id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.12127 | Fine-Grained Uncertainty Quantification via Collisions | [
"cs.LG",
"cs.IT",
"math.IT",
"math.ST",
"stat.ML",
"stat.TH"
] | We propose a new approach for fine-grained uncertainty quantification (UQ) using a collision matrix. For a classification problem involving $K$ classes, the $K\times K$ collision matrix $S$ measures the inherent (aleatoric) difficulty in distinguishing between each pair of classes. In contrast to existing UQ methods, t... | {
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2411.12128 | The Role of Accuracy and Validation Effectiveness in Conversational
Business Analytics | [
"cs.AI",
"econ.GN",
"q-fin.EC"
] | This study examines conversational business analytics, an approach that utilizes AI to address the technical competency gaps that hinder end users from effectively using traditional self-service analytics. By facilitating natural language interactions, conversational business analytics aims to empower end users to inde... | {
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2411.12130 | Adversarial Multi-Agent Reinforcement Learning for Proactive False Data
Injection Detection | [
"eess.SY",
"cs.SY"
] | Smart inverters are instrumental in the integration of renewable and distributed energy resources (DERs) into the electric grid. Such inverters rely on communication layers for continuous control and monitoring, potentially exposing them to cyber-physical attacks such as false data injection attacks (FDIAs). We propose... | {
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2411.12135 | Exact Risk Curves of signSGD in High-Dimensions: Quantifying
Preconditioning and Noise-Compression Effects | [
"stat.ML",
"cs.LG"
] | In recent years, signSGD has garnered interest as both a practical optimizer as well as a simple model to understand adaptive optimizers like Adam. Though there is a general consensus that signSGD acts to precondition optimization and reshapes noise, quantitatively understanding these effects in theoretically solvable ... | {
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2411.12136 | Visualizing Loss Functions as Topological Landscape Profiles | [
"cs.LG",
"cs.AI"
] | In machine learning, a loss function measures the difference between model predictions and ground-truth (or target) values. For neural network models, visualizing how this loss changes as model parameters are varied can provide insights into the local structure of the so-called loss landscape (e.g., smoothness) as well... | {
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2411.12142 | A Computational Method for Measuring "Open Codes" in Qualitative
Analysis | [
"cs.CL",
"cs.AI",
"cs.HC",
"cs.LG"
] | Qualitative analysis is critical to understanding human datasets in many social science disciplines. Open coding is an inductive qualitative process that identifies and interprets "open codes" from datasets. Yet, meeting methodological expectations (such as "as exhaustive as possible") can be challenging. While many ma... | {
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2411.12146 | Self-supervised denoising of visual field data improves detection of
glaucoma progression | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Perimetric measurements provide insight into a patient's peripheral vision and day-to-day functioning and are the main outcome measure for identifying progression of visual damage from glaucoma. However, visual field data can be noisy, exhibiting high variance, especially with increasing damage. In this study, we demon... | {
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2411.12147 | JuniperLiu at CoMeDi Shared Task: Models as Annotators in Lexical
Semantics Disagreements | [
"cs.CL"
] | We present the results of our system for the CoMeDi Shared Task, which predicts majority votes (Subtask 1) and annotator disagreements (Subtask 2). Our approach combines model ensemble strategies with MLP-based and threshold-based methods trained on pretrained language models. Treating individual models as virtual anno... | {
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2411.12150 | HEIGHT: Heterogeneous Interaction Graph Transformer for Robot Navigation
in Crowded and Constrained Environments | [
"cs.RO",
"cs.AI",
"cs.LG"
] | We study the problem of robot navigation in dense and interactive crowds with environmental constraints such as corridors and furniture. Previous methods fail to consider all types of interactions among agents and obstacles, leading to unsafe and inefficient robot paths. In this article, we leverage a graph-based repre... | {
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2411.12151 | Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot
Classification | [
"cs.CV"
] | This study aims to optimize the few-shot image classification task and improve the model's feature extraction and classification performance by combining self-supervised learning with the deep network model ResNet-101. During the training process, we first pre-train the model with self-supervision to enable it to learn... | {
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2411.12152 | Development of a Comprehensive Physics-Based Battery Model and Its
Multidimensional Comparison with an Equivalent-Circuit Model: Accuracy,
Complexity, and Real-World Performance under Varying Conditions | [
"eess.SY",
"cs.SY"
] | This paper develops a comprehensive physics-based model (PBM) that spans a wide operational range, including varying temperatures, charge/discharge conditions, and real-world field data cycles. The PBM incorporates key factors such as hysteresis effects, concentration-dependent diffusivity, and the Arrhenius law to pro... | {
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2411.12154 | Tangential Randomization in Linear Bandits (TRAiL): Guaranteed Inference
and Regret Bounds | [
"stat.ML",
"cs.LG",
"cs.SY",
"eess.SY"
] | We propose and analyze TRAiL (Tangential Randomization in Linear Bandits), a computationally efficient regret-optimal forced exploration algorithm for linear bandits on action sets that are sublevel sets of strongly convex functions. TRAiL estimates the governing parameter of the linear bandit problem through a standar... | {
