Results 81 to 90 of about 106,924 (261)

Learning Signed Network Embedding via Graph Attention

, 2020
Learning the low-dimensional representations of graphs (i.e., network embedding) plays a critical role in network analysis and facilitates many downstream tasks.
Li, Yu, Tian, Yuan, Zhang, Jiawei, Chang, Yi   +3 more
core   +1 more source

Mechanochemical Synthesis and Characterization of Nanostructured ErB4 and NdB4 Rare‐Earth Tetraborides

Advanced Engineering Materials, Volume 27, Issue 6, March 2025.
ErB4 and NdB4 nanostructured powders are produced by mechanochemical synthesis. 5 h mechanical alloying and 4 M HCl acid leaching are used in the production. ErB4 and NdB4 powders exhibit maximum magnetization of 0.4726 emu g−1 accompanied with an antiferromagnetic‐to‐paramagnetic phase transition at about TN = 18 K and 0.132 emu g−1 with a maximum at ...
Burçak Boztemur, Faruk Kaya, Bora Derin, Mustafa Lütfi Öveçoğlu, Ju Li, Duygu Ağaoğulları   +5 more
wiley   +1 more source

Hierarchical Line Graph Neural Network: A Study on Alternative Representations of Graph-Structured Data [PDF]

openThis thesis addresses the challenge of feature-smoothing common in deep graph neural networks (GNNs), a topic of considerable interest over the past decade.
MOHAMMADI, SOLMAZ
core  

Fostering Innovation: Streamlining Magnetocaloric Materials Research by Digitalization

Advanced Engineering Materials, EarlyView.
Magnetocaloric cooling (MCE) is an environmentally friendly refrigeration method with great potential. Optimizing MCE materials involves the preparation and screening of large quantities of samples, which in turn generates a large amount of data. A digitalization approach is presented that uses ontologies, knowledge graphs, and digital workflows to ...
Simon Bekemeier, Moritz Blum, Luana Caron, Alisa Chirkova, Philipp Cimiano, Basil Ell, Inga Ennen, Michael Feige, Maik Gaerner, Thomas Hilbig, Andreas Hütten, Günter Reiss, Tapas Samanta, Sonja Schöning, Christian Schröder, Lennart Schwan, Chris Taake, Martin Wortmann   +17 more
wiley   +1 more source

Flow-Attentional Graph Neural Networks

Trans. Mach. Learn. Res.
Accepted @ Transactions on Machine Learning Research (TMLR): https://openreview.net/forum?id ...
Pascal Plettenberg, Dominik Köhler, Bernhard Sick, Josephine M. Thomas   +3 more
openaire   +3 more sources

Soft Mechanical‐Electrical Logic Using Liquid Metal‐Filled 3D‐Printed Architectures

Advanced Engineering Materials, EarlyView.
We present 3D‐printed soft mechanical–electrical logic elements that use liquid metal–filled silicone tubes actuated by thermoplastic polyurethane/polylactic acid (TPU/PLA) architectures to produce Boolean operations. Complementary normally open and normally closed unit cells perform repeatable binary transitions and can be combined into more complex ...
Christoph Lehmann, Ryan Kalgreen, Laura M. Comella   +2 more
wiley   +1 more source

Contextual Graph Attention Network for Aspect-Level Sentiment Classification

, 2022
Aspect-level sentiment classification aims to predict the sentiment polarities towards the target aspects given in sentences. To address the issues of insufficient semantic information extraction and high computational complexity of attention mechanisms ...
Ming Zhou, Yuqing Miao, Guoyong Cai, Tonglai Liu, Wanzhen Zhang, Lin Zhu, Ronghai Luo   +6 more
core   +1 more source

Spiking Heterogeneous Graph Attention Networks

Proceedings of the AAAI Conference on Artificial Intelligence
Real-world graphs or networks are usually heterogeneous, involving multiple types of nodes and relationships. Heterogeneous graph neural networks (HGNNs) can effectively handle these diverse nodes and edges, capturing heterogeneous information within the graph, thus exhibiting outstanding performance.
Buqing Cao, Qian Peng, Xiang Xie, Liang Chen 0001, Min Shi 0001, Jianxun Liu 0001   +5 more
openaire   +2 more sources

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

Attention graph neural network on heterogeneous information network

, 2020
Graph Neural Network(GNN)is a kind of powerful deep learning network to analyse graph information. There are two types of graphs: Homogeneous Information Network and Heterogenous Information Network (HIN).
Wang, Kexin
core