id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.09289 | StreamAdapter: Efficient Test Time Adaptation from Contextual Streams | [
"cs.CL",
"cs.AI"
] | In-context learning (ICL) allows large language models (LLMs) to adapt to new tasks directly from the given demonstrations without requiring gradient updates. While recent advances have expanded context windows to accommodate more demonstrations, this approach increases inference costs without necessarily improving per... | {
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2411.09293 | LLV-FSR: Exploiting Large Language-Vision Prior for Face
Super-resolution | [
"cs.CV"
] | Existing face super-resolution (FSR) methods have made significant advancements, but they primarily super-resolve face with limited visual information, original pixel-wise space in particular, commonly overlooking the pluralistic clues, like the higher-order depth and semantics, as well as non-visual inputs (text capti... | {
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2411.09294 | Learning Hand State Estimation for a Light Exoskeleton | [
"cs.RO",
"cs.AI"
] | We propose a machine learning-based estimator of the hand state for rehabilitation purposes, using light exoskeletons. These devices are easy to use and useful for delivering domestic and frequent therapies. We build a supervised approach using information from the muscular activity of the forearm and the motion of the... | {
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2411.09296 | Enhancing generalization in high energy physics using white-box
adversarial attacks | [
"hep-ph",
"cs.LG"
] | Machine learning is becoming increasingly popular in the context of particle physics. Supervised learning, which uses labeled Monte Carlo (MC) simulations, remains one of the most widely used methods for discriminating signals beyond the Standard Model. However, this paper suggests that supervised models may depend exc... | {
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2411.09297 | DTELS: Towards Dynamic Granularity of Timeline Summarization | [
"cs.CL"
] | The rapid proliferation of online news has posed significant challenges in tracking the continuous development of news topics. Traditional timeline summarization constructs a chronological summary of the events but often lacks the flexibility to meet the diverse granularity needs. To overcome this limitation, we introd... | {
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2411.09299 | Hearing the Robot's Mind: Sonification for Explicit Feedback in
Human-Robot Interaction | [
"cs.RO"
] | Social robots are required not only to understand human intentions but also to effectively communicate their intentions or own internal states to users. This study explores the use of sonification to provide explicit auditory feedback, enhancing mutual understanding in HRI. We introduce a novel sonification approach th... | {
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2411.09301 | LHRS-Bot-Nova: Improved Multimodal Large Language Model for Remote
Sensing Vision-Language Interpretation | [
"cs.CV"
] | Automatically and rapidly understanding Earth's surface is fundamental to our grasp of the living environment and informed decision-making. This underscores the need for a unified system with comprehensive capabilities in analyzing Earth's surface to address a wide range of human needs. The emergence of multimodal larg... | {
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2411.09302 | EEG-Based Speech Decoding: A Novel Approach Using Multi-Kernel Ensemble
Diffusion Models | [
"cs.SD",
"cs.AI",
"eess.AS",
"eess.SP"
] | In this study, we propose an ensemble learning framework for electroencephalogram-based overt speech classification, leveraging denoising diffusion probabilistic models with varying convolutional kernel sizes. The ensemble comprises three models with kernel sizes of 51, 101, and 201, effectively capturing multi-scale t... | {
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2411.09307 | Model-Based Event-Triggered Implementation of Hybrid Controllers Using
Finite-Time Convergent Observers | [
"eess.SY",
"cs.SY"
] | In this paper, we explore the conditions for asymptotic stability of the hybrid closed-loop system resulting from the interconnection of a nonlinear plant, an intelligent sensor that generates finite-time convergent estimates of the plant state, and a controller node that receives opportunistic samples from the sensor ... | {
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2411.09308 | DT-JRD: Deep Transformer based Just Recognizable Difference Prediction
Model for Video Coding for Machines | [
"eess.IV",
"cs.CV"
] | Just Recognizable Difference (JRD) represents the minimum visual difference that is detectable by machine vision, which can be exploited to promote machine vision oriented visual signal processing. In this paper, we propose a Deep Transformer based JRD (DT-JRD) prediction model for Video Coding for Machines (VCM), wher... | {
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2411.09310 | Exploring Zero-Shot Anomaly Detection with CLIP in Medical Imaging: Are
We There Yet? | [
"cs.CV"
] | Zero-shot anomaly detection (ZSAD) offers potential for identifying anomalies in medical imaging without task-specific training. In this paper, we evaluate CLIP-based models, originally developed for industrial tasks, on brain tumor detection using the BraTS-MET dataset. Our analysis examines their ability to detect me... | {
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2411.09311 | Compression Method for Solar Polarization Spectra Collected from Hinode
SOT/SP Observations | [
"cs.LG",
"astro-ph.IM",
"astro-ph.SR"
] | The complex structure and extensive details of solar spectral data, combined with a recent surge in volume, present significant processing challenges. To address this, we propose a deep learning-based compression technique using deep autoencoder (DAE) and 1D-convolutional autoencoder (CAE) models developed with Hinode ... | {
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2411.09312 | Approximate Probabilistic Inference for Time-Series Data A Robust Latent
Gaussian Model With Temporal Awareness | [
"cs.LG"