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2411.12155 | Coarse-to-fine Q-Network with Action Sequence for Data-Efficient Robot
Learning | [
"cs.LG",
"cs.AI",
"cs.RO"
] | In reinforcement learning (RL), we train a value function to understand the long-term consequence of executing a single action. However, the value of taking each action can be ambiguous in robotics as robot movements are typically the aggregate result of executing multiple small actions. Moreover, robotic training data... | {
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2411.12156 | HNCSE: Advancing Sentence Embeddings via Hybrid Contrastive Learning
with Hard Negatives | [
"cs.CL",
"cs.AI"
] | Unsupervised sentence representation learning remains a critical challenge in modern natural language processing (NLP) research. Recently, contrastive learning techniques have achieved significant success in addressing this issue by effectively capturing textual semantics. Many such approaches prioritize the optimizati... | {
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2411.12157 | A Combined Encoder and Transformer Approach for Coherent and
High-Quality Text Generation | [
"cs.CL"
] | This research introduces a novel text generation model that combines BERT's semantic interpretation strengths with GPT-4's generative capabilities, establishing a high standard in generating coherent, contextually accurate language. Through the combined architecture, the model enhances semantic depth and maintains smoo... | {
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2411.12159 | Sensor-fusion based Prognostics Framework for Complex Engineering
Systems Exhibiting Multiple Failure Modes | [
"stat.ML",
"cs.LG",
"cs.SY",
"eess.SY",
"stat.AP"
] | Complex engineering systems are often subject to multiple failure modes. Developing a remaining useful life (RUL) prediction model that does not consider the failure mode causing degradation is likely to result in inaccurate predictions. However, distinguishing between causes of failure without manually inspecting the ... | {
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2411.12162 | Microsegmented Cloud Network Architecture Using Open-Source Tools for a
Zero Trust Foundation | [
"cs.CR",
"cs.DC",
"cs.NI",
"cs.SY",
"eess.SY"
] | This paper presents a multi-cloud networking architecture built on zero trust principles and micro-segmentation to provide secure connectivity with authentication, authorization, and encryption in transit. The proposed design includes the multi-cloud network to support a wide range of applications and workload use case... | {
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2411.12164 | UrbanDiT: A Foundation Model for Open-World Urban Spatio-Temporal
Learning | [
"cs.LG",
"cs.AI"
] | The urban environment is characterized by complex spatio-temporal dynamics arising from diverse human activities and interactions. Effectively modeling these dynamics is essential for understanding and optimizing urban systems In this work, we introduce UrbanDiT, a foundation model for open-world urban spatio-temporal ... | {
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2411.12168 | Sketch-guided Cage-based 3D Gaussian Splatting Deformation | [
"cs.CV",
"cs.GR"
] | 3D Gaussian Splatting (GS) is one of the most promising novel 3D representations that has received great interest in computer graphics and computer vision. While various systems have introduced editing capabilities for 3D GS, such as those guided by text prompts, fine-grained control over deformation remains an open ch... | {
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2411.12173 | SkillTree: Explainable Skill-Based Deep Reinforcement Learning for
Long-Horizon Control Tasks | [
"cs.LG",
"cs.AI"
] | Deep reinforcement learning (DRL) has achieved remarkable success in various research domains. However, its reliance on neural networks results in a lack of transparency, which limits its practical applications. To achieve explainability, decision trees have emerged as a popular and promising alternative to neural netw... | {
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2411.12174 | Just KIDDIN: Knowledge Infusion and Distillation for Detection of
INdecent Memes | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV"
] | Toxicity identification in online multimodal environments remains a challenging task due to the complexity of contextual connections across modalities (e.g., textual and visual). In this paper, we propose a novel framework that integrates Knowledge Distillation (KD) from Large Visual Language Models (LVLMs) and knowled... | {
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2411.12175 | AsynEIO: Asynchronous Monocular Event-Inertial Odometry Using Gaussian
Process Regression | [
"cs.RO",
"cs.CV"
] | Event cameras, when combined with inertial sensors, show significant potential for motion estimation in challenging scenarios, such as high-speed maneuvers and low-light environments. There are many methods for producing such estimations, but most boil down to a synchronous discrete-time fusion problem. However, the as... | {
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2411.12177 | Robust 3D Semantic Occupancy Prediction with Calibration-free Spatial
Transformation | [
"cs.CV"
] | 3D semantic occupancy prediction, which seeks to provide accurate and comprehensive representations of environment scenes, is important to autonomous driving systems. For autonomous cars equipped with multi-camera and LiDAR, it is critical to aggregate multi-sensor information into a unified 3D space for accurate and r... | {
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2411.12179 | Multi-Grained Preference Enhanced Transformer for Multi-Behavior
Sequential Recommendation | [
"cs.IR",
"cs.SI"
] | Sequential recommendation (SR) aims to predict the next purchasing item according to users' dynamic preference learned from their historical user-item interactions. To improve the performance of recommendation, learning dynamic heterogeneous cross-type behavior dependencies is indispensable for recommender system. Howe... | {
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2411.12180 | Quantifying the Innovativeness of Celebrated Scientists and Their
Embeddedness in Collaboration Networks | [
"cs.DL",
"cs.SI"
