id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.06003 | Learning to generate feasible graphs using graph grammars | [
"cs.LG"
] | Generative methods for graphs need to be sufficiently flexible to model complex dependencies between sets of nodes. At the same time, the generated graphs need to satisfy domain-dependent feasibility conditions, that is, they should not violate certain constraints that would make their interpretation impossible within ... | {
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2501.06004 | SeMi: When Imbalanced Semi-Supervised Learning Meets Mining Hard
Examples | [
"cs.CV"
] | Semi-Supervised Learning (SSL) can leverage abundant unlabeled data to boost model performance. However, the class-imbalanced data distribution in real-world scenarios poses great challenges to SSL, resulting in performance degradation. Existing class-imbalanced semi-supervised learning (CISSL) methods mainly focus on ... | {
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2501.06006 | CamCtrl3D: Single-Image Scene Exploration with Precise 3D Camera Control | [
"cs.CV"
] | We propose a method for generating fly-through videos of a scene, from a single image and a given camera trajectory. We build upon an image-to-video latent diffusion model. We condition its UNet denoiser on the camera trajectory, using four techniques. (1) We condition the UNet's temporal blocks on raw camera extrinsic... | {
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2501.06007 | CoNOAir: A Neural Operator for Forecasting Carbon Monoxide Evolution in
Cities | [
"cs.LG"
] | Carbon Monoxide (CO) is a dominant pollutant in urban areas due to the energy generation from fossil fuels for industry, automobile, and domestic requirements. Forecasting the evolution of CO in real-time can enable the deployment of effective early warning systems and intervention strategies. However, the computationa... | {
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2501.06014 | Pose-independent 3D Anthropometry from Sparse Data | [
"cs.CV"
] | 3D digital anthropometry is the study of estimating human body measurements from 3D scans. Precise body measurements are important health indicators in the medical industry, and guiding factors in the fashion, ergonomic and entertainment industries. The measuring protocol consists of scanning the whole subject in the s... | {
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2501.06016 | Investigating the Impact of Observation Space Design Choices On Training
Reinforcement Learning Solutions for Spacecraft Problems | [
"cs.LG",
"cs.SY",
"eess.SY"
] | Recent research using Reinforcement Learning (RL) to learn autonomous control for spacecraft operations has shown great success. However, a recent study showed their performance could be improved by changing the action space, i.e. control outputs, used in the learning environment. This has opened the door for finding m... | {
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2501.06019 | BRIGHT: A globally distributed multimodal building damage assessment
dataset with very-high-resolution for all-weather disaster response | [
"cs.CV",
"cs.AI",
"eess.IV",
"eess.SP"
] | Disaster events occur around the world and cause significant damage to human life and property. Earth observation (EO) data enables rapid and comprehensive building damage assessment (BDA), an essential capability in the aftermath of a disaster to reduce human casualties and to inform disaster relief efforts. Recent re... | {
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2501.06025 | How to Tune a Multilingual Encoder Model for Germanic Languages: A Study
of PEFT, Full Fine-Tuning, and Language Adapters | [
"cs.CL",
"cs.AI"
] | This paper investigates the optimal use of the multilingual encoder model mDeBERTa for tasks in three Germanic languages -- German, Swedish, and Icelandic -- representing varying levels of presence and likely data quality in mDeBERTas pre-training data. We compare full fine-tuning with the parameter-efficient fine-tuni... | {
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2501.06027 | Geometric-Based Nail Segmentation for Clinical Measurements | [
"cs.CV"
] | A robust segmentation method that can be used to perform measurements on toenails is presented. The proposed method is used as the first step in a clinical trial to objectively quantify the incidence of a particular pathology. For such an assessment, it is necessary to distinguish a nail, which locally appears to be si... | {
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2501.06030 | Resiliency metrics quantifying emergency response in a distribution
system | [
"eess.SY",
"cs.SY"
] | The electric distribution system is a cornerstone of modern life, playing a critical role in the daily activities and well-being of individuals. As the world transitions toward a decarbonized future, where even mobility relies on electricity, ensuring the resilience of the grid becomes paramount. This paper introduces ... | {
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2501.06031 | Generate, Transduct, Adapt: Iterative Transduction with VLMs | [
"cs.CV"
] | Transductive zero-shot learning with vision-language models leverages image-image similarities within the dataset to achieve better classification accuracy compared to the inductive setting. However, there is little work that explores the structure of the language space in this context. We propose GTA-CLIP, a novel tec... | {
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2501.06035 | Nonisotropic Gaussian Diffusion for Realistic 3D Human Motion Prediction | [
"cs.CV"
] | Probabilistic human motion prediction aims to forecast multiple possible future movements from past observations. While current approaches report high diversity and realism, they often generate motions with undetected limb stretching and jitter. To address this, we introduce SkeletonDiffusion, a latent diffusion model ... | {
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2501.06038 | A Holistically Point-guided Text Framework for Weakly-Supervised
Camouflaged Object Detection | [
"cs.CV"
] | Weakly-Supervised Camouflaged Object Detection (WSCOD) has gained popularity for its promise to train models with weak labels to segment objects that visually blend into their surroundings. Recently, some methods using sparsely-annotated supervision shown promising results through scribbling in WSCOD, while point-text ... | {