] | The development of robust generative models for highly varied non-stationary time series data is a complex yet important problem. Traditional models for time series data prediction, such as Long Short-Term Memory (LSTM), are inefficient and generalize poorly as they cannot capture complex temporal relationships. In thi... | {
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2411.09317 | Pie: Pooling CPU Memory for LLM Inference | [
"cs.LG",
"cs.DC"
] | The rapid growth of LLMs has revolutionized natural language processing and AI analysis, but their increasing size and memory demands present significant challenges. A common solution is to spill over to CPU memory; however, traditional GPU-CPU memory swapping often results in higher latency and lower throughput. Thi... | {
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2411.09318 | DriveThru: a Document Extraction Platform and Benchmark Datasets for
Indonesian Local Language Archives | [
"cs.CL"
] | Indonesia is one of the most diverse countries linguistically. However, despite this linguistic diversity, Indonesian languages remain underrepresented in Natural Language Processing (NLP) research and technologies. In the past two years, several efforts have been conducted to construct NLP resources for Indonesian lan... | {
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2411.09320 | AMARETTO: Enabling Efficient Quantum Algorithm Emulation on Low-Tier
FPGAs | [
"quant-ph",
"cs.SY",
"eess.SY"
] | Researchers and industries are increasingly drawn to quantum computing for its computational potential. However, validating new quantum algorithms is challenging due to the limitations of current quantum devices. Software simulators are time and memory-consuming, making hardware emulators an attractive alternative. Thi... | {
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2411.09329 | Improving hp-Variational Physics-Informed Neural Networks for
Steady-State Convection-Dominated Problems | [
"math.NA",
"cs.CE",
"cs.LG",
"cs.NA"
] | This paper proposes and studies two extensions of applying hp-variational physics-informed neural networks, more precisely the FastVPINNs framework, to convection-dominated convection-diffusion-reaction problems. First, a term in the spirit of a SUPG stabilization is included in the loss functional and a network archit... | {
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2411.09335 | Experimental Demonstration of Remote Synchronization in Coupled
Nonlinear Oscillator | [
"eess.SY",
"cs.SY"
] | This study investigates remote synchronization in scale-free networks of coupled nonlinear oscillators inspired by synchronization observed in the brain's cortical regions and power grid. We employ the Master Stability Function (MSF) approach to analyze network stability across various oscillator models. Synchronizatio... | {
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2411.09339 | Re-Parameterization of Lightweight Transformer for On-Device Speech
Emotion Recognition | [
"cs.SD",
"cs.CL",
"eess.AS"
] | With the increasing implementation of machine learning models on edge or Internet-of-Things (IoT) devices, deploying advanced models on resource-constrained IoT devices remains challenging. Transformer models, a currently dominant neural architecture, have achieved great success in broad domains but their complexity hi... | {
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2411.09341 | Approximated Variational Bayesian Inverse Reinforcement Learning for
Large Language Model Alignment | [
"cs.LG"
] | The alignment of large language models (LLMs) is crucial for generating helpful and harmless content. Existing approaches leverage preference-based human feedback data to learn the reward function and align the LLM with the feedback data. However, these approaches focus on modeling the reward difference between the cho... | {
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2411.09344 | Adaptively Augmented Consistency Learning: A Semi-supervised
Segmentation Framework for Remote Sensing | [
"cs.CV"
] | Remote sensing (RS) involves the acquisition of data about objects or areas from a distance, primarily to monitor environmental changes, manage resources, and support planning and disaster response. A significant challenge in RS segmentation is the scarcity of high-quality labeled images due to the diversity and comple... | {
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2411.09355 | Prices, Bids, Values: One ML-Powered Combinatorial Auction to Rule Them
All | [
"cs.GT",
"cs.AI",
"cs.LG"
] | We study the design of iterative combinatorial auctions (ICAs). The main challenge in this domain is that the bundle space grows exponentially in the number of items. To address this, recent work has proposed machine learning (ML)-based preference elicitation algorithms that aim to elicit only the most critical informa... | {
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2411.09356 | Multi-scale Generative Modeling for Fast Sampling | [
"cs.AI"
] | While working within the spatial domain can pose problems associated with ill-conditioned scores caused by power-law decay, recent advances in diffusion-based generative models have shown that transitioning to the wavelet domain offers a promising alternative. However, within the wavelet domain, we encounter unique cha... | {
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2411.09359 | Your Semantic-Independent Watermark is Fragile: A Semantic Perturbation
Attack against EaaS Watermark | [
"cs.CR",
"cs.AI"
] | Embedding-as-a-Service (EaaS) has emerged as a successful business pattern but faces significant challenges related to various forms of copyright infringement, particularly, the API misuse and model extraction attacks. Various studies have proposed backdoor-based watermarking schemes to protect the copyright of EaaS se... | {
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2411.09360 | D4W: Dependable Data-Driven Dynamics for Wheeled Robots | [
"cs.RO"
] | Wheeled robots have gained significant attention due to their wide range of applications in manufacturing, logistics, and service industries. However, due to the difficulty of building a highly accurate dynamics model for wheeled robots, developing and testing control algorithms for them remains challenging and time-co... | {
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2411.09361 | Time-to-Event Pretraining for 3D Medical Imaging | [
