision | [
"cond-mat.mtrl-sci",
"cs.LG"
] | The development of high-performance materials for microelectronics, energy storage, and extreme environments depends on our ability to describe and direct property-defining microstructural order. Our present understanding is typically derived from laborious manual analysis of imaging and spectroscopy data, which is dif... | {
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2411.09900 | Statistical Analysis of Policy Space Compression Problem | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Policy search methods are crucial in reinforcement learning, offering a framework to address continuous state-action and partially observable problems. However, the complexity of exploring vast policy spaces can lead to significant inefficiencies. Reducing the policy space through policy compression emerges as a powerf... | {
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2411.09904 | Self-Supervised Learning of Grasping Arbitrary Objects On-the-Move | [
"cs.RO",
"cs.LG"
] | Mobile grasping enhances manipulation efficiency by utilizing robots' mobility. This study aims to enable a commercial off-the-shelf robot for mobile grasping, requiring precise timing and pose adjustments. Self-supervised learning can develop a generalizable policy to adjust the robot's velocity and determine grasp po... | {
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2411.09906 | A Survey of Machine Learning-based Physical-Layer Authentication in
Wireless Communications | [
"cs.CR",
"cs.SY",
"eess.SY"
] | To ensure secure and reliable communication in wireless systems, authenticating the identities of numerous nodes is imperative. Traditional cryptography-based authentication methods suffer from issues such as low compatibility, reliability, and high complexity. Physical-Layer Authentication (PLA) is emerging as a promi... | {
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2411.09909 | AMXFP4: Taming Activation Outliers with Asymmetric Microscaling
Floating-Point for 4-bit LLM Inference | [
"cs.AI"
] | Scaling Large Language Models (LLMs) with extended context lengths has increased the need for efficient low-bit quantization to manage their substantial computational demands. However, reducing precision to 4 bits frequently degrades performance due to activation outliers. To address this, we propose Asymmetric Microsc... | {
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2411.09911 | DiffFNO: Diffusion Fourier Neural Operator | [
"cs.CV"
] | We introduce DiffFNO, a novel diffusion framework for arbitrary-scale super-resolution strengthened by a Weighted Fourier Neural Operator (WFNO). Mode Re-balancing in WFNO effectively captures critical frequency components, significantly improving the reconstruction of high-frequency image details that are crucial for ... | {
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2411.09913 | A Graph-based Strategic Sensor Deployment Approach for k-coverage in WSN | [
"eess.SY",
"cs.NI",
"cs.SY"
] | This paper studies a graph-based sensor deployment approach in wireless sensor networks (WSNs). Specifically, in today's world, where sensors are everywhere, detecting various attributes like temperature and movement, their deteriorating lifetime is indeed a very concerning issue. In many scenarios, these sensors are p... | {
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2411.09914 | mmSpyVR: Exploiting mmWave Radar for Penetrating Obstacles to Uncover
Privacy Vulnerability of Virtual Reality | [
"cs.CR",
"cs.CV"
] | Virtual reality (VR), while enhancing user experiences, introduces significant privacy risks. This paper reveals a novel vulnerability in VR systems that allows attackers to capture VR privacy through obstacles utilizing millimeter-wave (mmWave) signals without physical intrusion and virtual connection with the VR devi... | {
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2411.09915 | Physics-informed Machine Learning for Battery Pack Thermal Management | [
"cs.LG"
] | With the popularity of electric vehicles, the demand for lithium-ion batteries is increasing. Temperature significantly influences the performance and safety of batteries. Battery thermal management systems can effectively control the temperature of batteries; therefore, the performance and safety can be ensured. Howev... | {
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2411.09921 | Motion-Grounded Video Reasoning: Understanding and Perceiving Motion at
Pixel Level | [
"cs.CV",
"cs.AI"
] | In this paper, we introduce Motion-Grounded Video Reasoning, a new motion understanding task that requires generating visual answers (video segmentation masks) according to the input question, and hence needs implicit spatiotemporal reasoning and grounding. This task extends existing spatiotemporal grounding work focus... | {
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2411.09924 | A Polarization Image Dehazing Method Based on the Principle of Physical
Diffusion | [
"cs.CV",
"eess.IV"
] | Computer vision is increasingly used in areas such as unmanned vehicles, surveillance systems and remote sensing. However, in foggy scenarios, image degradation leads to loss of target details, which seriously affects the accuracy and effectiveness of these vision tasks. Polarized light, due to the fact that its electr... | {
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2411.09928 | Is Precise Recovery Necessary? A Task-Oriented Imputation Approach for
Time Series Forecasting on Variable Subset | [
"cs.LG"
