Abstract: We propose a novel discrete signal processing framework for structured datasets that arise from social, economic, biological, and physical networks. Our framework extends traditional ...

Abstract: We introduce a nonlocal discrete regularization framework on weighted graphs of the arbitrary topologies for image and manifold processing. The approach considers the problem as a ...

This repository contains the solution for Lab 4: Graphs and Trees, assigned for the (CSE214) Discrete Structures course. The assignment consists of three main tasks related to graphs and trees. Each ...

The main objective of work package 3 is to improve the understanding of the transition between discrete and continuum nonlocal models, which are investigated as part of the first two main objectives ...

One of my favorite tricks to understand discrete objects is studying their continuous limits. We can study Brownian Motion instead of random walks, Neural ODEs instead of neural networks or mean-field ...

Abstract: The aim of the talk is to discuss the phenomenon of ”quantum ergodicity” of eigenfunctions on large expander graphs. We will start with the work of Anantharaman-Le Masson and ...

ABSTRACT: In this paper, sharp upper bounds for the domination number, total domination number and connected domination number for the Cayley graph G = Cay(D2n, Ω) constructed on the finite dihedral ...

ABSTRACT: The rank of a graph is defined to be the rank of its adjacency matrix. In this paper, the Matlab was used to explore the graphs with rank no more than 5; the performance of the proposed ...

Discrete data in data analysis refers to quantitative information that consists of distinct, countable, and non-divisible whole numbers or fixed values. Examples include the number of customers, ...