Results 71 to 80 of about 121,348 (314)

Network Coherence in a Family of Book Graphs

Frontiers in Physics, 2020
In this paper, we study network coherence characterizing the consensus behaviors with additive noise in a family of book graphs. It is shown that the network coherence is determined by the eigenvalues of the Laplacian matrix.
Jing Chen, Yifan Li, Weigang Sun
doaj   +1 more source

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

Perfect State Transfer in Laplacian Quantum Walk [PDF]

, 2014
For a graph $G$ and a related symmetric matrix $M$, the continuous-time quantum walk on $G$ relative to $M$ is defined as the unitary matrix $U(t) = \exp(-itM)$, where $t$ varies over the reals.
Alvir, R., Dever, S., Lovitz, B, Myer, J., Tamon, C., Xu, Y., Zhan, H.   +6 more
core  

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

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

New Spectral Results for Laplacian Harary Matrix and the Harary Laplacian-Energy-like Applying a Matrix Order Reduction

Mathematics, 2023
In this paper, we introduce the concepts of Harary Laplacian-energy-like for a simple undirected and connected graph G with order n. We also establish novel matrix results in this regard. Furthermore, by employing matrix order reduction techniques, we derive upper and lower bounds utilizing existing graph invariants and vertex connectivity. Finally, we
Luis Medina, Jonnathan Rodríguez, Macarena Trigo   +2 more
openaire   +2 more sources

Random walks with long-range steps generated by functions of Laplacian matrices

, 2018
In this paper, we explore different Markovian random walk strategies on networks with transition probabilities between nodes defined in terms of functions of the Laplacian matrix.
Collet, B. A., Michelitsch, T. M., Nicolleau, F. C. G. A., Nowakowski, A. F., Riascos, A. P.   +4 more
core   +1 more source

Upsampling DINOv2 Features for Unsupervised Vision Tasks and Weakly Supervised Materials Segmentation

Advanced Intelligent Systems, EarlyView.
Feature from recent image foundation models (DINOv2) are useful for vision tasks (segmentation, object localization) with little or no human input. Once upsampled, they can be used for weakly supervised micrograph segmentation, achieving strong results when compared to classical features (blurs, edge detection) across a range of material systems.
Ronan Docherty, Antonis Vamvakeros, Samuel J. Cooper   +2 more
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

Efficient Dynamics: Reduced‐Order Modeling of the Time‐Dependent Schrödinger Equation

Advanced Physics Research, EarlyView.
Reduced‐order modeling (ROM) approaches for the time‐dependent Schrödinger equation are investigated, highlighting their ability to simulate quantum dynamics efficiently. Proper Orthogonal Decomposition, Dynamic Mode Decomposition, and Reduced Basis Methods are compared across canonical systems and extended to higher dimensions.
Kolade M. Owolabi
wiley   +1 more source