id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.07762 | PSReg: Prior-guided Sparse Mixture of Experts for Point Cloud
Registration | [
"cs.CV",
"cs.AI"
] | The discriminative feature is crucial for point cloud registration. Recent methods improve the feature discriminative by distinguishing between non-overlapping and overlapping region points. However, they still face challenges in distinguishing the ambiguous structures in the overlapping regions. Therefore, the ambiguo... | {
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2501.07763 | On the Statistical Capacity of Deep Generative Models | [
"stat.ML",
"cs.AI",
"cs.LG",
"math.ST",
"stat.TH"
] | Deep generative models are routinely used in generating samples from complex, high-dimensional distributions. Despite their apparent successes, their statistical properties are not well understood. A common assumption is that with enough training data and sufficiently large neural networks, deep generative model sample... | {
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2501.07764 | Deep Learning for Disease Outbreak Prediction: A Robust Early Warning
Signal for Transcritical Bifurcations | [
"cs.LG",
"cs.AI"
] | Early Warning Signals (EWSs) are vital for implementing preventive measures before a disease turns into a pandemic. While new diseases exhibit unique behaviors, they often share fundamental characteristics from a dynamical systems perspective. Moreover, measurements during disease outbreaks are often corrupted by diffe... | {
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2501.07765 | PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions
in Physics-Informed Neural Networks | [
"cs.LG",
"physics.comp-ph",
"stat.ML"
] | Physics-Informed Neural Networks (PINNs) solve partial differential equations (PDEs) by embedding governing equations and boundary/initial conditions into the loss function. However, enforcing Dirichlet boundary conditions accurately remains challenging, often leading to soft enforcement that compromises convergence an... | {
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2501.07766 | Large Language Models for Knowledge Graph Embedding Techniques, Methods,
and Challenges: A Survey | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) have attracted a lot of attention in various fields due to their superior performance, aiming to train hundreds of millions or more parameters on large amounts of text data to understand and generate natural language. As the superior performance of LLMs becomes apparent, they are increasing... | {
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2501.07769 | BMIP: Bi-directional Modality Interaction Prompt Learning for VLM | [
"cs.LG",
"cs.CV"
] | Vision-language models (VLMs) have exhibited remarkable generalization capabilities, and prompt learning for VLMs has attracted great attention for the ability to adapt pre-trained VLMs to specific downstream tasks. However, existing studies mainly focus on single-modal prompts or uni-directional modality interaction, ... | {
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2501.07771 | An Empirical Evaluation of Serverless Cloud Infrastructure for
Large-Scale Data Processing | [
"cs.DB"
] | Data processing systems are increasingly deployed in the cloud. While monolithic systems run fully on virtual servers, recent systems embrace cloud infrastructure and utilize the disaggregation of compute and storage to scale them independently. The introduction of serverless compute services, such as AWS Lambda, enabl... | {
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2501.07773 | Symmetry-Aware Generative Modeling through Learned Canonicalization | [
"cs.LG"
] | Generative modeling of symmetric densities has a range of applications in AI for science, from drug discovery to physics simulations. The existing generative modeling paradigm for invariant densities combines an invariant prior with an equivariant generative process. However, we observe that this technique is not neces... | {
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2501.07774 | Transforming Indoor Localization: Advanced Transformer Architecture for
NLOS Dominated Wireless Environments with Distributed Sensors | [
"cs.LG",
"cs.AI",
"eess.SP"
] | Indoor localization in challenging non-line-of-sight (NLOS) environments often leads to mediocre accuracy with traditional approaches. Deep learning (DL) has been applied to tackle these challenges; however, many DL approaches overlook computational complexity, especially for floating-point operations (FLOPs), making t... | {
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2501.07783 | Parameter-Inverted Image Pyramid Networks for Visual Perception and
Multimodal Understanding | [
"cs.CV",
"cs.CL"
] | Image pyramids are widely adopted in top-performing methods to obtain multi-scale features for precise visual perception and understanding. However, current image pyramids use the same large-scale model to process multiple resolutions of images, leading to significant computational cost. To address this challenge, we p... | {
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2501.07793 | Unsupervised Query Routing for Retrieval Augmented Generation | [
"cs.IR"
] | Query routing for retrieval-augmented generation aims to assign an input query to the most suitable search engine. Existing works rely heavily on supervised datasets that require extensive manual annotation, resulting in high costs and limited scalability, as well as poor generalization to out-of-distribution scenarios... | {
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2501.07794 | Linearly Convergent Mixup Learning | [
"cs.LG"
] | Learning in the reproducing kernel Hilbert space (RKHS) such as the support vector machine has been recognized as a promising technique. It continues to be highly effective and competitive in numerous prediction tasks, particularly in settings where there is a shortage of training data or computational limitations exis... | {
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2501.07800 | BioPose: Biomechanically-accurate 3D Pose Estimation from Monocular
Videos | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Recent advancements in 3D human pose estimation from single-camera images and videos have relied on parametric models, like SMPL. However, these models oversimplify anatomical structures, limiting their accuracy in capturing true joint locations and movements, which reduces their applicability in biomechanics, healthca... | {
