Results 81 to 90 of about 437,099 (249)

Graph neural network structural limitation for thermal simulation and architecture optimization through rating system

Advanced Modeling and Simulation in Engineering Sciences
Graph neural networks are well suited for physics based simulation. Among other features, graphs can accurately represent thermal effects, with energy conservation operating on the nodes (vertices) and heat flow coursing through edges.
Pierre Hembert, Chady Ghnatios, Julien Cotton, Francisco Chinesta   +3 more
doaj   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

Gated Graph Recurrent Neural Networks

IEEE Transactions on Signal Processing, 2020
Graph processes exhibit a temporal structure determined by the sequence index and and a spatial structure determined by the graph support. To learn from graph processes, an information processing architecture must then be able to exploit both underlying structures.
Luana Ruiz, Fernando Gama, Alejandro Ribeiro   +2 more
openaire   +2 more sources

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Structured Sequence Modeling with Graph Convolutional Recurrent Networks

, 2016
This paper introduces Graph Convolutional Recurrent Network (GCRN), a deep learning model able to predict structured sequences of data. Precisely, GCRN is a generalization of classical recurrent neural networks (RNN) to data structured by an arbitrary ...
Bresson, Xavier, Defferrard, Michaël, Seo, Youngjoo, Vandergheynst, Pierre   +3 more
core  

An All‐Optical Driven Bio‐Photovoltaic Interface for Active Control of Live Cells

Advanced Functional Materials, EarlyView.
Bio‐photovoltaic Interface (BIO‐PV‐I) for live cell manipulation is presented. BIO‐PV‐I can be activated non‐invasively and remotely to control the spatial motility, adhesion, and morphology of cells adhering to it. BIO‐PV‐I uses a patterned light‐induced electric potential in iron‐doped lithium niobate crystals whose light‐driven and reversible nature,
Lisa Miccio, Martina Mugnano, Valentina Marchesano, Alejandro Calabuig, Zhe Wang, Gennaro Giordano, Francesca Borrelli, Simonetta Grilli, Pietro Ferraro   +8 more
wiley   +1 more source

Interconnected Porous Hydrogels with Tunable Anisotropy Through Aqueous Emulsion Bioprinting

Advanced Functional Materials, EarlyView.
A 3D bioprintable microporous bioink is developed using an aqueous two‐phase system (ATPS) composed of extracellular matrix (ECM) mimetic biopolymers. The ATPS bioink enables the fabrication of interconnected porous architectures with up to 70% porosity, supporting long‐term cell viability and 3D cell alignment, enabling a simultaneous generation of ...
Hugo Edgar‐Vilar, Francisca P. Correia, Inês M. Freitas, Vítor M. Gaspar, João F. Mano   +4 more
wiley   +1 more source

Knowledge mapping of graph neural networks for drug discovery: a bibliometric and visualized analysis

Frontiers in Pharmacology
IntroductionIn recent years, graph neural network has been extensively applied to drug discovery research. Although researchers have made significant progress in this field, there is less research on bibliometrics. The purpose of this study is to conduct
Rufan Yao, Zhenhua Shen, Xinyi Xu, Guixia Ling, Rongwu Xiang, Tingyan Song, Fei Zhai, Yuxuan Zhai   +7 more
doaj   +1 more source

Hyperbolic Graph Convolutional Neural Networks

Advances in neural information processing systems, 2019
Published at Conference NeurIPS 2019.
Chami, Ines, Ying, Rex, Ré, Christopher, Leskovec, Jure   +3 more
openaire   +3 more sources

Predicting Atomic Charges in MOFs by Topological Charge Equilibration

Advanced Functional Materials, EarlyView.
An atomic charge prediction method is presented that is able to accurately reproduce ab‐initio‐derived reference charges for a large number of metal–organic frameworks. Based on a topological charge equilibration scheme, static charges that fulfill overall neutrality are quickly generated.
Babak Farhadi Jahromi, Sumukh Shankar Sharadaprasad, Rochus Schmid   +2 more
wiley   +1 more source