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2501.06039 | AI-powered virtual tissues from spatial proteomics for clinical
diagnostics and biomedical discovery | [
"q-bio.QM",
"cs.AI",
"cs.CV",
"cs.LG"
] | Spatial proteomics technologies have transformed our understanding of complex tissue architectures by enabling simultaneous analysis of multiple molecular markers and their spatial organization. The high dimensionality of these data, varying marker combinations across experiments and heterogeneous study designs pose un... | {
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2501.06040 | MSCViT: A Small-size ViT architecture with Multi-Scale Self-Attention
Mechanism for Tiny Datasets | [
"cs.CV"
] | Vision Transformer (ViT) has demonstrated significant potential in various vision tasks due to its strong ability in modelling long-range dependencies. However, such success is largely fueled by training on massive samples. In real applications, the large-scale datasets are not always available, and ViT performs worse ... | {
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2501.06042 | The improvement in transmission resilience metrics from reduced outages
or faster restoration can be calculated by rerunning historical outage data | [
"physics.soc-ph",
"cs.SY",
"eess.SY"
] | Transmission utilities routinely collect detailed outage data, including resilience events in which outages bunch up due to weather. The resilience events and their resilience metrics can readily be extracted from this historical outage data. Improvements such as grid hardening or investments in restoration lead to red... | {
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2501.06047 | Learning Affordances from Interactive Exploration using an Object-level
Map | [
"cs.RO"
] | Many robotic tasks in real-world environments require physical interactions with an object such as pick up or push. For successful interactions, the robot needs to know the object's affordances, which are defined as the potential actions the robot can perform with the object. In order to learn a robot-specific affordan... | {
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2501.06051 | Benchmarking Rotary Position Embeddings for Automatic Speech Recognition | [
"cs.CL",
"cs.AI",
"eess.AS"
] | Rotary Position Embedding (RoPE) encodes relative and absolute positional information in Transformer-based models through rotation matrices applied to input vectors within sequences. While RoPE has demonstrated superior performance compared to other positional embedding technologies in natural language processing tasks... | {
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2501.06053 | Enhancing, Refining, and Fusing: Towards Robust Multi-Scale and Dense
Ship Detection | [
"cs.CV"
] | Synthetic aperture radar (SAR) imaging, celebrated for its high resolution, all-weather capability, and day-night operability, is indispensable for maritime applications. However, ship detection in SAR imagery faces significant challenges, including complex backgrounds, densely arranged targets, and large scale variati... | {
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2501.06058 | Learning Flexible Heterogeneous Coordination with Capability-Aware
Shared Hypernetworks | [
"cs.MA",
"cs.LG"
] | Cooperative heterogeneous multi-agent tasks require agents to effectively coordinate their behaviors while accounting for their relative capabilities. Learning-based solutions to this challenge span between two extremes: i) shared-parameter methods, which encode diverse behaviors within a single architecture by assigni... | {
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2501.06059 | COMIX: Compositional Explanations using Prototypes | [
"cs.LG"
] | Aligning machine representations with human understanding is key to improving interpretability of machine learning (ML) models. When classifying a new image, humans often explain their decisions by decomposing the image into concepts and pointing to corresponding regions in familiar images. Current ML explanation techn... | {
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2501.06062 | Personalized Language Model Learning on Text Data Without User
Identifiers | [
"cs.LG"
] | In many practical natural language applications, user data are highly sensitive, requiring anonymous uploads of text data from mobile devices to the cloud without user identifiers. However, the absence of user identifiers restricts the ability of cloud-based language models to provide personalized services, which are e... | {
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2501.06066 | Distilling Calibration via Conformalized Credal Inference | [
"cs.LG",
"cs.AI",
"eess.SP"
] | Deploying artificial intelligence (AI) models on edge devices involves a delicate balance between meeting stringent complexity constraints, such as limited memory and energy resources, and ensuring reliable performance in sensitive decision-making tasks. One way to enhance reliability is through uncertainty quantificat... | {
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2501.06067 | Decentralized Multi-Antenna Architectures with Unitary Constraints | [
"eess.SP",
"cs.IT",
"math.IT"
] | The increase in the number of base station (BS) antennas calls for efficient solutions to deal with the increased interconnection bandwidth and processing complexity of traditional centralized approaches. Decentralized approaches are thus gaining momentum, since they achieve important reductions in data/processing volu... | {
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2501.06074 | Geometry and Optimization of Shallow Polynomial Networks | [
"cs.LG",
"math.AG"
] | We study shallow neural networks with polynomial activations. The function space for these models can be identified with a set of symmetric tensors with bounded rank. We describe general features of these networks, focusing on the relationship between width and optimization. We then consider teacher-student problems, t... | {
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2501.06076 | A monthly sub-national Harmonized Food Insecurity Dataset for
comprehensive analysis and predictive modeling | [
"cs.LG"
