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A measure-scaling quasi-isometry between two connected graphs is a quasi-isometry that is quasi-$\kappa$-to-one in a natural sense for some $\kappa>0$. For non-amenable graphs, all quasi-isometries are quasi-$\kappa$-to-one for any…

Group Theory · Mathematics 2021-05-12 Anthony Genevois , Romain Tessera

Supersymmetric quantum mechanics is constructed in a new non-Hermitian representation. Firstly, the map between the partner operators $H^{(\pm)}$ is chosen antilinear. Secondly, both these components of a super-Hamiltonian ${\cal H}$ are…

Mathematical Physics · Physics 2015-05-13 Miloslav Znojil , Vit Jakubsky

The Kullback-Leibler divergence offers an information-theoretic basis for measuring the difference between two given distributions. Its quantum analog, however, fails to play a corresponding role for comparing two density matrices, if the…

Quantum Physics · Physics 2009-11-10 Sumiyoshi Abe

Designing experiments that systematically gather data from complex physical systems is central to accelerating scientific discovery. While Bayesian experimental design (BED) provides a principled, information-based framework that integrates…

Machine Learning · Computer Science 2026-01-26 Huchen Yang , Xinghao Dong , Jin-Long Wu

Almost contact structures can be identified with sections of a twistor bundle and this allows to define their harmonicity, as sections or maps. We consider the class of nearly cosymplectic almost contact structures on a Riemannian manifold…

Differential Geometry · Mathematics 2011-09-14 E. Loubeau , E. Vergara-Diaz

We classify all negatively curved $\R^n \rtimes \R$ up to quasiisometry. We show that all quasiisometries between such manifolds (except when they are biLipschitz to the real hyperbolic spaces) are almost similarities. We prove these…

Group Theory · Mathematics 2014-11-11 Xiangdong Xie

The Manifold Hypothesis is a widely accepted tenet of Machine Learning which asserts that nominally high-dimensional data are in fact concentrated near a low-dimensional manifold, embedded in high-dimensional space. This phenomenon is…

Methodology · Statistics 2025-03-24 Nick Whiteley , Annie Gray , Patrick Rubin-Delanchy

The standard topological approach to indistinguishable particles formulates exchange statistics by using the fundamental group to analyze the connectedness of the configuration space. Although successful in two and more dimensions, this…

Quantum Physics · Physics 2022-10-28 N. L. Harshman , A. C. Knapp

A new method for analyzing the morphological features of point patterns is presented. The method is taken from the study of molecular liquids, where it has been introduced for making a statistical description of anisotropic distributions.…

Astrophysics · Physics 2007-05-23 R. Valdarnini

It is introduced a differentiable manifold with almost contact 3-structure which consists of an almost contact metric structure and two almost contact B-metric structures. The product of this manifold and a real line is an almost…

Differential Geometry · Mathematics 2017-11-21 Mancho Manev

We introduce the notion of negative topological dimension and the notion of weight for the asymptotic topological dimension. Quantizing of spaces of negative dimension is applied to linguistic statistics.

General Mathematics · Mathematics 2007-05-23 V. P. Maslov

Considering quasisymmetric mappings between b-metric spaces we have found a new estimation for the ratio of diameters of two subsets which are images of two bounded subsets. This result generalizes the well-known Tukia-V\"{a}is\"{a}l\"{a}…

General Topology · Mathematics 2025-01-08 Evgeniy A. Petrov , Ruslan R. Salimov

Optimal transport is a geometrically intuitive, robust and flexible metric for sample comparison in data analysis and machine learning. Its formal Riemannian structure allows for a local linearization via a tangent space approximation. This…

Optimization and Control · Mathematics 2024-06-07 Clément Sarrazin , Bernhard Schmitzer

The paper introduces a new kernel-based Maximum Mean Discrepancy (MMD) statistic for measuring the distance between two distributions given finitely-many multivariate samples. When the distributions are locally low-dimensional, the proposed…

Machine Learning · Statistics 2018-09-03 Xiuyuan Cheng , Alexander Cloninger , Ronald R. Coifman

We give an overview of statistical models and likelihood, together with two of its variants: penalized and hierarchical likelihood. The Kullback-Leibler divergence is referred to repeatedly, for defining the misspecification risk of a…

Statistics Theory · Mathematics 2008-09-01 Daniel Commenges

The problem of finding an appropriate geometrical/physical index for measuring a degree of inhomogeneity for a given space-time manifold is posed. Interrelations with the problem of understanding the gravitational/informational entropy are…

General Relativity and Quantum Cosmology · Physics 2007-05-23 Roustam Zalaletdinov

We present some product representations for random variables with the Linnik, Mittag-Leffler and Weibull distributions and establish the relationship between the mixing distributions in these representations. Based on these representations,…

Probability · Mathematics 2016-02-09 V. Yu. Korolev , A. I. Zeifman

In this article, we describe various aspects of categorification of the structures appearing in information theory. These aspects include probabilistic models both of classical and quantum physics, emergence of F-manifolds, and motivic…

Information Theory · Computer Science 2021-08-12 Noemie Combe , Yuri I. Manin , Matilde Marcolli

We propose a tomographic approach to study quantum nonlocality in continuous variable quantum systems. On one hand we derive a Bell-like inequality for measured tomograms. On the other hand, we introduce pseudospin operators whose…

Quantum Physics · Physics 2009-11-10 Stefano Mancini , Vladimir I. Man'ko , Evgeny V. Shchukin , Paolo Tombesi

The purpose of this paper is twofold. On a technical side, we propose an extension of the Hausdorff distance from metric spaces to spaces equipped with asymmetric distance measures. Specifically, we focus on the family of Bregman…

Machine Learning · Computer Science 2025-04-11 Tuyen Pham , Hana Dal Poz Kouřimská , Hubert Wagner
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