"cs.CV",
"cs.LG"
] | With the rise of medical foundation models and the growing availability of imaging data, scalable pretraining techniques offer a promising way to identify imaging biomarkers predictive of future disease risk. While current self-supervised methods for 3D medical imaging models capture local structural features like orga... | {
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2411.09365 | Stability and Generalization for Distributed SGDA | [
"cs.LG",
"math.OC"
] | Minimax optimization is gaining increasing attention in modern machine learning applications. Driven by large-scale models and massive volumes of data collected from edge devices, as well as the concern to preserve client privacy, communication-efficient distributed minimax optimization algorithms become popular, such ... | {
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2411.09366 | LTLf+ and PPLTL+: Extending LTLf and PPLTL to Infinite Traces | [
"cs.LO",
"cs.AI",
"cs.FL"
] | We introduce LTLf+ and PPLTL+, two logics to express properties of infinite traces, that are based on the linear-time temporal logics LTLf and PPLTL on finite traces. LTLf+/PPLTL+ use levels of Manna and Pnueli's LTL safety-progress hierarchy, and thus have the same expressive power as LTL. However, they also retain a ... | {
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2411.09371 | DSCformer: A Dual-Branch Network Integrating Enhanced Dynamic Snake
Convolution and SegFormer for Crack Segmentation | [
"cs.CV"
] | In construction quality monitoring, accurately detecting and segmenting cracks in concrete structures is paramount for safety and maintenance. Current convolutional neural networks (CNNs) have demonstrated strong performance in crack segmentation tasks, yet they often struggle with complex backgrounds and fail to captu... | {
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2411.09373 | Are nuclear masks all you need for improved out-of-domain
generalisation? A closer look at cancer classification in histopathology | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Domain generalisation in computational histopathology is challenging because the images are substantially affected by differences among hospitals due to factors like fixation and staining of tissue and imaging equipment. We hypothesise that focusing on nuclei can improve the out-of-domain (OOD) generalisation in cancer... | {
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2411.09387 | Instruction-Driven Fusion of Infrared-Visible Images: Tailoring for
Diverse Downstream Tasks | [
"cs.CV"
] | The primary value of infrared and visible image fusion technology lies in applying the fusion results to downstream tasks. However, existing methods face challenges such as increased training complexity and significantly compromised performance of individual tasks when addressing multiple downstream tasks simultaneousl... | {
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2411.09388 | A survey of probabilistic generative frameworks for molecular
simulations | [
"cs.LG",
"cond-mat.dis-nn",
"cond-mat.soft",
"cond-mat.stat-mech"
] | Generative artificial intelligence is now a widely used tool in molecular science. Despite the popularity of probabilistic generative models, numerical experiments benchmarking their performance on molecular data are lacking. In this work, we introduce and explain several classes of generative models, broadly sorted in... | {
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2411.09389 | Less is More: Unseen Domain Fake News Detection via Causal Propagation
Substructures | [
"cs.SI",
"cs.AI",
"cs.CL",
"cs.CY",
"cs.LG"
] | The spread of fake news on social media poses significant threats to individuals and society. Text-based and graph-based models have been employed for fake news detection by analysing news content and propagation networks, showing promising results in specific scenarios. However, these data-driven models heavily rely o... | {
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2411.09393 | Inherently Interpretable and Uncertainty-Aware Models for Online
Learning in Cyber-Security Problems | [
"cs.LG"
] | In this paper, we address the critical need for interpretable and uncertainty-aware machine learning models in the context of online learning for high-risk industries, particularly cyber-security. While deep learning and other complex models have demonstrated impressive predictive capabilities, their opacity and lack o... | {
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2411.09400 | Imagined Speech and Visual Imagery as Intuitive Paradigms for
Brain-Computer Interfaces | [
"cs.AI"
] | Brain-computer interfaces (BCIs) have shown promise in enabling communication for individuals with motor impairments. Recent advancements like brain-to-speech technology aim to reconstruct speech from neural activity. However, decoding communication-related paradigms, such as imagined speech and visual imagery, using n... | {
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2411.09402 | Automated Segmentation of Ischemic Stroke Lesions in Non-Contrast
Computed Tomography Images for Enhanced Treatment and Prognosis | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Stroke is the second leading cause of death worldwide, and is increasingly prevalent in low- and middle-income countries (LMICs). Timely interventions can significantly influence stroke survivability and the quality of life after treatment. However, the standard and most widely available imaging method for confirming s... | {
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2411.09403 | Quantum Machine Learning: An Interplay Between Quantum Computing and
Machine Learning | [
"quant-ph",
"cs.AI"
] | Quantum machine learning (QML) is a rapidly growing field that combines quantum computing principles with traditional machine learning. It seeks to revolutionize machine learning by harnessing the unique capabilities of quantum mechanics and employs machine learning techniques to advance quantum computing research. Thi... | {
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2411.09410 | LLM-assisted Explicit and Implicit Multi-interest Learning Framework for
Sequential Recommendation | [
"cs.IR"
