Results 221 to 230 of about 1,718,101 (355)

Ultra‐Wide‐Field Noninvasive Imaging Through Scattering Media Via Physics‐Guided Deep Learning

Advanced Science, EarlyView.
We propose a physics‐guided adaptive dual‐domain learning method for ultra‐wide‐field noninvasive imaging through scattering media, namely UNI‐Net. Our method not only reduces the requirement for real experimental data by an order of magnitude but also enables clear imaging of complex scenes with an ultra‐large field of view, which is 164 times the OME
Lintao Peng, Mingwei He, Jeff Zhu, Sujit K. Sahoo, Liheng Bian, Cuong Dang   +5 more
wiley   +1 more source

Nonintrusive Power Load Decomposition Based on Adaptive Graph Convolutional Neural Network. [PDF]

Sensors (Basel)
Zhao P, Wei J, Wang L, Qiu Y.
europepmc   +1 more source

Engineering Neuronal Network Connectivity Through Precise and Scalable Electrical Modulation

Advanced Science, EarlyView.
This study presents a scalable all‐electrical method for precise neuronal‐circuit reconfiguration based on high‐density microelectrode arrays. By employing biologically inspired plasticity rules, targeted connectivity changes were successfully induced and quantified across diverse neuronal preparations.
Sreedhar S. Kumar, Yannaël Bossard, Rachel Sava, Tobias Gänswein, Lorenca Sadiraj, Jean‐Samuel Dupré, Manuel Schröter, J. Gray Camp, Fernando Cardes, Julian Bartram, Andreas Hierlemann   +10 more
wiley   +1 more source

ConvCGP: A convolutional neural network to predict genetic values of agronomic traits from compressed genome-wide polymorphisms. [PDF]

Plant Genome
Raihan T, Kim CH, Shimono H, Kimura A, Iwata H.   +4 more
europepmc   +1 more source

SpaMode: A Broadly Applicable Framework for Deciphering Spatial Multi‐Omics Using Multimodal Mixture of Disentangled Experts

Advanced Science, EarlyView.
SpaMode introduces a versatile framework for spatial multi‐omics integration across vertical, horizontal, and mosaic scenarios. By disentangling modality‐invariant and variant features through a mixture‐of‐experts mechanism, it adaptively reconfigures spatially heterogeneous signals.
Xubin Zheng, Xinlei Huang, Xiang Zhou, Dian Meng, Ziyue Qiao, Zhiyuan Yuan, Lixin Cheng   +6 more
wiley   +1 more source

A Novel Convolutional Neural Network for Explainable Diabetic Retinopathy Detection and Grade Identification. [PDF]

Sensors (Basel)
Correra S, Sorgente V, Cesarelli M, Martinelli F, Santone A, Mercaldo F.   +5 more
europepmc   +1 more source

Graph Convolutional Neural Network [PDF]

Procedings of the British Machine Vision Conference 2016, 2016
Michael Edwards, Xianghua Xie
openaire   +2 more sources

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

, 2018
Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space.
Chmiela, S., Tkatchenko, A., Schütt, K., Sauceda, H., Müller, K., Kindermans, P.   +5 more
core  

Physics‐Informed Machine Learning for Sustainable Alloy Design: Toward a Recyclable Unified Q&P Steel

Advanced Science, EarlyView.
A physics‐informed property‐bridging framework links high‐throughput hardness screening to tensile performance in quenching and partitioning steels. By transferring metallurgically guided representations across properties, a single alloy composition is designed to achieve multiple strength grades through heat‐treatment tuning alone, offering a ...
Xiaolu Wei, Yong Li, Chenchong Wang, Lingyu Wang, Xiang Song, Keming Mao, Yu Zhang, Wei Xu   +7 more
wiley   +1 more source

AssiST: convolutional neural network for analysis of antibiotic susceptibility testing. [PDF]

Bioinform Adv
Li C, Schock S, Costa A, Mitchell A.
europepmc   +1 more source