] | Food security is a complex, multidimensional concept challenging to measure comprehensively. Effective anticipation, monitoring, and mitigation of food crises require timely and comprehensive global data. This paper introduces the Harmonized Food Insecurity Dataset (HFID), an open-source resource consolidating four key... | {
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2501.06077 | Explainable Federated Bayesian Causal Inference and Its Application in
Advanced Manufacturing | [
"cs.LG",
"stat.AP"
] | Causal inference has recently gained notable attention across various fields like biology, healthcare, and environmental science, especially within explainable artificial intelligence (xAI) systems, for uncovering the causal relationships among multiple variables and outcomes. Yet, it has not been fully recognized and ... | {
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2501.06078 | Explaining k-Nearest Neighbors: Abductive and Counterfactual
Explanations | [
"cs.LG",
"cs.AI"
] | Despite the wide use of $k$-Nearest Neighbors as classification models, their explainability properties remain poorly understood from a theoretical perspective. While nearest neighbors classifiers offer interpretability from a "data perspective", in which the classification of an input vector $\bar{x}$ is explained by ... | {
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2501.06080 | Scale-up Unlearnable Examples Learning with High-Performance Computing | [
"cs.LG",
"cs.AI",
"cs.DC"
] | Recent advancements in AI models are structured to retain user interactions, which could inadvertently include sensitive healthcare data. In the healthcare field, particularly when radiologists use AI-driven diagnostic tools hosted on online platforms, there is a risk that medical imaging data may be repurposed for fut... | {
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2501.06081 | Averaged Adam accelerates stochastic optimization in the training of
deep neural network approximations for partial differential equation and
optimal control problems | [
"math.OC",
"cs.LG",
"cs.NA",
"math.NA"
] | Deep learning methods - usually consisting of a class of deep neural networks (DNNs) trained by a stochastic gradient descent (SGD) optimization method - are nowadays omnipresent in data-driven learning problems as well as in scientific computing tasks such as optimal control (OC) and partial differential equation (PDE... | {
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2501.06086 | All AI Models are Wrong, but Some are Optimal | [
"cs.AI",
"cs.LG"
] | AI models that predict the future behavior of a system (a.k.a. predictive AI models) are central to intelligent decision-making. However, decision-making using predictive AI models often results in suboptimal performance. This is primarily because AI models are typically constructed to best fit the data, and hence to p... | {
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2501.06088 | Non-planar 3D Printing of Double Shells | [
"cs.RO",
"cs.CG"
] | We present a method to fabricate double shell structures printed in trans-versal directions using multi-axis fused-deposition-modeling (FDM) robot-ic 3D printing. Shell structures, characterized by lightweight, thin walls, fast buildup, and minimal material usage, find diverse applications in pro-totyping and architect... | {
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2501.06089 | Towards Developing Socially Compliant Automated Vehicles: State of the
Art, Experts Expectations, and A Conceptual Framework | [
"cs.RO",
"cs.AI",
"cs.LG",
"cs.MA",
"cs.SY",
"eess.SY"
] | Automated Vehicles (AVs) hold promise for revolutionizing transportation by improving road safety, traffic efficiency, and overall mobility. Despite the steady advancement in high-level AVs in recent years, the transition to full automation entails a period of mixed traffic, where AVs of varying automation levels coexi... | {
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2501.06092 | Molecular Communication-Inspired Particle Collector-Transmitter (PaCoT)
for Heavy Metal Removal from Human Circulatory System | [
"eess.SY",
"cs.SY"
] | This study proposes a novel molecular communication (MC)-inspired nanomachine, PArticle COllector-Transmitter (PaCoT), to remove toxic heavy metals from the human circulatory system. PaCoT collects these toxic metals and transmits them to release nodes, such as lymph capillaries, before they reach critical organs. The ... | {
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2501.06099 | Explaining Deep Learning-based Anomaly Detection in Energy Consumption
Data by Focusing on Contextually Relevant Data | [
"cs.LG",
"cs.AI"
] | Detecting anomalies in energy consumption data is crucial for identifying energy waste, equipment malfunction, and overall, for ensuring efficient energy management. Machine learning, and specifically deep learning approaches, have been greatly successful in anomaly detection; however, they are black-box approaches tha... | {
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2501.06100 | Practical Quantum Circuit Implementation for Simulating Coupled
Classical Oscillators | [
"quant-ph",
"cs.CE"
] | Simulating large-scale coupled-oscillator systems presents substantial computational challenges for classical algorithms, particularly when pursuing first-principles analyses in the thermodynamic limit. Motivated by the quantum algorithm framework proposed by Babbush et al., we present and implement a detailed quantum ... | {
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2501.06101 | From Conversation to Automation: Leveraging LLMs for Problem-Solving
Therapy Analysis | [
"cs.CL"
] | Problem-solving therapy (PST) is a structured psychological approach that helps individuals manage stress and resolve personal issues by guiding them through problem identification, solution brainstorming, decision-making, and outcome evaluation. As mental health care increasingly adopts technologies like chatbots and ... | {
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2501.06103 | Finite-Horizon Single-Pull Restless Bandits: An Efficient Index Policy
For Scarce Resource Allocation | [
"cs.MA",
"cs.LG"