] | Variable Subset Forecasting (VSF) refers to a unique scenario in multivariate time series forecasting, where available variables in the inference phase are only a subset of the variables in the training phase. VSF presents significant challenges as the entire time series may be missing, and neither inter- nor intra-var... | {
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2411.09929 | Autonomous Robotic Pepper Harvesting: Imitation Learning in Unstructured
Agricultural Environments | [
"cs.RO"
] | Automating tasks in outdoor agricultural fields poses significant challenges due to environmental variability, unstructured terrain, and diverse crop characteristics. We present a robotic system for autonomous pepper harvesting designed to operate in these unprotected, complex settings. Utilizing a custom handheld shea... | {
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2411.09933 | JRadiEvo: A Japanese Radiology Report Generation Model Enhanced by
Evolutionary Optimization of Model Merging | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.NE"
] | With the rapid advancement of large language models (LLMs), foundational models (FMs) have seen significant advancements. Healthcare is one of the most crucial application areas for these FMs, given the significant time and effort required for physicians to analyze large volumes of patient data. Recent efforts have foc... | {
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2411.09935 | Whole-Body Impedance Coordinative Control of Wheel-Legged Robot on
Uncertain Terrain | [
"cs.RO"
] | This article propose a whole-body impedance coordinative control framework for a wheel-legged humanoid robot to achieve adaptability on complex terrains while maintaining robot upper body stability. The framework contains a bi-level control strategy. The outer level is a variable damping impedance controller, which opt... | {
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2411.09937 | Refined and Segmented Price Sentiment Indices from Survey Comments | [
"cs.CL",
"q-fin.CP"
] | We aim to enhance a price sentiment index and to more precisely understand price trends from the perspective of not only consumers but also businesses. We extract comments related to prices from the Economy Watchers Survey conducted by the Cabinet Office of Japan and classify price trends using a large language model (... | {
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2411.09942 | ALPHA-$\alpha$ and Bi-ACT Are All You Need: Importance of Position and
Force Information/Control for Imitation Learning of Unimanual and Bimanual
Robotic Manipulation with Low-Cost System | [
"cs.RO"
] | Autonomous manipulation in everyday tasks requires flexible action generation to handle complex, diverse real-world environments, such as objects with varying hardness and softness. Imitation Learning (IL) enables robots to learn complex tasks from expert demonstrations. However, a lot of existing methods rely on posit... | {
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2411.09943 | Zero-shot Voice Conversion with Diffusion Transformers | [
"cs.SD",
"cs.LG",
"eess.AS"
] | Zero-shot voice conversion aims to transform a source speech utterance to match the timbre of a reference speech from an unseen speaker. Traditional approaches struggle with timbre leakage, insufficient timbre representation, and mismatches between training and inference tasks. We propose Seed-VC, a novel framework tha... | {
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2411.09944 | SlimLM: An Efficient Small Language Model for On-Device Document
Assistance | [
"cs.CL"
] | While small language models (SLMs) show promises for mobile deployment, their real-world performance and applications on smartphones remains underexplored. We present SlimLM, a series of SLMs optimized for document assistance tasks on mobile devices. Through extensive experiments on a Samsung Galaxy S24, we identify th... | {
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2411.09945 | TEESlice: Protecting Sensitive Neural Network Models in Trusted
Execution Environments When Attackers have Pre-Trained Models | [
"cs.CR",
"cs.AI",
"cs.LG"
] | Trusted Execution Environments (TEE) are used to safeguard on-device models. However, directly employing TEEs to secure the entire DNN model is challenging due to the limited computational speed. Utilizing GPU can accelerate DNN's computation speed but commercial widely-available GPUs usually lack security protection. ... | {
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2411.09946 | Assessing Response Disparities in California Wildland-Urban-Interface
(WUI) Cities Using the Compartmental Model | [
"cs.SI"
] | The increasing frequency and severity of wildfires pose significant risks to communities, infrastructure, and the environment, especially in Wildland-Urban Interface (WUI) areas. Effective disaster management requires understanding how the public perceives and responds to wildfire threats in real-time. This study uses ... | {
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2411.09947 | LoRA-LiteE: A Computationally Efficient Framework for Chatbot
Preference-Tuning | [
"cs.CL"
] | Effective preference tuning is pivotal in aligning chatbot responses with human expectations, enhancing user satisfaction and engagement. Traditional approaches, notably Reinforcement Learning from Human Feedback (RLHF) as employed in advanced models like GPT-4, have demonstrated considerable success in this domain. Ho... | {
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2411.09952 | GGAvatar: Reconstructing Garment-Separated 3D Gaussian Splatting Avatars
from Monocular Video | [
"cs.CV",
"cs.AI",
"cs.MM"