] | Multi-interest modeling in current recommender systems (RS) is mainly based on user behavioral data, capturing user interest preferences from multiple dimensions. However, since behavioral data is implicit and often highly sparse, it is challenging to understand users' complex and diverse interests. Recent studies have... | {
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2411.09411 | Building Height Estimation Using Shadow Length in Satellite Imagery | [
"cs.CV",
"eess.IV"
] | Estimating building height from satellite imagery poses significant challenges, especially when monocular images are employed, resulting in a loss of essential 3D information during imaging. This loss of spatial depth further complicates the height estimation process. We addressed this issue by using shadow length as a... | {
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2411.09413 | Script-centric behavior understanding for assisted autism spectrum
disorder diagnosis | [
"cs.CV",
"cs.AI"
] | Observing and analyzing children's social behaviors is crucial for the early diagnosis of Autism Spectrum Disorders (ASD). This work focuses on automatically detecting ASD using computer vision techniques and large language models (LLMs). Existing methods typically rely on supervised learning. However, the scarcity of ... | {
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2411.09420 | SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph
Attention for Vision Transformers | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Vision Transformers (ViTs) have redefined image classification by leveraging self-attention to capture complex patterns and long-range dependencies between image patches. However, a key challenge for ViTs is efficiently incorporating multi-scale feature representations, which is inherent in convolutional neural network... | {
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2411.09422 | OpenLS-DGF: An Adaptive Open-Source Dataset Generation Framework for
Machine Learning Tasks in Logic Synthesis | [
"cs.AI"
] | This paper introduces OpenLS-DGF, an adaptive logic synthesis dataset generation framework, to enhance machine learning~(ML) applications within the logic synthesis process. Previous dataset generation flows were tailored for specific tasks or lacked integrated machine learning capabilities. While OpenLS-DGF supports v... | {
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2411.09425 | MARM: Unlocking the Future of Recommendation Systems through Memory
Augmentation and Scalable Complexity | [
"cs.IR"
] | Scaling-law has guided the language model designing for past years, however, it is worth noting that the scaling laws of NLP cannot be directly applied to RecSys due to the following reasons: (1) The amount of training samples and model parameters is typically not the bottleneck for the model. Our recommendation system... | {
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2411.09429 | AI-driven inverse design of materials: Past, present and future | [
"cond-mat.mtrl-sci",
"cond-mat.supr-con",
"cs.AI"
] | The discovery of advanced materials is the cornerstone of human technological development and progress. The structures of materials and their corresponding properties are essentially the result of a complex interplay of multiple degrees of freedom such as lattice, charge, spin, symmetry, and topology. This poses signif... | {
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2411.09431 | Everyone deserves their voice to be heard: Analyzing Predictive Gender
Bias in ASR Models Applied to Dutch Speech Data | [
"cs.CL"
] | Recent research has shown that state-of-the-art (SotA) Automatic Speech Recognition (ASR) systems, such as Whisper, often exhibit predictive biases that disproportionately affect various demographic groups. This study focuses on identifying the performance disparities of Whisper models on Dutch speech data from the Com... | {
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2411.09434 | Mediffusion: Joint Diffusion for Self-Explainable Semi-Supervised
Classification and Medical Image Generation | [
"cs.CV",
"cs.LG"
] | We introduce Mediffusion -- a new method for semi-supervised learning with explainable classification based on a joint diffusion model. The medical imaging domain faces unique challenges due to scarce data labelling -- insufficient for standard training, and critical nature of the applications that require high perform... | {
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2411.09435 | ReMP: Reusable Motion Prior for Multi-domain 3D Human Pose Estimation
and Motion Inbetweening | [
"cs.CV"
] | We present Reusable Motion prior (ReMP), an effective motion prior that can accurately track the temporal evolution of motion in various downstream tasks. Inspired by the success of foundation models, we argue that a robust spatio-temporal motion prior can encapsulate underlying 3D dynamics applicable to various sensor... | {
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2411.09436 | Robot Tasks with Fuzzy Time Requirements from Natural Language
Instructions | [
"cs.RO",
"cs.CL",
"cs.HC"
] | Natural language allows robot programming to be accessible to everyone. However, the inherent fuzziness in natural language poses challenges for inflexible, traditional robot systems. We focus on instructions with fuzzy time requirements (e.g., "start in a few minutes"). Building on previous robotics research, we intro... | {
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2411.09439 | Spider: Any-to-Many Multimodal LLM | [
"cs.CV"
] | Multimodal LLMs (MLLMs) have emerged as an extension of Large Language Models (LLMs), enabling the integration of various modalities. However, Any-to-Any MLLMs are limited to generating pairwise modalities 'Text + X' within a single response, such as Text + {Image or Audio or Video}. To address this limitation, we intr... | {
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2411.09440 | Evaluation of RIS-Enabled B5G/6G Indoor Positioning and Mapping using
Ray Tracing Models | [
"cs.IT",
"eess.SP",
"math.IT"
] | A Reconfigurable Intelligent Surface (RIS) can significantly enhance network positioning and mapping, acting as an additional anchor point in the reference system and improving signal strength and measurement diversity through the generation of favorable scattering conditions and virtual line-of-sight paths. In this pa... | {
