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Person# Adélie Eliane Garin

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MATH-106(f): Analysis II

Étudier les concepts fondamentaux d'analyse et le calcul différentiel et intégral des fonctions réelles de plusieurs
variables.

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In applied mathematics, topological data analysis (TDA) is an approach to the analysis of datasets using techniques from topology. Extraction of information from datasets that are high-dimensional, i

:See homology for an introduction to the notation.
Persistent homology is a method for computing topological features of a space at different spatial resolutions. More persistent features are detect

In mathematics, topology (from the Greek words τόπος, and λόγος) is concerned with the properties of a geometric object that are preserved under continuous deformations, such as stretching, twisting

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Digital images enable quantitative analysis of material properties at micro and macro length scales, but choosing an appropriate resolution when acquiring the image is challenging. A high resolution means longer image acquisition and larger data requirements for a given sample, but if the resolution is too low, significant information may be lost. This paper studies the impact of changes in resolution on persistent homology, a tool from topological data analysis that provides a signature of structure in an image across all length scales. Given prior information about a function, the geometry of an object, or its density distribution at a given resolution, we provide methods to select the coarsest resolution yielding results within an acceptable tolerance. We present numerical case studies for an illustrative synthetic example and samples from porous materials where the theoretical bounds are unknown.

In this thesis, we investigate the inverse problem of trees and barcodes from a combinatorial, geometric, probabilistic and statistical point of view.Computing the persistent homology of a merge tree yields a barcode B. Reconstructing a tree from B involves gluing the branches back together. We are able to define combinatorial equivalence classes of merge trees and barcodes that allow us to completely solve this inverse problem. A barcode can be associated with an element in the symmetric group, and the number of trees with the same barcode, the tree realization number, depends only on the permutation type. We compare these combinatorial definitions of barcodes and trees to those of phylogenetic trees, thus describing the subtle differences between these spaces. The result is a clear combinatorial distinction between the phylogenetic tree space and the merge tree space.The representation of a barcode by a permutation not only gives a formula for the tree realization number, but also opens the door to deeper connections between inverse problems in topological data analysis, group theory, and combinatorics.Based on the combinatorial classes of barcodes, we construct a stratification of the barcode space. We define coordinates that partition the space of barcodes into regions indexed by the averages and the standard deviations of birth and death times and by the permutation type of a barcode. By associating to a barcode the coordinates of its region, we define a new invariant of barcodes.These equivalence classes define a stratification of the space of barcodes with n bars where the strata are indexed by the symmetric group on n letters and its parabolic subgroups.We study the realization numbers computed from barcodes with uniform permutation type (i.e., drawn from the uniform distribution on the symmetric group) and establish a fundamental null hypothesis for this invariant. We show that the tree realization number can be used as a statistic to distinguish distributions of trees by comparing neuronal trees to random barcode distributions.

Adélie Eliane Garin, Kathryn Hess Bellwald, Lida Kanari

Methods of topological data analysis have been successfully applied in a wide range of fields to provide useful summaries of the structure of complex data sets in terms of topological descriptors, such as persistence diagrams. While there are many powerful techniques for computing topological descriptors, the inverse problem, i.e., recovering the input data from topological descriptors, has proved to be challenging. In this article, we study in detail the Topological Morphology Descriptor (TMD), which assigns a persistence diagram to any tree embedded in Euclidean space, and a sort of stochastic inverse to the TMD, the Topological Neuron Synthesis (TNS) algorithm, gaining both theoretical and computational insights into the relation between the two. We propose a new approach to classify barcodes using symmetric groups, which provides a concrete language to formulate our results. We investigate to what extent the TNS recovers a geometric tree from its TMD and describe the effect of different types of noise on the process of tree generation from persistence diagrams. We prove moreover that the TNS algorithm is stable with respect to specific types of noise.

2020