Results 291 to 300 of about 2,956,415 (357)
Exploring neurodevelopment via spatiotemporal collation of anatomical networks with NeuroSC. [PDF]
ElifeKoonce NL +10 more
europepmc +1 more source
Graph-Theoretic Limits of Distributed Computation: Entropy, Eigenvalues, and Chromatic Numbers. [PDF]
Entropy (Basel)Deylam Salehi MR, Malak D.
europepmc +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Membership Problem with Adjacency Matrix
Computación y Sistemas, 2021In this article, proposed a algorithm to solve the membership problem in Hyperedge Replacement Grammars (HRG). Given a hypergraph H with labeled nodes rooted and directed hyperedges, the problem consists in determining if H 2 L(G), where G is in HRG, this is to say, if H is in the language generated by G, for this the analysis is done directly in the ...
Yolanda Moyao Martinez +3 more
openaire +2 more sources
Arc Adjacency Matrix-Based Fast Ellipse Detection
IEEE Transactions on Image Processing, 2020Fast and accurate ellipse detection is critical in certain computer vision tasks. In this paper, we propose an arc adjacency matrix-based ellipse detection (AAMED) method to fulfill this requirement. At first, after segmenting the edges into elliptic arcs, the digraph-based arc adjacency matrix (AAM) is constructed to describe their triple sequential ...
Cai Meng +3 more
openaire +3 more sources
DAMR: Dynamic Adjacency Matrix Representation Learning for Multivariate Time Series Imputation
Proc. ACM Manag. Data, 2023Missing data imputation for location-based sensor data has attracted much attention in recent years. The state-of-the-art imputation methods based on graph neural networks have a priori assumption that the spatial correlations between sensor locations ...
Xiaobin Ren +5 more
semanticscholar +1 more source
Contrastive Graph Convolutional Networks With Generative Adjacency Matrix
IEEE Transactions on Signal Processing, 2023Semi-supervised node classification with Graph Convolutional Network (GCN) is an attractive topic in social media analysis and applications. Recent studies show that GCN-based classification methods can facilitate the accuracy increase of learning ...
Luying Zhong +3 more
semanticscholar +1 more source
FPGA Acceleration of GCN in Light of the Symmetry of Graph Adjacency Matrix
Design, Automation and Test in Europe, 2023Graph Convolutional Neural Networks (GCNs) are widely used to process large-scale graph data. Different from deep neural networks (DNNs), GCNs are sparse, irregular, and unstructured, posing unique challenges to hardware acceleration with regular ...
Gopikrishnan Raveendran Nair +5 more
semanticscholar +1 more source
IEEE Sensors Letters, 2023
Chronic obstructive pulmonary disease (COPD) is one of the most severe respiratory diseases and can be diagnosed by several clinical modalities such as spirometric measures, lung function tests, parametric response mapping, wheezing events of lung sounds
Arka Roy, Arushi Thakur, Udit Satija
semanticscholar +1 more source
Chronic obstructive pulmonary disease (COPD) is one of the most severe respiratory diseases and can be diagnosed by several clinical modalities such as spirometric measures, lung function tests, parametric response mapping, wheezing events of lung sounds
Arka Roy, Arushi Thakur, Udit Satija
semanticscholar +1 more source