Results 191 to 200 of about 1,080,302 (365)

Learning to Rank With Bregman Divergences and Monotone Retargeting

, 2012
Sreangsu Acharyya, Oluwasanmi Koyejo, Joydeep Ghosh   +2 more
openalex   +2 more sources

Consolidate Overview of Ribonucleic Acid Molecular Dynamics: From Molecular Movements to Material Innovations

Advanced Engineering Materials, EarlyView.
Molecular dynamics simulations are advancing the study of ribonucleic acid (RNA) and RNA‐conjugated molecules. These developments include improvements in force fields, long‐timescale dynamics, and coarse‐grained models, addressing limitations and refining methods.
Kanchan Yadav, Iksoo Jang, Jong Bum Lee
wiley   +1 more source

Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials

Advanced Engineering Materials, EarlyView.
This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani, Surabhi Nadigotti, Erfan Fatehi, Derek Aranguren van Egmond, Behnam Ashrafi   +4 more
wiley   +1 more source

Low‐Activation Compositionally Complex Alloys for Advanced Nuclear Applications—A Review

Advanced Engineering Materials, EarlyView.
Low‐activation compositionally complex alloys (LACCAs) are advanced metallic materials primarily composed of low‐activation elements, offering advantages such as rapid compliance with operational standards and safe recyclability. This review highlights their potential for extreme high‐temperature irradiation environments as structural materials for ...
Yangfan Wang, Hanliang Zhu, Zhiyang Wang, Zhijun Qiu, Xiao Wang, Zengxi Pan, Hongtao Zhu, Mingxing Zhang, Huijun Li   +8 more
wiley   +1 more source

Incorporating query-specific feedback into learning-to-rank models

, 2014
Ethem F. Can, W. Bruce Croft, R. Manmatha   +2 more
openalex   +2 more sources

Multiscale Modeling of Process‐Induced Defects in Fused Filament Fabrication‐Printed Materials

Advanced Engineering Materials, EarlyView.
This study presents a predictive multiscale modeling tool for defect analysis of fused filament fabricated‐printed materials and their performance prediction using a mechanistic data science‐based reduced‐order modeling approach. Process‐induced defects are inherent to additively manufactured parts and significantly influence the performance of printed
Satyajit Mojumder, Derick Suarez, Trevor Abbott, Wing Kam Liu   +3 more
wiley   +1 more source

Smart learning: A search-based approach to rank change and defect prone classes

, 2015
Carol V. Alexandru, Annibale Panichella, Sebastiano Panichella, Alberto Bacchelli, Harald C. Gall   +4 more
openalex   +1 more source

Quad-Networks: Unsupervised Learning to Rank for Interest Point Detection [PDF]

, 2017
Nikolay Savinov, Akihito Seki, Ľubor Ladický, Torsten Sattler, Marc Pollefeys   +4 more
openalex   +1 more source

First‐Principles Modeling of Solid Solution Softening and Hardening Effects in Al–Mg–Zr–Si Aluminum Alloys

Advanced Engineering Materials, EarlyView.
The role of various alloying elements in face‐centered cubic aluminum on the barrier of a Shockley partial dislocation during its motion is presented. The study aims to understand how alloying atoms such as Mg, Si, and Zr affect the energy landscape for dislocation motion, thus influencing the solid solution hardening and softening in aluminum, which ...
Inna Plyushchay, Zhengqing Wei, Nebahat Bulut, Sibylle Gemming   +3 more
wiley   +1 more source

Incorporating Radiologist Knowledge Into MRI Quality Metrics for Machine Learning Using Rank-Based Ratings. [PDF]

J Magn Reson Imaging
Tang C, Eisenmenger LB, Rivera-Rivera L, Huo E, Junn JC, Kuner AD, Oechtering TH, Peret A, Starekova J, Johnson KM.   +9 more
europepmc   +1 more source