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2501.07801 | A Comparative Analysis of DNN-based White-Box Explainable AI Methods in
Network Security | [
"cs.CR",
"cs.AI"
] | New research focuses on creating artificial intelligence (AI) solutions for network intrusion detection systems (NIDS), drawing its inspiration from the ever-growing number of intrusions on networked systems, increasing its complexity and intelligibility. Hence, the use of explainable AI (XAI) techniques in real-world ... | {
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2501.07802 | Visual Language Models as Operator Agents in the Space Domain | [
"cs.AI",
"physics.space-ph"
] | This paper explores the application of Vision-Language Models (VLMs) as operator agents in the space domain, focusing on both software and hardware operational paradigms. Building on advances in Large Language Models (LLMs) and their multimodal extensions, we investigate how VLMs can enhance autonomous control and deci... | {
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2501.07804 | Balance Divergence for Knowledge Distillation | [
"cs.CV"
] | Knowledge distillation has been widely adopted in computer vision task processing, since it can effectively enhance the performance of lightweight student networks by leveraging the knowledge transferred from cumbersome teacher networks. Most existing knowledge distillation methods utilize Kullback-Leibler divergence t... | {
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2501.07806 | Learning Motion and Temporal Cues for Unsupervised Video Object
Segmentation | [
"cs.CV"
] | In this paper, we address the challenges in unsupervised video object segmentation (UVOS) by proposing an efficient algorithm, termed MTNet, which concurrently exploits motion and temporal cues. Unlike previous methods that focus solely on integrating appearance with motion or on modeling temporal relations, our method... | {
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2501.07808 | A Low-cost and Ultra-lightweight Binary Neural Network for Traffic
Signal Recognition | [
"cs.AI",
"cs.CV",
"eess.IV"
] | The deployment of neural networks in vehicle platforms and wearable Artificial Intelligence-of-Things (AIOT) scenarios has become a research area that has attracted much attention. With the continuous evolution of deep learning technology, many image classification models are committed to improving recognition accuracy... | {
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2501.07809 | Conformal mapping Coordinates Physics-Informed Neural Networks
(CoCo-PINNs): learning neural networks for designing neutral inclusions | [
"cs.LG",
"cs.AI",
"math.AP"
] | We focus on designing and solving the neutral inclusion problem via neural networks. The neutral inclusion problem has a long history in the theory of composite materials, and it is exceedingly challenging to identify the precise condition that precipitates a general-shaped inclusion into a neutral inclusion. Physics-i... | {
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2501.07810 | AVS-Mamba: Exploring Temporal and Multi-modal Mamba for Audio-Visual
Segmentation | [
"cs.CV"
] | The essence of audio-visual segmentation (AVS) lies in locating and delineating sound-emitting objects within a video stream. While Transformer-based methods have shown promise, their handling of long-range dependencies struggles due to quadratic computational costs, presenting a bottleneck in complex scenarios. To ove... | {
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2501.07813 | Talk to Right Specialists: Routing and Planning in Multi-agent System
for Question Answering | [
"cs.MA",
"cs.AI",
"cs.CL"
] | Leveraging large language models (LLMs), an agent can utilize retrieval-augmented generation (RAG) techniques to integrate external knowledge and increase the reliability of its responses. Current RAG-based agents integrate single, domain-specific knowledge sources, limiting their ability and leading to hallucinated or... | {
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2501.07814 | STTS-EAD: Improving Spatio-Temporal Learning Based Time Series
Prediction via | [
"cs.LG",
"cs.AI"
] | Handling anomalies is a critical preprocessing step in multivariate time series prediction. However, existing approaches that separate anomaly preprocessing from model training for multivariate time series prediction encounter significant limitations. Specifically, these methods fail to utilize auxiliary information cr... | {
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2501.07815 | Agent-Centric Projection of Prompting Techniques and Implications for
Synthetic Training Data for Large Language Models | [
"cs.AI",
"cs.CL",
"cs.MA"
] | Recent advances in prompting techniques and multi-agent systems for Large Language Models (LLMs) have produced increasingly complex approaches. However, we lack a framework for characterizing and comparing prompting techniques or understanding their relationship to multi-agent LLM systems. This position paper introduce... | {
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2501.07818 | A Multi-Encoder Frozen-Decoder Approach for Fine-Tuning Large Language
Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Among parameter-efficient fine-tuning methods, freezing has emerged as a popular strategy for speeding up training, reducing catastrophic forgetting, and improving downstream performance. We investigate the impact of freezing the decoder in a multi-task setup comprising diverse natural language tasks, aiming to reduce ... | {
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2501.07819 | 3UR-LLM: An End-to-End Multimodal Large Language Model for 3D Scene
Understanding | [
"cs.CV"
] | Multi-modal Large Language Models (MLLMs) exhibit impressive capabilities in 2D tasks, yet encounter challenges in discerning the spatial positions, interrelations, and causal logic in scenes when transitioning from 2D to 3D representations. We find that the limitations mainly lie in: i) the high annotation cost restri... | {
