id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.08469 | Building Trustworthy AI: Transparent AI Systems via Large Language
Models, Ontologies, and Logical Reasoning (TranspNet) | [
"cs.AI",
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] | Growing concerns over the lack of transparency in AI, particularly in high-stakes fields like healthcare and finance, drive the need for explainable and trustworthy systems. While Large Language Models (LLMs) perform exceptionally well in generating accurate outputs, their "black box" nature poses significant challenge... | {
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2411.08470 | HyperFace: Generating Synthetic Face Recognition Datasets by Exploring
Face Embedding Hypersphere | [
"cs.CV"
] | Face recognition datasets are often collected by crawling Internet and without individuals' consents, raising ethical and privacy concerns. Generating synthetic datasets for training face recognition models has emerged as a promising alternative. However, the generation of synthetic datasets remains challenging as it e... | {
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2411.08472 | A survey on Graph Deep Representation Learning for Facial Expression
Recognition | [
"cs.CV"
] | This comprehensive review delves deeply into the various methodologies applied to facial expression recognition (FER) through the lens of graph representation learning (GRL). Initially, we introduce the task of FER and the concepts of graph representation and GRL. Afterward, we discuss some of the most prevalent and va... | {
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2411.08473 | Fractional Fourier Domain PAPR Reduction | [
"cs.IT",
"eess.SP",
"math.IT"
] | High peak-to-average power ratio (PAPR) has long posed a challenge for multi-carrier systems, impacting amplifier efficiency and overall system performance. This paper introduces dynamic angle fractional Fourier division multiplexing (DA-FrFDM), an innovative multi-carrier system that effectively reduces PAPR for both ... | {
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2411.08474 | A Cost-effective, Stand-alone, and Real-time TinyML-Based Gait Diagnosis
Unit Aimed at Lower-limb Robotic Prostheses and Exoskeletons | [
"cs.RO"
] | Robotic prostheses and exoskeletons can do wonders compared to their non-robotic counterpart. However, in a cost-soaring world where 1 in every 10 patients has access to normal medical prostheses, access to advanced ones is, unfortunately, extremely limited especially due to their high cost, a significant portion of wh... | {
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2411.08478 | Learning Model Agnostic Explanations via Constraint Programming | [
"cs.LG",
"cs.AI"
] | Interpretable Machine Learning faces a recurring challenge of explaining the predictions made by opaque classifiers such as ensemble models, kernel methods, or neural networks in terms that are understandable to humans. When the model is viewed as a black box, the objective is to identify a small set of features that j... | {
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2411.08481 | Variable-Length Feedback Codes via Deep Learning | [
"cs.IT",
"math.IT"
] | Variable-length feedback coding has the potential to significantly enhance communication reliability in finite block length scenarios by adapting coding strategies based on real-time receiver feedback. Designing such codes, however, is challenging. While deep learning (DL) has been employed to design sophisticated feed... | {
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2411.08482 | Methodology for a Statistical Analysis of Influencing Factors on 3D
Object Detection Performance | [
"cs.CV",
"cs.LG"
] | In automated driving, object detection is an essential task to perceive the environment by localizing and classifying objects. Most object detection algorithms are based on deep learning for superior performance. However, their black-box nature makes it challenging to ensure safety. In this paper, we propose a first-of... | {
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2411.08488 | UNSCT-HRNet: Modeling Anatomical Uncertainty for Landmark Detection in
Total Hip Arthroplasty | [
"eess.IV",
"cs.CV"
] | Total hip arthroplasty (THA) relies on accurate landmark detection from radiographic images, but unstructured data caused by irregular patient postures or occluded anatomical markers pose significant challenges for existing methods. To address this, we propose UNSCT-HRNet (Unstructured CT - High-Resolution Net), a deep... | {
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2411.08490 | Impact of Iris Pigmentation on Performance Bias in Visible Iris
Verification Systems: A Comparative Study | [
"cs.CV"
] | Iris recognition technology plays a critical role in biometric identification systems, but their performance can be affected by variations in iris pigmentation. In this work, we investigate the impact of iris pigmentation on the efficacy of biometric recognition systems, focusing on a comparative analysis of blue and d... | {
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2411.08499 | Learning Robust Grasping Strategy Through Tactile Sensing and Adaption
Skill | [
"cs.RO"
] | Robust grasping represents an essential task in robotics, necessitating tactile feedback and reactive grasping adjustments for robust grasping of objects. Previous research has extensively combined tactile sensing with grasping, primarily relying on rule-based approaches, frequently neglecting post-grasping difficultie... | {
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2411.08504 | Towards Objective and Unbiased Decision Assessments with LLM-Enhanced
Hierarchical Attention Networks | [
"cs.CL",
"cs.AI"
] | How objective and unbiased are we while making decisions? This work investigates cognitive bias identification in high-stake decision making process by human experts, questioning its effectiveness in real-world settings, such as candidates assessments for university admission. We begin with a statistical analysis asses... | {
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2411.08506 | Towards Operationalizing Right to Data Protection | [
"cs.LG",
"cs.AI",
"cs.CL"
