Results 111 to 120 of about 1,049,471 (309)

Attention Graph Convolution Network for Image Segmentation in Big SAR Imagery Data

Remote Sensing, 2019
The recent emergence of high-resolution Synthetic Aperture Radar (SAR) images leads to massive amounts of data. In order to segment these big remotely sensed data in an acceptable time frame, more and more segmentation algorithms based on deep learning ...
Fei Ma, Fei Gao, Jinping Sun, Huiyu Zhou, Amir Hussain   +4 more
doaj   +1 more source

Learnable Graph Convolutional Attention Networks

, 2022
Existing Graph Neural Networks (GNNs) compute the message exchange between nodes by either aggregating uniformly (convolving) the features of all the neighboring nodes, or by applying a non-uniform score (attending) to the features. Recent works have shown the strengths and weaknesses of the resulting GNN architectures, respectively, GCNs and GATs.
Javaloy, Adrián, Sanchez-Martin, Pablo, Levi, Amit, Valera, Isabel   +3 more
openaire   +2 more sources

Posterior Cortical Atrophy in the Asia‐Pacific: A Report From the PCA Asian Workgroup

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Posterior Cortical Atrophy (PCA) is a distinct dementia syndrome primarily affecting spatial abilities and visual processing. It is associated with degeneration in the posterior part of the brain. PCA is subclassified into PCA‐pure and PCA‐plus syndromes based on consensus criteria.
Yuttachai Likitjaroen, Ya‐Ting Chang, Levinia Lim, Pai‐Yi Chiu, Jung‐Lung Hsu, SangYun Kim, Wataru Narita, Na Young Ryoo, Kyoko Suzuki, Nagaendran Kandiah, Sebastian Crutch, Ming‐Chyi Pai   +11 more
wiley   +1 more source

Fusing multiplex heterogeneous networks using graph attention-aware fusion networks

Scientific Reports
Graph Neural Networks (GNN) emerged as a deep learning framework to generate node and graph embeddings for downstream machine learning tasks. Popular GNN-based architectures operate on networks of single node and edge type.
Ziynet Nesibe Kesimoglu, Serdar Bozdag
doaj   +1 more source

Innate Immune Reprogramming Mediated by Endogenous Retroelement Dysregulation Drives Multiple Sclerosis Progression

Annals of Clinical and Translational Neurology, EarlyView.
Epigenetic reprogramming in hematopoietic stem and progenitor cells (HSPCs) and downstream myeloid cells, mediated by H3.3 downregulation and endogenous retroelement (ERE) overexpression, contributes to the progression of multiple sclerosis (MS). ABSTRACT Background Skewed myelopoiesis in the bone marrow has been identified as a key driver of multiple ...
Li‐Mei Xiao, Qiu‐Ping Zhao, Run‐Yun Li, Wei Chen, Huan‐Huan Song, Wan‐Jin Chen, Ying Fu   +6 more
wiley   +1 more source

Accelerating Transistor Simulations With Self-Supervised Graph Attention Networks

IEEE Access
Technology CAD (TCAD) tools are pivotal for transistor modeling, enabling physics-based simulations with high accuracy essential for developing next-generation technology nodes. However, their high computational cost and low throughput severely constrain
Tarek Mohamed, Hussam Amrouch
doaj   +1 more source

A Depolarizing Leak in Sodium Bicarbonate Cotransporter NBCe1 Causes Brain Edema

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objectives SLC4A4 encodes electrogenic sodium bicarbonate cotransporter NBCe1, prominently expressed in kidney and brain. Recessive loss‐of‐function variants in SLC4A4 cause proximal renal tubular acidosis, no brain edema. In the brain, NBCe1 is expressed by astrocytes, where it regulates pH and mediates astrocyte volume changes.
Quinty Bisseling, Mark D. Parker, Sven Kerst, Richard A. Pasternack, Jacob Tondreau, Marjolein Breur, Gemma M. van Rooijen‐van Leeuwen, Davide Tonduti, Ettore Salsano, Alejandra Darling, Joanna A. E. van Wijk, Susanna Törnroth‐Horsefield, Marianna Bugiani, Petra J. W. Pouwels, Quinten Waisfisz, Marjo S. van der Knaap, Rogier Min   +16 more
wiley   +1 more source

Context-Aware Graph Attention Networks

, 2019
Graph Neural Networks (GNNs) have been widely studied for graph data representation and learning. However, existing GNNs generally conduct context-aware learning on node feature representation only which usually ignores the learning of edge (weight) representation.
Jiang, Bo, Wang, Leiling, Tang, Jin, Luo, Bin   +3 more
openaire   +2 more sources

Effectiveness of rTMS on Working Memory and Inhibitory Impairments in Patients With Post‐Stroke Executive Deficits

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Considerable efforts have been dedicated to developing effective treatments for post‐stroke executive impairment (PSEI), among which repetitive transcranial magnetic stimulation (rTMS) has shown great potential. This study aimed to investigate the therapeutic effects of high‐frequency rTMS on working memory (WM) and response ...
Mengting Lao, Rongwei Du, Zhujun Wang, Yang Gao, Meiling Cheng, Xianglong Wang, Wen Wu   +6 more
wiley   +1 more source

Artificial Intelligence in Systemic Sclerosis: Clinical applications, challenges, and future directions

Arthritis Care &Research, Accepted Article.
Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality.
Cristiana Sieiro Santos, José Miguel Sequí‐Sabater, Diego Benavent   +2 more
wiley   +1 more source