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2501.07824 | Real-time Verification and Refinement of Language Model Text Generation | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Large language models (LLMs) have shown remarkable performance across a wide range of natural language tasks. However, a critical challenge remains in that they sometimes generate factually incorrect answers. To address this, while many previous work has focused on identifying errors in their generation and further ref... | {
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2501.07827 | Prediction Interval Construction Method for Electricity Prices | [
"cs.LG",
"cs.SY",
"eess.SY"
] | Accurate prediction of electricity prices plays an essential role in the electricity market. To reflect the uncertainty of electricity prices, price intervals are predicted. This paper proposes a novel prediction interval construction method. A conditional generative adversarial network is first presented to generate e... | {
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2501.07832 | Low-Contact Grasping of Soft Tissue with Complex Geometry using a Vortex
Gripper | [
"cs.RO"
] | Soft tissue manipulation is an integral aspect of most surgical procedures; however, the vast majority of surgical graspers used today are made of hard materials, such as metals or hard plastics. Furthermore, these graspers predominately function by pinching tissue between two hard objects as a method for tissue manipu... | {
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2501.07834 | Flow: A Modular Approach to Automated Agentic Workflow Generation | [
"cs.AI",
"cs.LG",
"cs.MA"
] | Multi-agent frameworks powered by large language models (LLMs) have demonstrated great success in automated planning and task execution. However, the effective adjustment of Agentic workflows during execution has not been well-studied. A effective workflow adjustment is crucial, as in many real-world scenarios, the ini... | {
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2501.07837 | A Driver Advisory System Based on Large Language Model for High-speed
Train | [
"cs.AI"
] | With the rapid development of China high-speed railway, drivers face increasingly significant technical challenges during operations, such as fault handling. Currently, drivers depend on the onboard mechanic when facing technical issues, for instance, traction loss or sensor faults. This dependency can hinder effective... | {
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2501.07839 | Social Media Data Mining With Natural Language Processing on Public
Dream Contents | [
"cs.CY",
"cs.AI",
"cs.CL",
"cs.SI"
] | The COVID-19 pandemic has significantly transformed global lifestyles, enforcing physical isolation and accelerating digital adoption for work, education, and social interaction. This study examines the pandemic's impact on mental health by analyzing dream content shared on the Reddit r/Dreams community. With over 374,... | {
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2501.07845 | Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs
Reasoning | [
"cs.CL"
] | Large language models (LLMs) have demonstrated remarkable success across a wide range of tasks; however, they still encounter challenges in reasoning tasks that require understanding and inferring relationships between distinct pieces of information within text sequences. This challenge is particularly pronounced in ta... | {
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2501.07849 | Unveiling Provider Bias in Large Language Models for Code Generation | [
"cs.SE",
"cs.AI",
"cs.CR"
] | Large Language Models (LLMs) have emerged as the new recommendation engines, outperforming traditional methods in both capability and scope, particularly in code generation applications. Our research reveals a novel provider bias in LLMs, namely without explicit input prompts, these models show systematic preferences f... | {
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2501.07850 | An Intra- and Cross-frame Topological Consistency Scheme for
Semi-supervised Atherosclerotic Coronary Plaque Segmentation | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Enhancing the precision of segmenting coronary atherosclerotic plaques from CT Angiography (CTA) images is pivotal for advanced Coronary Atherosclerosis Analysis (CAA), which distinctively relies on the analysis of vessel cross-section images reconstructed via Curved Planar Reformation. This task presents significant c... | {
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2501.07853 | Optimizing Language Models for Grammatical Acceptability: A Comparative
Study of Fine-Tuning Techniques | [
"cs.CL",
"cs.AI"
] | This study explores the fine-tuning (FT) of the Open Pre-trained Transformer (OPT-125M) for grammatical acceptability tasks using the CoLA dataset. By comparing Vanilla-Fine-Tuning (VFT), Pattern-Based-Fine-Tuning (PBFT), and Parameter-Efficient Fine-Tuning techniques (PEFT) like Low-Rank Adaptation (LoRA), we demonstr... | {
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2501.07855 | State-of-the-Art Transformer Models for Image Super-Resolution:
Techniques, Challenges, and Applications | [
"cs.CV",
"cs.AI",
"cs.ET",
"cs.LG",
"cs.NE"
] | Image Super-Resolution (SR) aims to recover a high-resolution image from its low-resolution counterpart, which has been affected by a specific degradation process. This is achieved by enhancing detail and visual quality. Recent advancements in transformer-based methods have remolded image super-resolution by enabling h... | {
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2501.07857 | Hierarchical Repository-Level Code Summarization for Business
Applications Using Local LLMs | [
"cs.SE",
"cs.AI"
] | In large-scale software development, understanding the functionality and intent behind complex codebases is critical for effective development and maintenance. While code summarization has been widely studied, existing methods primarily focus on smaller code units, such as functions, and struggle with larger code artif... | {
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2501.07859 | deepTerra -- AI Land Classification Made Easy | [
"cs.CV",
"cs.AI",
"cs.LG"
] | deepTerra is a comprehensive platform designed to facilitate the classification of land surface features using machine learning and satellite imagery. The platform includes modules for data collection, image augmentation, training, testing, and prediction, streamlining the entire workflow for image classification tasks... | {