] | Avatar modelling has broad applications in human animation and virtual try-ons. Recent advancements in this field have focused on high-quality and comprehensive human reconstruction but often overlook the separation of clothing from the body. To bridge this gap, this paper introduces GGAvatar (Garment-separated 3D Gaus... | {
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2411.09953 | Brain-inspired Action Generation with Spiking Transformer Diffusion
Policy Model | [
"cs.RO"
] | Spiking Neural Networks (SNNs) has the ability to extract spatio-temporal features due to their spiking sequence. While previous research has primarily foucus on the classification of image and reinforcement learning. In our paper, we put forward novel diffusion policy model based on Spiking Transformer Neural Networks... | {
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2411.09954 | Reaching Resilient Leader-Follower Consensus in Time-Varying Networks
via Multi-Hop Relays | [
"cs.MA",
"cs.SY",
"eess.SY"
] | We study resilient leader-follower consensus of multi-agent systems (MASs) in the presence of adversarial agents, where agents' communication is modeled by time-varying topologies. The objective is to develop distributed algorithms for the nonfaulty/normal followers to track an arbitrary reference value propagated by a... | {
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2411.09955 | Instruction-Guided Editing Controls for Images and Multimedia: A Survey
in LLM era | [
"cs.CV",
"cs.AI",
"cs.HC",
"cs.LG",
"cs.MM"
] | The rapid advancement of large language models (LLMs) and multimodal learning has transformed digital content creation and manipulation. Traditional visual editing tools require significant expertise, limiting accessibility. Recent strides in instruction-based editing have enabled intuitive interaction with visual cont... | {
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2411.09956 | A Secure Estimator with Gaussian Bernoulli Mixture Model | [
"eess.SY",
"cs.SY"
] | The implementation of cyber-physical systems in real-world applications is challenged by safety requirements in the presence of sensor threats. Most cyber-physical systems, in particular the vulnerable multi-sensor systems, struggle to detect the attack in observation signals. In this paper, we tackle this issue by pro... | {
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2411.09961 | Dense ReLU Neural Networks for Temporal-spatial Model | [
"stat.ML",
"cs.LG",
"math.ST",
"stat.TH"
] | In this paper, we focus on fully connected deep neural networks utilizing the Rectified Linear Unit (ReLU) activation function for nonparametric estimation. We derive non-asymptotic bounds that lead to convergence rates, addressing both temporal and spatial dependence in the observed measurements. By accounting for dep... | {
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2411.09968 | Seeing Clearly by Layer Two: Enhancing Attention Heads to Alleviate
Hallucination in LVLMs | [
"cs.CV",
"cs.AI"
] | The hallucination problem in multimodal large language models (MLLMs) remains a common issue. Although image tokens occupy a majority of the input sequence of MLLMs, there is limited research to explore the relationship between image tokens and hallucinations. In this paper, we analyze the distribution of attention sco... | {
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2411.09969 | Steering AI-Driven Personalization of Scientific Text for General
Audiences | [
"cs.HC",
"cs.AI"
] | Digital media platforms (e.g., social media, science blogs) offer opportunities to communicate scientific content to general audiences at scale. However, these audiences vary in their scientific expertise, literacy levels, and personal backgrounds, making effective science communication challenging. To address this cha... | {
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2411.09971 | Explanation for Trajectory Planning using Multi-modal Large Language
Model for Autonomous Driving | [
"cs.CV",
"cs.RO"
] | End-to-end style autonomous driving models have been developed recently. These models lack interpretability of decision-making process from perception to control of the ego vehicle, resulting in anxiety for passengers. To alleviate it, it is effective to build a model which outputs captions describing future behaviors ... | {
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2411.09972 | Large Language Models as User-Agents for Evaluating
Task-Oriented-Dialogue Systems | [
"cs.CL",
"cs.AI"
] | Traditionally, offline datasets have been used to evaluate task-oriented dialogue (TOD) models. These datasets lack context awareness, making them suboptimal benchmarks for conversational systems. In contrast, user-agents, which are context-aware, can simulate the variability and unpredictability of human conversations... | {
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2411.09973 | Establishing and Evaluating Trustworthy AI: Overview and Research
Challenges | [
"cs.LG",
"cs.IR"
] | Artificial intelligence (AI) technologies (re-)shape modern life, driving innovation in a wide range of sectors. However, some AI systems have yielded unexpected or undesirable outcomes or have been used in questionable manners. As a result, there has been a surge in public and academic discussions about aspects that A... | {
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2411.09975 | Express Yourself: Enabling large-scale public events involving
multi-human-swarm interaction for social applications with MOSAIX | [
"cs.RO"
] | Robot swarms have the potential to help groups of people with social tasks, given their ability to scale to large numbers of robots and users. Developing multi-human-swarm interaction is therefore crucial to support multiple people interacting with the swarm simultaneously - which is an area that is scarcely researched... | {