] | Matthew effects, or the tendency for early achievements in science to lead to more recognition and opportunities, are a potential source of stratification and lost innovation when they draw unreasonable attention away from equally innovative but less celebrated scholars. Here, we analyze whether prizewinners produce mo... | {
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2411.12181 | Enhancing Low Dose Computed Tomography Images Using Consistency Training
Techniques | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Diffusion models have significant impact on wide range of generative tasks, especially on image inpainting and restoration. Although the improvements on aiming for decreasing number of function evaluations (NFE), the iterative results are still computationally expensive. Consistency models are as a new family of genera... | {
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2411.12182 | Diffusion-Inspired Cold Start with Sufficient Prior in Computerized
Adaptive Testing | [
"cs.LG",
"cs.AI",
"cs.CY"
] | Computerized Adaptive Testing (CAT) aims to select the most appropriate questions based on the examinee's ability and is widely used in online education. However, existing CAT systems often lack initial understanding of the examinee's ability, requiring random probing questions. This can lead to poorly matched question... | {
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2411.12183 | Action-Attentive Deep Reinforcement Learning for Autonomous Alignment of
Beamlines | [
"eess.SY",
"cs.LG",
"cs.SY"
] | Synchrotron radiation sources play a crucial role in fields such as materials science, biology, and chemistry. The beamline, a key subsystem of the synchrotron, modulates and directs the radiation to the sample for analysis. However, the alignment of beamlines is a complex and time-consuming process, primarily carried ... | {
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2411.12184 | Testability of Instrumental Variables in Additive Nonlinear,
Non-Constant Effects Models | [
"stat.ME",
"cs.AI",
"cs.LG"
] | We address the issue of the testability of instrumental variables derived from observational data. Most existing testable implications are centered on scenarios where the treatment is a discrete variable, e.g., instrumental inequality (Pearl, 1995), or where the effect is assumed to be constant, e.g., instrumental vari... | {
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2411.12185 | LiV-GS: LiDAR-Vision Integration for 3D Gaussian Splatting SLAM in
Outdoor Environments | [
"cs.RO"
] | We present LiV-GS, a LiDAR-visual SLAM system in outdoor environments that leverages 3D Gaussian as a differentiable spatial representation. Notably, LiV-GS is the first method that directly aligns discrete and sparse LiDAR data with continuous differentiable Gaussian maps in large-scale outdoor scenes, overcoming the ... | {
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2411.12188 | Constant Rate Schedule: Constant-Rate Distributional Change for
Efficient Training and Sampling in Diffusion Models | [
"cs.CV",
"cs.LG"
] | We propose a noise schedule that ensures a constant rate of change in the probability distribution of diffused data throughout the diffusion process. To obtain this schedule, we measure the probability-distributional change of diffused data by simulating the forward process and use it to determine the noise schedule be... | {
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2411.12193 | Hierarchical Spatio-Temporal Uncertainty Quantification for Distributed
Energy Adoption | [
"stat.AP",
"cs.LG",
"stat.ML"
] | The rapid deployment of distributed energy resources (DER) has introduced significant spatio-temporal uncertainties in power grid management, necessitating accurate multilevel forecasting methods. However, existing approaches often produce overly conservative uncertainty intervals at individual spatial units and fail t... | {
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2411.12195 | A Survey of Medical Vision-and-Language Applications and Their
Techniques | [
"cs.CV"
] | Medical vision-and-language models (MVLMs) have attracted substantial interest due to their capability to offer a natural language interface for interpreting complex medical data. Their applications are versatile and have the potential to improve diagnostic accuracy and decision-making for individual patients while als... | {
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2411.12196 | A More Advanced Group Polarization Measurement Approach Based on
LLM-Based Agents and Graphs | [
"cs.CY",
"cs.AI"
] | Group polarization is an important research direction in social media content analysis, attracting many researchers to explore this field. Therefore, how to effectively measure group polarization has become a critical topic. Measuring group polarization on social media presents several challenges that have not yet been... | {
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2411.12197 | MTFusion: Reconstructing Any 3D Object from Single Image Using
Multi-word Textual Inversion | [
"cs.CV",
"cs.MM"
] | Reconstructing 3D models from single-view images is a long-standing problem in computer vision. The latest advances for single-image 3D reconstruction extract a textual description from the input image and further utilize it to synthesize 3D models. However, existing methods focus on capturing a single key attribute of... | {
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2411.12198 | CCIS-Diff: A Generative Model with Stable Diffusion Prior for Controlled
Colonoscopy Image Synthesis | [
"cs.CV",
"cs.AI"
] | Colonoscopy is crucial for identifying adenomatous polyps and preventing colorectal cancer. However, developing robust models for polyp detection is challenging by the limited size and accessibility of existing colonoscopy datasets. While previous efforts have attempted to synthesize colonoscopy images, current methods... | {
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2411.12199 | Rethinking Text-Promptable Surgical Instrument Segmentation with Robust
Framework | [
"cs.CV"
] | Surgical instrument segmentation (SIS) is essential in computer-assisted surgeries, with deep learning methods improving accuracy in complex environments. Recently, text-promptable segmentation methods have been introduced, generating masks based on textual descriptions. However, they assume the text-described object i... | {