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2501.07861 | ReARTeR: Retrieval-Augmented Reasoning with Trustworthy Process
Rewarding | [
"cs.CL"
] | Retrieval-Augmented Generation (RAG) systems for Large Language Models (LLMs) hold promise in knowledge-intensive tasks but face limitations in complex multi-step reasoning. While recent methods have integrated RAG with chain-of-thought reasoning or test-time search using Process Reward Models (PRMs), these approaches ... | {
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2501.07870 | Make-A-Character 2: Animatable 3D Character Generation From a Single
Image | [
"cs.CV"
] | This report introduces Make-A-Character 2, an advanced system for generating high-quality 3D characters from single portrait photographs, ideal for game development and digital human applications. Make-A-Character 2 builds upon its predecessor by incorporating several significant improvements for image-based head gener... | {
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2501.07875 | Continual Learning with Embedding Layer Surgery and Task-wise Beam
Search using Whisper | [
"cs.CL",
"cs.AI"
] | Current Multilingual ASR models only support a fraction of the world's languages. Continual Learning (CL) aims to tackle this problem by adding new languages to pre-trained models while avoiding the loss of performance on existing languages, also known as Catastrophic Forgetting (CF). However, existing CL methods overl... | {
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2501.07879 | Distributed Nonparametric Estimation: from Sparse to Dense Samples per
Terminal | [
"cs.LG",
"cs.IT",
"math.IT",
"math.ST",
"stat.TH"
] | Consider the communication-constrained problem of nonparametric function estimation, in which each distributed terminal holds multiple i.i.d. samples. Under certain regularity assumptions, we characterize the minimax optimal rates for all regimes, and identify phase transitions of the optimal rates as the samples per t... | {
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2501.07884 | MD-Syn: Synergistic drug combination prediction based on the
multidimensional feature fusion method and attention mechanisms | [
"cs.LG",
"q-bio.QM"
] | Drug combination therapies have shown promising therapeutic efficacy in complex diseases and have demonstrated the potential to reduce drug resistance. However, the huge number of possible drug combinations makes it difficult to screen them all in traditional experiments. In this study, we proposed MD-Syn, a computatio... | {
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2501.07885 | Mitigating Algorithmic Bias in Multiclass CNN Classifications Using
Causal Modeling | [
"cs.LG",
"cs.CV"
] | This study describes a procedure for applying causal modeling to detect and mitigate algorithmic bias in a multiclass classification problem. The dataset was derived from the FairFace dataset, supplemented with emotional labels generated by the DeepFace pre-trained model. A custom Convolutional Neural Network (CNN) was... | {
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2501.07886 | Iterative Label Refinement Matters More than Preference Optimization
under Weak Supervision | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Language model (LM) post-training relies on two stages of human supervision: task demonstrations for supervised finetuning (SFT), followed by preference comparisons for reinforcement learning from human feedback (RLHF). As LMs become more capable, the tasks they are given become harder to supervise. Will post-training ... | {
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2501.07888 | Tarsier2: Advancing Large Vision-Language Models from Detailed Video
Description to Comprehensive Video Understanding | [
"cs.CV",
"cs.AI"
] | We introduce Tarsier2, a state-of-the-art large vision-language model (LVLM) designed for generating detailed and accurate video descriptions, while also exhibiting superior general video understanding capabilities. Tarsier2 achieves significant advancements through three key upgrades: (1) Scaling pre-training data fro... | {
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2501.07890 | GRAPHMOE: Amplifying Cognitive Depth of Mixture-of-Experts Network via
Introducing Self-Rethinking Mechanism | [
"cs.CL",
"cs.AI"
] | Traditional Mixture-of-Experts (MoE) networks benefit from utilizing multiple smaller expert models as opposed to a single large network. However, these experts typically operate independently, leaving a question open about whether interconnecting these models could enhance the performance of MoE networks. In response,... | {
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2501.07892 | Leveraging Metamemory Mechanisms for Enhanced Data-Free Code Generation
in LLMs | [
"cs.SE",
"cs.AI"
] | Automated code generation using large language models (LLMs) has gained attention due to its efficiency and adaptability. However, real-world coding tasks or benchmarks like HumanEval and StudentEval often lack dedicated training datasets, challenging existing few-shot prompting approaches that rely on reference exampl... | {
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2501.07896 | Anytime Cooperative Implicit Hitting Set Solving | [
"cs.AI"
] | The Implicit Hitting Set (HS) approach has shown to be very effective for MaxSAT, Pseudo-boolean optimization and other boolean frameworks. Very recently, it has also shown its potential in the very similar Weighted CSP framework by means of the so-called cost-function merging. The original formulation of the HS approa... | {
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2501.07898 | Demographic Variability in Face Image Quality Measures | [
"cs.CV"
] | Face image quality assessment (FIQA) algorithms are being integrated into online identity management applications. These applications allow users to upload a face image as part of their document issuance process, where the image is then run through a quality assessment process to make sure it meets the quality and comp... | {
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2501.07901 | Cloud Removal With PolSAR-Optical Data Fusion Using A Two-Flow Residual
Network | [
"cs.CV",
"eess.IV"
