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An extra large metric is a spherical cone metric with all cone angles greater than 2 pi and every closed geodesic longer than 2pi. We show that every two-dimensional extra large metric can be triangulated with vertices at cone points only.…

Geometric Topology · Mathematics 2007-05-23 Igor Rivin

In this paper, we prove that all doubling measures on the unit disk $\mathbb{D}$ are Carleson measures for the standard Dirichlet space $\mathcal{D}$. The proof has three ingredients. The first one is a characterization of Carleson measures…

Functional Analysis · Mathematics 2018-04-24 Guozheng Cheng , Xiang Fang , Zipeng Wang , Jiayang Yu

On metric spaces equipped with doubling measures, we prove that a differentiability theorem holds for Lipschitz functions if and only if the space supports nontrivial (metric) derivations in the sense of Weaver that satisfy an additional…

Metric Geometry · Mathematics 2012-08-15 Jasun Gong

We study metric measure spaces that have quantitative topological control, as well as a weak form of differentiable structure. In particular, let $X$ be a pointwise doubling metric measure space. Let $U$ be a Borel subset on which the…

Metric Geometry · Mathematics 2016-11-17 Guy C. David , Bruce Kleiner

We consider the problem of choosing Euclidean points to maximize the sum of their weighted pairwise distances, when each point is constrained to a ball centered at the origin. We derive a dual minimization problem and show strong duality…

Data Structures and Algorithms · Computer Science 2010-07-02 Neal E. Young

Prompted by a recent question of G. Hjorth as to whether a bounded Urysohn space is indivisible, that is to say has the property that any partition into finitely many pieces has one piece which contains an isometric copy of the space, we…

Combinatorics · Mathematics 2007-05-23 Christian Delhomme , Claude Laflamme , Maurice Pouzet , Norbert Sauer

We examine double successive approximations on a set, which we denote by $L_2L_1, \ U_2U_1, U_2L_1,$ $L_2U_1$ where $L_1, U_1$ and $L_2, U_2$ are based on generally non-equivalent equivalence relations $E_1$ and $E_2$ respectively, on a…

Logic in Computer Science · Computer Science 2016-12-13 Alexa Gopaulsingh

A point set $M$ in $m$-dimensional Euclidean space is called an integral point set if all the distances between the elements of $M$ are integers, and $M$ is not situated on an $(m-1)$-dimensional hyperplane. We improve the linear lower…

Combinatorics · Mathematics 2025-12-02 Nikolai Avdeev

This paper introduces a natural definition for the volume of the unit ball in $n$-dimensional normed spaces $\mathbb{R}^n$. This definition preserves the Euclidean relation $P(B)/V(B)=n$ between the perimiter and the volume of the unit ball…

Metric Geometry · Mathematics 2026-05-05 Gershon Wolansky

A round metric space is the one in which closure of each open ball is the corresponding closed ball. By a sleek metric space, we mean a metric space in which interior of each closed ball is the corresponding open ball. In this, article we…

General Topology · Mathematics 2023-04-28 Jitender Singh , T. D. Narang

Motivated by the expectation that relativistic symmetries might acquire quantum features in Quantum Gravity, we take the first steps towards a theory of ''Doubly'' Quantum Mechanics, a modification of Quantum Mechanics in which the…

Quantum Physics · Physics 2025-04-30 Vittorio D'Esposito , Giuseppe Fabiano , Domenico Frattulillo , Flavio Mercati

Several researchers proposed using non-Euclidean metrics on point sets in Euclidean space for clustering noisy data. Almost always, a distance function is desired that recognizes the closeness of the points in the same cluster, even if the…

Computational Geometry · Computer Science 2015-03-02 Michael B. Cohen , Brittany Terese Fasy , Gary L. Miller , Amir Nayyeri , Donald R. Sheehy , Ameya Velingker

The connection between several hyperbolic type metrics is studied in subdomains of the Euclidean space. In particular, a new metric is introduced and compared to the distance ratio metric.

Metric Geometry · Mathematics 2018-01-29 Oleksiy Dovgoshey , Parisa Hariri , Matti Vuorinen

A finite set of distinct vectors $\mathcal{X}$ in the $d$-dimensional Euclidean space $\mathbb{R}^d$ is called a $2$-distance set, if the set of mutual distances between distinct elements of $\mathcal{X}$ has cardinality exactly $2$. In…

Metric Geometry · Mathematics 2018-06-21 Ferenc Szöllősi

The purpose of this paper is to give a survey on the notions of distance between subsets either of a metric space or of a measure space, including definitions, a classification, and a discussion of the best-known distance functions, which…

Functional Analysis · Mathematics 2018-08-09 A. Conci , C. S. Kubrusly

The Hilbert metric is a distance function defined for points lying within the interior of a convex body. It arises in the analysis and processing of convex bodies, machine learning, and quantum information theory. In this paper, we show how…

Computational Geometry · Computer Science 2023-12-12 Auguste Gezalyan , Soo Kim , Carlos Lopez , Daniel Skora , Zofia Stefankovic , David M. Mount

In this paper, an approach for generalizing the Gromov-Hausdorff metric is presented, which applies to metric spaces equipped with some additional structure. A special case is the Gromov-Hausdorff-Prokhorov metric between measured metric…

Metric Geometry · Mathematics 2023-11-30 Ali Khezeli

As a generalization of Hausdorff's extension theorem of metrics, we prove an interpolation theorem of a family of metrics defined on closed subsets of metrizable spaces. As an application, we investigate typicality of subsets of moduli…

Metric Geometry · Mathematics 2026-01-14 Yoshito Ishiki

Given a two-level regular fractional factorial design of resolution IV, the method of doubling produces another design of resolution IV which doubles both the run size and the number of factors of the initial design. On the other hand, the…

Statistics Theory · Mathematics 2007-06-13 Hegang H. Chen , Ching-Shui Cheng

Deep metric learning (DML) is a cornerstone of many computer vision applications. It aims at learning a mapping from the input domain to an embedding space, where semantically similar objects are located nearby and dissimilar objects far…

Computer Vision and Pattern Recognition · Computer Science 2021-09-10 Artsiom Sanakoyeu , Pingchuan Ma , Vadim Tschernezki , Björn Ommer
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