] | Restless multi-armed bandits (RMABs) have been highly successful in optimizing sequential resource allocation across many domains. However, in many practical settings with highly scarce resources, where each agent can only receive at most one resource, such as healthcare intervention programs, the standard RMAB framewo... | {
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2501.06104 | Weather-Driven Priority Charging for Battery Storage Systems in Hybrid
Renewable Energy Grids | [
"eess.SY",
"cs.SY"
] | The integration of renewable energy into the power grid is often hindered by its fragmented infrastructure, leading to inefficient utilization due to the variability of energy production and its reliance on weather conditions. Battery storage systems, while essential for stabilizing energy supply, face challenges like ... | {
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2501.06108 | Inferring High-Order Couplings with Neural Networks | [
"cond-mat.dis-nn",
"cond-mat.stat-mech",
"cs.LG"
] | Maximum entropy methods, based on the inverse Ising/Potts problem from statistical mechanics, are essential for modeling interactions between pairs of variables in data-driven problems across disciplines such as bioinformatics, ecology, and neuroscience. Despite their considerable success, these methods typically fail ... | {
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2501.06112 | Optimizing Experiments for Accurate Battery Circuit Parameters
Estimation: Reduction and Adjustment of Frequency Set Used in Electrochemical
Impedance Spectroscopy | [
"eess.SY",
"cs.SY"
] | In this paper, we study a suitable experimental design of electrochemical impedance spectroscopy (EIS) to reduce the number of frequency points while not significantly affecting the uncertainties of the estimated cell's equivalent circuit model (ECM) parameters. It is based on an E-optimal experimental design that aims... | {
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2501.06113 | Vehicle-in-Virtual-Environment (VVE) Based Autonomous Driving Function
Development and Evaluation Methodology for Vulnerable Road User Safety | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Traditional methods for developing and evaluating autonomous driving functions, such as model-in-the-loop (MIL) and hardware-in-the-loop (HIL) simulations, heavily depend on the accuracy of simulated vehicle models and human factors, especially for vulnerable road user safety systems. Continuation of development during... | {
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2501.06115 | Development of an Advisory System for Parking of a Car and Trailer | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Trailer parking is a challenging task due to the unstable nature of the vehicle-trailer system in reverse motion and the unintuitive steering actions required at the vehicle to accomplish the parking maneuver. This paper presents a strategy to tackle this kind of maneuver with an advisory graphic aid to help the human ... | {
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2501.06117 | Fleurs-SLU: A Massively Multilingual Benchmark for Spoken Language
Understanding | [
"cs.CL",
"cs.AI"
] | Spoken language understanding (SLU) is indispensable for half of all living languages that lack a formal writing system, since these languages cannot pair automatic speech recognition (ASR) with language models to benefit from language technology. Even if low-resource languages possess a writing system, ASR for these l... | {
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2501.06118 | Nonlinear port-Hamiltonian system identification from input-state-output
data | [
"eess.SY",
"cs.SY",
"math.DS",
"math.OC",
"nlin.CD"
] | A framework for identifying nonlinear port-Hamiltonian systems using input-state-output data is introduced. The framework utilizes neural networks' universal approximation capacity to effectively represent complex dynamics in a structured way. We show that using the structure helps to make long-term predictions compare... | {
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2501.06121 | kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search | [
"cs.IR"
] | Approximate Nearest Neighbors (ANN) search is a crucial task in several applications like recommender systems and information retrieval. Current state-of-the-art ANN libraries, although being performance-oriented, often lack modularity and ease of use. This translates into them not being fully suitable for easy prototy... | {
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2501.06122 | NDOB-Based Control of a UAV with Delta-Arm Considering Manipulator
Dynamics | [
"cs.RO"
] | Aerial Manipulators (AMs) provide a versatile platform for various applications, including 3D printing, architecture, and aerial grasping missions. However, their operational speed is often sacrificed to uphold precision. Existing control strategies for AMs often regard the manipulator as a disturbance and employ robus... | {
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2501.06126 | Merging Feed-Forward Sublayers for Compressed Transformers | [
"cs.CL",
"cs.LG"
] | With the rise and ubiquity of larger deep learning models, the need for high-quality compression techniques is growing in order to deploy these models widely. The sheer parameter count of these models makes it difficult to fit them into the memory constraints of different hardware. In this work, we present a novel appr... | {
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2501.06129 | Contextual ASR Error Handling with LLMs Augmentation for Goal-Oriented
Conversational AI | [
"cs.CL",
"cs.AI"
] | General-purpose automatic speech recognition (ASR) systems do not always perform well in goal-oriented dialogue. Existing ASR correction methods rely on prior user data or named entities. We extend correction to tasks that have no prior user data and exhibit linguistic flexibility such as lexical and syntactic variatio... | {
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2501.06130 | A Mixed-Integer Conic Program for the Multi-Agent Moving-Target
Traveling Salesman Problem | [
"cs.RO"
] | The Moving-Target Traveling Salesman Problem (MT-TSP) aims to find a shortest path for an agent that starts at a stationary depot, visits a set of moving targets exactly once, each within one of their respective time windows, and then returns to the depot. In this paper, we introduce a new Mixed-Integer Conic Program (... | {