] | Optical remote sensing images play a crucial role in the observation of the Earth's surface. However, obtaining complete optical remote sensing images is challenging due to cloud cover. Reconstructing cloud-free optical images has become a major task in recent years. This paper presents a two-flow Polarimetric Syntheti... | {
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2501.07903 | Optimal Classification Trees for Continuous Feature Data Using Dynamic
Programming with Branch-and-Bound | [
"cs.LG",
"cs.AI",
"cs.DS"
] | Computing an optimal classification tree that provably maximizes training performance within a given size limit, is NP-hard, and in practice, most state-of-the-art methods do not scale beyond computing optimal trees of depth three. Therefore, most methods rely on a coarse binarization of continuous features to maintain... | {
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2501.07905 | Logarithmic Memory Networks (LMNs): Efficient Long-Range Sequence
Modeling for Resource-Constrained Environments | [
"cs.AI",
"cs.LG"
] | Long-range sequence modeling is a crucial aspect of natural language processing and time series analysis. However, traditional models like Recurrent Neural Networks (RNNs) and Transformers suffer from computational and memory inefficiencies, especially when dealing with long sequences. This paper introduces Logarithmic... | {
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2501.07911 | Deep Learning and Natural Language Processing in the Field of
Construction | [
"cs.AI"
] | This article presents a complete process to extract hypernym relationships in the field of construction using two main steps: terminology extraction and detection of hypernyms from these terms. We first describe the corpus analysis method to extract terminology from a collection of technical specifications in the field... | {
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2501.07913 | Governing AI Agents | [
"cs.AI"
] | The field of AI is undergoing a fundamental transition from generative models that can produce synthetic content to artificial agents that can plan and execute complex tasks with only limited human involvement. Companies that pioneered the development of language models have now built AI agents that can independently n... | {
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2501.07919 | Large Language Model Interface for Home Energy Management Systems | [
"cs.AI"
] | Home Energy Management Systems (HEMSs) help households tailor their electricity usage based on power system signals such as energy prices. This technology helps to reduce energy bills and offers greater demand-side flexibility that supports the power system stability. However, residents who lack a technical background ... | {
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2501.07922 | VENOM: Text-driven Unrestricted Adversarial Example Generation with
Diffusion Models | [
"cs.CV"
] | Adversarial attacks have proven effective in deceiving machine learning models by subtly altering input images, motivating extensive research in recent years. Traditional methods constrain perturbations within $l_p$-norm bounds, but advancements in Unrestricted Adversarial Examples (UAEs) allow for more complex, genera... | {
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2501.07923 | Aviation Safety Enhancement via NLP & Deep Learning: Classifying Flight
Phases in ATSB Safety Reports | [
"cs.LG",
"cs.CL"
] | Aviation safety is paramount, demanding precise analysis of safety occurrences during different flight phases. This study employs Natural Language Processing (NLP) and Deep Learning models, including LSTM, CNN, Bidirectional LSTM (BLSTM), and simple Recurrent Neural Networks (sRNN), to classify flight phases in safety ... | {
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2501.07924 | Exploring Aviation Incident Narratives Using Topic Modeling and
Clustering Techniques | [
"cs.AI",
"cs.CL"
] | Aviation safety is a global concern, requiring detailed investigations into incidents to understand contributing factors comprehensively. This study uses the National Transportation Safety Board (NTSB) dataset. It applies advanced natural language processing (NLP) techniques, including Latent Dirichlet Allocation (LDA)... | {
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2501.07925 | Phase of Flight Classification in Aviation Safety using LSTM, GRU, and
BiLSTM: A Case Study with ASN Dataset | [
"cs.LG"
] | Safety is the main concern in the aviation industry, where even minor operational issues can lead to serious consequences. This study addresses the need for comprehensive aviation accident analysis by leveraging natural language processing (NLP) and advanced AI models to classify the phase of flight from unstructured a... | {
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2501.07927 | Gandalf the Red: Adaptive Security for LLMs | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CR"
] | Current evaluations of defenses against prompt attacks in large language model (LLM) applications often overlook two critical factors: the dynamic nature of adversarial behavior and the usability penalties imposed on legitimate users by restrictive defenses. We propose D-SEC (Dynamic Security Utility Threat Model), whi... | {
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2501.07930 | An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN
Architectures | [
"cs.AI",
"cs.NE"
] | Orthogonal convolutional layers are the workhorse of multiple areas in machine learning, such as adversarial robustness, normalizing flows, GANs, and Lipschitzconstrained models. Their ability to preserve norms and ensure stable gradient propagation makes them valuable for a large range of problems. Despite their promi... | {
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2501.07931 | Advice for Diabetes Self-Management by ChatGPT Models: Challenges and
Recommendations | [
"cs.AI"
] | Given their ability for advanced reasoning, extensive contextual understanding, and robust question-answering abilities, large language models have become prominent in healthcare management research. Despite adeptly handling a broad spectrum of healthcare inquiries, these models face significant challenges in deliverin... | {
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2501.07945 | Early prediction of the transferability of bovine embryos from
