Results 81 to 90 of about 18,314 (299)

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Advanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour, Sara Soltanabadi Farshi, Tuanna Ozcan, Amir Mohammad Ketabchi, Alper Kiraz, Savas Tasoglu   +5 more
wiley   +1 more source

Moment-Based Spectral Analysis of Large-Scale Generalized Random Graphs

IEEE Access, 2017
This paper investigates the spectra of the adjacency matrix and Laplacian matrix for an artificial complex network model-the generalized random graph. We deduce explicit expressions for the first four asymptotic spectral moments of the adjacency matrix ...
Qun Liu, Zhishan Dong, En Wang
doaj   +1 more source

On Laplacian Energy of r-Uniform Hypergraphs [PDF]

, 2023
The matrix representations of hypergraphs have been defined via hypermatrices initially. In recent studies, the Laplacian matrix of hypergraphs, a generalization of the Laplacian matrix, has been introduced.
N. Feyza Yalçın
core   +1 more source

Laplacian versus adjacency matrix in quantum walk search [PDF]

Quantum Information Processing, 2016
A quantum particle evolving by Schrödinger's equation contains, from the kinetic energy of the particle, a term in its Hamiltonian proportional to Laplace's operator. In discrete space, this is replaced by the discrete or graph Laplacian, which gives rise to a continuous-time quantum walk.
Thomas G. Wong, Luís Tarrataca, Nikolay Nahimov   +2 more
openaire   +3 more sources

A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws

Advanced Intelligent Discovery, EarlyView.
The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows, G. Zhang, S. Anand, Z. Chen, J. Lin, A. Desai, S. Martiniani, F. Caravelli   +7 more
wiley   +1 more source

Permanent of the laplacian matrix of trees with a given matching

Discrete Mathematics, 1986
Let \(G=(V,E)\) be an arbitrary graph, and \(d_ i>0\) be the degree of the vertex \(v_ i\in V\). D(G) denotes the diagonal matrix whose (i,i)-entry is \(d_ i\), and A(G) denotes the adjacency matrix of the graph G. The matrix \(L(G)=D(G)-A(G)\) will be called the Laplacian matrix of the graph G.
openaire   +2 more sources

Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real‐World Data

Advanced Intelligent Discovery, EarlyView.
This article investigates how persistent homology, persistent Laplacians, and persistent commutative algebra reveal complementary geometric, topological, and algebraic invariants or signatures of real‐world data. By analyzing shapes, synthetic complexes, fullerenes, and biomolecules, the article shows how these mathematical frameworks enhance ...
Yiming Ren, Guo‐Wei Wei
wiley   +1 more source

On the Eigenvalues and Energy of the Seidel and Seidel Laplacian Matrices of Graphs

Discrete Dynamics in Nature and Society
Let SΓ be a Seidel matrix of a graph Γ of order n and let DΓ=diagn−1−2d1,n−1−2d2,…,n−1−2dn be a diagonal matrix with di denoting the degree of a vertex vi in Γ. The Seidel Laplacian matrix of Γ is defined as SLΓ=DΓ−SΓ.
J. Askari, Kinkar Chandra Das, Yilun Shang   +2 more
doaj   +1 more source

Mapping directed networks [PDF]

, 2010
We develop and test a new mapping that can be applied to directed unweighted networks. Although not a “matrix function” in the classical matrix theory sense, this mapping converts an unsymmetric matrix with entries of zero or one into a symmetric real ...
Crofts, JJ, Estrada, E, Crofts, Jonathan, Estrada, Ernesto, Higham, Desmond, Taylor, Alan, Higham, DJ, Taylor, A   +7 more
core  

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

Advanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source