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2411.09441 | A ROS~2-based Navigation and Simulation Stack for the Robotino | [
"cs.RO"
] | The Robotino, developed by Festo Didactic, serves as a versatile platform in education and research for mobile robotics tasks. However, there currently is no ROS2 integration for the Robotino available. In this paper, we describe our work on a Webots simulation environment for a Robotino platform extended by LIDAR sens... | {
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2411.09444 | Learning efficient and provably convergent splitting methods | [
"math.NA",
"cs.LG",
"cs.NA"
] | Splitting methods are widely used for solving initial value problems (IVPs) due to their ability to simplify complicated evolutions into more manageable subproblems which can be solved efficiently and accurately. Traditionally, these methods are derived using analytic and algebraic techniques from numerical analysis, i... | {
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2411.09449 | Image Regeneration: Evaluating Text-to-Image Model via Generating
Identical Image with Multimodal Large Language Models | [
"cs.CV"
] | Diffusion models have revitalized the image generation domain, playing crucial roles in both academic research and artistic expression. With the emergence of new diffusion models, assessing the performance of text-to-image models has become increasingly important. Current metrics focus on directly matching the input te... | {
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2411.09451 | DiffRoad: Realistic and Diverse Road Scenario Generation for Autonomous
Vehicle Testing | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Generating realistic and diverse road scenarios is essential for autonomous vehicle testing and validation. Nevertheless, owing to the complexity and variability of real-world road environments, creating authentic and varied scenarios for intelligent driving testing is challenging. In this paper, we propose DiffRoad, a... | {
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2411.09453 | Long-Tailed Object Detection Pre-training: Dynamic Rebalancing
Contrastive Learning with Dual Reconstruction | [
"cs.CV",
"cs.LG"
] | Pre-training plays a vital role in various vision tasks, such as object recognition and detection. Commonly used pre-training methods, which typically rely on randomized approaches like uniform or Gaussian distributions to initialize model parameters, often fall short when confronted with long-tailed distributions, esp... | {
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2411.09459 | Caravan MultiMet: Extending Caravan with Multiple Weather Nowcasts and
Forecasts | [
"cs.LG"
] | The Caravan large-sample hydrology dataset (Kratzert et al., 2023) was created to standardize and harmonize streamflow data from various regional datasets, combined with globally available meteorological forcing and catchment attributes. This community-driven project also allows researchers to conveniently extend the d... | {
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2411.09460 | Analysis Methodology for Age of Information under Sequence Based
Scheduling | [
"cs.IT",
"math.IT"
] | We focus on the Age of Information (AoI) performance in a system where each user generates packets periodically to send to a common access point (AP) for status updating. To avoid heavy overhead, we assume that channel sensing, feedback information from the AP, and time synchronization are not available in the system. ... | {
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2411.09462 | SINETRA: a Versatile Framework for Evaluating Single Neuron Tracking in
Behaving Animals | [
"cs.CV"
] | Accurately tracking neuronal activity in behaving animals presents significant challenges due to complex motions and background noise. The lack of annotated datasets limits the evaluation and improvement of such tracking algorithms. To address this, we developed SINETRA, a versatile simulator that generates synthetic t... | {
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2411.09468 | Harnessing Machine Learning for Single-Shot Measurement of Free Electron
Laser Pulse Power | [
"cs.LG",
"physics.acc-ph"
] | Electron beam accelerators are essential in many scientific and technological fields. Their operation relies heavily on the stability and precision of the electron beam. Traditional diagnostic techniques encounter difficulties in addressing the complex and dynamic nature of electron beams. Particularly in the context o... | {
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2411.09469 | An Explainable Attention Model for Cervical Precancer Risk
Classification using Colposcopic Images | [
"eess.IV",
"cs.AI"
] | Cervical cancer remains a major worldwide health issue, with early identification and risk assessment playing critical roles in effective preventive interventions. This paper presents the Cervix-AID-Net model for cervical precancer risk classification. The study designs and evaluates the proposed Cervix-AID-Net model b... | {
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2411.09471 | Renal Cell Carcinoma subtyping: learning from multi-resolution
localization | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Renal Cell Carcinoma is typically asymptomatic at the early stages for many patients. This leads to a late diagnosis of the tumor, where the curability likelihood is lower, and makes the mortality rate of Renal Cell Carcinoma high, with respect to its incidence rate. To increase the survival chance, a fast and correct ... | {
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2411.09475 | ResidualDroppath: Enhancing Feature Reuse over Residual Connections | [
"cs.LG",
"cs.AI"
] | Residual connections are one of the most important components in neural network architectures for mitigating the vanishing gradient problem and facilitating the training of much deeper networks. One possible explanation for how residual connections aid deeper network training is by promoting feature reuse. However, we ... | {
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2411.09476 | Graph Neural Networks and Differential Equations: A hybrid approach for