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2501.06132 | CoDriveVLM: VLM-Enhanced Urban Cooperative Dispatching and Motion
Planning for Future Autonomous Mobility on Demand Systems | [
"cs.RO",
"cs.AI",
"cs.MA"
] | The increasing demand for flexible and efficient urban transportation solutions has spotlighted the limitations of traditional Demand Responsive Transport (DRT) systems, particularly in accommodating diverse passenger needs and dynamic urban environments. Autonomous Mobility-on-Demand (AMoD) systems have emerged as a p... | {
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2501.06137 | Supervision policies can shape long-term risk management in
general-purpose AI models | [
"cs.AI",
"cs.CY",
"cs.SI"
] | The rapid proliferation and deployment of General-Purpose AI (GPAI) models, including large language models (LLMs), present unprecedented challenges for AI supervisory entities. We hypothesize that these entities will need to navigate an emergent ecosystem of risk and incident reporting, likely to exceed their supervis... | {
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2501.06138 | MS-Temba : Multi-Scale Temporal Mamba for Efficient Temporal Action
Detection | [
"cs.CV"
] | Action detection in real-world scenarios is particularly challenging due to densely distributed actions in hour-long untrimmed videos. It requires modeling both short- and long-term temporal relationships while handling significant intra-class temporal variations. Previous state-of-the-art (SOTA) Transformer-based arch... | {
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2501.06141 | Emergent Symbol-like Number Variables in Artificial Neural Networks | [
"cs.LG",
"cs.AI",
"cs.SC"
] | What types of numeric representations emerge in Neural Networks (NNs)? To what degree do NNs induce abstract, mutable, slot-like numeric variables, and in what situations do these representations emerge? How do these representations change over learning, and how can we understand the neural implementations in ways that... | {
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2501.06143 | Multilingual Performance of a Multimodal Artificial Intelligence System
on Multisubject Physics Concept Inventories | [
"physics.ed-ph",
"cs.AI"
] | We investigate the multilingual and multimodal performance of a large language model-based artificial intelligence (AI) system, GPT-4o, on a diverse set of physics concept inventories spanning multiple languages and subject areas. The inventories taken from the PhysPort website cover the classical physics topics of mec... | {
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2501.06146 | xLSTM-SENet: xLSTM for Single-Channel Speech Enhancement | [
"cs.SD",
"cs.AI",
"eess.AS"
] | While attention-based architectures, such as Conformers, excel in speech enhancement, they face challenges such as scalability with respect to input sequence length. In contrast, the recently proposed Extended Long Short-Term Memory (xLSTM) architecture offers linear scalability. However, xLSTM-based models remain unex... | {
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2501.06148 | From discrete-time policies to continuous-time diffusion samplers:
Asymptotic equivalences and faster training | [
"cs.LG",
"stat.ML"
] | We study the problem of training neural stochastic differential equations, or diffusion models, to sample from a Boltzmann distribution without access to target samples. Existing methods for training such models enforce time-reversal of the generative and noising processes, using either differentiable simulation or off... | {
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2501.06151 | PySpatial: A High-Speed Whole Slide Image Pathomics Toolkit | [
"eess.IV",
"cs.CV"
] | Whole Slide Image (WSI) analysis plays a crucial role in modern digital pathology, enabling large-scale feature extraction from tissue samples. However, traditional feature extraction pipelines based on tools like CellProfiler often involve lengthy workflows, requiring WSI segmentation into patches, feature extraction ... | {
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2501.06158 | GenMol: A Drug Discovery Generalist with Discrete Diffusion | [
"cs.LG"
] | Drug discovery is a complex process that involves multiple scenarios and stages, such as fragment-constrained molecule generation, hit generation and lead optimization. However, existing molecular generative models can only tackle one or two of these scenarios and lack the flexibility to address various aspects of the ... | {
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2501.06159 | Efficient Transition State Searches by Freezing String Method with Graph
Neural Network Potentials | [
"physics.chem-ph",
"cs.LG"
] | Transition states are a critical bottleneck in chemical transformations. Significant efforts have been made to develop algorithms that efficiently locate transition states on potential energy surfaces. However, the computational cost of ab-initio potential energy surface evaluation limits the size of chemical systems t... | {
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2501.06164 | Model Alignment Search | [
"cs.LG",
"cs.AI"
] | When can we say that two neural systems are the same? The answer to this question is goal-dependent, and it is often addressed through correlative methods such as Representational Similarity Analysis (RSA) and Centered Kernel Alignment (CKA). How do we target functionally relevant similarity, and how do we isolate spec... | {
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2501.06167 | Meta-Learning for Physically-Constrained Neural System Identification | [
"cs.LG",
"cs.SY",
"eess.SY",
"math.OC"
] | We present a gradient-based meta-learning framework for rapid adaptation of neural state-space models (NSSMs) for black-box system identification. When applicable, we also incorporate domain-specific physical constraints to improve the accuracy of the NSSM. The major benefit of our approach is that instead of relying s... | {
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2501.06171 | Machine Learning Force-Field Approach for Itinerant Electron Magnets | [
"cond-mat.str-el",
"cs.LG",
"physics.comp-ph"