videomicroscopy | [
"eess.IV",
"cs.AI",
"cs.CV",
"q-bio.QM"
] | Videomicroscopy is a promising tool combined with machine learning for studying the early development of in vitro fertilized bovine embryos and assessing its transferability as soon as possible. We aim to predict the embryo transferability within four days at most, taking 2D time-lapse microscopy videos as input. We fo... | {
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2501.07947 | "Wait, did you mean the doctor?": Collecting a Dialogue Corpus for
Topical Analysis | [
"cs.CL"
] | Dialogue is at the core of human behaviour and being able to identify the topic at hand is crucial to take part in conversation. Yet, there are few accounts of the topical organisation in casual dialogue and of how people recognise the current topic in the literature. Moreover, analysing topics in dialogue requires con... | {
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2501.07948 | Synchronization of Kuramoto oscillators via HEOL, and a discussion on AI | [
"math.OC",
"cs.SY",
"eess.SY"
] | Artificial neural networks and their applications in deep learning have recently made an incursion into the field of control. Deep learning techniques in control are often related to optimal control, which relies on Pontryagin maximum principle or the Hamilton-Jacobi-Bellman equation. They imply control schemes that ar... | {
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2501.07952 | Spiking Neural Network Accelerator Architecture for Differential-Time
Representation using Learned Encoding | [
"cs.NE",
"eess.SP"
] | Spiking Neural Networks (SNNs) have garnered attention over recent years due to their increased energy efficiency and advantages in terms of operational complexity compared to traditional Artificial Neural Networks (ANNs). Two important questions when implementing SNNs are how to best encode existing data into spike tr... | {
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2501.07953 | Robust Hyperspectral Image Panshapring via Sparse Spatial-Spectral
Representation | [
"cs.CV",
"eess.IV"
] | High-resolution hyperspectral imaging plays a crucial role in various remote sensing applications, yet its acquisition often faces fundamental limitations due to hardware constraints. This paper introduces S$^{3}$RNet, a novel framework for hyperspectral image pansharpening that effectively combines low-resolution hype... | {
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2501.07954 | Many-Objective Neuroevolution for Testing Games | [
"cs.SE",
"cs.NE"
] | Generating tests for games is challenging due to the high degree of randomisation inherent to games and hard-to-reach program states that require sophisticated gameplay. The test generator NEATEST tackles these challenges by combining search-based software testing principles with neuroevolution to optimise neural netwo... | {
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2501.07957 | AI Guide Dog: Egocentric Path Prediction on Smartphone | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.HC",
"cs.LG"
] | This paper presents AI Guide Dog (AIGD), a lightweight egocentric (first-person) navigation system for visually impaired users, designed for real-time deployment on smartphones. AIGD employs a vision-only multi-label classification approach to predict directional commands, ensuring safe navigation across diverse enviro... | {
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2501.07959 | Self-Instruct Few-Shot Jailbreaking: Decompose the Attack into Pattern
and Behavior Learning | [
"cs.AI"
] | Recently, several works have been conducted on jailbreaking Large Language Models (LLMs) with few-shot malicious demos. In particular, Zheng et al. focus on improving the efficiency of Few-Shot Jailbreaking (FSJ) by injecting special tokens into the demos and employing demo-level random search, known as Improved Few-Sh... | {
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2501.07960 | SkipClick: Combining Quick Responses and Low-Level Features for
Interactive Segmentation in Winter Sports Contexts | [
"cs.CV"
] | In this paper, we present a novel architecture for interactive segmentation in winter sports contexts. The field of interactive segmentation deals with the prediction of high-quality segmentation masks by informing the network about the objects position with the help of user guidance. In our case the guidance consists ... | {
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2501.07964 | Derivation of Output Correlation Inferences for Multi-Output (aka
Multi-Task) Gaussian Process | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Gaussian process (GP) is arguably one of the most widely used machine learning algorithms in practice. One of its prominent applications is Bayesian optimization (BO). Although the vanilla GP itself is already a powerful tool for BO, it is often beneficial to be able to consider the dependencies of multiple outputs. To... | {
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2501.07970 | Comprehensive Metapath-based Heterogeneous Graph Transformer for
Gene-Disease Association Prediction | [
"cs.AI"
] | Discovering gene-disease associations is crucial for understanding disease mechanisms, yet identifying these associations remains challenging due to the time and cost of biological experiments. Computational methods are increasingly vital for efficient and scalable gene-disease association prediction. Graph-based learn... | {
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2501.07972 | Zero-shot Video Moment Retrieval via Off-the-shelf Multimodal Large
Language Models | [
"cs.MM",
"cs.CV"
] | The target of video moment retrieval (VMR) is predicting temporal spans within a video that semantically match a given linguistic query. Existing VMR methods based on multimodal large language models (MLLMs) overly rely on expensive high-quality datasets and time-consuming fine-tuning. Although some recent studies intr... | {
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2501.07973 | An Open Source Validation System for Continuous Arterial Blood Pressure
Measuring Sensors | [
"physics.med-ph",
"cs.SY",
"eess.SY"
] | Measuring the blood pressure waveform is becoming a more frequently studied area. The development of sensor technologies opens many new ways to be able to measure high-quality signals. The development of such an aim-specific sensor can be time-consuming, expensive, and difficult to test or validate with known and consi... | {