] | The widespread practice of indiscriminate data scraping to fine-tune language models (LMs) raises significant legal and ethical concerns, particularly regarding compliance with data protection laws such as the General Data Protection Regulation (GDPR). This practice often results in the unauthorized use of personal inf... | {
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2411.08508 | BillBoard Splatting (BBSplat): Learnable Textured Primitives for Novel
View Synthesis | [
"cs.CV"
] | We present billboard Splatting (BBSplat) - a novel approach for 3D scene representation based on textured geometric primitives. BBSplat represents the scene as a set of optimizable textured planar primitives with learnable RGB textures and alpha-maps to control their shape. BBSplat primitives can be used in any Gaussia... | {
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2411.08509 | Sum Rate Maximization for Movable Antenna-Aided Downlink RSMA Systems | [
"cs.IT",
"eess.SP",
"math.IT"
] | Rate splitting multiple access (RSMA) is regarded as a crucial and powerful physical layer (PHY) paradigm for next-generation communication systems. Particularly, users employ successive interference cancellation (SIC) to decode part of the interference while treating the remainder as noise. However, conventional RSMA ... | {
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2411.08514 | Explainers' Mental Representations of Explainees' Needs in Everyday
Explanations | [
"cs.AI"
] | In explanations, explainers have mental representations of explainees' developing knowledge and shifting interests regarding the explanandum. These mental representations are dynamic in nature and develop over time, thereby enabling explainers to react to explainees' needs by adapting and customizing the explanation. X... | {
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2411.08516 | Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale
Table Understanding | [
"cs.CL"
] | The ubiquity and value of tables as semi-structured data across various domains necessitate advanced methods for understanding their complexity and vast amounts of information. Despite the impressive capabilities of large language models (LLMs) in advancing the natural language understanding frontier, their application... | {
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2411.08520 | On the Design of Variable Modulation and Adaptive Modulation for Uplink
Sparse Code Multiple Access | [
"cs.IT",
"math.IT"
] | Sparse code multiple access (SCMA) is a promising non-orthogonal multiple access scheme for enabling massive connectivity in next generation wireless networks. However, current SCMA codebooks are designed with the same size, leading to inflexibility of user grouping and supporting diverse data rates. To address this is... | {
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2411.08521 | SAD-TIME: a Spatiotemporal-fused network for depression detection with
Automated multi-scale Depth-wise and TIME-interval-related common feature
extractor | [
"cs.LG",
"cs.AI"
] | Background and Objective: Depression is a severe mental disorder, and accurate diagnosis is pivotal to the cure and rehabilitation of people with depression. However, the current questionnaire-based diagnostic methods could bring subjective biases and may be denied by subjects. In search of a more objective means of di... | {
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2411.08526 | Gendered Words and Grant Rates: A Textual Analysis of Disparate Outcomes
in the Patent System | [
"cs.CY",
"cs.AI"
] | Text is a vehicle to convey information that reflects the writer's linguistic style and communicative patterns. By studying these attributes, we can discover latent insights about the author and their underlying message. This article uses such an approach to better understand patent applications and their inventors. Wh... | {
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2411.08530 | Efficient Whole Slide Image Classification through Fisher Vector
Representation | [
"cs.CV",
"cs.LG"
] | The advancement of digital pathology, particularly through computational analysis of whole slide images (WSI), is poised to significantly enhance diagnostic precision and efficiency. However, the large size and complexity of WSIs make it difficult to analyze and classify them using computers. This study introduces a no... | {
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2411.08531 | Classification and Morphological Analysis of DLBCL Subtypes in
H\&E-Stained Slides | [
"cs.CV"
] | We address the challenge of automated classification of diffuse large B-cell lymphoma (DLBCL) into its two primary subtypes: activated B-cell-like (ABC) and germinal center B-cell-like (GCB). Accurate classification between these subtypes is essential for determining the appropriate therapeutic strategy, given their di... | {
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2411.08533 | ACROSS: A Deformation-Based Cross-Modal Representation for Robotic
Tactile Perception | [
"cs.RO",
"cs.AI"
] | Tactile perception is essential for human interaction with the environment and is becoming increasingly crucial in robotics. Tactile sensors like the BioTac mimic human fingertips and provide detailed interaction data. Despite its utility in applications like slip detection and object identification, this sensor is now... | {
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2411.08534 | Neural Topic Modeling with Large Language Models in the Loop | [
"cs.CL"
] | Topic modeling is a fundamental task in natural language processing, allowing the discovery of latent thematic structures in text corpora. While Large Language Models (LLMs) have demonstrated promising capabilities in topic discovery, their direct application to topic modeling suffers from issues such as incomplete top... | {
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2411.08537 | MLV$^2$-Net: Rater-Based Majority-Label Voting for Consistent Meningeal
Lymphatic Vessel Segmentation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Meningeal lymphatic vessels (MLVs) are responsible for the drainage of waste products from the human brain. An impairment in their functionality has been associated with aging as well as brain disorders like multiple sclerosis and Alzheimer's disease. However, MLVs have only recently been described for the first time i... | {
