Data Science

UMKC Announces New Master of Science in Artificial Intelligence

UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...

Scientists uncover new method to generate protein datasets for training AI

6 Months Using AI for Actual Work: What's Incredible, What's Overhyped, and What's Quietly Dangerous

Six months ago I committed to using AI tools for everything I possibly could in my work. Every day, every task, every workflow. Here's th...

[2602.12292] A Gradient Boosted Mixed-Model Machine Learning Framework for Vessel Speed in the U.S. Arctic

This paper presents a gradient boosted mixed-model machine learning framework to analyze vessel speed in the U.S. Arctic, utilizing AIS d...

[2602.12289] String-Level Ground Fault Localization for TN-Earthed Three-Phase Photovoltaic Systems

This article presents a novel edge-AI approach for localizing ground faults in TN-earthed three-phase photovoltaic systems, enhancing eff...

[2602.13174] Learning functional components of PDEs from data using neural networks

The paper explores how neural networks can be integrated into partial differential equations (PDEs) to recover unknown functions from dat...

[2602.13155] Learning to Approximate Uniform Facility Location via Graph Neural Networks

This paper explores a novel approach using Graph Neural Networks (GNNs) to solve the Uniform Facility Location problem, merging learning-...

[2602.13140] FlashSchNet: Fast and Accurate Coarse-Grained Neural Network Molecular Dynamics

FlashSchNet presents a novel framework for molecular dynamics simulations, enhancing speed and accuracy through innovative techniques in ...

[2602.13136] Order Matters in Retrosynthesis: Structure-aware Generation via Reaction-Center-Guided Discrete Flow Matching

The paper presents a novel framework for retrosynthesis, emphasizing the importance of atom ordering in neural representations to enhance...

[2602.13075] Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding

This paper presents a unified approach to graph pre-training that effectively integrates both homogeneous and heterogeneous graphs, addre...

[2602.13010] Probabilistic Wind Power Forecasting with Tree-Based Machine Learning and Weather Ensembles

This paper presents a method for probabilistic wind power forecasting using tree-based machine learning and weather ensembles, demonstrat...

[2602.13008] Machine Learning-Based Classification of Jhana Advanced Concentrative Absorption Meditation (ACAM-J) using 7T fMRI

This study explores the use of machine learning to classify Jhana advanced concentration absorption meditation (ACAM-J) through 7T fMRI, ...

[2602.13004] Uncertainty in Federated Granger Causality: From Origins to Systemic Consequences

This paper presents a novel methodology for quantifying uncertainty in Federated Granger Causality, addressing limitations in existing al...

[2602.12982] Multi-Dimensional Visual Data Recovery: Scale-Aware Tensor Modeling and Accelerated Randomized Computation

The paper presents a novel approach to multi-dimensional visual data recovery using Scale-Aware Tensor Modeling and accelerated randomize...

[2602.12980] MAUNet-Light: A Concise MAUNet Architecture for Bias Correction and Downscaling of Precipitation Estimates

The paper presents MAUNet-Light, a lightweight neural network architecture designed for bias correction and downscaling of precipitation ...

[2602.12961] Ca-MCF: Category-level Multi-label Causal Feature selection

The paper introduces Ca-MCF, a novel method for category-level multi-label causal feature selection, enhancing predictive accuracy while ...

[2602.12744] Adaptive Structured Pruning of Convolutional Neural Networks for Time Series Classification

This article presents Dynamic Structured Pruning (DSP), an innovative method for optimizing convolutional neural networks in time series ...

[2602.12753] Hierarchical Successor Representation for Robust Transfer

The paper introduces the Hierarchical Successor Representation (HSR), addressing limitations of classical successor representation in dyn...

[2602.12756] Closing the Loop: A Control-Theoretic Framework for Provably Stable Time Series Forecasting with LLMs

This paper introduces F-LLM, a control-theoretic framework for stable time series forecasting using large language models, addressing iss...

[2602.12706] Physics-Informed Laplace Neural Operator for Solving Partial Differential Equations

The paper presents the Physics-Informed Laplace Neural Operator (PILNO), a novel approach to solving partial differential equations (PDEs...

[2602.12708] Mixture of Predefined Experts: Maximizing Data Usage on Vertical Federated Learning

The paper introduces Split-MoPE, a novel framework for Vertical Federated Learning that maximizes data usage by integrating predefined ex...

[2602.12704] QTabGAN: A Hybrid Quantum-Classical GAN for Tabular Data Synthesis

QTabGAN introduces a hybrid quantum-classical generative adversarial network designed for synthesizing tabular data, addressing challenge...

[2602.12693] Leverage-Weighted Conformal Prediction

The paper introduces Leverage-Weighted Conformal Prediction (LWCP), a method that enhances prediction intervals by adapting to variance w...