Results 111 to 120 of about 35,453 (267)

AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling

Advanced Science, EarlyView.
This review unites experiments, physics‐based simulations, and AI as a synergistic triad for protein dynamics modeling. It highlights integrative strategies, resolves sampling and forcefield bottlenecks, and outlines challenges and future directions for accurate, interpretable conformational ensemble prediction.
Chen Shi, Minying Low, Peng Xiu, Kresten Lindorff‐Larsen, Yong Wang   +4 more
wiley   +1 more source

Rotation Invariance in Graph Convolutional Networks [PDF]

Annals of computer science and information systems, 2021
Nguyen Anh Mac, Hung Son Nguyen
doaj   +1 more source

Variational Graph Convolutional Networks for Dynamic Graph Representation Learning

IEEE Access
The ubiquitous and ever-evolving nature of cyber threats demands innovative approaches that can adapt to the dynamic relationships and structures within network data.
Aabid A. Mir, Megat F. Zuhairi, Shahrulniza Musa, Meshari H. Alanazi, Abdallah Namoun   +4 more
doaj   +1 more source

Synergistic Integration of Artificial Merkel Disc and Meissner Corpuscle via Dermal Papillary Structures for Mechanically Filtered Multimodal Tactile Sensing

Advanced Science, EarlyView.
A multimodal tactile sensor module that mimics the spatial arrangement and function of Merkel discs and Meissner corpuscles within the human papillary structure operates in a self‐powered manner, responding to both dynamic and static stimuli, achieving tactile perception more similar to human skin.
Jaehyeong Kim, Bohee Maeng, Seunghwan Seo, Kyoung‐Yong Chun, Chang‐Soo Han   +4 more
wiley   +1 more source

Dual‐Module Near‐Infrared Fluorophores Discovery System via Knowledge Transfer

Advanced Science, EarlyView.
This study presents a dual‐module deep learning system for the design of near‐infrared (NIR) fluorophores. A large molecular library is generated and analyzed, leading to the suggestions of promising candidates. The effectiveness of the system is further validated through the synthesis, characterization, and in vivo imaging, demonstrating its potential
Yixin Zhu, Xia Ling, Xianhe Zhang, Chuanjiang Jian, Leilei Shi, Wentao Song, Xiaonan Wang, Bin Liu   +7 more
wiley   +1 more source

Review of Blockchain Application With Graph Neural Networks, Graph Convolutional Networks and Convolutional Neural Networks

This paper reviews the applications of Graph Neural Networks (GNNs), Graph Convolutional Networks (GCNs), and Convolutional Neural Networks (CNNs) in blockchain technology. As the complexity and adoption of blockchain networks continue to grow, traditional analytical methods are proving inadequate in capturing the intricate relationships and dynamic ...
Amy Ancelotti, Claudia Liason
openaire   +2 more sources

Causal‐Guided Ultra‐Long‐Term Time Series Forecasting Via Anticipated Covariates

Advanced Science, EarlyView.
Often treated as unknown, information from the future remains underutilized.We demonstrate that in a coupled dynamical system, providing the future state of the effect enables accurate forecasting of the cause for a long timesteps. A time series forecasting paradigm that introduces anticipated covariates to represent such known future states is ...
Jintong Zhao, Yufei Liu, Ruixi Huang, Zhongxue Gan, Siyang Leng   +4 more
wiley   +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

A review on the applications of graph neural networks in materials science at the atomic scale

Materials Genome Engineering Advances
In recent years, interdisciplinary research has become increasingly popular within the scientific community. The fields of materials science and chemistry have also gradually begun to apply the machine learning technology developed by scientists from ...
Xingyue Shi, Linming Zhou, Yuhui Huang, Yongjun Wu, Zijian Hong   +4 more
doaj   +1 more source

Advancing the Design of High‐Efficiency Printable Hole‐Conductor‐Free Mesoscopic Perovskite Solar Cells Through Machine Learning

Advanced Science, EarlyView.
Based on the largest printable mesoscopic perovskite solar cells database we established, stacking model achieved precise PCE prediction (R2 = 0.73, MAE = 2.18%). Multiple experiments verified the accuracy of the model, which guided the fabrication of high‐PCE devices with an efficiency of 19.36%.
Hao Meng, Jingzi Zhang, Xu Zhu, Yuelin Wang, Antai Yang, Kailong Hu, Chengquan Zhong, Jiakai Liu, Menghan Dun, Xi Lin   +9 more
wiley   +1 more source