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2411.08538 | Intelligent Adaptive Metasurface in Complex Wireless Environments | [
"physics.app-ph",
"cs.SY",
"eess.SY"
] | The programmable metasurface is regarded as one of the most promising transformative technologies for next-generation wireless system applications. Due to the lack of effective perception ability of the external electromagnetic environment, there are numerous challenges in the intelligent regulation of wireless channel... | {
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2411.08544 | Deeper Insights into Learning Performance of Stochastic Configuration
Networks | [
"cs.AI"
] | Stochastic Configuration Networks (SCNs) are a class of randomized neural networks that integrate randomized algorithms within an incremental learning framework. A defining feature of SCNs is the supervisory mechanism, which adaptively adjusts the distribution to generate effective random basis functions, thereby enabl... | {
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2411.08545 | APDDv2: Aesthetics of Paintings and Drawings Dataset with Artist Labeled
Scores and Comments | [
"cs.CV"
] | Datasets play a pivotal role in training visual models, facilitating the development of abstract understandings of visual features through diverse image samples and multidimensional attributes. However, in the realm of aesthetic evaluation of artistic images, datasets remain relatively scarce. Existing painting dataset... | {
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2411.08549 | A Framework for Robust Lossy Compression of Heavy-Tailed Sources | [
"cs.IT",
"eess.SP",
"math.IT"
] | We study the rate-distortion problem for both scalar and vector memoryless heavy-tailed $\alpha$-stable sources ($0 < \alpha < 2$). Using a recently defined notion of ``strength" as a power measure, we derive the rate-distortion function for $\alpha$-stable sources subject to a constraint on the strength of the error, ... | {
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2411.08550 | Graph Neural Networks in Supply Chain Analytics and Optimization:
Concepts, Perspectives, Dataset and Benchmarks | [
"cs.LG",
"cs.CE",
"stat.ML"
] | Graph Neural Networks (GNNs) have recently gained traction in transportation, bioinformatics, language and image processing, but research on their application to supply chain management remains limited. Supply chains are inherently graph-like, making them ideal for GNN methodologies, which can optimize and solve comple... | {
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2411.08552 | Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with
Variational Quantum Circuits | [
"cs.LG",
"cs.AI",
"quant-ph"
] | Quantum Machine Learning (QML) offers tremendous potential but is currently limited by the availability of qubits. We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC). This technique effectively separates approximation error from qubit count and re... | {
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2411.08553 | CorrSynth -- A Correlated Sampling Method for Diverse Dataset Generation
from LLMs | [
"cs.CL"
] | Large language models (LLMs) have demonstrated remarkable performance in diverse tasks using zero-shot and few-shot prompting. Even though their capabilities of data synthesis have been studied well in recent years, the generated data suffers from a lack of diversity, less adherence to the prompt, and potential biases ... | {
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2411.08557 | Learning Locally Adaptive Metrics that Enhance Structural Representation
with $\texttt{LAMINAR}$ | [
"cs.LG"
] | We present $\texttt{LAMINAR}$, a novel unsupervised machine learning pipeline designed to enhance the representation of structure within data via producing a more-informative distance metric. Analysis methods in the physical sciences often rely on standard metrics to define geometric relationships in data, which may fa... | {
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2411.08561 | LogLLM: Log-based Anomaly Detection Using Large Language Models | [
"cs.SE",
"cs.AI"
] | Software systems often record important runtime information in logs to help with troubleshooting. Log-based anomaly detection has become a key research area that aims to identify system issues through log data, ultimately enhancing the reliability of software systems. Traditional deep learning methods often struggle to... | {
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2411.08562 | Neural Corrective Machine Unranking | [
"cs.IR",
"cs.AI"
] | Machine unlearning in neural information retrieval (IR) systems requires removing specific data whilst maintaining model performance. Applying existing machine unlearning methods to IR may compromise retrieval effectiveness or inadvertently expose unlearning actions due to the removal of particular items from the retri... | {
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2411.08563 | Leveraging LLMs for Predictive Insights in Food Policy and Behavioral
Interventions | [
"cs.AI"
] | Food consumption and production contribute significantly to global greenhouse gas emissions, making them crucial entry points for mitigating climate change and maintaining a liveable planet. Over the past two decades, food policy initiatives have explored interventions to reshape production and consumption patterns, fo... | {
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2411.08566 | Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space
Exploration by Reinforcement Learning Agent | [
"cs.RO",
"cs.LG"
] | Grasping by a robot in unstructured environments is deemed a critical challenge because of the requirement for effective adaptation to a wide variation in object geometries, material properties, and other environmental factors. In this paper, we propose a novel framework for robotic grasping based on the idea of compre... | {
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2411.08567 | Saliency Map-based Image Retrieval using Invariant Krawtchouk Moments | [
"cs.CV"