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2411.09978 | HistoLens: An LLM-Powered Framework for Multi-Layered Analysis of
Historical Texts -- A Case Application of Yantie Lun | [
"cs.CL"
] | This paper proposes HistoLens, a multi-layered analysis framework for historical texts based on Large Language Models (LLMs). Using the important Western Han dynasty text "Yantie Lun" as a case study, we demonstrate the framework's potential applications in historical research and education. HistoLens integrates NLP te... | {
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2411.09979 | Fully Dynamic Adversarially Robust Correlation Clustering in
Polylogarithmic Update Time | [
"cs.DS",
"cs.LG"
] | We study the dynamic correlation clustering problem with $\textit{adaptive}$ edge label flips. In correlation clustering, we are given a $n$-vertex complete graph whose edges are labeled either $(+)$ or $(-)$, and the goal is to minimize the total number of $(+)$ edges between clusters and the number of $(-)$ edges wit... | {
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2411.09986 | Unlocking Transfer Learning for Open-World Few-Shot Recognition | [
"cs.CV",
"cs.AI"
] | Few-Shot Open-Set Recognition (FSOSR) targets a critical real-world challenge, aiming to categorize inputs into known categories, termed closed-set classes, while identifying open-set inputs that fall outside these classes. Although transfer learning where a model is tuned to a given few-shot task has become a prominen... | {
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2411.09990 | Exploring the Influence of Residential Electric Vehicle Charging on
Distribution System Hosting Capacity -- A Case-Study in Arizona | [
"eess.SY",
"cs.SY"
] | The installation of high-capacity fast chargers for electric vehicles (EVs) is posing a significant risk to the distribution grid as the increased demand from widespread residential EV charging could exceed the technical limits of the distribution system. Addressing this issue is critical, given that current infrastruc... | {
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2411.09996 | Building 6G Radio Foundation Models with Transformer Architectures | [
"eess.SP",
"cs.AI",
"cs.NI"
] | Foundation deep learning (DL) models are general models, designed to learn general, robust and adaptable representations of their target modality, enabling finetuning across a range of downstream tasks. These models are pretrained on large, unlabeled datasets using self-supervised learning (SSL). Foundation models have... | {
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2411.09997 | DBenVis: A Visual Analytics System for Comparing DBMS Performance via
Benchmark Programs | [
"cs.HC",
"cs.DB"
] | Database benchmarking is an essential method for evaluating and comparing the performance characteristics of a database management system (DBMS). It helps researchers and developers to evaluate the efficacy of their optimizations or newly developed DBMS solutions. Also, companies can benefit by analyzing the performanc... | {
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2411.09998 | Adaptive Non-Uniform Timestep Sampling for Diffusion Model Training | [
"cs.LG",
"cs.CV"
] | As a highly expressive generative model, diffusion models have demonstrated exceptional success across various domains, including image generation, natural language processing, and combinatorial optimization. However, as data distributions grow more complex, training these models to convergence becomes increasingly com... | {
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2411.09999 | Understanding Graph Databases: A Comprehensive Tutorial and Survey | [
"cs.DB",
"cs.PL"
] | This tutorial serves as a comprehensive guide for understanding graph databases, focusing on the fundamentals of graph theory while showcasing practical applications across various fields. It starts by introducing foundational concepts and delves into the structure of graphs through nodes and edges, covering different ... | {
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2411.10000 | DuSEGO: Dual Second-order Equivariant Graph Ordinary Differential
Equation | [
"cs.LG",
"cs.AI"
] | Graph Neural Networks (GNNs) with equivariant properties have achieved significant success in modeling complex dynamic systems and molecular properties. However, their expressiveness ability is limited by: (1) Existing methods often overlook the over-smoothing issue caused by traditional GNN models, as well as the grad... | {
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2411.10004 | EyeDiff: text-to-image diffusion model improves rare eye disease
diagnosis | [
"eess.IV",
"cs.AI",
"cs.CV"
] | The rising prevalence of vision-threatening retinal diseases poses a significant burden on the global healthcare systems. Deep learning (DL) offers a promising solution for automatic disease screening but demands substantial data. Collecting and labeling large volumes of ophthalmic images across various modalities enco... | {
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2411.10005 | Analyzing Performance Characteristics of PostgreSQL and MariaDB on
NVMeVirt | [
"cs.DB",
"cs.AR"
] | The NVMeVirt paper analyzes the implication of storage performance on database engine performance to promote the tunable performance of NVMeVirt. They perform analysis on two very popular database engines, MariaDB and PostgreSQL. The result shows that MariaDB is more efficient when the storage is slow, but PostgreSQL o... | {
