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Related papers: Topologies on unparameterised rough path space

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We expose a class of discrete metric spaces, for which bounded geometry is equivalent to the property A of G. Yu. This class includes the coarse disjoint union of $(\mathbb Z/2\mathbb Z)^n$, $n\in\mathbb N$, and consists of spaces of simple…

Metric Geometry · Mathematics 2025-11-21 V. Manuilov

We prove that a large class of metrizable group topologies for subgroups of $\mathbb{R}^n$ and the completions of the subgroups are locally isometric to, respectively, metrizable group topologies for $\mathbb{Z}$ and their completions,…

General Topology · Mathematics 2007-05-23 Jon W. Short

We discuss measures on spaces of unparametrized paths related to the Wiener measure. These measures arise naturally in the study of one-dimensional gravity coupled to scalar fields. Two kinds of discrete approximations are defined, the…

Mathematical Physics · Physics 2015-06-26 B. Durhuus , T. Jonsson

We study a notion of strict pseudoconvexity in the context of topologically (often unsmoothably) embedded 3-manifolds in complex surfaces. Topologically pseudoconvex (TPC) 3-manifolds behave similarly to their smooth analogues, cutting out…

Geometric Topology · Mathematics 2023-04-18 Robert E. Gompf

Most real-world networks are embedded in latent geometries. If a node in a network is found in the vicinity of another node in the latent geometry, the two nodes have a disproportionately high probability of being connected by a link. The…

Physics and Society · Physics 2024-06-19 Bukyoung Jhun

The 'contracting boundary' of a proper geodesic metric space consists of equivalence classes of geodesic rays that behave like rays in a hyperbolic space. We introduce a geometrically relevant, quasi-isometry invariant topology on the…

Metric Geometry · Mathematics 2019-08-21 Christopher H. Cashen , John M. Mackay

Topological data analysis is a powerful tool for describing topological signatures in real world data. An important challenge in topological data analysis is matching significant topological signals across distinct systems. In geometry and…

Algebraic Topology · Mathematics 2025-02-19 Stephen Y Zhang , Michael P H Stumpf , Tom Needham , Agnese Barbensi

Persistence diagrams (PDs), often characterized as sets of death and birth of homology class, have been known for providing a topological representation of a graph structure, which is often useful in machine learning tasks. Prior works rely…

Machine Learning · Computer Science 2022-09-29 Chau Pham , Trung Dang , Peter Chin

Comparing two geometric graphs embedded in space is important in the field of transportation network analysis. Given street maps of the same city collected from different sources, researchers often need to know how and where they differ.…

Computational Geometry · Computer Science 2015-02-17 Mahmuda Ahmed , Brittany Terese Fasy , Kyle S. Hickmann , Carola Wenk

Signatures provide a succinct description of certain features of paths in a reparametrization invariant way. We propose a method for classifying shapes based on signatures, and compare it to current approaches based on the SRV transform and…

Differential Geometry · Mathematics 2020-01-15 Elena Celledoni , Pål Erik Lystad , Nikolas Tapia

For two not necessarily commutative topological groups G and T, let H(G,T) denote the space of all continuous homomorphisms from G to T with the compact-open topology. We prove that if G is metrizable and T is compact then H(G,T) is a…

General Topology · Mathematics 2007-05-23 Gabor Lukacs

Topological methods for data analysis present opportunities for enforcing certain invariances of broad interest in computer vision, including view-point in activity analysis, articulation in shape analysis, and measurement invariance in…

Computer Vision and Pattern Recognition · Computer Science 2018-07-30 Anirudh Som , Kowshik Thopalli , Karthikeyan Natesan Ramamurthy , Vinay Venkataraman , Ankita Shukla , Pavan Turaga

Persistent homology is a multiscale method for analyzing the shape of sets and functions from point cloud data arising from an unknown distribution supported on those sets. When the size of the sample is large, direct computation of the…

We investigate topological descriptors for 3D surface analysis, i.e. the classification of surfaces according to their geometric fine structure. On a dataset of high-resolution 3D surface reconstructions we compute persistence diagrams for…

Computer Vision and Pattern Recognition · Computer Science 2016-01-25 Matthias Zeppelzauer , Bartosz Zieliński , Mateusz Juda , Markus Seidl

Our objective is to illuminate the global structure of non-orientable manifolds with signature-changing metrics, with particular emphasis on global topological obstructions. Using explicit geometric constructions based on the topology of…

Differential Geometry · Mathematics 2026-05-04 Nathalie E. Rieger

This article discusses the existence problem of a compact quotient of a symmetric space by a properly discontinuous group with emphasis on the non-Riemannian case. Discontinuous groups are not always abundant in a homogeneous space $G/H$ if…

Differential Geometry · Mathematics 2011-06-22 Toshiyuki Kobayashi , Taro Yoshino

The motion of a quantum particle constrained to a two-dimensional non-compact Riemannian manifold with non-trivial metric can be described by a flat-space Schroedinger-type equation at the cost of introducing local mass and metric and…

Mesoscale and Nanoscale Physics · Physics 2025-12-19 Benjamin Schwager , Theresa Appel , Jamal Berakdar

A Polish group $G$ has the generic point property if any minimal $G$-flow admits a comeager orbit, or equivalently if the universal minimal flow (UMF) does. The class $\mathsf{GPP}$ of such Polish groups is a proper extension of the class…

Dynamical Systems · Mathematics 2025-09-11 Gianluca Basso , Andy Zucker

We introduce a systematic mathematical language for describing fixed point models and apply it to the study to topological phases of matter. The framework is reminiscent of state-sum models and lattice topological quantum field theories,…

Quantum Physics · Physics 2022-07-28 A. Bauer , J. Eisert , C. Wille

Modern representation learning increasingly relies on unsupervised and self-supervised methods trained on large-scale unlabeled data. While these approaches achieve impressive generalization across tasks and domains, evaluating embedding…