The theory of persistent homology (PH), introduced by Edelsbrunner, Letscher, and Zomorodian in [1], provides a framework for extracting topological information from experimental data. This framework was then expanded by Carlsson and Zomorodian in [2] to allow for multiple parameters of analysis with the theory of multidimensional persistent homology (MPH). This particular generalization is considerably more difficult to compute and to apply than its predecessor. We introduce an intermediate theory, coordinated persistent homology (CPH), that allows for multiple parameters while still preserving the clarity and coherence of PH. In addition to introducing the basic theory, we provide a polynomial time algorithm to compute CPH for time series and prove several important theorems about the nature of CPH. We also describe an application of the theory to a problem in seismology.

One goal of persistent homology is to recover meaningful information from point-cloud data by examining long-lived topological features of filtered simplicial complexes built over the point-cloud. Motivated by real-world applications, the classic setting for this approach has been on finite metric spaces where many suitable complexes can be defined, and a natural filtration exists via sublevel sets of the metric. We consider the extension of persistent homology to dissimilarity networks equipped with a relaxed metric that does not assume symmetry nor the triangle inequality, by computing persistent homology on the directed clique complex defined over weighted directed graphs induced from a dissimilarity network and filtered by an adapted Rips filtration. We characterize digraph maps that induce maps on homology, describe a procedure to lift any digraph map to one that does induce maps on homology, and present a homotopy classification that provides a condition for two such digraph maps to induce the same map at the homology level. We also prove functoriality of directed clique homology and describe filtrations of digraphs induced by digraph maps. We then prove stability of persistent directed clique homology by showing that the persistence modules of a digraph and that of an admissible perturbation are interleaved. These admissible perturbations include perturbing dissimilarity measures in the network that either preserve the digraph structure or collapse series of arrows. We also explore similar constructions for maps between digraphs that allow reversal of arrows and show that while such maps, in general, produce unstable persistence barcodes, one can recover stability by inducing a reverse filtration and truncating at an appropriate threshold. Finally, we present an application of persistent directed clique homology to trace patterns and shapes embedded in migration and remittance networks.

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<p>The work presented in this dissertation includes the study of cohomology and cohomological operations within the framework of Persistence. Although Persistence was originally defined for homology, recent research has developed persistent approaches to other algebraic topology invariants. The work in this document extends the field of persistence to include cohomology classes, cohomology operations and characteristic classes. </p><p>By starting with presenting a combinatorial formula to compute the Stiefel-Whitney homology class, we set up the groundwork for Persistent Characteristic Classes. To discuss persistence for the more general cohomology classes, we construct an algorithm that allows us to find the Poincar'{e} Dual to a homology class. Then, we develop two algorithms that compute persistent cohomology, the general case and one for a specific cohomology class. We follow this with defining and composing an algorithm for extended persistent cohomology. </p><p>In addition, we construct an algorithm for determining when a cohomology class is decomposible and compose it in the context of persistence. Lastly, we provide a proof for a concise formula for the first Steenrod Square of a given cohomology class and then develop an algorithm to determine when a cohomology class is a Steenrod Square of a lower dimensional cohomology class.</p> / Dissertation

archives@tulane.edu / The current system for evaluating prostate cancer architecture is the Gleason Grade system, which divides the morphology of cancer into five distinct architectural patterns, labeled numerically in increasing levels of cancer aggressiveness and generates a score by summing the labels of the two most dominant patterns. The Gleason score is currently the most powerful prognostic predictor of patient outcomes; however, it suffers from problems in reproducibility and consistency due to the high intra-observer and inter-observer variability among pathologists. In addition, the Gleason system lacks the granularity to address potentially prognostic architectural features beyond Gleason patterns. We look towards persistent homology, a tool from topological data analysis, to provide a means of evaluating prostate cancer glandular architecture. The objective of this work is to demonstrate the capacity of persistent homology to capture architectural features independently of Gleason patterns in a representation suitable for unsupervised and supervised machine learning. Specifically, using persistent homology, we compute topological representations of purely graded prostate cancer histopathology images of Gleason patterns and show that persistent homology is capable of clustering prostate cancer histology into architectural groups through discrete representations of persistent homology in both two-dimensional and three-dimensional histopathology. We then demonstrate the performance of persistent homology based features in common machine learning classifiers, indicating that persistent homology can both separate unique architectures in prostate cancer, but is also predictive of prostate cancer aggressiveness. Our results indicate the ability of persistent homology to cluster into unique groups with dominant architectural patterns consistent with the continuum of Gleason patterns. In addition, of particular interest, is the sensitivity of persistent homology to identify specific sub-architectural groups within single Gleason patterns, suggesting that persistent homology could represent a robust quantification method for prostate cancer architecture with higher granularity than the existing semi-quantitative measures. This work develops a framework for segregating prostate cancer aggressiveness by architectural subtype using topological representations, in a supervised machine learning setting, and lays the groundwork for augmenting traditional approaches with topological features for improved diagnosis and prognosis. / 1 / Peter Lawson

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Persistent homology is a technique of topological data analysis that seeks to understand the shape of data. We study the effectiveness of a single-layer perceptron and gradient boosted classification trees in classifying perhaps the most well-known data set in machine learning, the MNIST-Digits, or MNIST. An alternative representation is constructed, called MNIST-PD. This construction captures the topology of the digits using persistence diagrams, a product of persistent homology. We show that the models are more effective when trained on MNIST compared to MNIST-PD. Promising evidence reveals that the topology is learned by the algorithms. / Thesis / Master of Science (MSc)