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2411.12201 | Invariant Shape Representation Learning For Image Classification | [
"cs.CV"
] | Geometric shape features have been widely used as strong predictors for image classification. Nevertheless, most existing classifiers such as deep neural networks (DNNs) directly leverage the statistical correlations between these shape features and target variables. However, these correlations can often be spurious an... | {
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2411.12205 | Sparser Training for On-Device Recommendation Systems | [
"cs.IR"
] | Recommender systems often rely on large embedding tables that map users and items to dense vectors of uniform size, leading to substantial memory consumption and inefficiencies. This is particularly problematic in memory-constrained environments like mobile and Web of Things (WoT) applications, where scalability and re... | {
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2411.12206 | Safe Navigation in Dynamic Environments using Density Functions | [
"cs.RO",
"math.DS",
"math.OC"
] | This work uses density functions for safe navigation in dynamic environments. The dynamic environment consists of time-varying obstacles as well as time-varying target sets. We propose an analytical construction of time-varying density functions to solve these navigation problems. The proposed approach leads to a time-... | {
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2411.12207 | CMBAnalysis: A Modern Framework for High-Precision Cosmic Microwave
Background Analysis | [
"astro-ph.CO",
"astro-ph.IM",
"cs.CE"
] | I present CMBAnalysis, a state-of-the-art Python framework designed for high-precision analysis of Cosmic Microwave Background (CMB) radiation data. This comprehensive package implements parallel Markov Chain Monte Carlo (MCMC) techniques for robust cosmological parameter estimation, featuring adaptive integration meth... | {
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2411.12220 | DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in
Federated Learning | [
"cs.LG",
"cs.AI",
"cs.CR"
] | Federated Learning (FL) enables collaborative model training across distributed devices while preserving local data privacy, making it ideal for mobile and embedded systems. However, the decentralized nature of FL also opens vulnerabilities to model poisoning attacks, particularly backdoor attacks, where adversaries im... | {
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2411.12222 | Contrast Similarity-Aware Dual-Pathway Mamba for Multivariate Time
Series Node Classification | [
"cs.LG",
"cs.AI"
] | Multivariate time series (MTS) data is generated through multiple sensors across various domains such as engineering application, health monitoring, and the internet of things, characterized by its temporal changes and high dimensional characteristics. Over the past few years, many studies have explored the long-range ... | {
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2411.12227 | INDIANA: Personalized Travel Recommendations Using Wearables and AI | [
"cs.HC",
"cs.IR"
] | This work presents a personalized travel recommendation system developed as part of the INDIANA platform, designed to enhance the tourist experience through tailored activity suggestions, by leveraging data from wearable devices, user preferences, current location, weather forecasts, and activity history to provide rea... | {
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2411.12229 | SymphonyQG: Towards Symphonious Integration of Quantization and Graph
for Approximate Nearest Neighbor Search | [
"cs.DB",
"cs.IR"
] | Approximate nearest neighbor (ANN) search in high-dimensional Euclidean space has a broad range of applications. Among existing ANN algorithms, graph-based methods have shown superior performance in terms of the time-accuracy trade-off. However, they face performance bottlenecks due to the random memory accesses caused... | {
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2411.12235 | BoolQuestions: Does Dense Retrieval Understand Boolean Logic in
Language? | [
"cs.IR",
"cs.CL"
] | Dense retrieval, which aims to encode the semantic information of arbitrary text into dense vector representations or embeddings, has emerged as an effective and efficient paradigm for text retrieval, consequently becoming an essential component in various natural language processing systems. These systems typically fo... | {
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2411.12239 | A Control Lyapunov Function Approach to Event-Triggered Parameterized
Control for Discrete-Time Linear Systems | [
"math.OC",
"cs.SY",
"eess.SY"
] | This paper proposes an event-triggered parameterized control method using a control Lyapunov function approach for discrete time linear systems with external disturbances. In this control method, each control input to the plant is a linear combination of a fixed set of linearly independent scalar functions. The control... | {
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2411.12240 | Evaluating Tokenizer Performance of Large Language Models Across
Official Indian Languages | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) based on transformer architectures have revolutionized a variety of domains, with tokenization playing a pivotal role in their pre-processing and fine-tuning stages. In multilingual models, particularly those tailored for Indic languages, effective tokenization is crucial for optimizing per... | {
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2411.12244 | Hyper-parameter Optimization for Federated Learning with Step-wise
Adaptive Mechanism | [
"cs.LG",
"cs.DC"
] | Federated Learning (FL) is a decentralized learning approach that protects sensitive information by utilizing local model parameters rather than sharing clients' raw datasets. While this privacy-preserving method is widely employed across various applications, it still requires significant development and optimization.... | {
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2411.12246 | Efficient Training in Multi-Agent Reinforcement Learning: A
Communication-Free Framework for the Box-Pushing Problem | [