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2411.10006 | Orca: Enhancing Role-Playing Abilities of Large Language Models by
Integrating Personality Traits | [
"cs.CL",
"cs.AI"
] | Large language models has catalyzed the development of personalized dialogue systems, numerous role-playing conversational agents have emerged. While previous research predominantly focused on enhancing the model's capability to follow instructions by designing character profiles, neglecting the psychological factors t... | {
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2411.10008 | Graph-based Complexity for Causal Effect by Empirical Plug-in | [
"cs.AI"
] | This paper focuses on the computational complexity of computing empirical plug-in estimates for causal effect queries. Given a causal graph and observational data, any identifiable causal query can be estimated from an expression over the observed variables, called the estimand. The estimand can then be evaluated by pl... | {
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2411.10010 | DeepMedcast: A Deep Learning Method for Generating Intermediate Weather
Forecasts among Multiple NWP Models | [
"cs.LG",
"cs.AI"
] | Numerical weather prediction (NWP) centers around the world operate a variety of NWP models, and recent advances in AI-driven NWP models have increased the availability of diverse NWP outputs. While this expansion holds the potential to improve forecast accuracy, it also raises a critical challenge of identifying the m... | {
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2411.10013 | Efficient Depth Estimation for Unstable Stereo Camera Systems on AR
Glasses | [
"cs.CV",
"cs.LG"
] | Stereo depth estimation is a fundamental component in augmented reality (AR) applications. Although AR applications require very low latency for their real-time applications, traditional depth estimation models often rely on time-consuming preprocessing steps such as rectification to achieve high accuracy. Also, non st... | {
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2411.10015 | MicroCrackAttentionNeXt: Advancing Microcrack Detection in Wave Field
Analysis Using Deep Neural Networks through Feature Visualization | [
"cs.CV",
"cs.AI",
"cs.ET",
"cs.LG"
] | Micro Crack detection using deep neural networks (DNNs) through an automated pipeline using wave fields interacting with the damaged areas is highly sought after. These high-dimensional spatio-temporal crack data are limited, and these datasets have large dimensions in the temporal domain. The dataset presents a substa... | {
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2411.10016 | 'What did the Robot do in my Absence?' Video Foundation Models to
Enhance Intermittent Supervision | [
"cs.RO"
] | This paper investigates the application of Video Foundation Models (ViFMs) for generating robot data summaries to enhance intermittent human supervision of robot teams. We propose a novel framework that produces both generic and query-driven summaries of long-duration robot vision data in three modalities: storyboards,... | {
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2411.10017 | Difficulties of the NSGA-II with the Many-Objective LeadingOnes Problem | [
"cs.NE"
] | The NSGA-II is the most prominent multi-objective evolutionary algorithm (cited more than 50,000 times). Very recently, a mathematical runtime analysis has proven that this algorithm can have enormous difficulties when the number of objectives is larger than two (Zheng, Doerr. IEEE Transactions on Evolutionary Computat... | {
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2411.10018 | Once More, With Feeling: Measuring Emotion of Acting Performances in
Contemporary American Film | [
"cs.CL"
] | Narrative film is a composition of writing, cinematography, editing, and performance. While much computational work has focused on the writing or visual style in film, we conduct in this paper a computational exploration of acting performance. Applying speech emotion recognition models and a variationist sociolinguisti... | {
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2411.10019 | Towards Utilising a Range of Neural Activations for Comprehending
Representational Associations | [
"cs.CV",
"cs.LG"
] | Recent efforts to understand intermediate representations in deep neural networks have commonly attempted to label individual neurons and combinations of neurons that make up linear directions in the latent space by examining extremal neuron activations and the highest direction projections. In this paper, we show that... | {
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2411.10020 | Information Extraction from Clinical Notes: Are We Ready to Switch to
Large Language Models? | [
"cs.CL"
] | Backgrounds: Information extraction (IE) is critical in clinical natural language processing (NLP). While large language models (LLMs) excel on generative tasks, their performance on extractive tasks remains debated. Methods: We investigated Named Entity Recognition (NER) and Relation Extraction (RE) using 1,588 clinic... | {
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2411.10023 | Model Inversion Attacks: A Survey of Approaches and Countermeasures | [
"cs.LG"
] | The success of deep neural networks has driven numerous research studies and applications from Euclidean to non-Euclidean data. However, there are increasing concerns about privacy leakage, as these networks rely on processing private data. Recently, a new type of privacy attack, the model inversion attacks (MIAs), aim... | {
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2411.10028 | MOT FCG++: Enhanced Representation of Spatio-temporal Motion and
Appearance Features | [
"cs.CV",
