Results 111 to 120 of about 4,082,283 (306)

Node-attribute graph layout for small-world networks [PDF]

, 2011
Small-world networks are a very commonly occurring type of graph in the real-world, which exhibit a clustered structure that is not well represented by current graph layout algorithms. In many cases we also have information about the nodes in such graphs,
Joe Faith, Helen Gibson, Gibson, Helen, Faith, Joe   +3 more
core   +1 more source

A Numerical–Experimental Approach for Multi‐Matrix Fiber‐Reinforced Plastics Characterization Using Finite Element Model Updating

Advanced Engineering Materials, EarlyView.
A numerical–experimental framework is developed for characterizing multi‐matrix fiber‐reinforced polymers (MM‐FRPs) combining epoxy and polyurethane matrices. Harmonic bending tests are integrated with finite element model updating (FEMU) to simultaneously identify elastic and viscoelastic material parameters.
Rodrigo M. Dartora, Tales V. Lisbôa, Axel Spickenheuer, Rogério J. Marczak, Jakson M. Vassoler   +4 more
wiley   +1 more source

Survey of Breast Cancer Pathological Image Analysis Methods Based on Graph Neural Networks [PDF]

Jisuanji kexue
Pathological diagnosis is the gold standard for cancer diagnosis and treatment,the use of artificial intelligence(AI) models for analyzing pathological images has the potential to not only reduce the workload of pathologists but also improve the accuracy
CHEN Sishuo, WANG Xiaodong, LIU Xiyang
doaj   +1 more source

Uncertainty-Aware Graph Neural Networks: A Multihop Evidence Fusion Approach

Graph neural networks (GNNs) excel in graph representation learning by integrating graph structure and node features. Existing GNNs, unfortunately, fail to account for the uncertainty of class probabilities that vary with the depth of the model, leading ...
Webb, Geoffrey I, Li, Shiyuan, Chen, Qingfeng, Zhang, Shichao, Pan, Shirui, Liu, Yixin   +5 more
core   +1 more source

Fabrication Routes for Ionic Conducting Fiber Strain Sensors

Advanced Engineering Materials, EarlyView.
Ionic conducting fiber strain sensors (ICFSs) offer compliant, textile‐integrable sensing. Thus far, the commercialization of ICFSs has been constrained by fiber fabrication routes. This review provides a fabrication‐centric analysis of ICFSs correlating processing strategies with material properties and scalability.
Leo John Kershaw, Zhanhong Yang, Anne Ladegaard Skov, Magdalena Skowyra   +3 more
wiley   +1 more source

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

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

A graph neural network with negative message passing and uniformity maximization for graph coloring

Complex & Intelligent Systems
Graph neural networks have received increased attention over the past years due to their promising ability to handle graph-structured data, which can be found in many real-world problems such as recommender systems and drug synthesis.
Xiangyu Wang, Xueming Yan, Yaochu Jin
doaj   +1 more source

Multi-view Attributed Graph Clustering Based on Contrast Consensus Graph Learning [PDF]

Jisuanji kexue
Multi-view attribute graph clustering can divide nodes of graph data with multiple views into different clusters,which has attracted widespread attention from researchers in recent years.At present,many multi-view attribute graph clustering me-thods ...
LIU Pengyi, HU Jie, WANG Hongjun, PENG Bo
doaj   +1 more source

Multiresolution Reservoir Graph Neural Network

, 2021
Graph neural networks are receiving increasing attention as state-of-the-art methods to process graph-structured data. However, similar to other neural networks, they tend to suffer from a high computational cost to perform training. Reservoir computing (
Pasa L., Sperduti A., Navarin N.
core   +1 more source