#machine learning
88 resultsA Distributed Acoustic Sensing Dataset for Vessel Detection and Localization in Submarine Cable Protection
Erick Eduardo Ramirez-Torres, Javier Macias-Guarasa, Daniel Pizarro +7
The paper introduces a dataset of distributed acoustic sensing measurements from a submarine fiber-optic cable, paired with AIS vessel data, to support machine‑learning research on…
A comparative analysis of automated techniques for security bug report identification
Muhammad Laiq
The paper compares various automated methods, including traditional machine‑learning models and large language models, for identifying security‑related bug reports, finding that th…
Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches
Laura B. Balzer, Lei Nie, Issa J. Dahabreh +15
The paper discusses how to improve precision in randomized clinical trials by using covariate adjustment, comparing traditional fixed parametric methods with flexible data‑adaptive…
$αβ$-Tessarine Toolbox: A high-performance MATLAB framework for hypercomplex tensor algebra
José Domingo Jiménez-López, Jesús Navarro-Moreno, Juan Carlos Ruiz-Molina
The paper presents the αβ‑Tessarine Toolbox, a high‑performance MATLAB framework for performing hypercomplex tensor algebra, including exact matrix factorizations, aimed at large‑s…
Predictability of Human Movements across Industry Sectors using Multilayer Networks
Maisha Islam Sejunti, Melissa Butler, Yingjie Hu +1
The paper evaluates how well human movement across different industry sectors can be predicted using demographic, socioeconomic, and infrastructure data, comparing ten statistical…
Learning features from Newton's algorithm: a way to accelerate nonlinear parametrized PDE solvers
Rémy Vallot, Florian de Vuyst, Thibault Dairay +1
The paper introduces a two‑stage method that learns features from precomputed Newton trajectories to predict a surrogate solution and then apply a cheap corrective step, providing…
AI-based scoring systematically underestimates conceptual understanding of linguistically weak students' explanations in physics
Markus S. Feser, Paul L. Tschisgale
The study evaluates AI-based scoring of secondary students' physics explanations and finds that these systems systematically underestimate conceptual understanding for explanations…
Finite-size effects and interaction-driven crossovers in quarter-filled attractive Hubbard model: Exact diagonalization, DMRG and machine-learning analysis
Md Fahad Equbal, M. A. H. Ahsan, Satoru Hayami
The paper studies the quarter‑filled attractive Hubbard model on finite cylindrical lattices using exact diagonalization, DMRG, and unsupervised machine‑learning methods to identif…
Learning to Trace Seiberg Dualities
Jonathan J. Heckman, Shani Meynet, Alessandro Mininno +1
The paper applies machine learning, including transformers and MLPs, to identify Seiberg dualities in supersymmetric quiver gauge theories by learning quiver mutations, showing imp…
Optimizing the extraction of information from redshift probability distribution functions
Rodrigo Duarte, Valerio Marra
The paper presents turboPDZ, a machine‑learning framework that extracts optimized point estimates and reliability scores directly from photometric redshift probability distribution…
Extracting informative vortical structures of turbulent wake-extreme vortex gust interactions with machine learning
Ryo Koshikawa, Kai Fukami
The paper applies a convolutional information‑theoretic machine‑learning method to separate informative vortical structures from residual flow in turbulent wake and vortex‑gust int…
Physics-informed Machine Learning Prediction of Hubbard Interaction Parameters
Jiyeon Kim, Indukuru Ramesh Reddy, Bongjae Kim +1
The paper develops machine‑learning models that predict cRPA‑derived Hubbard interaction parameters (U_eff, V, and J) for transition‑metal oxides, providing both accurate predictio…
