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Related papers: P-adic numbers and kernels

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In this article, we study a large class of radial probability density functions defined on the p-adic numbers from which it is possible to obtain certain non-archimedean pseudo-differential operators. These operators are associated with…

Mathematical Physics · Physics 2019-07-05 Anselmo Torresblanca-Badillo , Ismael Gutierrez Garcia

We discuss a spin glass reminiscent of the Random Energy Model, which allows in particular to recast the Parisi minimization into a more classical Gibbs variational principle, thereby shedding some light on the physical meaning of the order…

Probability · Mathematics 2009-11-13 Erwin Bolthausen , Nicola Kistler

In an earlier work, the statistical physics associated with finite--temperature decoding of code ensembles, along with the relation to their random coding error exponents, were explored in a framework that is analogous to Derrida's random…

Information Theory · Computer Science 2016-11-15 Neri Merhav

We will see that key concepts of number theory can be defined for arbitrary operations. We give a generalized distributivity for hyperoperations (usual arithmetic operations and operations going beyond exponentiation) and a generalization…

Rings and Algebras · Mathematics 2011-01-06 Patrick St-Amant

In this short paper I consider relation between measurements, numbers and p-adic mathematical physics. p-Adic numbers are not result of measurements, but nevertheless they play significant role in description of some systems and phenomena.…

General Physics · Physics 2012-06-15 Branko Dragovich

Positive semi-definite kernels are used to induce pseudo-metrics, or ``distances'', between measures. We write these as an expected quadratic variation of, or expected inner product between, a random field and the difference of measures.…

Probability · Mathematics 2025-05-30 Ian Langmore

We present Random Partition Kernels, a new class of kernels derived by demonstrating a natural connection between random partitions of objects and kernels between those objects. We show how the construction can be used to create kernels…

Machine Learning · Statistics 2014-02-19 Alex Davies , Zoubin Ghahramani

We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be used to both predict and train…

Computational Physics · Physics 2019-02-05 Michele Ceriotti , Michael J. Willatt , Gábor Csányi

In this paper, we offer a brief introduction to the $p$-adic numbers and operations in the metric space defined under the $p$-adic norm. Specifically, we provide a clear description of the derivation of the $p$-adic number via the…

History and Overview · Mathematics 2017-10-25 Joel Abraham

These are notes for a mini-course on the extremes of correlated random fields which I gave in the spring 2013 in Marseille. The first chapter recalls the paradigmatic random energy models with finitely many scales introduced by B. Derrida…

Probability · Mathematics 2014-12-03 Nicola Kistler

In this note, we develop some of the basic theory of s-finite (measures and) kernels, a little-studied class that Staton has recently argued convincingly to be precisely the semantic counterpart of (first-order) probabilistic programs. We…

Probability · Mathematics 2026-05-06 Matthijs Vákár , Luke Ong

This paper examines the problem of learning with a finite and possibly large set of p base kernels. It presents a theoretical and empirical analysis of an approach addressing this problem based on ensembles of kernel predictors. This…

Machine Learning · Computer Science 2012-02-20 Corinna Cortes , Mehryar Mohri , Afshin Rostamizadeh

In this thesis, we consider several Random Energy Models. This includes Derrida's Random Energy Model (REM) and Generalized Random Energy Model (GREM) and a nonhierarchical version (BK-GREM) by Bolthausen and Kistler. The limiting free…

Probability · Mathematics 2007-11-09 Nabin Kumar Jana

We consider the free energy of a class of spin glass models with $ p$-spin interactions in a transverse magnetic field. As $ p \to \infty $, the infinite system-size free energy is proven to converge to that of the quantum random energy…

Mathematical Physics · Physics 2026-03-10 Anouar Kouraich , Chokri Manai , Simone Warzel

The mathematical basis of p-adic Higgs mechanism discussed in papers [email protected] 9410058-62 is considered in this paper. The basic properties of p-adic numbers, of their algebraic extensions and the so called canonical…

High Energy Physics - Theory · Physics 2008-02-03 M. Pitkänen

In a digraph, a kernel is a subset of vertices that is both independent and absorbing. Kernels have important applications in combinatorics and outside. Kernels do not always exist and finding sufficient conditions ensuring their existence…

Combinatorics · Mathematics 2025-02-05 Hélène Langlois , Frédéric Meunier

We introduce the notion of multiplication kernels of birational and $D$-module type and give various examples. We also introduce the notion of a semi-classical multiplication kernel associated with an integrable system and discuss its…

Algebraic Geometry · Mathematics 2022-01-05 Maxim Kontsevich , Alexander Odesskii

We study pseudodifferential equations and Riesz kernels attached to certain quadratic forms over p-adic fields. We attach to an elliptic quadratic form of dimension two or four a family of distributions depending on a complex parameter, the…

Functional Analysis · Mathematics 2014-06-20 O. Casas-Sanchez , W. A. Zuniga-Galindo

A comprehensive review will be given about the rich mathematical structure of mean field spin glass theory, mostly developed, until now, in the frame of the methods of theoretical physics, based on deep physical intuition and hints coming…

Disordered Systems and Neural Networks · Physics 2007-05-23 F. Guerra

This review presents various aspects of a mean-field spin glass model known as the p-spin spherical spin glass model, which has raised a lot of interest in the study of spin glasses, and also for its possible links with a mean-field theory…

Disordered Systems and Neural Networks · Physics 2008-02-03 A. Barrat
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