] | With the widespread adoption of digital devices equipped with cameras and the rapid development of Internet technology, numerous content-based image retrieval systems and novel image feature extraction techniques have emerged in recent years. This paper introduces a saliency map-based image retrieval approach using inv... | {
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2411.08569 | UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot
Object Detection and Instance Segmentation | [
"cs.CV"
] | This paper introduces a novel framework for unified incremental few-shot object detection (iFSOD) and instance segmentation (iFSIS) using the Transformer architecture. Our goal is to create an optimal solution for situations where only a few examples of novel object classes are available, with no access to training dat... | {
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2411.08576 | Future state prediction based on observer for missile system | [
"eess.SY",
"cs.SY"
] | Guided missile accuracy and precision is negatively impacted by seeker delay, more specifically by the delay introduced by a mechanical seeker gimbal and the computational time taken to process the raw data. To meet the demands and expectations of modern missiles systems, the impact of this hardware limitation must be ... | {
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2411.08579 | NavAgent: Multi-scale Urban Street View Fusion For UAV Embodied
Vision-and-Language Navigation | [
"cs.CV",
"cs.RO"
] | Vision-and-Language Navigation (VLN), as a widely discussed research direction in embodied intelligence, aims to enable embodied agents to navigate in complicated visual environments through natural language commands. Most existing VLN methods focus on indoor ground robot scenarios. However, when applied to UAV VLN in ... | {
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2411.08582 | Intelligent Algorithms For Signature Diagnostics Of Three-Phase Motors | [
"eess.SP",
"cs.AI",
"cs.LG"
] | The application of machine learning (ML) algorithms in the intelligent diagnosis of three-phase engines has the potential to significantly enhance diagnostic performance and accuracy. Traditional methods largely rely on signature analysis, which, despite being a standard practice, can benefit from the integration of ad... | {
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2411.08583 | An Empirical Examination of the Evaluative AI Framework | [
"cs.HC",
"cs.AI"
] | This study empirically examines the "Evaluative AI" framework, which aims to enhance the decision-making process for AI users by transitioning from a recommendation-based approach to a hypothesis-driven one. Rather than offering direct recommendations, this framework presents users pro and con evidence for hypotheses t... | {
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2411.08586 | Optimizing Automatic Summarization of Long Clinical Records Using
Dynamic Context Extension:Testing and Evaluation of the NBCE Method | [
"cs.AI"
] | Summarizing patient clinical notes is vital for reducing documentation burdens. Current manual summarization makes medical staff struggle. We propose an automatic method using LLMs, but long inputs cause LLMs to lose context, reducing output quality especially in small size model. We used a 7B model, open-calm-7b, enha... | {
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2411.08587 | DeepUQ: Assessing the Aleatoric Uncertainties from two Deep Learning
Methods | [
"cs.LG",
"cs.AI"
] | Assessing the quality of aleatoric uncertainty estimates from uncertainty quantification (UQ) deep learning methods is important in scientific contexts, where uncertainty is physically meaningful and important to characterize and interpret exactly. We systematically compare aleatoric uncertainty measured by two UQ tech... | {
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2411.08590 | Hopfield-Fenchel-Young Networks: A Unified Framework for Associative
Memory Retrieval | [
"cs.LG"
] | Associative memory models, such as Hopfield networks and their modern variants, have garnered renewed interest due to advancements in memory capacity and connections with self-attention in transformers. In this work, we introduce a unified framework-Hopfield-Fenchel-Young networks-which generalizes these models to a br... | {
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2411.08592 | Slender Object Scene Segmentation in Remote Sensing Image Based on
Learnable Morphological Skeleton with Segment Anything Model | [
"cs.CV"
] | Morphological methods play a crucial role in remote sensing image processing, due to their ability to capture and preserve small structural details. However, most of the existing deep learning models for semantic segmentation are based on the encoder-decoder architecture including U-net and Segment Anything Model (SAM)... | {
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2411.08599 | A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for
Text-to-SQL | [
"cs.AI",
"cs.CL",
"cs.DB",
"cs.LG"
] | To tackle the challenges of large language model performance in natural language to SQL tasks, we introduce XiYan-SQL, an innovative framework that employs a multi-generator ensemble strategy to improve candidate generation. We introduce M-Schema, a semi-structured schema representation method designed to enhance the u... | {
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2411.08603 | Generalized Pose Space Embeddings for Training In-the-Wild using
Anaylis-by-Synthesis | [
"cs.CV",
"cs.HC"
] | Modern pose estimation models are trained on large, manually-labelled datasets which are costly and may not cover the full extent of human poses and appearances in the real world. With advances in neural rendering, analysis-by-synthesis and the ability to not only predict, but also render the pose, is becoming an appea... | {
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2411.08605 | Lo-MARVE: A Low Cost Autonomous Underwater Vehicle for Marine
Exploration | [
"cs.RO",
"cs.AI"
] | This paper presents Low-cost Marine Autonomous Robotic Vehicle Explorer (Lo-MARVE), a novel autonomous underwater vehicle (AUV) designed to provide a low cost solution for underwater exploration and environmental monitoring in shallow water environments. Lo-MARVE offers a cost-effective alternative to existing AUVs, fe... | {
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2411.08606 | LG-Gaze: Learning Geometry-aware Continuous Prompts for Language-Guided