"cs.AI"
] | Self-organizing systems consist of autonomous agents that can perform complex tasks and adapt to dynamic environments without a central controller. Prior research often relies on reinforcement learning to enable agents to gain the skills needed for task completion, such as in the box-pushing environment. However, when ... | {
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2411.12248 | Neuro-3D: Towards 3D Visual Decoding from EEG Signals | [
"cs.CV"
] | Human's perception of the visual world is shaped by the stereo processing of 3D information. Understanding how the brain perceives and processes 3D visual stimuli in the real world has been a longstanding endeavor in neuroscience. Towards this goal, we introduce a new neuroscience task: decoding 3D visual perception fr... | {
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2411.12250 | ADV2E: Bridging the Gap Between Analogue Circuit and Discrete Frames in
the Video-to-Events Simulator | [
"cs.CV",
"cs.RO"
] | Event cameras operate fundamentally differently from traditional Active Pixel Sensor (APS) cameras, offering significant advantages. Recent research has developed simulators to convert video frames into events, addressing the shortage of real event datasets. Current simulators primarily focus on the logical behavior of... | {
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2411.12254 | Predicting User Intents and Musical Attributes from Music Discovery
Conversations | [
"cs.CL",
"cs.LG",
"cs.SD",
"eess.AS"
] | Intent classification is a text understanding task that identifies user needs from input text queries. While intent classification has been extensively studied in various domains, it has not received much attention in the music domain. In this paper, we investigate intent classification models for music discovery conve... | {
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2411.12255 | Error-Feedback Model for Output Correction in Bilateral Control-Based
Imitation Learning | [
"cs.RO",
"cs.AI",
"cs.LG"
] | In recent years, imitation learning using neural networks has enabled robots to perform flexible tasks. However, since neural networks operate in a feedforward structure, they do not possess a mechanism to compensate for output errors. To address this limitation, we developed a feedback mechanism to correct these error... | {
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2411.12256 | Restructuring Tractable Probabilistic Circuits | [
"cs.AI",
"cs.LG"
] | Probabilistic circuits (PCs) is a unifying representation for probabilistic models that support tractable inference. Numerous applications of PCs like controllable text generation depend on the ability to efficiently multiply two circuits. Existing multiplication algorithms require that the circuits respect the same st... | {
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2411.12259 | Prototype Optimization with Neural ODE for Few-Shot Learning | [
"cs.CV"
] | Few-Shot Learning (FSL) is a challenging task, which aims to recognize novel classes with few examples. Pre-training based methods effectively tackle the problem by pre-training a feature extractor and then performing class prediction via a cosine classifier with mean-based prototypes. Nevertheless, due to the data sca... | {
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2411.12262 | Low-resource Machine Translation: what for? who for? An observational
study on a dedicated Tetun language translation service | [
"cs.CL"
] | Low-resource machine translation (MT) presents a diversity of community needs and application challenges that remain poorly understood. To complement surveys and focus groups, which tend to rely on small samples of respondents, we propose an observational study on actual usage patterns of a specialized MT service for t... | {
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2411.12265 | On the Accuracy and Precision of Moving Averages to Estimate Wi-Fi Link
Quality | [
"cs.NI",
"cs.LG"
] | The radio spectrum is characterized by a noticeable variability, which impairs performance and determinism of every wireless communication technology. To counteract this aspect, mechanisms like Minstrel are customarily employed in real Wi-Fi devices, and the adoption of machine learning for optimization is envisaged in... | {
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2411.12270 | KDC-MAE: Knowledge Distilled Contrastive Mask Auto-Encoder | [
"cs.CV"
] | In this work, we attempted to extend the thought and showcase a way forward for the Self-supervised Learning (SSL) learning paradigm by combining contrastive learning, self-distillation (knowledge distillation) and masked data modelling, the three major SSL frameworks, to learn a joint and coordinated representation. T... | {
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2411.12273 | Acquire Precise and Comparable Fundus Image Quality Score: FTHNet and
FQS Dataset | [
"eess.IV",
"cs.CV"
] | The retinal fundus images are utilized extensively in the diagnosis, and their quality can directly affect the diagnosis results. However, due to the insufficient dataset and algorithm application, current fundus image quality assessment (FIQA) methods are not powerful enough to meet ophthalmologists` demands. In this ... | {
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2411.12274 | A Review on Generative AI Models for Synthetic Medical Text, Time
Series, and Longitudinal Data | [
"cs.LG",
"cs.CL"
] | This paper presents the results of a novel scoping review on the practical models for generating three different types of synthetic health records (SHRs): medical text, time series, and longitudinal data. The innovative aspects of the review, which incorporate study objectives, data modality, and research methodology o... | {
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2411.12275 | Building Trust: Foundations of Security, Safety and Transparency in AI | [
"cs.CY",
"cs.AI",
"cs.CL"
] | This paper explores the rapidly evolving ecosystem of publicly available AI models, and their potential implications on the security and safety landscape. As AI models become increasingly prevalent, understanding their potential risks and vulnerabilities is crucial. We review the current security and safety scenarios w... | {