data assimilation of fluid flows | [
"physics.flu-dyn",
"cs.LG"
] | This study presents a novel hybrid approach that combines Graph Neural Networks (GNNs) with Reynolds-Averaged Navier Stokes (RANS) equations to enhance the accuracy of mean flow reconstruction across a range of fluid dynamics applications. Traditional purely data-driven Neural Networks (NNs) models, often struggle main... | {
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2411.09481 | What makes a good BIM design: quantitative linking between design
behavior and quality | [
"cs.LG"
] | In the Architecture Engineering & Construction (AEC) industry, how design behaviors impact design quality remains unclear. This study proposes a novel approach, which, for the first time, identifies and quantitatively describes the relationship between design behaviors and quality of design based on Building Informatio... | {
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2411.09483 | Sparse Bayesian Generative Modeling for Compressive Sensing | [
"stat.ML",
"cs.LG",
"eess.IV"
] | This work addresses the fundamental linear inverse problem in compressive sensing (CS) by introducing a new type of regularizing generative prior. Our proposed method utilizes ideas from classical dictionary-based CS and, in particular, sparse Bayesian learning (SBL), to integrate a strong regularization towards sparse... | {
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2411.09484 | Image Matching Filtering and Refinement by Planes and Beyond | [
"cs.CV"
] | This paper introduces a modular, non-deep learning method for filtering and refining sparse correspondences in image matching. Assuming that motion flow within the scene can be approximated by local homography transformations, matches are aggregated into overlapping clusters corresponding to virtual planes using an ite... | {
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2411.09486 | An Approach to Twinning and Mining Collaborative Network of Construction
Projects | [
"cs.SI"
] | Understanding complex collaboration processes is essential for the success of construction projects. However, there is still a lack of efficient methods for timely collection and analysis of collaborative networks. Therefore, an integrated framework consisting three parts, namely, system updating for data collection, d... | {
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2411.09492 | MM-Eval: A Hierarchical Benchmark for Modern Mongolian Evaluation in
LLMs | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) excel in high-resource languages but face notable challenges in low-resource languages like Mongolian. This paper addresses these challenges by categorizing capabilities into language abilities (syntax and semantics) and cognitive abilities (knowledge and reasoning). To systematically evalu... | {
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2411.09493 | Strategic Sacrifice: Self-Organized Robot Swarm Localization for
Inspection Productivity | [
"cs.RO",
"cs.MA"
] | Robot swarms offer significant potential for inspecting diverse infrastructure, ranging from bridges to space stations. However, effective inspection requires accurate robot localization, which demands substantial computational resources and limits productivity. Inspired by biological systems, we introduce a novel coop... | {
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2411.09495 | ISAC Super-Resolution Receiver via Lifted Atomic Norm Minimization | [
"cs.IT",
"eess.SP",
"math.IT"
] | This paper introduces an off-the-grid estimator for integrated sensing and communication (ISAC) systems, utilizing lifted atomic norm minimization (LANM). The key challenge in this scenario is that neither the transmit signals nor the radar-and-communication channels are known. We prove that LANM can simultaneously ach... | {
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2411.09497 | The Use of Readability Metrics in Legal Text: A Systematic Literature
Review | [
"cs.CL"
] | Understanding the text in legal documents can be challenging due to their complex structure and the inclusion of domain-specific jargon. Laws and regulations are often crafted in such a manner that engagement with them requires formal training, potentially leading to vastly different interpretations of the same texts. ... | {
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2411.09499 | Developement of Reinforcement Learning based Optimisation Method for
Side-Sill Design | [
"cs.LG"
] | Optimisation for crashworthiness is a critical part of the vehicle development process. Due to stringent regulations and increasing market demands, multiple factors must be considered within a limited timeframe. However, for optimal crashworthiness design, multiobjective optimisation is necessary, and for complex parts... | {
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2411.09502 | Golden Noise for Diffusion Models: A Learning Framework | [
"cs.LG",
"cs.CV"
] | Text-to-image diffusion model is a popular paradigm that synthesizes personalized images by providing a text prompt and a random Gaussian noise. While people observe that some noises are ``golden noises'' that can achieve better text-image alignment and higher human preference than others, we still lack a machine learn... | {
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2411.09507 | Toward a Cohesive AI and Simulation Software Ecosystem for Scientific
Innovation | [
"cs.SE",
"cs.AI"
] | In this paper, we discuss the need for an integrated software stack that unites artificial intelligence (AI) and modeling and simulation (ModSim) tools to advance scientific discovery. The authors advocate for a unified AI/ModSim software ecosystem that ensures compatibility across a wide range of software on diverse h... | {
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2411.09510 | Communication Compression for Tensor Parallel LLM Inference | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Large Language Models (LLMs) have pushed the frontier of artificial intelligence but are comprised of hundreds of billions of parameters and operations. For faster inference latency, LLMs are deployed on multiple hardware accelerators through various Model Parallelism strategies. Our paper looks into the details on one... | {