] | We review the recent development of machine-learning (ML) force-field frameworks for Landau-Lifshitz-Gilbert (LLG) dynamics simulations of itinerant electron magnets, focusing on the general theory and implementations of symmetry-invariant representations of spin configurations. The crucial properties that such magneti... | {
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2501.06173 | VideoAuteur: Towards Long Narrative Video Generation | [
"cs.CV"
] | Recent video generation models have shown promising results in producing high-quality video clips lasting several seconds. However, these models face challenges in generating long sequences that convey clear and informative events, limiting their ability to support coherent narrations. In this paper, we present a large... | {
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2501.06181 | Best Response Convergence for Zero-sum Stochastic Dynamic Games with
Partial and Asymmetric Information | [
"eess.SY",
"cs.SY",
"math.OC"
] | We analyze best response dynamics for finding a Nash equilibrium of an infinite horizon zero-sum stochastic linear quadratic dynamic game (LQDG) with partial and asymmetric information. We derive explicit expressions for each player's best response within the class of pure linear dynamic output feedback control strateg... | {
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2501.06184 | PEACE: Empowering Geologic Map Holistic Understanding with MLLMs | [
"cs.CV",
"cs.MA"
] | Geologic map, as a fundamental diagram in geology science, provides critical insights into the structure and composition of Earth's subsurface and surface. These maps are indispensable in various fields, including disaster detection, resource exploration, and civil engineering. Despite their significance, current Multi... | {
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2501.06186 | LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs | [
"cs.CV"
] | Reasoning is a fundamental capability for solving complex multi-step problems, particularly in visual contexts where sequential step-wise understanding is essential. Existing approaches lack a comprehensive framework for evaluating visual reasoning and do not emphasize step-wise problem-solving. To this end, we propose... | {
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2501.06187 | Multi-subject Open-set Personalization in Video Generation | [
"cs.CV"
] | Video personalization methods allow us to synthesize videos with specific concepts such as people, pets, and places. However, existing methods often focus on limited domains, require time-consuming optimization per subject, or support only a single subject. We present Video Alchemist $-$ a video model with built-in mul... | {
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2501.06189 | A Multimodal Social Agent | [
"cs.AI",
"cs.CL"
] | In recent years, large language models (LLMs) have demonstrated remarkable progress in common-sense reasoning tasks. This ability is fundamental to understanding social dynamics, interactions, and communication. However, the potential of integrating computers with these social capabilities is still relatively unexplore... | {
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2501.06192 | A Computational Model of Learning and Memory Using Structurally Dynamic
Cellular Automata | [
"cs.AI",
"cs.NE",
"math.DS",
"q-bio.NC"
] | In the fields of computation and neuroscience, much is still unknown about the underlying computations that enable key cognitive functions including learning, memory, abstraction and behavior. This paper proposes a mathematical and computational model of learning and memory based on a small set of bio-plausible functio... | {
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2501.06193 | A Novel Task-Driven Method with Evolvable Interactive Agents Using Event
Trees for Enhanced Emergency Decision Support | [
"cs.AI",
"cs.CL"
] | As climate change and other global challenges increase the likelihood of unforeseen emergencies, the limitations of human-driven strategies in critical situations become more pronounced. Inadequate pre-established emergency plans can lead operators to become overwhelmed during complex systems malfunctions. This study a... | {
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2501.06196 | How Do Artificial Intelligences Think? The Three Mathematico-Cognitive
Factors of Categorical Segmentation Operated by Synthetic Neurons | [
"q-bio.NC",
"cs.AI",
"cs.NE"
] | How do the synthetic neurons in language models create "thought categories" to segment and analyze their informational environment? What are the cognitive characteristics, at the very level of formal neurons, of this artificial categorical thought? Based on the mathematical nature of algebraic operations inherent to ne... | {
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2501.06201 | A Novel Method for Pignistic Information Fusion in the View of Z-number | [
"cs.AI"
] | How to properly fuse information from complex sources is still an open problem. Lots of methods have been put forward to provide a effective solution in fusing intricate information. Among them, Dempster-Shafer evidences theory (DSET) is one of the representatives, it is widely used to handle uncertain information. Bas... | {
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2501.06205 | Leveraging Edge Intelligence and LLMs to Advance 6G-Enabled Internet of
Automated Defense Vehicles | [
"cs.NI",
"cs.AI",
"cs.CL"
] | The evolution of Artificial Intelligence (AI) and its subset Deep Learning (DL), has profoundly impacted numerous domains, including autonomous driving. The integration of autonomous driving in military settings reduces human casualties and enables precise and safe execution of missions in hazardous environments while ... | {
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2501.06208 | Enhancing AI Safety Through the Fusion of Low Rank Adapters | [
"cs.CL"
] | Instruction fine-tuning of large language models (LLMs) is a powerful method for improving task-specific performance, but it can inadvertently lead to a phenomenon where models generate harmful responses when faced with malicious prompts. In this paper, we explore Low-Rank Adapter Fusion (LoRA) as a means to mitigate t... | {
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2501.06210 | Applications of natural language processing in aviation safety: A review
and qualitative analysis | [
"cs.CL",
"cs.LG"
