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Computational Topology: An Introduction

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TLDR
In this article, the authors present an introduction to the field of computational topology, combining concepts from topology and algorithms, and the main approach is the discovery of topology through algorithms.
Abstract
Combining concepts from topology and algorithms, this book delivers what its title promises: an introduction to the field of computational topology. Starting with motivating problems in both mathematics and computer science and building up from classic topics in geometric and algebraic topology, the third part of the text advances to persistent homology. This point of view is critically important in turning a mostly theoretical field of mathematics into one that is relevant to a multitude of disciplines in the sciences and engineering. The main approach is the discovery of topology through algorithms. The book is ideal for teaching a graduate or advanced undergraduate course in computational topology, as it develops all the background of both the mathematical and algorithmic aspects of the subject from first principles. Thus the text could serve equally well in a course taught in a mathematics department or computer science department.

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Journal ArticleDOI

A roadmap for the computation of persistent homology

TL;DR: A friendly introduction to PH is given, the pipeline for the computation of PH is navigated with an eye towards applications, and a range of synthetic and real-world data sets are used to evaluate currently available open-source implementations for the computations of PH.
Journal ArticleDOI

Homological scaffolds of brain functional networks

TL;DR: The results show that the homological structure of the brain's functional patterns undergoes a dramatic change post-psilocybin, characterized by the appearance of many transient structures of low stability and of a small number of persistent ones that are not observed in the case of placebo.
Journal ArticleDOI

Geometric frustration of icosahedron in metallic glasses

TL;DR: Experimental observation of local icosahedral order in metallic glasses by means of angstrom-beam electron diffraction of single icosahedra found to be distorted with partially face-centered cubic symmetry, presenting compelling evidence on geometric frustration of local Icosahedron order in Metallic glasses.
Proceedings ArticleDOI

A stable multi-scale kernel for topological machine learning

TL;DR: In this paper, a multi-scale kernel for persistence diagrams, a stable summary representation of topological features in data, is proposed for 3D shape classification/retrieval and texture recognition.
Journal Article

Persistence images: a stable vector representation of persistent homology

TL;DR: In this article, a persistence diagram (PD) is converted to a finite-dimensional vector representation which is called a persistence image (PI) and proved the stability of this transformation with respect to small perturbations in the inputs.
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