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2411.09512 | GAN-Based Architecture for Low-dose Computed Tomography Imaging
Denoising | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Generative Adversarial Networks (GANs) have surfaced as a revolutionary element within the domain of low-dose computed tomography (LDCT) imaging, providing an advanced resolution to the enduring issue of reconciling radiation exposure with image quality. This comprehensive review synthesizes the rapid advancements in G... | {
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2411.09517 | Randomized Truthful Auctions with Learning Agents | [
"cs.GT",
"cs.LG",
"econ.TH"
] | We study a setting where agents use no-regret learning algorithms to participate in repeated auctions. \citet{kolumbus2022auctions} showed, rather surprisingly, that when bidders participate in second-price auctions using no-regret bidding algorithms, no matter how large the number of interactions $T$ is, the runner-up... | {
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2411.09523 | Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats
in LLM-Based Agents | [
"cs.AI"
] | With the continuous development of large language models (LLMs), transformer-based models have made groundbreaking advances in numerous natural language processing (NLP) tasks, leading to the emergence of a series of agents that use LLMs as their control hub. While LLMs have achieved success in various tasks, they face... | {
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2411.09524 | FlowNav: Learning Efficient Navigation Policies via Conditional Flow
Matching | [
"cs.RO"
] | Effective robot navigation in dynamic environments is a challenging task that depends on generating precise control actions at high frequencies. Recent advancements have framed navigation as a goal-conditioned control problem. Current state-of-the-art methods for goal-based navigation, such as diffusion policies, eithe... | {
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2411.09538 | Marker-free Human Gait Analysis using a Smart Edge Sensor System | [
"cs.CV"
] | The human gait is a complex interplay between the neuronal and the muscular systems, reflecting an individual's neurological and physiological condition. This makes gait analysis a valuable tool for biomechanics and medical experts. Traditional observational gait analysis is cost-effective but lacks reliability and acc... | {
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2411.09539 | A Practical Guide to Fine-tuning Language Models with Limited Data | [
"cs.CL",
"cs.LG"
] | Employing pre-trained Large Language Models (LLMs) has become the de facto standard in Natural Language Processing (NLP) despite their extensive data requirements. Motivated by the recent surge in research focused on training LLMs with limited data, particularly in low-resource domains and languages, this paper surveys... | {
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2411.09540 | Prompting the Unseen: Detecting Hidden Backdoors in Black-Box Models | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Visual prompting (VP) is a new technique that adapts well-trained frozen models for source domain tasks to target domain tasks. This study examines VP's benefits for black-box model-level backdoor detection. The visual prompt in VP maps class subspaces between source and target domains. We identify a misalignment, term... | {
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2411.09543 | OpenGeMM: A High-Utilization GeMM Accelerator Generator with Lightweight
RISC-V Control and Tight Memory Coupling | [
"cs.AR",
"cs.AI"
] | Deep neural networks (DNNs) face significant challenges when deployed on resource-constrained extreme edge devices due to their computational and data-intensive nature. While standalone accelerators tailored for specific application scenarios suffer from inflexible control and limited programmability, generic hardware ... | {
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2411.09545 | Equation-informed data-driven identification of flow budgets and
dynamics | [
"physics.flu-dyn",
"cs.LG"
] | Computational Fluid Dynamics (CFD) is an indispensable method of fluid modelling in engineering applications, reducing the need for physical prototypes and testing for tasks such as design optimisation and performance analysis. Depending on the complexity of the system under consideration, models ranging from low to hi... | {
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2411.09546 | Architectural Exploration of Application-Specific Resonant SRAM
Compute-in-Memory (rCiM) | [
"cs.AR",
"cs.CY",
"cs.ET",
"cs.SY",
"eess.SY"
] | While general-purpose computing follows Von Neumann's architecture, the data movement between memory and processor elements dictates the processor's performance. The evolving compute-in-memory (CiM) paradigm tackles this issue by facilitating simultaneous processing and storage within static random-access memory (SRAM)... | {
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2411.09547 | Piecing It All Together: Verifying Multi-Hop Multimodal Claims | [
"cs.CL",
"cs.AI"
] | Existing claim verification datasets often do not require systems to perform complex reasoning or effectively interpret multimodal evidence. To address this, we introduce a new task: multi-hop multimodal claim verification. This task challenges models to reason over multiple pieces of evidence from diverse sources, inc... | {
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2411.09550 | A small-gain criterion for 2-contraction of large scale interconnected
systems | [
"eess.SY",
"cs.SY"
] | Despite modular conditions to guarantee stability for large-scale systems have been widely studied, few methods are available to tackle the case of networks with multiple equilibria. This paper introduces small-gain like sufficient conditions for 2-contraction of large-scale interconnected systems on the basis of a fam... | {
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2411.09551 | MFTIQ: Multi-Flow Tracker with Independent Matching Quality Estimation | [
"cs.CV"
