Results 141 to 150 of about 55,999 (288)

Graphics Processing Units (GPU) acceleration on acoustic 3D simulations

, 2011
Graphics Processing Units (GPU) feedback on acoustic 3D code, more precisely with a sparse inverse matrix kernel in ...
openaire   +1 more source

Usability of a deep learning platform for detecting radiographic bone loss and furcation involvement

Journal of Periodontology, EarlyView.
Abstract Background Assessing radiographic bone condition is important for periodontal diagnosis. The accuracy of radiographic interpretation depends highly on a clinician's experience and knowledge. This study aimed to develop a deep learning‐based online platform that aids clinicians in diagnosing periodontitis based on periapical radiographs and to ...
Chun‐Teh Lee, Tanjida Kabir, Alaa Alsaffar, Jiman Nelson, Sally Sheng, Hsiu‐Wan Meng, Mohammad Qali, Muhammad F. Walji, Xiaoqian Jiang, Shayan Shams   +9 more
wiley   +1 more source

A Power‐Efficient Monte Carlo Framework for Nonlinear Light–Matter Interactions: In Silico Modeling of Spontaneous and Stimulated Raman Scattering in Turbid Media

Journal of Raman Spectroscopy, EarlyView.
Power‐efficient Monte Carlo modeling of nonlinear light–matter interactions in turbid media is demonstrated using Apple Silicon–accelerated photon transport. The Metal‐base framework enables accurate simulation of spontaneous and stimulated Raman scattering, revealing detection‐dependent SRS efficiency while providing a scalable, energy‐efficient ...
Ilya Vladyko, Vladislav Yakovlev, Alexander Doronin   +2 more
wiley   +1 more source

Benchmarking Large Language Models for Polymer Property Predictions

Macromolecular Rapid Communications, EarlyView.
Large language models (LLMs) are fine‐tuned on polymer thermal property datasets to directly predict glass transition, melting, and decomposition temperatures from SMILES inputs. Compared to state‐of‐the‐art models such as Polymer Genome, polyGNN, and polyBERT, LLMs achieve competitive yet lower accuracy.
Sonakshi Gupta, Akhlak Mahmood, Shivank Shukla, Rampi Ramprasad   +3 more
wiley   +1 more source

CCC-GPU: a graphics processing unit (GPU)-accelerated nonlinear correlation coefficient for large-scale transcriptomic analyses. [PDF]

Bioinformatics
Zhang H, Fotso KT, Subirana-Granés M, Pividori M.   +3 more
europepmc   +1 more source

DeepSeek‐Lattice‐KG: A Compact Language Model With Knowledge Graph Augmentation for Lattice Structure Design

Materials Genome Engineering Advances, EarlyView.
DeepSeek‐Lattice‐KG integrates a domain‐adapted 14B LLM with a Neo4j lattice knowledge graph distilled from 50,000 papers. It analyzes queries, retrieves supporting subgraphs, and generates grounded answers; on a 2100‐question, six‐domain benchmark, it achieves 94.8% accuracy.
Zhiyang Shu, Chao Wang, Changsheng Feng, Feiyu Zhou, Xueyan Chen, Yan Chen, Yilun Liu   +6 more
wiley   +1 more source

Efficient numerical approaches with accelerated graphics processing unit (GPU) computations for Poisson problems and Cahn-Hilliard equations. [PDF]

AIMS Math
Orizaga S, Fabien M, Millard M.
europepmc   +1 more source

Free‐Breathing Magnetization Transfer Imaging of the Lung at 0.55 T Using bSTAR

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To develop a clinically applicable method for magnetization transfer (MT) imaging of the lung at 0.55 T. Methods MT imaging of the chest was explored in healthy volunteers at 0.55 T using a self‐gated 3D half‐radial dual‐echo balanced steady‐state free precession sequence (bSTAR), a 2D multi‐slice gradient echo method (GRE) acquired in
Alexandra Braun, Grzegorz Bauman, Maurice Pradella, Jonathan Röcken, Katrin E. Hostettler, Oliver Bieri   +5 more
wiley   +1 more source

Global optimization tailored for graphics processing units: Complete and rigorous search for large-scale nonlinear minimization. [PDF]

PNAS Nexus
Zhang G, Shan Q, Cagan J.
europepmc   +1 more source

Massive Sampling Strategy for Antibody–Antigen Targets in CAPRI Round 55 With MassiveFold

Proteins: Structure, Function, and Bioinformatics, EarlyView.
ABSTRACT Massive sampling with AlphaFold2 improves protein–protein complex predictions. This has been shown during the last CASP15‐CAPRI blind prediction round by the AFsample tool. However, more difficult targets including antibody–antigen binding remain challenging. CAPRI Round 55 consisted of three antibody–antigen targets and one heterotrimer.
Nessim Raouraoua, Marc F. Lensink, Guillaume Brysbaert   +2 more
wiley   +1 more source