Reinforcement Learning applied to Optimization of LHC beams in the CERN Proton Synchrotron
Joel Axel Wulff, Alexandre Lasheen
The paper describes using a convolutional neural network and reinforcement‑learning agents to automatically optimize the longitudinal triple‑splitting of proton beams in CERN's Pro…
Tangling Pull Requests: Curating a Commit Untangling Dataset from Merged PRs
Yuki Ueno, Profir-Petru Pârţachi, Takashi Kobayashi
The paper presents a method to automatically build a large dataset of tangled and untangled commits by extracting and filtering commits from merged pull requests, showing that this…
A Data-Driven Vibration Analysis Framework for Micro-Motor Fault Diagnosis and Quality Control
Xuan Chen, Xinjun Zuo, Yancheng Bi +2
The paper presents a vibration‑based method that uses a custom accelerometer setup, feature extraction, random‑forest feature selection, and an SVM classifier to detect faults in m…
An Enhanced Catalog of Gaia DR3 Galaxy Candidates with Spectroscopic and Machine-Learning Photometric Redshifts
Junghyun Hwang, Ho Seong Hwang
The paper presents an updated all‑sky galaxy catalog based on Gaia DR3, adding spectroscopic redshifts where available and machine‑learning photometric redshifts for the rest, usin…
Multi-scale radiomics in pelvic MRI for endometriosis subtyping: highlighting data heterogeneity constraints
Eliot Leguy, Chloe Mallet, Nicolas Normand +1
The paper evaluates a radiomics pipeline on pelvic MRI to subtype endometriosis, comparing multi‑scale feature representations and showing modest classification performance but lim…
Rotational equivariance and locality in data-driven subgrid-scale closures
Ryley McConkey, Julia Balla, Elyssa Hofgard +2
The paper evaluates how enforcing rotational equivariance in data‑driven subgrid‑scale models for large‑eddy simulation impacts accuracy, parameter efficiency, and generalization,…
Toward Multi-Modal Deep Learning for Pulmonary Disease Classification: A Texture-Based Machine Learning Pilot Study on Public Chest X-Ray Data
Yogisri Pujitha Chinthoti
The study evaluates classical texture (GLCM) and gradient (HOG) features with standard classifiers to distinguish COVID-19 from other pneumonia on a public chest X‑ray dataset, ach…
MatCreatioNN: Machine learning-guided computational discovery of photocatalysts for environmental applications
Satya Kokonda
The paper presents a machine‑learning workflow that combines reinforcement‑learning generation of metal‑organic frameworks with a crystal‑graph convolutional neural network to pred…
Testing of machine learning wavefront sensing algorithms on the Tiny Observatory for Telescope Optimization (TOTO) testbed
Sanchit Sabhlok, Solvay A. Blomquist, Maggie Y +16
The paper evaluates a machine‑learning based wavefront sensing algorithm on the Tiny Observatory for Telescope Optimization (TOTO) testbed, comparing its low‑order Zernike coeffici…
GSpyNetTree-O4: an event validation tool used in the fourth LIGO-Virgo-KAGRA observing run
Sofia Alvarez-Lopez, Man Leong Chan, Franz S. Herbst +7
The paper presents GSpyNetTree-O4, a machine‑learning tool deployed in the fourth LIGO‑Virgo‑KAGRA observing run to classify detector glitches and validate gravitational‑wave event…
A Density-Matrix Framework for Electronic-Structure Analysis of Functional-Group and Salt Effects in Lithium-Metal Electrolytes
Mingkang Liu, Huize Yu, Yanbin Gao +3
The paper introduces EMolStudio, an AI-driven density‑matrix framework that predicts electronic‑structure properties of lithium‑metal electrolyte molecules and their explicit Li⁺ s…
Multi-Dimensional Entropy for Vibration Measurement Data Quality Assessment and Erroneous Signal Identification in Wind Turbines
Xiao-Ming Yuan, Zishun Wang, Donghui Zhao +2
The paper introduces a Multi-Dimensional Entropy (MDE) metric that evaluates the quality of vibration measurements in wind turbines by analyzing time‑domain, spectral, and frequenc…