Results 91 to 100 of about 533,476 (267)

GCL-ALG: graph contrastive learning with adaptive learnable view generators [PDF]

PeerJ Computer Science
Data augmentation is a pivotal part of graph contrastive learning, which can mine implicit graph data information to improve the quality of representation learning.
Yafang Li, Jie Kang, Zhihua Chu, Baokai Zu   +3 more
doaj   +2 more sources

Large-scale knowledge graph representation learning

Knowledge and Information Systems
Abstract The knowledge graph emerges as powerful data structures that provide a deep representation and understanding of the knowledge presented in networks. In the pursuit of representation learning of the knowledge graph, entities and relationships undergo an embedding process, where they are mapped onto a vector space with reduced dimensions.
Badrouni, Marwa, Katar, Chaker, Inoubli, Wissem   +2 more
openaire   +2 more sources

Deep Learning for Learning Graph Representations

, 2019
Mining graph data has become a popular research topic in computer science and has been widely studied in both academia and industry given the increasing amount of network data in the recent years. However, the huge amount of network data has posed great challenges for efficient analysis.
Zhu, Wenwu, Wang, Xin, Cui, Peng
openaire   +2 more sources

Engineering Strategies for Stable and Long‐Life Alkaline Zinc‐Based Flow Batteries

Advanced Functional Materials, EarlyView.
Alkaline zinc‐based flow batteries face persistent challenges from unstable zinc deposition, including dendrite growth, passivation, corrosion, and hydrogen evolution, which severely limit cycling stability. Current research addresses these issues through coordinated electrode structuring, electrolyte regulation, and membrane design to control zinc ...
Yuran Bai, Xiaoyu Huo, Xin Long, Enkang Fu, Xusong Gong, Lei Wei, Liang An   +6 more
wiley   +1 more source

Bioprinting Organs—Science or Fiction?—A Review From Students to Students

Advanced Healthcare Materials, EarlyView.
Bioprinting artificial organs has the potential to revolutionize the medical field. This is a comprehensive review of the bioprinting workflow delving into the latest advancements in bioinks, materials and bioprinting techniques, exploring the critical stages of tissue maturation and functionality.
Nicoletta Murenu, Camilla Mussoni, Mateo S. Andrade Mier, Paula Buettner, Nathaly Chicaiza‐Cabezas, Yi‐Yu Robin Dai, Jessica Faber, Maren Fiedler, Zan Lamberger, Xuen Jen Ng, Vanessa Moessler, Anna Rederer, Jonas Roeder, Sabrina Stecher, Katinka Theis, Jeanette Weigelt, Silvia Budday, Gregor Lang, Natascha Schaefer   +18 more
wiley   +1 more source

GTAT: empowering graph neural networks with cross attention

Scientific Reports
Graph Neural Networks (GNNs) serve as a powerful framework for representation learning on graph-structured data, capturing the information of nodes by recursively aggregating and transforming the neighboring nodes’ representations.
Jiahao Shen, Qura Tul Ain, Yaohua Liu, Banqing Liang, Xiaoli Qiang, Zheng Kou   +5 more
doaj   +1 more source

Smart Catheters for Diagnosis, Monitoring, and Therapy

Advanced Healthcare Materials, EarlyView.
This study presents a comprehensive review of smart catheters, an emerging class of medical devices that integrate embedded sensors, robotics, and communication systems, offering increased functionality and complexity to enable real‐time health monitoring, diagnostics, and treatment. Abstract This review explores smart catheters as an emerging class of
Azra Yaprak Tarman, Samiha Ahmed, Majed Othman Althumayri, Megan Guy, Darlenne Chavez Lugo, Frances S. Ligler, George T. Ligler, Rahmi Oklu, Hiroshi Kawahira, Michael J. McShane, Jun Kameoka, Jonathan Bova, Hatice Ceylan Koydemir   +12 more
wiley   +1 more source

Survey of Knowledge Graph Representation Learning for Relation Feature Modeling [PDF]

Jisuanji kexue
Knowledge graph representation learning techniques can transform symbolic knowledge graphs into numerical representations of entities and relations,and then effectively combine various deep learning models to facilitate downstream applications of ...
NIU Guanglin, LIN Zhen
doaj   +1 more source

Redefining Therapies for Drug‐Resistant Tuberculosis: Synergistic Effects of Antimicrobial Peptides, Nanotechnology, and Computational Design

Advanced Healthcare Materials, EarlyView.
Antimicrobial peptide (AMP)‐loaded nanocarriers provide a multifunctional strategy to combat drug‐resistant Mycobacterium tuberculosis. By enhancing intracellular delivery, bypassing efflux pumps, and disrupting bacterial membranes, this platform restores phagolysosome fusion and macrophage function.
Christian S. Carnero Canales, Jessica Ingrid Marquez Cazorla, Renzo Marianito Marquez Cazorla, Aline Martins dos Santos, Jonatas Lobato Duarte, Letícia Oliveira Catarin Nunes, Túlio Custódio Reis, Lara Cerazi Salvador, Norival Alves Santos‐Filho, Rafael Miguel Sábio, Hélder A. Santos, Fernando Rogério Pavan   +11 more
wiley   +1 more source

Molecular subgraph representation learning based on spatial structure transformer

Complex & Intelligent Systems
In the field of molecular biology, graph representation learning is crucial for molecular structure analysis. However, challenges arise in recognising functional groups and distinguishing isomers due to a lack of spatial structure information. To address
Shaoguang Zhang, Jianguang Lu, Xianghong Tang   +2 more
doaj   +1 more source