Results 201 to 210 of about 8,038,825 (297)

On‐Chip Photonic Neural Network Architectures

Advanced Optical Materials, EarlyView.
This review presents a comprehensive overview of on‐chip photonic neural network architectures, covering key photonic building blocks, representative network types, and emerging applications. Recent advances, implementation challenges, and future directions are examined, highlighting the potential of integrated photonics to enable ultrafast, energy ...
Seokjin Hong, Berkay Neseli, Seungyeon Cho, Seungsoo Lee, Yeonjun Kim, Seungyoon Choi, Hyo‐Hoon Park, Hamza Kurt   +7 more
wiley   +1 more source

Knowledge Graph Representation Learning by Text Encoding and Graph Structure

Knowledge graph representation learning aims to embed entities and relationships into low-dimensional space through knowledge graph embedding methods. Because knowledge graphs are incomplete, it is often necessary to complete the knowledge graph through ...
Li, Song, Zhong, Chengyu, Zhang, Liping
core  

Graph representation learning via enhanced GNNs and transformers. [PDF]

Sci Rep
Mu H, Zhou C, Yu Q, Mu Q.
europepmc   +1 more source

A knowledge graph representation learning approach to predict novel kinase-substrate interactions. [PDF]

Mol Omics, 2022
Gavali S, Ross K, Chen C, Cowart J, Wu CH.   +4 more
europepmc   +1 more source

Scalable Task Planning via Large Language Models and Structured World Representations

Advanced Robotics Research, EarlyView.
This work efficiently combines graph‐based world representations with the commonsense knowledge in Large Language Models to enhance planning techniques for the large‐scale environments that modern robots will need to face. Planning methods often struggle with computational intractability when solving task‐level problems in large‐scale environments ...
Rodrigo Pérez‐Dattari, Zhaoting Li, Robert Babuška, Jens Kober, Cosimo Della Santina   +4 more
wiley   +1 more source

A high-dimensional steady-state structural framework for regional transmission interface capacity planning using physics-embedded graph representation learning. [PDF]

Sci Rep
Zhang D, Mu Y, Guan D, Xue W.
europepmc   +1 more source

iGRLDTI: an improved graph representation learning method for predicting drug-target interactions over heterogeneous biological information network. [PDF]

Bioinformatics, 2023
Zhao BW, Su XR, Hu PW, Huang YA, You ZH, Hu L.   +5 more
europepmc   +1 more source

AI‐Powered Framework for Evaluating Drug Efficacy for Three‐Dimensional In Vitro Cancer Models in Robot‐Assisted Production

Advanced Robotics Research, EarlyView.
An AI‐powered, robot‐assisted framework automatically produces, images, and analyzes 3D tumor spheroids to evaluate drug efficacy. Integrated modules handle spheroid formation, live/dead staining, brightfield imaging, and automated image analysis, including spheroid segmentation, viability and metrics to assess the drug treatment efficacy. The workflow
Dalia Mahdy, Jesus Nieves, Lukas Königer, Matthias Peindl, Fabio Erhard, Marc Möllmann, Anke Klingner, Markus Münig, Bibin Babu, Thomas Schwarz, Marco Metzger, Gudrun Dandekar, Maximiliane Wußmann, Jan Hansmann   +13 more
wiley   +1 more source

Characterization of the heterogeneity in SARS-CoV-2 fitness dynamics via graph representation learning. [PDF]

PLoS Comput Biol
Wang Z, Zhou Z, Wang J, Yang L, Zhang Z, Xu W, Liu Z, Ge Y, Yang L, Wang X, Yang P, Wang Q, Cao Y, Guo Y, Tian H.   +14 more
europepmc   +1 more source

Graph representation learning for structural proteomics. [PDF]

Emerg Top Life Sci, 2021
Fasoulis R, Paliouras G, Kavraki LE.
europepmc   +1 more source