Results 111 to 120 of about 110,849 (310)

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

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

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

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

Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels

, 2021
Graph Neural Networks (GNNs) have achieved remarkable performance in the task of semi-supervised node classification. However, most existing GNN models require sufficient labeled data for effective network training.
Wan, S, Zhan, Y, Liu, L, Yu, B, Pan, S, Gong, C   +5 more
core  

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

Intrinsic Photoactive Star ZnPc–Poly(glutamate) Nanoplatforms for Multimodal Glioblastoma Therapy and Brain‐Targeted Delivery

Advanced Functional Materials, EarlyView.
An intrinsic photoactive star‐shaped zinc phtalocyanine‐poly(L‐glutamic acid) (ZnPc‐PGA) nanoplatform for multimodal glioblastoma (GBM) therapy and brain‐targeted elivery. A ZnPc‐PGA‐based multifunctional theranostic nanocarrier platform enables image‐guided, multimodal GBM therapy. ZnPc‐PGA nanocarriers support the integration of fluorescence imaging,
Amina Benaicha‐Fernández, Teodora Ranđelović, Ana Armiñán, Víctor Sebastián, Manuel Fuentes, Cristián Huck‐Iriart, Esther Masiá, Paula Carrascosa‐Marco, Pablo Juanes‐Velasco, Snežana Đorđević, Clara Bayona, Ignacio Ochoa, Maria Medel, Inmaculada Conejos‐Sánchez, María J. Vicent   +14 more
wiley   +1 more source

Hyperbolic graph neural networks

, 2019
Learning from graph-structured data is an important task in machine learning and artificial intelligence, for which Graph Neural Networks (GNNs) have shown great promise.
Nickel, Maximilian, Kiela, Douwe, Liu, Qi   +2 more
core  

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