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...

Accelerating science with AI and simulations

MIT Professor Rafael Gómez-Bombarelli discusses the transformative potential of AI in scientific research, emphasizing its role in materi...

Scientists uncover new method to generate protein datasets for training AI

[2602.14699] Qute: Towards Quantum-Native Database

The paper presents Qute, a quantum-native database that integrates quantum computation into database operations, enhancing performance ov...

[2602.14947] Gradient Networks for Universal Magnetic Modeling of Synchronous Machines

This paper introduces a physics-informed neural network approach for modeling saturable synchronous machines, enhancing dynamic modeling ...

[2602.14975] Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Non-Conservative Forces

The paper presents a novel approach, DMTS-NC, for accelerating molecular dynamics simulations using neural network potentials, achieving ...

[2602.14939] Fault Detection in Electrical Distribution System using Autoencoders

This paper presents an anomaly-based approach for fault detection in electrical distribution systems using deep autoencoders, achieving h...

[2602.14655] Breaking Data Efficiency Dilemma: A Federated and Augmented Learning Framework For Alzheimer's Disease Detection via Speech

This article presents a novel framework, FAL-AD, that enhances data efficiency in Alzheimer's Disease detection through federated and aug...

[2602.14934] Activation-Space Uncertainty Quantification for Pretrained Networks

The paper presents Gaussian Process Activations (GAPA), a novel method for uncertainty quantification in pretrained networks, enhancing e...

[2602.14928] From Classical to Quantum: Extending Prometheus for Unsupervised Discovery of Phase Transitions in Three Dimensions and Quantum Systems

This article presents an extension of the Prometheus framework for unsupervised discovery of phase transitions, applying it to both class...

[2602.14907] Adjoint-based Shape Optimization, Machine Learning based Surrogate Models, Conditional Variational Autoencoder (CVAE), Voith Schneider propulsion (VSP), Self-propelled Ship, Propulsion Model, Hull Optimization

This paper presents a machine learning-assisted framework for optimizing ship hull designs using adjoint-based methods, addressing challe...

[2602.14885] Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks

The paper introduces Drift-Diffusion Matching, a framework for training recurrent neural networks (RNNs) to model complex stochastic dyna...

[2602.14867] Fast and accurate quasi-atom method for simultaneous atomistic and continuum simulation of solids

This paper presents a novel quasi-atom method for simultaneous atomistic and continuum simulations of solids, demonstrating improved comp...

[2602.14862] The Well-Tempered Classifier: Some Elementary Properties of Temperature Scaling

The paper explores the properties of temperature scaling in probabilistic models, particularly its impact on classifier calibration and l...

[2602.14833] RF-GPT: Teaching AI to See the Wireless World

RF-GPT introduces a novel radio-frequency language model that bridges the gap between RF signal processing and high-level reasoning using...

[2602.14536] Explainable Token-level Noise Filtering for LLM Fine-tuning Datasets

The paper presents XTF, an explainable token-level noise filtering framework designed to enhance the fine-tuning of Large Language Models...

[2602.14828] Exploring the limits of pre-trained embeddings in machine-guided protein design: a case study on predicting AAV vector viability

This study evaluates the effectiveness of pre-trained embeddings in machine-guided protein design, focusing on predicting AAV vector viab...

[2602.14785] SA-SSL-MOS: Self-supervised Learning MOS Prediction with Spectral Augmentation for Generalized Multi-Rate Speech Assessment

The paper presents a novel approach to speech quality assessment using self-supervised learning and spectral augmentation, addressing cha...

[2602.14488] BETA-Labeling for Multilingual Dataset Construction in Low-Resource IR

This article presents the BETA-labeling framework for constructing a Bangla IR dataset, addressing challenges in low-resource languages a...

[2602.14743] LLMStructBench: Benchmarking Large Language Model Structured Data Extraction

LLMStructBench introduces a benchmark for evaluating large language models on structured data extraction, emphasizing the impact of promp...

[2602.14757] Solving Inverse Parametrized Problems via Finite Elements and Extreme Learning Networks

This paper presents a novel framework for solving inverse parametrized problems using finite element methods and extreme learning network...

[2602.14481] On the Rate-Distortion-Complexity Tradeoff for Semantic Communication

This paper explores the rate-distortion-complexity tradeoff in semantic communication, proposing a framework that balances semantic dista...

[2602.14642] GenPANIS: A Latent-Variable Generative Framework for Forward and Inverse PDE Problems in Multiphase Media

GenPANIS introduces a generative framework for solving forward and inverse PDE problems in multiphase media, enhancing accuracy and effic...