] | This study explores using Natural Language Processing in aviation safety, focusing on machine learning algorithms to enhance safety measures. There are currently May 2024, 34 Scopus results from the keyword search natural language processing and aviation safety. Analyzing these studies allows us to uncover trends in th... | {
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2501.06211 | FLAME: Financial Large-Language Model Assessment and Metrics Evaluation | [
"cs.CL",
"cs.AI",
"cs.CE"
] | LLMs have revolutionized NLP and demonstrated potential across diverse domains. More and more financial LLMs have been introduced for finance-specific tasks, yet comprehensively assessing their value is still challenging. In this paper, we introduce FLAME, a comprehensive financial LLMs evaluation system in Chinese, wh... | {
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2501.06214 | Path Space Partitioning and Guided Image Sampling for MCMC | [
"cs.CV",
"cs.GR"
] | Rendering algorithms typically integrate light paths over path space. However, integrating over this one unified space is not necessarily the most efficient approach, and we show that partitioning path space and integrating each of these partitioned spaces with a separate estimator can have advantages. We propose an ap... | {
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2501.06215 | Fitting Different Interactive Information: Joint Classification of
Emotion and Intention | [
"cs.CV",
"cs.CL",
"cs.LG",
"cs.MM",
"eess.AS"
] | This paper is the first-place solution for ICASSP MEIJU@2025 Track I, which focuses on low-resource multimodal emotion and intention recognition. How to effectively utilize a large amount of unlabeled data, while ensuring the mutual promotion of different difficulty levels tasks in the interaction stage, these two poin... | {
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2501.06216 | Understanding colors of Dufaycolor: Can we recover them using historical
colorimetric and spectral data? | [
"cs.CV",
"cs.GR"
] | Dufaycolor, an additive color photography process produced from 1935 to the late 1950s, represents one of the most advanced iterations of this technique. This paper presents ongoing research and development of an open-source Color-Screen tool designed to reconstruct the original colors of additive color photographs. We... | {
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2501.06218 | Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models | [
"cs.CV"
] | Vision generative models have recently made significant advancements along two primary paradigms: diffusion-style and language-style, both of which have demonstrated excellent scaling laws. Quantization is crucial for efficiently deploying these models, as it reduces memory and computation costs. In this work, we syste... | {
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2501.06219 | WhACC: Whisker Automatic Contact Classifier with Expert Human-Level
Performance | [
"cs.CV",
"cs.LG"
] | The rodent vibrissal system is pivotal in advancing neuroscience research, particularly for studies of cortical plasticity, learning, decision-making, sensory encoding, and sensorimotor integration. Despite the advantages, curating touch events is labor intensive and often requires >3 hours per million video frames, ev... | {
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2501.06220 | Powerful Design of Small Vision Transformer on CIFAR10 | [
"cs.LG",
"cs.CV"
] | Vision Transformers (ViTs) have demonstrated remarkable success on large-scale datasets, but their performance on smaller datasets often falls short of convolutional neural networks (CNNs). This paper explores the design and optimization of Tiny ViTs for small datasets, using CIFAR-10 as a benchmark. We systematically ... | {
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2501.06221 | Optimizing Supply Chain Networks with the Power of Graph Neural Networks | [
"cs.LG",
"econ.GN",
"q-fin.EC"
] | Graph Neural Networks (GNNs) have emerged as transformative tools for modeling complex relational data, offering unprecedented capabilities in tasks like forecasting and optimization. This study investigates the application of GNNs to demand forecasting within supply chain networks using the SupplyGraph dataset, a benc... | {
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2501.06222 | Can Explainable AI Assess Personalized Health Risks from Indoor Air
Pollution? | [
"cs.LG"
] | Acknowledging the effects of outdoor air pollution, the literature inadequately addresses indoor air pollution's impacts. Despite daily health risks, existing research primarily focused on monitoring, lacking accuracy in pinpointing indoor pollution sources. In our research work, we thoroughly investigated the influenc... | {
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2501.06223 | Interpretable Auto Window Setting for Deep-Learning-Based CT Analysis | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Whether during the early days of popularization or in the present, the window setting in Computed Tomography (CT) has always been an indispensable part of the CT analysis process. Although research has investigated the capabilities of CT multi-window fusion in enhancing neural networks, there remains a paucity of domai... | {
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2501.06224 | Detection, Retrieval, and Explanation Unified: A Violence Detection
System Based on Knowledge Graphs and GAT | [
"cs.CV",
"cs.AI"
] | Recently, violence detection systems developed using unified multimodal models have achieved significant success and attracted widespread attention. However, most of these systems face two critical challenges: the lack of interpretability as black-box models and limited functionality, offering only classification or re... | {
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} |
2501.06225 | A Distributed Hybrid Quantum Convolutional Neural Network for Medical
Image Classification | [
"cs.CV",
"cs.LG"
] | Medical images are characterized by intricate and complex features, requiring interpretation by physicians with medical knowledge and experience. Classical neural networks can reduce the workload of physicians, but can only handle these complex features to a limited extent. Theoretically, quantum computing can explore ... | {
