arXiv research

MuRiT efficiently computes multi-parameter persistence barcodes.

We define notions of differentiability for maps from and to the space of persistence barcodes. Inspired by the theory of diffeological spaces, the proposed framework uses lifts to the space of ordered barcodes, from which derivatives can be computed. The two derived notions of differentiability (respectively from and t…

Introduces TSI, a variance-based measure for persistence barcodes.

We introduce a new feature map for barcodes that arise in persistent homology computation. The main idea is to first realize each barcode as a path in a convenient vector space, and to then compute its path signature which takes values in the tensor algebra of that vector space. The composition of these two operations …

This article introduces an application of Ghrist barcodes in the study of persistent Betti numbers derived from vortex nerve complexes found in triangulations of video frames. A Ghrist barcode is a topology of data pictograph useful in representing the persistence of the features of changing shapes. The basic approach …

Paper stabilizes persistent homology rank functions for statistical inference.

We consider different notions of equivalence for Morse functions on the sphere in the context of persistent homology, and introduce new invariants to study these equivalence classes. These new invariants are as simple, but more discerning than existing topological invariants, such as persistence barcodes and Reeb graph…

Paper introduces stable vectorization for multiparameter PH using signed barcodes.

Homotopy types of Vietoris-Rips metric thickenings of the circle confirmed.

Stable topological summary captures evolving dependency structure in dynamic Bayesian networks.

We apply the barcodes of persistent homology theory to the Chekanov-Eliashberg algebra of a Legendrian submanifold to deduce displacement energy bounds for arbitrary Legendrians. We do not require the full Chekanov-Eliashberg algebra to admit an augmentation as we linearize the algebra only below a certain action level…

Persistent entropy detects phase transitions in complex systems.

New metrics and coordinates for barcode space using group theory.

We propose to study neural networks' loss surfaces by methods of topological data analysis. We suggest to apply barcodes of Morse complexes to explore topology of loss surfaces. An algorithm for calculations of the loss function's barcodes of local minima is described. We have conducted experiments for calculating barc…

A new method clusters complex networks using topological and geometric structure.

Topology-GS improves 3D GS for better structural and feature integrity.

Entropy measures geodesic flow complexity.

Paper compares dimension reduction methods using topological analysis on EEG data.

The fingerprint classification problem is to sort fingerprints into pre-determined groups, such as arch, loop, and whorl. It was asserted in the literature that minutiae points, which are commonly used for fingerprint matching, are not useful for classification. We show that, to the contrary, near state-of-the-art clas…

Floer theory constructs filtrations on quantum cohomology for symplectic manifolds.

Quantum-enhanced barcode decoding and pattern recognition outperforms classical methods.

Persistent homology has emerged as a novel tool for data analysis in the past two decades. However, there are still very few shapes or even manifolds whose persistent homology barcodes (say of the Vietoris-Rips complex) are fully known. Towards this direction, let $P_n$ be the boundary of a regular polygon in the plane…

Symplectic homology matches dual capacities for convex domains.

Detection of rare variants by resequencing is important for the identification of individuals carrying disease variants. Rapid sequencing by new technologies enables low-cost resequencing of target regions, although it is still prohibitive to test more than a few individuals. In order to improve cost trade-offs, it has…

Approaches for approximating persistent homology for large datasets.

Persistence landscapes map persistence diagrams into a function space, which may often be taken to be a Banach space or even a Hilbert space. In the latter case, it is a feature map and there is an associated kernel. The main advantage of this summary is that it allows one to apply tools from statistics and machine lea…

Proposes deep graph persistence to address neural persistence issues in deep learning.

Paper proves $k$-means clustering works on persistence diagrams.

Formula for interleaving distance of rectangle persistence modules.

This paper demonstrates the flaws of co-persistence theory proposed by Bollerslev and Engle (1993) which cause the theory can hardly be applied. With the introduction of the half-life of decay coefficient as the measure of the persistence, and both the weak definition of persistence and co-persistence in variance, this…

Optimizes wavelets for graph classification using spectral wavelet signatures and persistence diagrams.

New method for analyzing multiparameter persistence modules from smooth functions.

Develops robust persistence diagrams using kernel methods.

This paper interprets critical scales in persistent homology for compact metric spaces.

Persistent homology can recognize knotting in curves.

This article analyzes the relationship between co-persistence and hedging which indicates co-persistence ratio is just the long-term hedging ratio. The new method of exhaustive search algorithm for deriving co-persistence ratio is derived in the article. And we also develop a new hedging strategy of combining co-persis…

Despite the obvious similarities between the metrics used in topological data analysis and those of optimal transport, an optimal-transport based formalism to study persistence diagrams and similar topological descriptors has yet to come. In this article, by considering the space of persistence diagrams as a space of d…

Persistence diagrams are important descriptors in Topological Data Analysis. Due to the nonlinearity of the space of persistence diagrams equipped with their {\em diagram distances}, most of the recent attempts at using persistence diagrams in machine learning have been done through kernel methods, i.e., embeddings of …

Computational topology has recently known an important development toward data analysis, giving birth to the field of topological data analysis. Topological persistence, or persistent homology, appears as a fundamental tool in this field. In this paper, we study topological persistence in general metric spaces, with a …

A new approach to reinforcement learning improves policy performance by adjusting control frequency.

Given a compact geodesic space $X$ we apply the fundamental group and alternatively the first homology group functor to the corresponding Rips or Čech filtration of $X$ to obtain what we call a persistence. This paper contains the theory describing such persistence: properties of the set of critical points, their preci…

A new method compares persistent cycles in topological data.

Improved persistence spheres map measures to functions, stable under partial transport.

We introduce several geometric notions, including the width of a homology class, to the theory of persistent homology. These ideas provide geometric interpretations of persistence diagrams. Indeed, we give quantitative and geometric descriptions of the "life span" or "persistence" of a homology class. As a case study, …

This article addresses persistent tangles. These are tangles whose presence in a knot diagram forces that diagram to be knotted. We provide new methods for constructing persistent tangles. Our techniques rely mainly on the existence of non-trivial colorings for the tangles in question. Our main result in this article i…

Study cosmic structures using Topological Data Analysis and Persistence Energy.

New lattice path method for statistical inference of persistent diagrams.

The paper examines how long-memory dynamics, rough-volatility, and persistence affect equity volatility forecasting.