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2411.12276 | libcll: an Extendable Python Toolkit for Complementary-Label Learning | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Complementary-label learning (CLL) is a weakly supervised learning paradigm for multiclass classification, where only complementary labels -- indicating classes an instance does not belong to -- are provided to the learning algorithm. Despite CLL's increasing popularity, previous studies highlight two main challenges: ... | {
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2411.12278 | Versatile Cataract Fundus Image Restoration Model Utilizing Unpaired
Cataract and High-quality Images | [
"eess.IV",
"cs.CV"
] | Cataract is one of the most common blinding eye diseases and can be treated by surgery. However, because cataract patients may also suffer from other blinding eye diseases, ophthalmologists must diagnose them before surgery. The cloudy lens of cataract patients forms a hazy degeneration in the fundus images, making it ... | {
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2411.12279 | HouseLLM: LLM-Assisted Two-Phase Text-to-Floorplan Generation | [
"cs.CV"
] | This paper proposes a two-phase text-to-floorplan generation method, which guides a Large Language Model (LLM) to generate an initial layout (Layout-LLM) and refines them into the final floorplans through conditional diffusion model. We incorporate a Chain-of-Thought approach to prompt the LLM based on user text specif... | {
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2411.12286 | GLOVER: Generalizable Open-Vocabulary Affordance Reasoning for
Task-Oriented Grasping | [
"cs.RO",
"cs.CV"
] | Inferring affordable (i.e., graspable) parts of arbitrary objects based on human specifications is essential for robots advancing toward open-vocabulary manipulation. Current grasp planners, however, are hindered by limited vision-language comprehension and time-consuming 3D radiance modeling, restricting real-time, op... | {
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2411.12287 | CUE-M: Contextual Understanding and Enhanced Search with Multimodal
Large Language Model | [
"cs.CL"
] | The integration of Retrieval-Augmented Generation (RAG) with Multimodal Large Language Models (MLLMs) has revolutionized information retrieval and expanded the practical applications of AI. However, current systems struggle in accurately interpreting user intent, employing diverse retrieval strategies, and effectively ... | {
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2411.12290 | SSEditor: Controllable Mask-to-Scene Generation with Diffusion Model | [
"cs.CV",
"cs.AI"
] | Recent advancements in 3D diffusion-based semantic scene generation have gained attention. However, existing methods rely on unconditional generation and require multiple resampling steps when editing scenes, which significantly limits their controllability and flexibility. To this end, we propose SSEditor, a controlla... | {
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2411.12292 | The Soft-PVTOL: modeling and control | [
"eess.SY",
"cs.SY"
] | This paper presents, for the first time, the soft planar vertical take-off and landing (Soft-PVTOL) aircraft. This concept captures the soft aerial vehicle's fundamental dynamics with a minimum number of states and inputs but retains the main features to consider when designing control laws. Unlike conventional PVTOL a... | {
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2411.12293 | Generative Timelines for Instructed Visual Assembly | [
"cs.CV",
"cs.HC",
"cs.MM"
] | The objective of this work is to manipulate visual timelines (e.g. a video) through natural language instructions, making complex timeline editing tasks accessible to non-expert or potentially even disabled users. We call this task Instructed visual assembly. This task is challenging as it requires (i) identifying rele... | {
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2411.12295 | Consistency Regularization for Complementary Clothing Recommendations | [
"cs.IR"
] | This paper reports on the development of a Consistency Regularized model for Bayesian Personalized Ranking (CR-BPR), addressing to the drawbacks in existing complementary clothing recommendation methods, namely limited consistency and biased learning caused by diverse feature scale of multi-modal data. Compared to othe... | {
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2411.12301 | Physics-Guided Detector for SAR Airplanes | [
"cs.CV"
] | The disperse structure distributions (discreteness) and variant scattering characteristics (variability) of SAR airplane targets lead to special challenges of object detection and recognition. The current deep learning-based detectors encounter challenges in distinguishing fine-grained SAR airplanes against complex bac... | {
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2411.12304 | Emergence of Implicit World Models from Mortal Agents | [
"cs.NE",
"cs.LG"
] | We discuss the possibility of world models and active exploration as emergent properties of open-ended behavior optimization in autonomous agents. In discussing the source of the open-endedness of living things, we start from the perspective of biological systems as understood by the mechanistic approach of theoretical... | {
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2411.12306 | Diffusion Product Quantization | [
"cs.CV"
] | In this work, we explore the quantization of diffusion models in extreme compression regimes to reduce model size while maintaining performance. We begin by investigating classical vector quantization but find that diffusion models are particularly susceptible to quantization error, with the codebook size limiting gene... | {
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2411.12307 | Balancing Accuracy and Efficiency in Multi-Turn Intent Classification
for LLM-Powered Dialog Systems in Production | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Accurate multi-turn intent classification is essential for advancing conversational AI systems. However, challenges such as the scarcity of comprehensive datasets and the complexity of contextual dependencies across dialogue turns hinder progress. This paper presents two novel approaches leveraging Large Language Model... | {
