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A set $A$ in a finite dimensional Euclidean space is \emph{monovex} if for every two points $x,y \in A$ there is a continuous path within the set that connects $x$ and $y$ and is monotone (nonincreasing or nondecreasing) in each coordinate.…

General Topology · Mathematics 2016-09-29 Lev Buhovsky , Eilon Solan , Omri Nisan Solan

We give a sufficient condition for a projective metric on a subset of a Euclidean space to admit a bi-Lipschitz embedding into Euclidean space of the same dimension.

Metric Geometry · Mathematics 2014-06-17 Leonid V. Kovalev

Dimensional types of metric scattered spaces are investigated. Revised proofs of Mazurkiewicz-Sierpi\'nski and Knaster-Urbanik theorems are presented. Embeddable properties of countable metric spaces are generalized onto uncountable metric…

General Topology · Mathematics 2015-05-01 Szymon Plewik , Marta Walczyńska

In this paper we study the kaleidoscopic pseudo-randomness of finite Euclidean graphs using probabilistic methods. Roughly speaking, we show that sufficiently large subsets of d-dimensional vector spaces over finite fields contain every…

Combinatorics · Mathematics 2008-08-03 Le Anh Vinh

Recent literature has shown that symbolic data, such as text and graphs, is often better represented by points on a curved manifold, rather than in Euclidean space. However, geometrical operations on manifolds are generally more complicated…

Machine Learning · Computer Science 2019-02-06 Max Aalto , Nakul Verma

The deck of a topological space $X$ is the set $\mathcal{D}(X)=\{[X \setminus \{x\}] \colon x \in X\}$, where $[Z]$ denotes the homeomorphism class of $Z$. A space $X$ is topologically reconstructible if whenever…

General Topology · Mathematics 2015-09-28 Paul Gartside , Max F. Pitz , Rolf Suabedissen

Diversities are like metric spaces, except that every finite subset, instead of just every pair of points, is assigned a value. Just as there is a theory of minimal distortion embeddings of finite metric spaces into $L_1$, there is a…

Metric Geometry · Mathematics 2016-11-10 David Bryant , Paul F. Tupper

In recent years, considerable advances have been made in the study of properties of metric spaces in terms of their doubling dimension. This line of research has not only enhanced our understanding of finite metrics, but has also resulted…

Discrete Mathematics · Computer Science 2007-12-27 Anupam Gupta , Kunal Talwar

We present the Probabilistic Context Neighborhood model designed for two-dimensional lattices as a variation of a Markov Random Field assuming discrete values. In this model, the neighborhood structure has a fixed geometry but a variable…

Methodology · Statistics 2024-01-31 Debora F. Magalhaes , Aline M. Piroutek , Denise Duarte , Caio Alves

Every graph G can be embedded in a Euclidean space as a two-distance set. The Euclidean representation number of G is the smallest dimension in which G is representable by such an embedding. We consider spherical and J-spherical…

Metric Geometry · Mathematics 2019-06-26 Oleg R. Musin

In this article we introduce theory and algorithms for learning discrete representations that take on a lattice that is embedded in an Euclidean space. Lattice representations possess an interesting combination of properties: a) they can be…

Machine Learning · Computer Science 2020-06-25 Luis A. Lastras

The group-theoretic method for constructing symmetric isometric embeddings is used to describe all possible four-dimensional surfaces in flat $(1,9)$-dimensional space, whose induced metric is static and spherically symmetric. For such…

General Relativity and Quantum Cosmology · Physics 2025-06-26 S. S. Kuptsov , S. A. Paston , A. A. Sheykin

A `discrete differential manifold' we call a countable set together with an algebraic differential calculus on it. This structure has already been explored in previous work and provides us with a convenient framework for the formulation of…

High Energy Physics - Theory · Physics 2009-10-28 A. Dimakis , F. M"uller-Hoissen , F. Vanderseypen

The first main goal of this article is to give a new metrization of the Mukherjee--Varadhan topology, recently introduced as a translation-invariant compactification of the space of probability measures on Euclidean spaces. This new…

Probability · Mathematics 2020-05-12 Yuri Bakhtin , Donghyun Seo

In this paper, we prove that the topology induced by algebraic cone metric coincides with the topology induced by the metric obtained via a nonlinear scalarization function, i.e. any algebraic cone metric space is metrizable. Furthermore,…

Functional Analysis · Mathematics 2014-02-04 Saeedeh Shamsi Gamchi , Mohammad Janfada , Assadollah Niknam

This paper studies the configuration spaces of linkages whose underlying graph is a single cycle. Assume that the edge lengths are such that there are no configurations in which all the edges lie along a line. The main results are that,…

Computational Geometry · Computer Science 2008-11-11 Don Shimamoto , Mary Wootters

In dealing with asymptotic approximation of possibly divergent nets of probability distributions, we are led to study uniform structures on the set of distributions. This paper identifies a class of such uniform structures that may be…

Probability · Mathematics 2010-11-23 Jan Pachl

We construct model sets arising from cut and project schemes in Euclidean spaces whose associated Delone dynamical systems have positive toplogical entropy. The construction works both with windows that are proper and with windows that have…

Dynamical Systems · Mathematics 2018-06-26 Tobias Jäger , Daniel Lenz , Christian Oertel

The n-th symmetric product of a metric space is the set of its nonempty subsets with cardinality at most n, equipped with the Hausdorff metric. We prove that every symmetric product of the line is an absolute Lipschitz retract and admits a…

Metric Geometry · Mathematics 2018-07-10 Leonid V. Kovalev

Embedding graphs in continous spaces is a key factor in designing and developing algorithms for automatic information extraction to be applied in diverse tasks (e.g., learning, inferring, predicting). The reliability of graph embeddings…

Machine Learning · Computer Science 2023-11-30 Andrea Marinoni , Pietro Lio' , Alessandro Barp , Christian Jutten , Mark Girolami
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