Gaze Estimation | [
"cs.CV"
] | The ability of gaze estimation models to generalize is often significantly hindered by various factors unrelated to gaze, especially when the training dataset is limited. Current strategies aim to address this challenge through different domain generalization techniques, yet they have had limited success due to the ris... | {
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2411.08608 | Comparative study of random walks with one-step memory on complex
networks | [
"cs.CY",
"cs.SI",
"physics.soc-ph"
] | We investigate searching efficiency of different kinds of random walk on complex networks which rely on local information and one-step memory. For the studied navigation strategies we obtained theoretical and numerical values for the graph mean first passage times as an indicator for the searching efficiency. The exper... | {
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2411.08610 | Dynamic Subset Tuning: Expanding the Operational Range of
Parameter-Efficient Training for Large Language Models | [
"cs.CL",
"cs.LG"
] | We propose a novel parameter-efficient training (PET) method for large language models that adapts models to downstream tasks by optimizing a small subset of the existing model parameters. Unlike prior methods, this subset is not fixed in location but rather which parameters are modified evolves over the course of trai... | {
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2411.08618 | Robust Optimal Power Flow Against Adversarial Attacks: A Tri-Level
Optimization Approach | [
"eess.SY",
"cs.SY"
] | In power systems, unpredictable events like extreme weather, equipment failures, and cyberattacks present significant challenges to ensuring safety and reliability. Ensuring resilience in the face of these uncertainties is crucial for reliable and efficient operations. This paper presents a tri-level optimization appro... | {
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2411.08622 | Precision-Focused Reinforcement Learning Model for Robotic Object
Pushing | [
"cs.RO",
"cs.AI"
] | Non-prehensile manipulation, such as pushing objects to a desired target position, is an important skill for robots to assist humans in everyday situations. However, the task is challenging due to the large variety of objects with different and sometimes unknown physical properties, such as shape, size, mass, and frict... | {
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2411.08625 | A Conjecture on Group Decision Accuracy in Voter Networks through the
Regularized Incomplete Beta Function | [
"stat.ME",
"cs.GT",
"cs.SI"
] | This paper presents a conjecture on the regularized incomplete beta function in the context of majority decision systems modeled through a voter framework. We examine a network where voters interact, with some voters fixed in their decisions while others are free to change their states based on the influence of their n... | {
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2411.08629 | Zero-shot capability of SAM-family models for bone segmentation in CT
scans | [
"cs.CV"
] | The Segment Anything Model (SAM) and similar models build a family of promptable foundation models (FMs) for image and video segmentation. The object of interest is identified using prompts, such as bounding boxes or points. With these FMs becoming part of medical image segmentation, extensive evaluation studies are re... | {
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2411.08631 | Deep Generative Demand Learning for Newsvendor and Pricing | [
"stat.ML",
"cs.LG",
"math.OC"
] | We consider data-driven inventory and pricing decisions in the feature-based newsvendor problem, where demand is influenced by both price and contextual features and is modeled without any structural assumptions. The unknown demand distribution results in a challenging conditional stochastic optimization problem, furth... | {
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2411.08634 | On the Application of Model Predictive Control to a Weighted Coverage
Path Planning Problem | [
"eess.SY",
"cs.MA",
"cs.RO",
"cs.SY"
] | This paper considers the application of Model Predictive Control (MPC) to a weighted coverage path planning (WCPP) problem. The problem appears in a wide range of practical applications, such as search and rescue (SAR) missions. The basic setup is that one (or multiple) agents can move around a given search space and c... | {
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2411.08637 | Robot See, Robot Do: Imitation Reward for Noisy Financial Environments | [
"cs.LG",
"cs.RO",
"q-fin.TR"
] | The sequential nature of decision-making in financial asset trading aligns naturally with the reinforcement learning (RL) framework, making RL a common approach in this domain. However, the low signal-to-noise ratio in financial markets results in noisy estimates of environment components, including the reward function... | {
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2411.08638 | Gaussian Mixture Models Based Augmentation Enhances GNN Generalization | [
"cs.LG",
"cs.SI",
"stat.AP",
"stat.ML"
] | Graph Neural Networks (GNNs) have shown great promise in tasks like node and graph classification, but they often struggle to generalize, particularly to unseen or out-of-distribution (OOD) data. These challenges are exacerbated when training data is limited in size or diversity. To address these issues, we introduce a... | {
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2411.08640 | Towards Secure Intelligent O-RAN Architecture: Vulnerabilities, Threats
and Promising Technical Solutions using LLMs | [
"cs.CR",
"cs.LG"
] | The evolution of wireless communication systems will be fundamentally impacted by an open radio access network (O-RAN), a new concept defining an intelligent architecture with enhanced flexibility, openness, and the ability to slice services more efficiently. For all its promises, and like any technological advancement... | {
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2411.08641 | DipMe: Haptic Recognition of Granular Media for Tangible Interactive
Applications | [
"cs.HC",
"cs.AI"
