Results 151 to 160 of about 120,084 (296)

Accounting for data variability in multi-institutional distributed deep learning for medical imaging. [PDF]

J Am Med Inform Assoc, 2020
Balachandar N, Chang K, Kalpathy-Cramer J, Rubin DL.   +3 more
europepmc   +1 more source

Toward Knowledge‐Based Workflows: A Semantic Approach to Atomistic Simulations for Mechanical and Thermodynamic Properties

Advanced Engineering Materials, EarlyView.
Knowledge‐based atomistic workflows are presented for mechanical and thermodynamic properties. By coupling modular simulations with ontology‐aligned metadata and provenance, Fe case studies on elastic behavior, defects, thermal properties, and Hall–Petch strengthening reveal how FAIR, queryable, and reusable simulation data can be generated. Mechanical
Abril Azócar Guzmán, Hoang‐Thien Luu, Sarath Menon, Tilmann Hickel, Nina Merkert, Stefan Sandfeld   +5 more
wiley   +1 more source

ShmCaffe: A Distributed Deep Learning Platform with Shared Memory Buffer for HPC Architecture

, 2018
One of the reasons behind the tremendous success of deep learning theory and applications in the recent days is advances in distributed and parallel high performance computing (HPC).
Joongheon Kim, Wan Choi, Shinyoung Ahn, Aziz Mohaisen, Sungwon Kang, Ahn, Shinyoung, Lim, Eunji, Kim, Joongheon, Eunji Lim, Mohaisen, Aziz, Choi, Wan, Kang, Sungwon   +11 more
core   +1 more source

Corrigendum to: Accounting for data variability in multi-institutional distributed deep learning for medical imaging. [PDF]

J Am Med Inform Assoc, 2020
Balachandar N, Chang K, Kalpathy-Cramer J, Rubin DL.   +3 more
europepmc   +1 more source

An Experimental High‐Throughput Approach for the Screening of Hard Magnet Materials

Advanced Engineering Materials, EarlyView.
An entire workflow for the high‐throughput characterization and analysis of compositionally graded magnetic films is presented. Characterization protocols, data management tools and data analysis approaches are illustrated with test case Sm(Fe, V)12 based films.
William Rigaut, Pierre Le Berre, Hamdi Jaballah, Lukas Fink, Victor Poline, Richard Haettel, Stéphane Grenier, Gilbert Chahine, Ewen Bellec, Guillaume Buatier, Wilfried Hortschitz, Santa Pile, Thomas Schrefl, Nils Blanc, Yuan Hong, Thibaut Devillers, Nora M. Dempsey   +16 more
wiley   +1 more source

Resource Allocation and Workload Scheduling for Large-Scale Distributed Deep Learning: A Survey

With rapidly increasing distributed deep learning workloads in large-scale data centers, efficient distributed deep learning framework strategies for resource allocation and workload scheduling have become the key to high-performance deep learning.
Leung, Victor C. M., Hu, Xiping, Liang, Feng, Li, Chengming, Zhang, Zhen, Lu, Haifeng, Guo, Yanyi   +6 more
core   +1 more source

A distributed deep learning-driven edge caching strategy for industrial IoT networks

, 2022
The Industrial Internet-of-Things (IIoT) refers to the use of interconnected networks of industrial-grade devices to enhance productivities and improve the efficiency of industrial processes.
Shen, Li Qin
core  

Distributed deep learning across multisite datasets for generalized CT hemorrhage segmentation. [PDF]

Med Phys, 2020
Remedios SW, Roy S, Bermudez C, Patel MB, Butman JA, Landman BA, Pham DL.   +6 more
europepmc   +1 more source

Microstructure Reconstruction in Battery Electrodes Using Machine Learning Based on Low‐Voltage Focused Ion Beam–Scanning Electron Microscopy Tomography Images

Advanced Engineering Materials, EarlyView.
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran, Vincent Nebel, Dominik Perius, Kshitij Kumar, Yannis Heim, Laura Bies, Tobias Kraus   +6 more
wiley   +1 more source

Influence of Geometric Design on Mechanical Performance of Auxetic Metastructure

Advanced Engineering Materials, EarlyView.
Strategic geometric reinforcement transforms auxetic performance. This study evaluates 3D‐printed arrowhead metastructures, revealing that a modified design with local ring reinforcement suppresses premature failure to achieve superior energy absorption and structural efficiency.
Muhammad Gulzari, Supharoek Sornsomrit, Muhammad Ayaz Akbar, Stephan Rudykh   +3 more
wiley   +1 more source