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The logarithmic derivative of a point process plays a key role in the general approach, due to the third author, to constructing diffusions preserving a given point process. In this paper we explicitly compute the logarithmic derivative for…

Probability · Mathematics 2017-07-07 Alexander I. Bufetov , Andrey V. Dymov , Hirofumi Osada

A model of randomly distributed overlapping spheres of different radii is represented to describe a heterogeneous porous medium. Two-particle correlation function of the relative position of pores of different radii in the medium space was…

Soft Condensed Matter · Physics 2016-03-04 V. D. Borman , A. A. Belogorlov , V. A. Byrkin , V. N. Tronin , V. I. Troyan

When the number of particles is finite, the noncolliding Brownian motion (the Dyson model) and the noncolliding squared Bessel process are determinantal diffusion processes for any deterministic initial configuration $\xi=\sum_{j \in…

Probability · Mathematics 2011-12-07 Makoto Katori , Hideki Tanemura

We study energy integrals and discrete energies on the sphere, in particular, analogs of the Riesz energy with the geodesic distance in place of Euclidean, and observe that the range of exponents for which the uniform distribution optimizes…

Classical Analysis and ODEs · Mathematics 2016-12-28 Dmitriy Bilyk , Feng Dai

This paper is concerned with the detection of multiple change-points in the joint distribution of independent categorical variables. The procedures introduced rely on model selection and are based on a penalized least-squares criterion.…

Statistics Theory · Mathematics 2008-01-08 Nathalie Akakpo

Diffusion processes are a class of stochastic differential equations (SDEs) providing a rich family of expressive models that arise naturally in dynamic modelling tasks. Probabilistic inference and learning under generative models with…

Machine Learning · Computer Science 2024-02-28 Prakhar Verma , Vincent Adam , Arno Solin

Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory. In contrast to traditional structured models like Markov random fields, which become intractable and…

Machine Learning · Statistics 2013-01-11 Alex Kulesza , Ben Taskar

We analyse several constructions of random point sets on the sphere $\mathbb{S}^{3}\subset\mathbb{R}^4$ evaluating and comparing them through their discrete logarithmic energy: \begin{equation*} E_0(\omega_N) = \sum_{\substack{i, j=1\\ i…

Probability · Mathematics 2026-02-13 Ujué Etayo , Pablo G. Arce

We consider Riesz energy problems with radial external fields. We study the question of whether or not the equilibrium is the uniform distribution on a sphere. We develop general necessary as well as general sufficient conditions on the…

Classical Analysis and ODEs · Mathematics 2025-09-08 Djalil Chafaï , Ryan W. Matzke , Edward B. Saff , Minh Quan H. Vu , Robert S. Womersley

This study introduces a novel point-wise diffusion model that processes spatio-temporal points independently to efficiently predict complex physical systems with shape variations. This methodological contribution lies in applying forward…

Computational Physics · Physics 2025-08-05 Jiyong Kim , Sunwoong Yang , Namwoo Kang

We introduce a very general method for high-dimensional classification, based on careful combination of the results of applying an arbitrary base classifier to random projections of the feature vectors into a lower-dimensional space. In one…

Methodology · Statistics 2017-06-06 Timothy I. Cannings , Richard J. Samworth

This article is concerned with a geometric tool given by a pair of projector operators defined by almost product structures on finite dimensional manifolds, polarized by a distribution of constant rank and also endowed with some geometric…

Mathematical Physics · Physics 2011-10-11 Paulo Pitanga , Paulo R. Rodrigues

Determinantal point processes (DPPs) offer a powerful approach to modeling diversity in many applications where the goal is to select a diverse subset. We study the problem of learning the parameters (the kernel matrix) of a DPP from…

Machine Learning · Statistics 2014-11-10 Boqing Gong , Wei-lun Chao , Kristen Grauman , Fei Sha

Weyl-Heisenberg ensembles are a class of determinantal point processes associated with the Schr\"odinger representation of the Heisenberg group. Hyperuniformity characterizes a state of matter for which (scaled) density fluctuations…

Statistical Mechanics · Physics 2017-06-21 Luís Daniel Abreu , João M. Pereira , José Luis Romero , Salvatore Torquato

Number theorists have studied extensively the connections between the distribution of zeros of the Riemann $\zeta$-function, and of some generalizations, with the statistics of the eigenvalues of large random matrices. It is interesting to…

Mathematical Physics · Physics 2009-10-31 E. Brezin , S. Hikami

In this article, we construct discrete tight frames for $L^2(\mathbb{S}^{d-1})$, $d\geq3$, which consist of localized polynomial wavelets with adjustable degrees of directionality. In contrast to the well studied isotropic case, these…

Classical Analysis and ODEs · Mathematics 2025-12-09 Frederic Schoppert

We characterize the image of the Poisson transform on any distinguished boundary of a Riemannian symmetric space of the noncompact type by a system of differential equations. The system corresponds to a generator system of a two sided…

Representation Theory · Mathematics 2011-06-07 Toshio Oshima , Nobukazu Shimeno

We show that every 2nd order ODE defines a 4-parameter family of projective connections on its 2-dimensional solution space. In a special case of ODEs, for which a certain point transformation invariant vanishes, we find that this family of…

General Relativity and Quantum Cosmology · Physics 2007-05-23 Ezra T Newman , Pawel Nurowski

Cone spherical metrics, defined on compact Riemann surfaces, are conformal metrics with constant curvature one and finitely many cone singularities. Such a metric is termed \textit{reducible} if a developing map of the metric has monodromy…

Differential Geometry · Mathematics 2024-09-25 Yu Feng , Jijian Song , Bin Xu

Discrete delta functions define the limits of attainable spatial resolution for all imaging systems. Here we construct broad, multi-dimensional discrete functions that replicate closely the action of a Dirac delta function under aperiodic…

Image and Video Processing · Electrical Eng. & Systems 2020-09-01 I. D. Svalbe , D. M. Paganin , T. C. Petersen