This paper applies the major computational tool from Topological Data Analysis (TDA), persistent homology, to discover patterns in the data related to professional sports teams. I will use official game data from the North-American National Hockey League (NHL) 2013-2014 season to discover the correlation between the composition of NHL teams with the currently preferred offensive performance mar...

We present a novel framework for characterizing signals in images using techniques from computational algebraic topology. This technique is general enough for dealing with noisy multivariate data including geometric noise. The main tool is persistent homology which can be encoded in persistence diagrams. These are scatter plots of paired local critical values of the signal. One of these diagram...

An algorithm is presented that constructs an acyclic partial matching on the cells of a given simplicial complex from a vector-valued function defined on the vertices and extended to each simplex by taking the least common upper bound of the values on its vertices. The resulting acyclic partial matching may be used to construct a reduced filtered complex with the same multidimensional persisten...

Persistent homology computes topological invariants from point cloud data. Recent work has focused on developing statistical methods for data analysis in this framework. We show that, in certain models, parametric inference can be performed using statistics defined on the computed invariants. We develop this idea with a model from population genetics, the coalescent with recombination. We apply...

We present a novel framework for characterizing signals in images using techniques from computational algebraic topology. This technique is general enough for dealing with noisy multivariate data including geometric noise. The main tool is persistent homology which can be encoded in persistence diagrams. These are scatter plots of paired local critical values of the signal. One of these diagram...

The theory of multidimensional persistent homology was initially developed in the discrete setting, and involved the study of simplicial complexes filtered through an ordering of the simplices. Later, stability properties of multidimensional persistence have been proved to hold when topological spaces are filtered by continuous functions, i.e. for continuous data. This paper aims to provide a b...

In recent years, the application of network analysis to neuroimaging data has provided useful insights about the brain's functional and structural organization in both health and disease. This has proven a significant paradigm shift from the study of individual brain regions in isolation. Graph-based models of the brain consist of vertices, which represent distinct brain areas, and edges which ...

The topology of two-dimensional network materials is investigated by persistent homology analysis. constraint two dimensions allows for a direct comparison key metrics (persistence diagrams, cycles, and Betti numbers) with more traditional such as the ring-size distributions. Two different types networks are employed in which manipulated systematically. In first, comparatively rigid generated t...

We investigate the relations between algebraic structures, spectral invariants, and persistence modules, in context of monotone Lagrangian Floer homology with Hamiltonian term. Firstly, we use newly introduced method filtered continuation elements to prove that norm controls barcode perturbation submanifold, up shift, bottleneck distance. Moreover, show it satisfies Chekanov type low-energy int...

In this article, we show the existence of certain exact sequences with respect to two homology theories, called d-homology and extended d-homology. We present sufficient conditions for the existence of long exact extended d- homology sequence. Also we give some illustrative examples.