"cs.AI"
] | The goal of multi-object tracking (MOT) is to detect and track all objects in a scene across frames, while maintaining a unique identity for each object. Most existing methods rely on the spatial-temporal motion features and appearance embedding features of the detected objects in consecutive frames. Effectively and ro... | {
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2411.10029 | Toward Robust and Accurate Adversarial Camouflage Generation against
Vehicle Detectors | [
"cs.CV"
] | Adversarial camouflage is a widely used physical attack against vehicle detectors for its superiority in multi-view attack performance. One promising approach involves using differentiable neural renderers to facilitate adversarial camouflage optimization through gradient back-propagation. However, existing methods oft... | {
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2411.10031 | Enforcing Cooperative Safety for Reinforcement Learning-based
Mixed-Autonomy Platoon Control | [
"eess.SY",
"cs.SY"
] | It is recognized that the control of mixed-autonomy platoons comprising connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) can enhance traffic flow. Among existing methods, Multi-Agent Reinforcement Learning (MARL) appears to be a promising control strategy because it can manage complex scenarios ... | {
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2411.10032 | VMID: A Multimodal Fusion LLM Framework for Detecting and Identifying
Misinformation of Short Videos | [
"cs.CV",
"cs.AI"
] | Short video platforms have become important channels for news dissemination, offering a highly engaging and immediate way for users to access current events and share information. However, these platforms have also emerged as significant conduits for the rapid spread of misinformation, as fake news and rumors can lever... | {
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2411.10033 | GSEditPro: 3D Gaussian Splatting Editing with Attention-based
Progressive Localization | [
"cs.CV"
] | With the emergence of large-scale Text-to-Image(T2I) models and implicit 3D representations like Neural Radiance Fields (NeRF), many text-driven generative editing methods based on NeRF have appeared. However, the implicit encoding of geometric and textural information poses challenges in accurately locating and contro... | {
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2411.10036 | Rethinking Normalization Strategies and Convolutional Kernels for
Multimodal Image Fusion | [
"cs.CV",
"cs.AI"
] | Multimodal image fusion (MMIF) aims to integrate information from different modalities to obtain a comprehensive image, aiding downstream tasks. However, existing methods tend to prioritize natural image fusion and focus on information complementary and network training strategies. They ignore the essential distinction... | {
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2411.10038 | Remote Life Support Robot Interface System for Global Task Planning and
Local Action Expansion Using Foundation Models | [
"cs.RO"
] | Robot systems capable of executing tasks based on language instructions have been actively researched. It is challenging to convey uncertain information that can only be determined on-site with a single language instruction to the robot. In this study, we propose a system that includes ambiguous parts as template varia... | {
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2411.10047 | Nonlinear Neural Dynamics and Classification Accuracy in Reservoir
Computing | [
"cs.NE",
"q-bio.NC"
] | Reservoir computing - information processing based on untrained recurrent neural networks with random connections - is expected to depend on the nonlinear properties of the neurons and the resulting oscillatory, chaotic, or fixpoint dynamics of the network. However, the required degree of nonlinearity and the range of ... | {
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2411.10048 | Physics-informed neural networks need a physicist to be accurate: the
case of mass and heat transport in Fischer-Tropsch catalyst particles | [
"cs.LG",
"cs.AI",
"physics.chem-ph",
"physics.comp-ph"
] | Physics-Informed Neural Networks (PINNs) have emerged as an influential technology, merging the swift and automated capabilities of machine learning with the precision and dependability of simulations grounded in theoretical physics. PINNs are often employed to solve algebraic or differential equations to replace some ... | {
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2411.10049 | SPLIT: SE(3)-diffusion via Local Geometry-based Score Prediction for 3D
Scene-to-Pose-Set Matching Problems | [
"cs.RO"
] | To enable versatile robot manipulation, robots must detect task-relevant poses for different purposes from raw scenes. Currently, many perception algorithms are designed for specific purposes, which limits the flexibility of the perception module. We present a general problem formulation called 3D scene-to-pose-set mat... | {
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2411.10050 | Jal Anveshak: Prediction of fishing zones using fine-tuned LlaMa 2 | [
"cs.LG",
"cs.AI"
] | In recent years, the global and Indian government efforts in monitoring and collecting data related to the fisheries industry have witnessed significant advancements. Despite this wealth of data, there exists an untapped potential for leveraging artificial intelligence based technological systems to benefit Indian fish... | {
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2411.10053 | That Chip Has Sailed: A Critique of Unfounded Skepticism Around AI for
Chip Design | [
"cs.AI",
"cs.LG"