] | While tangible user interface has shown its power in naturally interacting with rigid or soft objects, users cannot conveniently use different types of granular materials as the interaction media. We introduce DipMe as a smart device to recognize the types of granular media in real time, which can be used to connect th... | {
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2411.08642 | Towards More Accurate Fake Detection on Images Generated from Advanced
Generative and Neural Rendering Models | [
"cs.CV",
"cs.AI"
] | The remarkable progress in neural-network-driven visual data generation, especially with neural rendering techniques like Neural Radiance Fields and 3D Gaussian splatting, offers a powerful alternative to GANs and diffusion models. These methods can produce high-fidelity images and lifelike avatars, highlighting the ne... | {
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2411.08645 | A System Level Performance Evaluation for Superconducting Digital
Systems | [
"cs.AR",
"cs.AI",
"cs.ET"
] | Superconducting Digital (SCD) technology offers significant potential for enhancing the performance of next generation large scale compute workloads. By leveraging advanced lithography and a 300 mm platform, SCD devices can reduce energy consumption and boost computational power. This paper presents a cross-layer model... | {
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2411.08647 | The Galactica database: an open, generic and versatile tool for the
dissemination of simulation data in astrophysics | [
"astro-ph.IM",
"cs.CE",
"cs.DB"
] | The Galactica simulation database is a platform designed to assist computational astrophysicists with their open science approach based on FAIR (Findable, Accessible, Interoperable, Reusable) principles. It offers the means to publish their numerical simulation projects, whatever their field of application or research ... | {
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2411.08651 | Estimating unknown parameters in differential equations with a
reinforcement learning based PSO method | [
"cs.LG",
"cs.AI"
] | Differential equations offer a foundational yet powerful framework for modeling interactions within complex dynamic systems and are widely applied across numerous scientific fields. One common challenge in this area is estimating the unknown parameters of these dynamic relationships. However, traditional numerical opti... | {
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2411.08652 | Accelerating Quasi-Static Time Series Simulations with Foundation Models | [
"cs.LG"
] | Quasi-static time series (QSTS) simulations have great potential for evaluating the grid's ability to accommodate the large-scale integration of distributed energy resources. However, as grids expand and operate closer to their limits, iterative power flow solvers, central to QSTS simulations, become computationally pr... | {
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2411.08656 | MikuDance: Animating Character Art with Mixed Motion Dynamics | [
"cs.CV"
] | We propose MikuDance, a diffusion-based pipeline incorporating mixed motion dynamics to animate stylized character art. MikuDance consists of two key techniques: Mixed Motion Modeling and Mixed-Control Diffusion, to address the challenges of high-dynamic motion and reference-guidance misalignment in character art anima... | {
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2411.08661 | Energy Optimal Traversal Between Hover Waypoints for Lift+Cruise
Electric Powered Aircraft | [
"cs.RO"
] | Advanced Air Mobility aircraft require energy efficient flight plans to be economically viable. This paper defines minimum energy direct trajectories between waypoints for Lift+Cruise electric Vertical Take-Off and Landing (eVTOL) aircraft. Energy consumption is optimized over accelerated and cruise flight profiles wit... | {
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2411.08663 | Toward Human Understanding with Controllable Synthesis | [
"cs.CV"
] | Training methods to perform robust 3D human pose and shape (HPS) estimation requires diverse training images with accurate ground truth. While BEDLAM demonstrates the potential of traditional procedural graphics to generate such data, the training images are clearly synthetic. In contrast, generative image models produ... | {
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2411.08664 | UniMat: Unifying Materials Embeddings through Multi-modal Learning | [
"cs.LG",
"cond-mat.mtrl-sci"
] | Materials science datasets are inherently heterogeneous and are available in different modalities such as characterization spectra, atomic structures, microscopic images, and text-based synthesis conditions. The advancements in multi-modal learning, particularly in vision and language models, have opened new avenues fo... | {
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2411.08665 | OSMLoc: Single Image-Based Visual Localization in OpenStreetMap with
Geometric and Semantic Guidances | [
"cs.CV"
] | OpenStreetMap (OSM), an online and versatile source of volunteered geographic information (VGI), is widely used for human self-localization by matching nearby visual observations with vectorized map data. However, due to the divergence in modalities and views, image-to-OSM (I2O) matching and localization remain challen... | {
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2411.08666 | A Survey on Vision Autoregressive Model | [
"cs.CV",
"cs.AI"
] | Autoregressive models have demonstrated great performance in natural language processing (NLP) with impressive scalability, adaptability and generalizability. Inspired by their notable success in NLP field, autoregressive models have been intensively investigated recently for computer vision, which perform next-token p... | {
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2411.08671 | Theoretical Analysis of Byte-Pair Encoding | [
"cs.DS",
"cs.CL"
] | Byte-Pair Encoding (BPE) is a widely used method for subword tokenization, with origins in grammar-based text compression. It is employed in a variety of language processing tasks such as machine translation or large language model (LLM) pretraining, to create a token dictionary of a prescribed size. Most evaluations o... | {
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2411.08674 | Reducing ADC Front-end Costs During Training of On-sensor Printed