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2501.07975 | Some observations on the ambivalent role of symmetries in Bayesian
inference problems | [
"cond-mat.dis-nn",
"cond-mat.stat-mech",
"cs.IT",
"math.IT",
"math.PR",
"math.ST",
"stat.TH"
] | We collect in this note some observations on the role of symmetries in Bayesian inference problems, that can be useful or detrimental depending on the way they act on the signal and on the observations. We emphasize in particular the need to gauge away unobservable invariances in the definition of a distance between a ... | {
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2501.07978 | Facial Dynamics in Video: Instruction Tuning for Improved Facial
Expression Perception and Contextual Awareness | [
"cs.CV",
"cs.AI"
] | Facial expression captioning has found widespread application across various domains. Recently, the emergence of video Multimodal Large Language Models (MLLMs) has shown promise in general video understanding tasks. However, describing facial expressions within videos poses two major challenges for these models: (1) th... | {
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2501.07981 | A resource management approach for concurrent operation of RF
functionalities | [
"eess.SP",
"cs.SY",
"eess.SY"
] | Future multifunction RF systems will be able to not only perform various different radar, communication and electronic warfare functionalities but also to perform them simultaneously on the same aperture. This ability of concurrent operations requires new, cognitive approaches of resource management compared to classic... | {
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2501.07983 | V-Trans4Style: Visual Transition Recommendation for Video Production
Style Adaptation | [
"cs.CV"
] | We introduce V-Trans4Style, an innovative algorithm tailored for dynamic video content editing needs. It is designed to adapt videos to different production styles like documentaries, dramas, feature films, or a specific YouTube channel's video-making technique. Our algorithm recommends optimal visual transitions to he... | {
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2501.07984 | Threshold Attention Network for Semantic Segmentation of Remote Sensing
Images | [
"cs.CV"
] | Semantic segmentation of remote sensing images is essential for various applications, including vegetation monitoring, disaster management, and urban planning. Previous studies have demonstrated that the self-attention mechanism (SA) is an effective approach for designing segmentation networks that can capture long-ran... | {
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2501.07985 | CHEQ-ing the Box: Safe Variable Impedance Learning for Robotic Polishing | [
"cs.RO",
"cs.LG"
] | Robotic systems are increasingly employed for industrial automation, with contact-rich tasks like polishing requiring dexterity and compliant behaviour. These tasks are difficult to model, making classical control challenging. Deep reinforcement learning (RL) offers a promising solution by enabling the learning of mode... | {
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2501.07988 | GAC-Net_Geometric and attention-based Network for Depth Completion | [
"cs.CV",
"cs.AI"
] | Depth completion is a key task in autonomous driving, aiming to complete sparse LiDAR depth measurements into high-quality dense depth maps through image guidance. However, existing methods usually treat depth maps as an additional channel of color images, or directly perform convolution on sparse data, failing to full... | {
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2501.07991 | Training Hybrid Neural Networks with Multimode Optical Nonlinearities
Using Digital Twins | [
"physics.optics",
"cs.AI"
] | The ability to train ever-larger neural networks brings artificial intelligence to the forefront of scientific and technical discoveries. However, their exponentially increasing size creates a proportionally greater demand for energy and computational hardware. Incorporating complex physical events in networks as fixed... | {
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2501.07992 | LLM-Ehnanced Holonic Architecture for Ad-Hoc Scalable SoS | [
"cs.AI",
"cs.ET",
"cs.MA",
"cs.SE"
] | As modern system of systems (SoS) become increasingly adaptive and human centred, traditional architectures often struggle to support interoperability, reconfigurability, and effective human system interaction. This paper addresses these challenges by advancing the state of the art holonic architecture for SoS, offerin... | {
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2501.07994 | Combining imaging and shape features for prediction tasks of Alzheimer's
disease classification and brain age regression | [
"cs.CV",
"cs.LG",
"eess.IV"
] | We investigate combining imaging and shape features extracted from MRI for the clinically relevant tasks of brain age prediction and Alzheimer's disease classification. Our proposed model fuses ResNet-extracted image embeddings with shape embeddings from a bespoke graph neural network. The shape embeddings are derived ... | {
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2501.07995 | MMAPs to model complex multi-state systems with vacation policies in the
repair facility | [
"stat.ME",
"cs.SY",
"eess.SY"
] | Two complex multi-state systems subject to multiple events are built in an algorithmic and computational way by considering phase-type distributions and Markovian arrival processes with marked arrivals. The internal performance of the system is composed of different degradation levels and internal repairable and non-re... | {
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2501.07996 | Reward Compatibility: A Framework for Inverse RL | [
"cs.LG"
] | We provide an original theoretical study of Inverse Reinforcement Learning (IRL) through the lens of reward compatibility, a novel framework to quantify the compatibility of a reward with the given expert's demonstrations. Intuitively, a reward is more compatible with the demonstrations the closer the performance of th... | {
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2501.07999 | Unsupervised Feature Construction for Anomaly Detection in Time Series