] | In 2020, we introduced a deep reinforcement learning method capable of generating superhuman chip layouts, which we then published in Nature and open-sourced on GitHub. AlphaChip has inspired an explosion of work on AI for chip design, and has been deployed in state-of-the-art chips across Alphabet and extended by exte... | {
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2411.10055 | Towards unearthing neglected climate innovations from scientific
literature using Large Language Models | [
"cs.IR",
"cs.AI",
"cs.CL"
] | Climate change poses an urgent global threat, needing the rapid identification and deployment of innovative solutions. We hypothesise that many of these solutions already exist within scientific literature but remain underutilised. To address this gap, this study employs a curated dataset sourced from OpenAlex, a compr... | {
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2411.10057 | KuaiFormer: Transformer-Based Retrieval at Kuaishou | [
"cs.IR",
"cs.AI",
"cs.LG"
] | In large-scale content recommendation systems, retrieval serves as the initial stage in the pipeline, responsible for selecting thousands of candidate items from billions of options to pass on to ranking modules. Traditionally, the dominant retrieval method has been Embedding-Based Retrieval (EBR) using a Deep Neural N... | {
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2411.10058 | Unsupervised Congestion Status Identification Using LMP Data | [
"eess.SY",
"cs.LG",
"cs.SY"
] | Having a better understanding of how locational marginal prices (LMPs) change helps in price forecasting and market strategy making. This paper investigates the fundamental distribution of the congestion part of LMPs in high-dimensional Euclidean space using an unsupervised approach. LMP models based on the lossless an... | {
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2411.10060 | CMATH: Cross-Modality Augmented Transformer with Hierarchical
Variational Distillation for Multimodal Emotion Recognition in Conversation | [
"cs.MM",
"cs.CL"
] | Multimodal emotion recognition in conversation (MER) aims to accurately identify emotions in conversational utterances by integrating multimodal information. Previous methods usually treat multimodal information as equal quality and employ symmetric architectures to conduct multimodal fusion. However, in reality, the q... | {
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2411.10061 | EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation | [
"cs.GR",
"cs.CV"
] | Recent work on human animation usually involves audio, pose, or movement maps conditions, thereby achieves vivid animation quality. However, these methods often face practical challenges due to extra control conditions, cumbersome condition injection modules, or limitation to head region driving. Hence, we ask if it is... | {
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2411.10063 | Federated Domain Generalization via Prompt Learning and Aggregation | [
"cs.AI",
"cs.CV",
"cs.LG"
] | Federated domain generalization (FedDG) aims to improve the global model generalization in unseen domains by addressing data heterogeneity under privacy-preserving constraints. A common strategy in existing FedDG studies involves sharing domain-specific knowledge among clients, such as spectrum information, class proto... | {
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2411.10064 | Adaptive Physics-Guided Neural Network | [
"stat.ME",
"cs.LG"
] | This paper introduces an adaptive physics-guided neural network (APGNN) framework for predicting quality attributes from image data by integrating physical laws into deep learning models. The APGNN adaptively balances data-driven and physics-informed predictions, enhancing model accuracy and robustness across different... | {
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2411.10067 | The Interference Channel with Entangled Transmitters | [
"quant-ph",
"cs.IT",
"math.IT"
] | This paper explores communication over a two-sender, two-receiver classical interference channel, enhanced by the availability of entanglement resources between transmitters. The central contributions are an inner and outer bound on the capacity region for a general interference channel with entangled transmitters. It ... | {
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2411.10068 | Diachronic Document Dataset for Semantic Layout Analysis | [
"cs.CV"
] | We present a novel, open-access dataset designed for semantic layout analysis, built to support document recreation workflows through mapping with the Text Encoding Initiative (TEI) standard. This dataset includes 7,254 annotated pages spanning a large temporal range (1600-2024) of digitised and born-digital materials ... | {
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2411.10069 | Layer Importance and Hallucination Analysis in Large Language Models via
Enhanced Activation Variance-Sparsity | [
"cs.CL",
"cs.PF"
] | Evaluating the importance of different layers in large language models (LLMs) is crucial for optimizing model performance and interpretability. This paper first explores layer importance using the Activation Variance-Sparsity Score (AVSS), which combines normalized activation variance and sparsity to quantify each laye... | {
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2411.10070 | Step-wise Distribution Alignment Guided Style Prompt Tuning for
Source-free Cross-domain Few-shot Learning | [
"cs.CV"
] | Existing cross-domain few-shot learning (CDFSL) methods, which develop source-domain training strategies to enhance model transferability, face challenges with large-scale pre-trained models (LMs) due to inaccessible source data and training strategies. Moreover, fine-tuning LMs for CDFSL demands substantial computatio... | {