Multilayer Perceptrons | [
"cs.AR",
"cs.NE"
] | Printed electronics technology offers a cost-effectiveand fully-customizable solution to computational needs beyondthe capabilities of traditional silicon technologies, offering ad-vantages such as on-demand manufacturing and conformal, low-cost hardware. However, the low-resolution fabrication of printedelectronics, w... | {
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2411.08678 | Identification of Power Systems with Droop-Controlled Units Using Neural
Ordinary Differential Equations | [
"eess.SY",
"cs.SY"
] | In future power systems, the detailed structure and dynamics may not always be fully known. This is due to an increasing number of distributed energy resources, such as photovoltaic generators, battery storage systems, heat pumps and electric vehicles, as well as a shift towards active distribution grids. Obtaining phy... | {
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2411.08680 | Integrated Precoder and Trajectory Design for MIMO UAV-Assisted Relay
System With Finite-Alphabet Inputs | [
"cs.IT",
"eess.SP",
"math.IT"
] | Unmanned aerial vehicles (UAVs) are gaining widespread use in wireless relay systems due to their exceptional flexibility and cost-effectiveness. This paper focuses on the integrated design of UAV trajectories and the precoders at both the transmitter and UAV in a UAV-assisted relay communication system, accounting for... | {
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2411.08684 | Analogical Reasoning Within a Conceptual Hyperspace | [
"cs.AI"
] | We propose an approach to analogical inference that marries the neuro-symbolic computational power of complex-sampled hyperdimensional computing (HDC) with Conceptual Spaces Theory (CST), a promising theory of semantic meaning. CST sketches, at an abstract level, approaches to analogical inference that go beyond the st... | {
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2411.08687 | Measuring similarity between embedding spaces using induced neighborhood
graphs | [
"cs.LG"
] | Deep Learning techniques have excelled at generating embedding spaces that capture semantic similarities between items. Often these representations are paired, enabling experiments with analogies (pairs within the same domain) and cross-modality (pairs across domains). These experiments are based on specific assumption... | {
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2411.08696 | Scholarly Wikidata: Population and Exploration of Conference Data in
Wikidata using LLMs | [
"cs.DL",
"cs.AI",
"cs.IR"
] | Several initiatives have been undertaken to conceptually model the domain of scholarly data using ontologies and to create respective Knowledge Graphs. Yet, the full potential seems unleashed, as automated means for automatic population of said ontologies are lacking, and respective initiatives from the Semantic Web co... | {
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2411.08699 | FedSub: Introducing class-aware Subnetworks Fusion to Enhance
Personalized Federated Learning in Ubiquitous Systems | [
"cs.LG"
] | Personalized Federated Learning is essential in AI-driven ubiquitous systems, supporting the distributed development of models able to adapt to diverse and evolving user behaviors while safeguarding privacy. Despite addressing heterogeneous user data distributions in collaborative model training, existing methods often... | {
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2411.08700 | Rethinking negative sampling in content-based news recommendation | [
"cs.IR",
"cs.AI",
"cs.LG"
] | News recommender systems are hindered by the brief lifespan of articles, as they undergo rapid relevance decay. Recent studies have demonstrated the potential of content-based neural techniques in tackling this problem. However, these models often involve complex neural architectures and often lack consideration for ne... | {
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2411.08701 | TRACE: Transformer-based Risk Assessment for Clinical Evaluation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | We present TRACE (Transformer-based Risk Assessment for Clinical Evaluation), a novel method for clinical risk assessment based on clinical data, leveraging the self-attention mechanism for enhanced feature interaction and result interpretation. Our approach is able to handle different data modalities, including contin... | {
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2411.08703 | MVKTrans: Multi-View Knowledge Transfer for Robust Multiomics
Classification | [
"cs.LG",
"cs.AI"
] | The distinct characteristics of multiomics data, including complex interactions within and across biological layers and disease heterogeneity (e.g., heterogeneity in etiology and clinical symptoms), drive us to develop novel designs to address unique challenges in multiomics prediction. In this paper, we propose the mu... | {
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2411.08706 | Searching Latent Program Spaces | [
"cs.LG",
"cs.AI"
] | Program synthesis methods aim to automatically generate programs restricted to a language that can explain a given specification of input-output pairs. While purely symbolic approaches suffer from a combinatorial search space, recent methods leverage neural networks to learn distributions over program structures to nar... | {
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2411.08708 | Are Triggers Needed for Document-Level Event Extraction? | [
"cs.CL"
] | Most existing work on event extraction has focused on sentence-level texts and presumes the identification of a trigger-span -- a word or phrase in the input that evokes the occurrence of an event of interest. Event arguments are then extracted with respect to the trigger. Indeed, triggers are treated as integral to, a... | {
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2411.08712 | High-resolution optical and acoustic remote sensing datasets of the Puck
Lagoon, Southern Baltic | [
"cs.CV",
"eess.SP"
] | The very shallow marine basin of Puck Lagoon in the southern Baltic Sea, on the Northern coast of Poland, hosts valuable benthic habitats and cultural heritage sites. These include, among others, protected Zostera marina meadows, one of the Baltic's major medieval harbours, a ship graveyard, and likely other submerged ... | {