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2411.12308 | SNN-Based Online Learning of Concepts and Action Laws in an Open World | [
"cs.AI",
"cs.LG",
"cs.NE",
"cs.RO"
] | We present the architecture of a fully autonomous, bio-inspired cognitive agent built around a spiking neural network (SNN) implementing the agent's semantic memory. The agent explores its universe and learns concepts of objects/situations and of its own actions in a one-shot manner. While object/situation concepts are... | {
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2411.12309 | DGTR: Distributed Gaussian Turbo-Reconstruction for Sparse-View Vast
Scenes | [
"cs.CV"
] | Novel-view synthesis (NVS) approaches play a critical role in vast scene reconstruction. However, these methods rely heavily on dense image inputs and prolonged training times, making them unsuitable where computational resources are limited. Additionally, few-shot methods often struggle with poor reconstruction qualit... | {
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2411.12310 | Variable-Frequency Imitation Learning for Variable-Speed Motion | [
"cs.RO"
] | Conventional methods of imitation learning for variable-speed motion have difficulty extrapolating speeds because they rely on learning models running at a constant sampling frequency. This study proposes variable-frequency imitation learning (VFIL), a novel method for imitation learning with learning models trained to... | {
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2411.12312 | Age of Information Minimization in UAV-Assisted Covert Communication:
Trajectory and Beamforming Design | [
"eess.SY",
"cs.IT",
"cs.SY",
"math.IT"
] | Unmanned aerial vehicles (UAVs) have the potential for time-sensitive applications. Due to wireless channel variation, received data may have an expiration time, particularly in critical situations such as rescue operations, natural disasters, or the military. Age of Information (AoI) is a metric that measures the fres... | {
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2411.12313 | C$^{2}$INet: Realizing Incremental Trajectory Prediction with
Prior-Aware Continual Causal Intervention | [
"cs.LG",
"cs.CV"
] | Trajectory prediction for multi-agents in complex scenarios is crucial for applications like autonomous driving. However, existing methods often overlook environmental biases, which leads to poor generalization. Additionally, hardware constraints limit the use of large-scale data across environments, and continual lear... | {
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2411.12319 | CLIP Unreasonable Potential in Single-Shot Face Recognition | [
"cs.CV",
"cs.AI"
] | Face recognition is a core task in computer vision designed to identify and authenticate individuals by analyzing facial patterns and features. This field intersects with artificial intelligence image processing and machine learning with applications in security authentication and personalization. Traditional approache... | {
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2411.12321 | Enhancing Blind Source Separation with Dissociative Principal Component
Analysis | [
"cs.CV"
] | Sparse principal component analysis (sPCA) enhances the interpretability of principal components (PCs) by imposing sparsity constraints on loading vectors (LVs). However, when used as a precursor to independent component analysis (ICA) for blind source separation (BSS), sPCA may underperform due to its focus on simplic... | {
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2411.12329 | Attributed Graph Clustering in Collaborative Settings | [
"cs.LG",
"cs.SI"
] | Graph clustering is an unsupervised machine learning method that partitions the nodes in a graph into different groups. Despite achieving significant progress in exploiting both attributed and structured data information, graph clustering methods often face practical challenges related to data isolation. Moreover, the ... | {
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2411.12330 | Graph as a feature: improving node classification with non-neural
graph-aware logistic regression | [
"cs.LG"
] | Graph Neural Networks (GNNs) and their message passing framework that leverages both structural and feature information, have become a standard method for solving graph-based machine learning problems. However, these approaches still struggle to generalise well beyond datasets that exhibit strong homophily, where nodes... | {
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2411.12331 | Accelerating UMAP for Large-Scale Datasets Through Spectral Coarsening | [
"cs.CV"
] | This paper introduces an innovative approach to dramatically accelerate UMAP using spectral data compression.The proposed method significantly reduces the size of the dataset, preserving its essential manifold structure through an advanced spectral compression technique. This allows UMAP to perform much faster while ma... | {
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2411.12334 | Learning from Label Proportions and Covariate-shifted Instances | [
"cs.LG"
] | In many applications, especially due to lack of supervision or privacy concerns, the training data is grouped into bags of instances (feature-vectors) and for each bag we have only an aggregate label derived from the instance-labels in the bag. In learning from label proportions (LLP) the aggregate label is the average... | {
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2411.12335 | Cities beyond proximity | [
"physics.soc-ph",
"cs.SI"
] | The concept of `proximity-based cities' has gained attention as a new urban organizational model. Most prominently, the 15-minute city contends that cities can function more effectively, equitably and sustainably if essential, everyday services and key amenities are within a 15-minute walk or cycle. However, focusing s... | {
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2411.12338 | Target Height Estimation Using a Single Acoustic Camera for Compensation
in 2D Seabed Mosaicking | [
"cs.RO",
"cs.CV"
] | This letter proposes a novel approach for compensating target height data in 2D seabed mosaicking for low-visibility underwater perception. Acoustic cameras are effective sensors for sensing the marine environments due to their high-resolution imaging capabilities and robustness to darkness and turbidity. However, the ... | {
