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In this article, we conduct a study of integral operators defined in terms of non-convolution type kernels with singularities of various degrees. The operators that fall within our scope of research include fractional integrals, fractional…

Functional Analysis · Mathematics 2018-01-16 Lucas Chaffee , Jarod Hart , Lucas Oliveira

We investigate the mean-field limit for interacting particle systems through a duality-based framework and obtain quantitative estimates on the convergence of marginals as well as on correlation functions. In particular, for merely…

Analysis of PDEs · Mathematics 2026-05-05 Nadia Khoury , P. -E. Jabin

We solve the initial value problem for the linearized mean field Kramers equation describing Brownian particles with long-range interactions in the $N\rightarrow +\infty$ limit. We show that the dielectric function can be expressed in terms…

Statistical Mechanics · Physics 2013-09-11 Pierre-Henri Chavanis

This paper is devoted to the design and analysis of a numerical algorithm for approximating solutions of a degenerate cross-diffusion system, which models particular instances of taxis-type migration processes under local sensing…

Numerical Analysis · Mathematics 2025-10-09 Juan Vicente Gutiérrez-Santacreu

In heavy-ion phenomenology, the nucleon density distribution in colliding nuclei is commonly described by a two-parameter Woods-Saxon (WS) distribution. However, this approach overlooks the detailed radial structure in the density…

Nuclear Theory · Physics 2024-10-10 Zhengxi Yan , Jun Xu , Jiangyong Jia

The ground-state properties of superfluid nuclear systems with ^1S_0 pairing are studied within a local energy-density functional (LEDF) approach. A new form of the LEDF is proposed with a volume part which fits the Friedman- Pandharipande…

Nuclear Theory · Physics 2011-04-15 S. A. Fayans , D. Zawischa

The paper considers probability distribution, density, conditional distribution and density and conditional moments as well as their kernel estimators in spaces of generalized functions. This approach does not require restrictions on…

Statistics Theory · Mathematics 2013-03-07 Victoria Zinde-Walsh

Hybrid density functionals show great promise for chemically-accurate first principles calculations, but their high computational cost limits their application in non-trivial studies, such as exploration of reaction pathways of adsorbents…

Materials Science · Physics 2013-04-18 Ravishankar Sundararaman , T. A. Arias

One proves the uniqueness of distributional solutions to nonlinear Fokker--Planck equations with monotone diffusion term and derive as a consequence (restricted) uniqueness in law for the corresponding McKean--Vlasov stochastic differential…

Probability · Mathematics 2021-04-19 Viorel Barbu , Michael Röckner

This article concerns testing for equality of distribution between groups. We focus on screening variables with shared distributional features such as common support, modes and patterns of skewness. We propose a Bayesian testing method…

Methodology · Statistics 2016-02-19 Eric F. Lock , David B. Dunson

Much of machine learning relies on comparing distributions with discrepancy measures. Stein's method creates discrepancy measures between two distributions that require only the unnormalized density of one and samples from the other. Stein…

Machine Learning · Statistics 2020-07-21 Raghav Singhal , Xintian Han , Saad Lahlou , Rajesh Ranganath

We study the concentration of random kernel matrices around their mean. We derive nonasymptotic exponential concentration inequalities for Lipschitz kernels assuming that the data points are independent draws from a class of multivariate…

Statistics Theory · Mathematics 2020-01-28 Arash A. Amini , Zahra S. Razaee

The initial inverse problem of finding solutions and their initial values ($t = 0$) appearing in a general class of fractional reaction-diffusion equations from the knowledge of solutions at the final time ($t = T$). Our work focuses on the…

Analysis of PDEs · Mathematics 2021-03-29 Tran Bao Ngoc , Yavar Kian , Nguyen Huy Tuan

We introduce kernel density machines (KDM), an agnostic kernel-based framework for learning the Radon-Nikodym derivative (density) between probability measures under minimal assumptions. KDM applies to general measurable spaces and avoids…

Machine Learning · Statistics 2026-03-27 Andrea Della Vecchia , Damir Filipovic , Paul Schneider

Finding a suitable density function is essential for density-based clustering algorithms such as DBSCAN and DPC. A naive density corresponding to the indicator function of a unit $d$-dimensional Euclidean ball is commonly used in these…

Machine Learning · Computer Science 2021-10-15 Chao Zheng , Yingjie Chen , Chong Chen , Jianqiang Huang , Xian-Sheng Hua

A recently proposed local second contact value theorem [Henderson D., Boda D., J. Electroanal. Chem., 2005, 582, 16] for the charge profile of an electric double layer is used in conjunction with the existing Monte Carlo data from the…

Soft Condensed Matter · Physics 2012-07-16 L. B. Bhuiyan , D. Henderson , S. Sokołowski

Using a nonperturbative approach we examine the large frequency asymptotics of the two-point level density correlator in weakly disordered metallic grains. This allows us to study the behavior of the two-level structure factor close to the…

Condensed Matter · Physics 2016-08-31 A. V. Andreev , B. L. Altshuler

We prove Holder regularity for solutions of non divergence integro-differential equations with non necessarily even kernels. The even/odd decomposition of the kernel can be understood as a sum of a diffusion and a drift term. In our case we…

Analysis of PDEs · Mathematics 2012-10-31 Hector A. Chang Lara

We establish the mean-field convergence for systems of points evolving along the gradient flow of their interaction energy when the interaction is the Coulomb potential or a super-coulombic Riesz potential, for the first time in arbitrary…

Analysis of PDEs · Mathematics 2020-12-23 Sylvia Serfaty , appendix with Mitia Duerinckx

Random objects are complex non-Euclidean data taking value in general metric space, possibly devoid of any underlying vector space structure. Such data are getting increasingly abundant with the rapid advancement in technology. Examples…

Methodology · Statistics 2023-10-13 Satarupa Bhattacharjee , Bing Li , Lingzhou Xue
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