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2411.08715 | Retrieval Augmented Recipe Generation | [
"cs.CV"
] | Given the potential applications of generating recipes from food images, this area has garnered significant attention from researchers in recent years. Existing works for recipe generation primarily utilize a two-stage training method, first generating ingredients and then obtaining instructions from both the image and... | {
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2411.08719 | Balancing Speed and Stability: The Trade-offs of FP8 vs. BF16 Training
in LLMs | [
"cs.LG"
] | Large Language Models (LLMs) have attracted significant attention due to their human-like language understanding and generation capabilities, as well as their applicability across various domains. These models, characterized by their massive scale and extensive training data, continue to push the boundaries of what i... | {
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2411.08724 | QCG-Rerank: Chunks Graph Rerank with Query Expansion in
Retrieval-Augmented LLMs for Tourism Domain | [
"cs.CL",
"cs.AI"
] | Retrieval-Augmented Generation (RAG) mitigates the issue of hallucination in Large Language Models (LLMs) by integrating information retrieval techniques. However, in the tourism domain, since the query is usually brief and the content in the database is diverse, existing RAG may contain a significant amount of irrelev... | {
"Other": 0,
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} |
2411.08726 | Analyst Reports and Stock Performance: Evidence from the Chinese Market | [
"cs.CL",
"q-fin.CP"
] | This article applies natural language processing (NLP) to extract and quantify textual information to predict stock performance. Using an extensive dataset of Chinese analyst reports and employing a customized BERT deep learning model for Chinese text, this study categorizes the sentiment of the reports as positive, ne... | {
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} |
2411.08727 | Voxeland: Probabilistic Instance-Aware Semantic Mapping with
Evidence-based Uncertainty Quantification | [
"cs.RO"
] | Robots in human-centered environments require accurate scene understanding to perform high-level tasks effectively. This understanding can be achieved through instance-aware semantic mapping, which involves reconstructing elements at the level of individual instances. Neural networks, the de facto solution for scene un... | {
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} |
2411.08728 | Polymetis:Large Language Modeling for Multiple Material Domains | [
"cs.AI"
] | As the application of large language models in various fields continues to expand, materials science also ushers in opportunities for AI-driven innovation. The traditional way of relying on manual search for materials science-related information is now using artificial intelligence technology as an auxiliary tool to im... | {
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} |
2411.08733 | Dynamic Rewarding with Prompt Optimization Enables Tuning-free
Self-Alignment of Language Models | [
"cs.CL"
] | Aligning Large Language Models (LLMs) traditionally relies on costly training and human preference annotations. Self-alignment seeks to reduce these expenses by enabling models to align themselves. To further lower costs and achieve alignment without any expensive tuning or annotations, we introduce a new tuning-free a... | {
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} |
2411.08734 | Recommender systems and reinforcement learning for human-building
interaction and context-aware support: A text mining-driven review of
scientific literature | [
"eess.SY",
"cs.LG",
"cs.SY"
] | The indoor environment significantly impacts human health and well-being; enhancing health and reducing energy consumption in these settings is a central research focus. With the advancement of Information and Communication Technology (ICT), recommendation systems and reinforcement learning (RL) have emerged as promisi... | {
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} |
2411.08735 | New advances in universal approximation with neural networks of minimal
width | [
"cs.NE",
"math.FA"
] | Deep neural networks have achieved remarkable success in diverse applications, prompting the need for a solid theoretical foundation. Recent research has identified the minimal width $\max\{2,d_x,d_y\}$ required for neural networks with input dimensions $d_x$ and output dimension $d_y$ that use leaky ReLU activations t... | {
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} |
2411.08739 | Bayesian Comparisons Between Representations | [
"cs.LG",
"q-bio.QM"
] | Which neural networks are similar is a fundamental question for both machine learning and neuroscience. Here, I propose to base comparisons on the predictive distributions of linear readouts from intermediate representations. In Bayesian statistics, the prior predictive distribution is a full description of the inducti... | {
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} |
2411.08742 | A Comparative Study of Discrete Speech Tokens for Semantic-Related Tasks
with Large Language Models | [
"cs.CL",
"cs.SD",
"eess.AS"
] | With the rise of Speech Large Language Models (Speech LLMs), there has been growing interest in discrete speech tokens for their ability to integrate with text-based tokens seamlessly. Compared to most studies that focus on continuous speech features, although discrete-token based LLMs have shown promising results on c... | {
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} |
2411.08745 | Separating Tongue from Thought: Activation Patching Reveals
Language-Agnostic Concept Representations in Transformers | [
"cs.CL",
"cs.AI"
] | A central question in multilingual language modeling is whether large language models (LLMs) develop a universal concept representation, disentangled from specific languages. In this paper, we address this question by analyzing latent representations (latents) during a word translation task in transformer-based LLMs. W... | {
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} |
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