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2501.06226 | asanAI: In-Browser, No-Code, Offline-First Machine Learning Toolkit | [
"cs.LG",
"cs.AI",
"cs.SE"
] | Machine learning (ML) has become crucial in modern life, with growing interest from researchers and the public. Despite its potential, a significant entry barrier prevents widespread adoption, making it challenging for non-experts to understand and implement ML techniques. The increasing desire to leverage ML is counte... | {
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2501.06227 | Generating and Detecting Various Types of Fake Image and Audio Content:
A Review of Modern Deep Learning Technologies and Tools | [
"cs.CR",
"cs.LG"
] | This paper reviews the state-of-the-art in deepfake generation and detection, focusing on modern deep learning technologies and tools based on the latest scientific advancements. The rise of deepfakes, leveraging techniques like Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion models a... | {
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} |
2501.06229 | Open-Source Manually Annotated Vocal Tract Database for Automatic
Segmentation from 3D MRI Using Deep Learning: Benchmarking 2D and 3D
Convolutional and Transformer Networks | [
"cs.CV",
"cs.SD",
"eess.AS"
] | Accurate segmentation of the vocal tract from magnetic resonance imaging (MRI) data is essential for various voice and speech applications. Manual segmentation is time intensive and susceptible to errors. This study aimed to evaluate the efficacy of deep learning algorithms for automatic vocal tract segmentation from 3... | {
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} |
2501.06230 | BEN: Using Confidence-Guided Matting for Dichotomous Image Segmentation | [
"cs.CV",
"eess.IV"
] | Current approaches to dichotomous image segmentation (DIS) treat image matting and object segmentation as fundamentally different tasks. As improvements in image segmentation become increasingly challenging to achieve, combining image matting and grayscale segmentation techniques offers promising new directions for arc... | {
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} |
2501.06231 | Sustainable and Intelligent Public Facility Failure Management System
Based on Large Language Models | [
"cs.AI"
] | This paper presents a new Large Language Model (LLM)-based Smart Device Management framework, a pioneering approach designed to address the intricate challenges of managing intelligent devices within public facilities, with a particular emphasis on applications to libraries. Our framework leverages state-of-the-art LLM... | {
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} |
2501.06232 | An Interpretable ML-based Model for Predicting p-y Curves of Monopile
Foundations in Sand | [
"cs.LG",
"cond-mat.soft"
] | Predicting the lateral pile response is challenging due to the complexity of pile-soil interactions. Machine learning (ML) techniques have gained considerable attention for their effectiveness in non-linear analysis and prediction. This study develops an interpretable ML-based model for predicting p-y curves of monopil... | {
"Other": 0,
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} |
2501.06233 | Mechanics and Design of Metastructured Auxetic Patches with Bio-inspired
Materials | [
"cs.LG",
"cond-mat.mtrl-sci"
] | Metastructured auxetic patches, characterized by negative Poisson's ratios, offer unique mechanical properties that closely resemble the behavior of human tissues and organs. As a result, these patches have gained significant attention for their potential applications in organ repair and tissue regeneration. This study... | {
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} |
2501.06235 | NextStop: An Improved Tracker For Panoptic LIDAR Segmentation Data | [
"cs.CV",
"cs.AI",
"cs.RO"
] | 4D panoptic LiDAR segmentation is essential for scene understanding in autonomous driving and robotics ,combining semantic and instance segmentation with temporal consistency.Current methods, like 4D-PLS and 4D-STOP, use a tracking-by-detection methodology, employing deep learning networks to perform semantic and insta... | {
"Other": 0,
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} |
2501.06236 | Data-Driven Radio Propagation Modeling using Graph Neural Networks | [
"cs.LG",
"cs.AI",
"cs.NI"
] | Modeling radio propagation is essential for wireless network design and performance optimization. Traditional methods rely on physics models of radio propagation, which can be inaccurate or inflexible. In this work, we propose using graph neural networks to learn radio propagation behaviors directly from real-world net... | {
"Other": 1,
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"cs.SY": 0
} |
2501.06237 | Forecasting Anonymized Electricity Load Profiles | [
"cs.CR",
"cs.AI",
"cs.LG"
] | In the evolving landscape of data privacy, the anonymization of electric load profiles has become a critical issue, especially with the enforcement of the General Data Protection Regulation (GDPR) in Europe. These electric load profiles, which are essential datasets in the energy industry, are classified as personal be... | {
"Other": 0,
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} |
2501.06238 | Multi-field Visualization: Trait design and trait-induced merge trees | [
"cs.LG",
"cs.GR"
] | Feature level sets (FLS) have shown significant potential in the analysis of multi-field data by using traits defined in attribute space to specify features in the domain. In this work, we address key challenges in the practical use of FLS: trait design and feature selection for rendering. To simplify trait design, we ... | {
"Other": 1,
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} |
2501.06239 | Towards a scalable AI-driven framework for data-independent Cyber Threat
Intelligence Information Extraction | [
"cs.CR",
"cs.AI",
"cs.CL"
] | Cyber Threat Intelligence (CTI) is critical for mitigating threats to organizations, governments, and institutions, yet the necessary data are often dispersed across diverse formats. AI-driven solutions for CTI Information Extraction (IE) typically depend on high-quality, annotated data, which are not always available.... | {
"Other": 0,
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} |
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