] | In this work, we present MFTIQ, a novel dense long-term tracking model that advances the Multi-Flow Tracker (MFT) framework to address challenges in point-level visual tracking in video sequences. MFTIQ builds upon the flow-chaining concepts of MFT, integrating an Independent Quality (IQ) module that separates correspo... | {
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2411.09553 | OOD-SEG: Out-Of-Distribution detection for image SEGmentation with
sparse multi-class positive-only annotations | [
"cs.CV"
] | Despite significant advancements, segmentation based on deep neural networks in medical and surgical imaging faces several challenges, two of which we aim to address in this work. First, acquiring complete pixel-level segmentation labels for medical images is time-consuming and requires domain expertise. Second, typica... | {
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2411.09555 | Image Processing for Motion Magnification | [
"cs.CV",
"cs.NA",
"math.NA"
] | Motion Magnification (MM) is a collection of relative recent techniques within the realm of Image Processing. The main motivation of introducing these techniques in to support the human visual system to capture relevant displacements of an object of interest; these motions can be in object color and in object location.... | {
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2411.09558 | Adaptive Deviation Learning for Visual Anomaly Detection with Data
Contamination | [
"cs.CV",
"cs.LG"
] | Visual anomaly detection targets to detect images that notably differ from normal pattern, and it has found extensive application in identifying defective parts within the manufacturing industry. These anomaly detection paradigms predominantly focus on training detection models using only clean, unlabeled normal sample... | {
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} |
2411.09565 | Vlimb: A Wire-Driven Wearable Robot for Bodily Extension, Balancing
Powerfulness and Reachability | [
"cs.RO"
] | Numerous wearable robots have been developed to meet the demands of physical assistance and entertainment. These wearable robots range from body-enhancing types that assist human arms and legs to body-extending types that have extra arms. This study focuses specifically on wearable robots of the latter category, aimed ... | {
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} |
2411.09567 | VPBSD:Vessel-Pattern-Based Semi-Supervised Distillation for Efficient 3D
Microscopic Cerebrovascular Segmentation | [
"cs.CV"
] | 3D microscopic cerebrovascular images are characterized by their high resolution, presenting significant annotation challenges, large data volumes, and intricate variations in detail. Together, these factors make achieving high-quality, efficient whole-brain segmentation particularly demanding. In this paper, we propos... | {
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} |
2411.09572 | Dynamic Reconstruction of Hand-Object Interaction with Distributed
Force-aware Contact Representation | [
"cs.CV"
] | We present ViTaM-D, a novel visual-tactile framework for dynamic hand-object interaction reconstruction, integrating distributed tactile sensing for more accurate contact modeling. While existing methods focus primarily on visual inputs, they struggle with capturing detailed contact interactions such as object deformat... | {
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} |
2411.09576 | Automating Reformulation of Essence Specifications via Graph Rewriting | [
"cs.AI"
] | Formulating an effective constraint model of a parameterised problem class is crucial to the efficiency with which instances of the class can subsequently be solved. It is difficult to know beforehand which of a set of candidate models will perform best in practice. This paper presents a system that employs graph rewri... | {
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} |
2411.09580 | Software Performance Engineering for Foundation Model-Powered Software
(FMware) | [
"cs.SE",
"cs.AI"
] | The rise of Foundation Models (FMs) like Large Language Models (LLMs) is revolutionizing software development. Despite the impressive prototypes, transforming FMware into production-ready products demands complex engineering across various domains. A critical but overlooked aspect is performance engineering, which aims... | {
"Other": 1,
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} |
2411.09582 | Safety Filter for Robust Disturbance Rejection via Online Optimization | [
"eess.SY",
"cs.SY",
"math.OC"
] | Disturbance rejection in high-precision control applications can be significantly improved upon via online convex optimization (OCO). This includes classical techniques such as recursive least squares (RLS) and more recent, regret-based formulations. However, these methods can cause instabilities in the presence of mod... | {
"Other": 0,
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"cs.SY": 1
} |
2411.09587 | BabyLM Challenge: Exploring the Effect of Variation Sets on Language
Model Training Efficiency | [
"cs.CL"
] | While current large language models have achieved a remarkable success, their data efficiency remains a challenge to overcome. Recently it has been suggested that child-directed speech (CDS) can improve training data efficiency of modern language models based on Transformer neural networks. However, it is not yet under... | {
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} |
2411.09590 | Adopting RAG for LLM-Aided Future Vehicle Design | [
"cs.SE",
"cs.AI"
] | In this paper, we explore the integration of Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to enhance automated design and software development in the automotive industry. We present two case studies: a standardization compliance chatbot and a design copilot, both utilizing RAG to provide accur... | {
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} |
2411.09591 | Handling missing values in clinical machine learning: Insights from an
expert study | [
"cs.LG"
] | Inherently interpretable machine learning (IML) models offer valuable support for clinical decision-making but face challenges when features contain missing values. Traditional approaches, such as imputation or discarding incomplete records, are often impractical in scenarios where data is missing at test time. We surv... | {
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"cs.SY": 0
} |
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