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A universal strategy for smoothing deceleration in deep graph neural networks.
Neural Netw
January 2025
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150001, China. Electronic address:
Graph neural networks (GNNs) have shown great promise in modeling graph-structured data, but the over-smoothing problem restricts their effectiveness in deep layers. Two key weaknesses of existing research on deep GNN models are: (1) ignoring the beneficial aspects of intra-class smoothing while focusing solely on reducing inter-class smoothing, and (2) inefficient computation of residual weights that neglect the influence of neighboring nodes' distributions. To address these weaknesses, we propose a novel Smoothing Deceleration (SD) strategy to reduce the smoothing speed rate of nodes as information propagates between layers, thereby mitigating over-smoothing.
View Article and Find Full Text PDFImplementing the discontinuous-Galerkin finite element method using graph neural networks with application to diffusion equations.
Neural Netw
December 2024
Department of Earth Science and Engineering, Imperial College London, Prince Consort Road, London SW7 2BP, UK; Centre for AI-Physics Modelling, Imperial-X, White City Campus, Imperial College London, W12 7SL, UK.
Machine learning (ML) has benefited from both software and hardware advancements, leading to increasing interest in capitalising on ML throughout academia and industry. There have been efforts in the scientific computing community to leverage this development via implementing conventional partial differential equation (PDE) solvers with machine learning packages, most of which rely on structured spatial discretisation and fast convolution algorithms. However, unstructured meshes are favoured in problems with complex geometries.
View Article and Find Full Text PDFDiscovering stochastic basin stability from data in a Filippov competition system with threshold control.
Chaos
January 2025
Department of Mathematics, Xi'an University of Science and Technology, Xi'an 710054, People's Republic of China.
The existing research studies on the basin stability of stochastic systems typically focus on smooth systems, or the attraction basins are pre-defined as easily solvable regular basins. In this work, we introduce a new framework to discover the basin stability from state time series in the non-smooth stochastic competition system under threshold control. Specifically, we approximate the drift and diffusion with threshold control parameters by an extended Kramers-Moyal expansion with initial state partitioning.
View Article and Find Full Text PDFInhibition of aortic CX3CR1+ macrophages mitigates thoracic aortic aneurysm progression in Marfan syndrome in mice.
J Clin Invest
January 2025
Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Peking University; State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China.
The pathogenesis of thoracic aortic aneurysm (TAA) in Marfan syndrome (MFS) is generally attributed to vascular smooth muscle cell (VSMC) pathologies. However, the role of immune cell-mediated inflammation remains elusive. Single-cell RNA sequencing identified a subset of CX3CR1+ macrophages mainly located in the intima in the aortic roots and ascending aortas of Fbn1C1041G/+ mice, further validated in MFS patients.
View Article and Find Full Text PDFDiaphragmatic Rupture in the Postpartum Period.
Cureus
December 2024
General Surgery, Ibn Rochd University Hospital/Hassan II University, Casablanca, MAR.
Diaphragmatic rupture during labor is an exceptionally rare condition, with a limited number of cases reported in the literature. A recent review underscores the rarity of this complication and emphasizes the associated challenges in diagnosis and management. This case report presents a postpartum diaphragmatic rupture, focusing on the diagnostic and therapeutic challenges it poses, particularly in the context of unsupervised deliveries.
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