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In Euclidean space, the asymptotic shape of large cells in various types of Poisson driven random tessellations has been the subject of a famous conjecture due to David Kendall. Since shape is a geometric concept and large cells are…

Probability · Mathematics 2025-09-01 Daniel Hug , Andreas Reichenbacher

Permutation entropy and its associated frameworks are remarkable examples of physics-inspired techniques adept at processing complex and extensive datasets. Despite substantial progress in developing and applying these tools, their use has…

Data Analysis, Statistics and Probability · Physics 2024-08-14 Leonardo G. J. M. Voltarelli , Arthur A. B. Pessa , Luciano Zunino , Rafael S. Zola , Ervin K. Lenzi , Matjaz Perc , Haroldo V. Ribeiro

A recent paper [CGT] studies the evolution of star-shaped mean convex hypersurfaces of the Euclidean space by a class of nonhomogeneous expanding curvature flows. In the present paper we consider the same problem in the real, complex and…

Differential Geometry · Mathematics 2020-10-08 Giuseppe Pipoli

We prove $\epsilon$-closeness of hypersurfaces to a sphere in Euclidean space under the assumption that the traceless second fundamental form is $\delta$-small compared to the mean curvature. We give the explicit dependence of $\delta$ on…

Differential Geometry · Mathematics 2015-07-08 Julian Scheuer

Coordination geometries describe how the neighbours of a central particle are arranged around it. Such geometries can be thought to lie in an abstract topological space; a model of this space could provide a mathematical basis for…

Mathematical Physics · Physics 2023-06-28 John Çamkıran , Fabian Parsch , Glenn D. Hibbard

Complex systems, ranging from soft materials to wireless communication, are often organised as random geometric networks in which nodes and edges evenly fill up the volume of some space. Studying such networks is difficult because they…

Probability · Mathematics 2022-07-19 Ivan Kryven , Rik Versendaal

Network complexity has been studied for over half a century and has found a wide range of applications. Many methods have been developed to characterize and estimate the complexity of networks. However, there has been little research with…

Machine Learning · Statistics 2021-01-13 Yann Issartel

The unit distance embeddability of a graph, like planarity, involves a mix of constraints that are combinatorial and geometric. We construct a unit distance embedding for $H-e$ in the hope that it will lead to an embedding for $H$. We then…

Combinatorics · Mathematics 2007-11-08 Mitchell A. Harris

A well-defined distance on the parameter space is key to evaluating estimators, ensuring consistency, and building confidence sets. While there are typically standard distances to adopt in a continuous space, this is not the case for…

Statistics Theory · Mathematics 2026-02-02 Armeen Taeb , F. Richard Guo , Leonard Henckel

Motivated by persistent homology and topological data analysis, we consider formal sums on a metric space with a distinguished subset. These formal sums, which we call persistence diagrams, have a canonical 1-parameter family of metrics…

Algebraic Topology · Mathematics 2025-02-19 Peter Bubenik , Iryna Hartsock

Examples show that Riemannian manifolds with almost-Euclidean lower bounds on scalar curvature and Perelman entropy need not be close to Euclidean space in any metric space sense. Here we show that if one additionally assumes an…

Differential Geometry · Mathematics 2022-11-09 Robin Neumayer

We prove estimates for the optimal volume of thick embeddings of finite graphs into symmetric spaces, generalising results of Kolmogorov-Barzdin and Gromov-Guth for embeddings into Euclidean spaces. We distinguish two very different…

Geometric Topology · Mathematics 2023-12-13 Benjamin Barrett , David Hume , Larry Guth , Elia Portnoy

Defects are a ubiquitous feature of ordered media. They have certain universal features, independent of the underlying physical system, reflecting their topological origins. While the topological properties of defects are robust, they…

Soft Condensed Matter · Physics 2021-02-24 Chiqun Zhang , Amit Acharya , Alan C. Newell , Shankar C. Venkataramani

A rigidity theory is developed for frameworks in a metric space with two types of distance constraints. Mixed sparsity graph characterisations are obtained for the infinitesimal and continuous rigidity of completely regular bar-joint…

Metric Geometry · Mathematics 2019-08-26 Anthony Nixon , Stephen Power

Consider the sum of the first $N$ eigenspaces for the Laplacian on a Riemannian manifold. A basis for this space determines a map to Euclidean space and for $N$ sufficiently large the map is an embedding. In analogy with a fruitful idea of…

Differential Geometry · Mathematics 2014-04-30 Eric Potash

Recent studies have experimentally shown that we can achieve in non-Euclidean metric space effective and efficient graph embedding, which aims to obtain the vertices' representations reflecting the graph's structure in the metric space.…

Machine Learning · Statistics 2023-05-16 Atsushi Suzuki , Atsushi Nitanda , Taiji Suzuki , Jing Wang , Feng Tian , Kenji Yamanishi

Distance functions of metric spaces with lower curvature bound, by definition, enjoy various metric inequalities; triangle comparison, quadruple comparison and the inequality of Lang-Schroeder-Sturm. The purpose of this paper is to study…

Differential Geometry · Mathematics 2009-12-02 Takumi Yokota

Sobolev embeddings, of arbitrary order, are considered into function spaces on domains of $\mathbb R^n$ endowed with measures whose decay on balls is dominated by a power $d$ of their radius. Norms in arbitrary rearrangement-invariant…

Functional Analysis · Mathematics 2019-12-10 Andrea Cianchi , Luboš Pick , Lenka Slavíková

We study the space complexity of sketching cuts and Laplacian quadratic forms of graphs. We show that any data structure which approximately stores the sizes of all cuts in an undirected graph on $n$ vertices up to a $1+\epsilon$ error must…

Data Structures and Algorithms · Computer Science 2018-01-01 Charles Carlson , Alexandra Kolla , Nikhil Srivastava , Luca Trevisan

Discrete forms of the mean and directed curvature are constructed on piecewise flat manifolds, providing local curvature approximations for smooth manifolds embedded in both Euclidean and non-Euclidean spaces. The resulting expressions take…

Differential Geometry · Mathematics 2023-04-04 Rory Conboye
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