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2411.12346 | A computational model for inelastic behaviour and fracture of refractory
industrial components under high-temperature conditions, application to slide
gate plates | [
"cs.CE",
"physics.class-ph"
] | This work aims to provide a computational model that can describe the complex behaviour of refractory industrial components under working conditions. Special attention is given to the asymmetric tension-compression behaviour and its evolution in the full range of working temperatures. The model accounts for inelastic f... | {
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2411.12347 | Leveraging NFTs for Spectrum Securitization in 6G Networks | [
"cs.CE"
] | Dynamic Spectrum Sharing can enhance spectrum resource utilization by promoting the dynamic distribution of spectrum resources. However, to effectively implement dynamic spectrum resource allocation, certain mechanisms are needed to incentivize primary users to proactively share their spectrum resources. This paper, ba... | {
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2411.12350 | DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for
Semi-supervised Medical Image Segmentation | [
"cs.CV",
"cs.AI"
] | As a technique to alleviate the pressure of data annotation, semi-supervised learning (SSL) has attracted widespread attention. In the specific domain of medical image segmentation, semi-supervised methods (SSMIS) have become a research hotspot due to their ability to reduce the need for large amounts of precisely anno... | {
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} |
2411.12352 | Perfecting Imperfect Physical Neural Networks with Transferable
Robustness using Sharpness-Aware Training | [
"physics.optics",
"cs.ET",
"cs.LG"
] | AI models are essential in science and engineering, but recent advances are pushing the limits of traditional digital hardware. To address these limitations, physical neural networks (PNNs), which use physical substrates for computation, have gained increasing attention. However, developing effective training methods f... | {
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} |
2411.12353 | Service Restoration for Distribution Systems Based on Semi-Analytical
Metamodeling of Decision-Dependent Interruption Cost and Cold Load Pickup | [
"eess.SY",
"cs.SY"
] | Developing optimized restoration strategies for power distribution systems (PDSs) is essential to meet the pressing demand for enhanced resilience. Prior knowledge of customer interruption cost (CIC) and load restoration behaviors, particularly cold load pickup (CLPU), is crucial for guiding effective restoration; howe... | {
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} |
2411.12354 | Scalable and Effective Negative Sample Generation for Hyperedge
Prediction | [
"cs.IR"
] | Hyperedge prediction is crucial in hypergraph analysis for understanding complex multi-entity interactions in various web-based applications, including social networks and e-commerce systems. Traditional methods often face difficulties in generating high-quality negative samples due to the imbalance between positive an... | {
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} |
2411.12355 | DynFocus: Dynamic Cooperative Network Empowers LLMs with Video
Understanding | [
"cs.CV"
] | The challenge in LLM-based video understanding lies in preserving visual and semantic information in long videos while maintaining a memory-affordable token count. However, redundancy and correspondence in videos have hindered the performance potential of existing methods. Through statistical learning on current datase... | {
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} |
2411.12357 | A Layered Architecture for Developing and Enhancing Capabilities in
Large Language Model-based Software Systems | [
"cs.SE",
"cs.AI",
"cs.CL",
"cs.MA"
] | Significant efforts has been made to expand the use of Large Language Models (LLMs) beyond basic language tasks. While the generalizability and versatility of LLMs have enabled widespread adoption, evolving demands in application development often exceed their native capabilities. Meeting these demands may involve a di... | {
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} |
2411.12359 | TactV: A Class of Hybrid Terrestrial/Aerial Coaxial Tilt-Rotor Vehicles | [
"cs.RO"
] | To enhance the obstacle-crossing and endurance capabilities of vehicles operating in complex environments, this paper presents the design of a hybrid terrestrial/aerial coaxial tilt-rotor vehicle, TactV, which integrates advantages such as lightweight construction and high maneuverability. Unlike existing tandem dual-r... | {
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} |
2411.12361 | Breathless: An 8-hour Performance Contrasting Human and Robot
Expressiveness | [
"cs.RO",
"cs.CV"
] | This paper describes the robot technology behind an original performance that pairs a human dancer (Cuan) with an industrial robot arm for an eight-hour dance that unfolds over the timespan of an American workday. To control the robot arm, we combine a range of sinusoidal motions with varying amplitude, frequency and o... | {
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} |
2411.12364 | Ultra-Sparse Memory Network | [
"cs.LG"
] | It is widely acknowledged that the performance of Transformer models is logarithmically related to their number of parameters and computational complexity. While approaches like Mixture of Experts (MoE) decouple parameter count from computational complexity, they still face challenges in inference due to high memory ac... | {
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} |
2411.12372 | RedPajama: an Open Dataset for Training Large Language Models | [
"cs.CL",
"cs.LG"
] | Large language models are increasingly becoming a cornerstone technology in artificial intelligence, the sciences, and society as a whole, yet the optimal strategies for dataset composition and filtering remain largely elusive. Many of the top-performing models lack transparency in their dataset curation and model deve... | {
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} |
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