-- An Evaluation | [
"cs.LG"
] | To detect anomalies with precision and without prior knowledge in time series, is it better to build a detector from the initial temporal representation, or to compute a new (tabular) representation using an existing automatic variable construction library? In this article, we address this question by conducting an in-... | {
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2501.08001 | GDiffRetro: Retrosynthesis Prediction with Dual Graph Enhanced Molecular
Representation and Diffusion Generation | [
"cs.AI"
] | Retrosynthesis prediction focuses on identifying reactants capable of synthesizing a target product. Typically, the retrosynthesis prediction involves two phases: Reaction Center Identification and Reactant Generation. However, we argue that most existing methods suffer from two limitations in the two phases: (i) Exist... | {
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"cs.SY": 0
} |
2501.08002 | Maximizing Uncertainty for Federated learning via Bayesian
Optimisation-based Model Poisoning | [
"cs.LG",
"cs.AI",
"cs.CV"
] | As we transition from Narrow Artificial Intelligence towards Artificial Super Intelligence, users are increasingly concerned about their privacy and the trustworthiness of machine learning (ML) technology. A common denominator for the metrics of trustworthiness is the quantification of uncertainty inherent in DL algori... | {
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} |
2501.08003 | Formalising lexical and syntactic diversity for data sampling in French | [
"cs.CL"
] | Diversity is an important property of datasets and sampling data for diversity is useful in dataset creation. Finding the optimally diverse sample is expensive, we therefore present a heuristic significantly increasing diversity relative to random sampling. We also explore whether different kinds of diversity -- lexica... | {
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} |
2501.08004 | Bridging financial gaps for infrastructure climate adaptation via
integrated carbon markets | [
"econ.GN",
"cs.CE",
"q-fin.EC"
] | Climate physical risks pose an increasing threat to urban infrastructure, necessitating urgent climate adaptation measures to protect lives and assets. Implementing such measures, including the development of resilient infrastructure and retrofitting existing systems, demands substantial financial investment. Unfortuna... | {
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} |
2501.08005 | DisCoPatch: Batch Statistics Are All You Need For OOD Detection, But
Only If You Can Trust Them | [
"cs.CV",
"cs.AI",
"eess.IV"
] | Out-of-distribution (OOD) detection holds significant importance across many applications. While semantic and domain-shift OOD problems are well-studied, this work focuses on covariate shifts - subtle variations in the data distribution that can degrade machine learning performance. We hypothesize that detecting these ... | {
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} |
2501.08008 | TriAdaptLoRA: Brain-Inspired Triangular Adaptive Low-Rank Adaptation for
Parameter-Efficient Fine-Tuning | [
"cs.CL",
"cs.AI"
] | The fine-tuning of Large Language Models (LLMs) is pivotal for achieving optimal performance across diverse downstream tasks. However, while full fine-tuning delivers superior results, it entails significant computational and resource costs. Parameter-Efficient Fine-Tuning (PEFT) methods, such as LoRA, address these ch... | {
"Other": 0,
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} |
2501.08009 | Tutorial: VAE as an inference paradigm for neuroimaging | [
"eess.IV",
"cs.AI"
] | In this tutorial, we explore Variational Autoencoders (VAEs), an essential framework for unsupervised learning, particularly suited for high-dimensional datasets such as neuroimaging. By integrating deep learning with Bayesian inference, VAEs enable the generation of interpretable latent representations. This tutorial ... | {
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} |
2501.08019 | An AI-driven framework for rapid and localized optimizations of urban
open spaces | [
"cs.LG",
"cs.AI",
"cs.CY"
] | As urbanization accelerates, open spaces are increasingly recognized for their role in enhancing sustainability and well-being, yet they remain underexplored compared to built spaces. This study introduces an AI-driven framework that integrates machine learning models (MLMs) and explainable AI techniques to optimize Sk... | {
"Other": 0,
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"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.08020 | Cooperative Patrol Routing: Optimizing Urban Crime Surveillance through
Multi-Agent Reinforcement Learning | [
"cs.AI"
] | The effective design of patrol strategies is a difficult and complex problem, especially in medium and large areas. The objective is to plan, in a coordinated manner, the optimal routes for a set of patrols in a given area, in order to achieve maximum coverage of the area, while also trying to minimize the number of pa... | {
"Other": 0,
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"cs.RO": 0,
"cs.SD": 0,
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"cs.SY": 0
} |
2501.08025 | Analysis of Power Losses and the Efficacy of Power Minimization
Strategies in Multichannel Electrical Stimulation Systems | [
"eess.SY",
"cs.SY"
] | Neuroprosthetic devices require multichannel stimulator systems with an increasing number of channels. However, there are inherent power losses in typical multichannel stimulation circuits caused by a mismatch between the power supply voltage and the voltage required at each electrode to successfully stimulate tissue. ... | {
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} |
2501.08026 | Orthogonal Delay-Doppler Division Multiplexing Modulation with
Hierarchical Mode-Based Index Modulation | [
"eess.SP",
"cs.IT",
"math.IT"
] | The orthogonal time frequency space with index modulation (OTFS-IM) offers flexible tradeoffs between spectral efficiency (SE) and bit error rate (BER) in doubly selective fading channels. While OTFS-IM schemes demonstrated such potential, a persistent challenge lies in the detection complexity. To address this problem... | {
"Other": 0,
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
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