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2411.10071 | Evidential Federated Learning for Skin Lesion Image Classification | [
"cs.CV",
"cs.AI",
"cs.LG"
] | We introduce FedEvPrompt, a federated learning approach that integrates principles of evidential deep learning, prompt tuning, and knowledge distillation for distributed skin lesion classification. FedEvPrompt leverages two sets of prompts: b-prompts (for low-level basic visual knowledge) and t-prompts (for task-specif... | {
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2411.10072 | Real-Time AI-Driven People Tracking and Counting Using Overhead Cameras | [
"cs.CV",
"cs.AI"
] | Accurate people counting in smart buildings and intelligent transportation systems is crucial for energy management, safety protocols, and resource allocation. This is especially critical during emergencies, where precise occupant counts are vital for safe evacuation. Existing methods struggle with large crowds, often ... | {
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} |
2411.10074 | Improving the accuracy of automated labeling of specimen images datasets
via a confidence-based process | [
"cs.CV",
"q-bio.PE"
] | The digitization of natural history collections over the past three decades has unlocked a treasure trove of specimen imagery and metadata. There is great interest in making this data more useful by further labeling it with additional trait data, and modern deep learning machine learning techniques utilizing convolutio... | {
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} |
2411.10077 | Uncertainty-Weighted Mutual Distillation for Multi-View Fusion | [
"cs.CV"
] | Multi-view learning often faces challenges in effectively leveraging images captured from different angles and locations. This challenge is particularly pronounced when addressing inconsistencies and uncertainties between views. In this paper, we propose a novel Multi-View Uncertainty-Weighted Mutual Distillation (MV-U... | {
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2411.10080 | Understanding The Effect Of Temperature On Alignment With Human Opinions | [
"cs.CL",
"cs.CY"
] | With the increasing capabilities of LLMs, recent studies focus on understanding whose opinions are represented by them and how to effectively extract aligned opinion distributions. We conducted an empirical analysis of three straightforward methods for obtaining distributions and evaluated the results across a variety ... | {
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} |
2411.10081 | Influence of Depth Camera Noise Models on Respiration Estimation | [
"cs.CV"
] | Depth cameras are an interesting modality for capturing vital signs such as respiratory rate. Plenty approaches exist to extract vital signs in a controlled setting, but in order to apply them more flexibly for example in multi-camera settings, a simulated environment is needed to generate enough data for training and ... | {
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} |
2411.10082 | Jointly Optimizing Power Allocation and Device Association for Robust
IoT Networks under Infeasible Circumstances | [
"cs.IT",
"math.IT"
] | Jointly optimizing power allocation and device association is crucial in Internet-of-Things (IoT) networks to ensure devices achieve their data throughput requirements. Device association, which assigns IoT devices to specific access points (APs), critically impacts resource allocation. Many existing works often assume... | {
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2411.10083 | Xmodel-1.5: An 1B-scale Multilingual LLM | [
"cs.CL"
] | We introduce Xmodel-1.5, a 1-billion-parameter multilingual large language model pretrained on 2 trillion tokens, designed for balanced performance and scalability. Unlike most large models that use the BPE tokenizer, Xmodel-1.5 employs a custom unigram tokenizer with 65,280 tokens, optimizing both efficiency and accur... | {
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} |
2411.10084 | Adapting the Biological SSVEP Response to Artificial Neural Networks | [
"cs.AI"
] | Neuron importance assessment is crucial for understanding the inner workings of artificial neural networks (ANNs) and improving their interpretability and efficiency. This paper introduces a novel approach to neuron significance assessment inspired by frequency tagging, a technique from neuroscience. By applying sinuso... | {
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} |
2411.10086 | CorrCLIP: Reconstructing Correlations in CLIP with Off-the-Shelf
Foundation Models for Open-Vocabulary Semantic Segmentation | [
"cs.CV"
] | Open-vocabulary semantic segmentation aims to assign semantic labels to each pixel without relying on a predefined set of categories. Contrastive Language-Image Pre-training (CLIP) demonstrates outstanding zero-shot classification capabilities but struggles with the pixel-wise segmentation task as the captured inter-pa... | {
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} |
2411.10087 | PFML: Self-Supervised Learning of Time-Series Data Without
Representation Collapse | [
"cs.LG",
"cs.AI"
] | Self-supervised learning (SSL) is a data-driven learning approach that utilizes the innate structure of the data to guide the learning process. In contrast to supervised learning, which depends on external labels, SSL utilizes the inherent characteristics of the data to produce its own supervisory signal. However, one ... | {
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} |
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