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In Random Matrix Theory the local correlations of the Laguerre and Jacobi Unitary Ensemble in the hard edge scaling limit can be described in terms of the Bessel kernel (containing a parameter $\alpha$). In particular, the so-called hard…

Functional Analysis · Mathematics 2010-01-15 Torsten Ehrhardt

We place ourselves in the setting of high-dimensional statistical inference where the number of variables $p$ in a dataset of interest is of the same order of magnitude as the number of observations $n$. We consider the spectrum of certain…

Statistics Theory · Mathematics 2010-01-05 Noureddine El Karoui

In this note we consider a family of nonlinear (conditional) expectations that can be understood as a multidimensional diffusion with uncertain drift and certain volatility. Here, the drift is prescribed by a set-valued function that…

Probability · Mathematics 2023-11-14 David Criens , Lars Niemann

Data sites selected from modeling high-dimensional problems often appear scattered in non-paternalistic ways. Except for sporadic clustering at some spots, they become relatively far apart as the dimension of the ambient space grows. These…

Numerical Analysis · Mathematics 2021-09-28 Shao-Bo Lin , Xiangyu Chang , Xingping Sun

We give a substitute to Feller property for semigroups of time-changed processes; under some conditions this leads to establish sufficient (new) conditions for the semigroups to be Feller. Moreover, given a standard process and a sequence…

Probability · Mathematics 2025-10-16 Ali BenAmor , Kazuhiro Kuwae

We measure electrical transport on networks of single wall nanotube of different origin as a function of temperature T, voltage V and pressure P . We observe Luttinger liquid (LL) behavior, a conductance T^alpha and a dynamic conductance…

Mesoscale and Nanoscale Physics · Physics 2013-10-22 F. Morales , M. Monteverde , M. Nunez-Regueiro

We consider a family of determinantal random point processes on the two-dimensional lattice and prove that members of our family can be interpreted as a kind of Gibbs ensembles of nonintersecting paths. Examples include probability measures…

Mathematical Physics · Physics 2015-05-13 Alexei Borodin , Senya Shlosman

The main result of this paper, Theorem 1.5, establishes a conjecture of Lyons and Peres: for a determinantal point process governed by a reproducing kernel, the system of kernels sampled at the particles of a random configuration is…

Probability · Mathematics 2018-12-19 Alexander I. Bufetov , Yanqi Qiu , Alexander Shamov

In this paper we derive an efficient algorithm to learn the parameters of structured predictors in general graphical models. This algorithm blends the learning and inference tasks, which results in a significant speedup over traditional…

Machine Learning · Computer Science 2013-09-02 Tamir Hazan , Alexander Schwing , David McAllester , Raquel Urtasun

We extend inner fluctuations to spectral triples that do not fulfill the first-order condition. This involves the addition of a quadratic term to the usual linear terms. We find a semi-group of inner fluctuations, which only depends on the…

Mathematical Physics · Physics 2013-12-02 Ali H. Chamseddine , Alain Connes , Walter D. van Suijlekom

It is long known that the rational Calogero model describing n identical particles on a line with inverse-square mutual interaction potential is quantum superintegrable. We review the (nonlinear) algebra of the conserved quantum charges and…

High Energy Physics - Theory · Physics 2015-06-18 Francisco Correa , Olaf Lechtenfeld , Mikhail Plyushchay

We define Laguerre and Jacobi analogues of the Warren process. That is, we construct local dynamics on a triangular array of particles so that the projections to each level recover the Laguerre and Jacobi eigenvalue processes of…

Probability · Mathematics 2017-07-25 Yi Sun

A recently developed wavelet based approach is employed to characterize the scaling behavior of spectral fluctuations of random matrix ensembles, as well as complex atomic systems. Our study clearly reveals anti-persistent behavior and…

Chaotic Dynamics · Physics 2009-11-11 P. Manimaran , Prasanta K. Panigrahi , P. Anantha Lakshmi

This work proposed kernel selection approaches for probabilistic classifiers based on features produced by the convolutional encoder of a variational autoencoder. Particularly, the developed methodologies allow the selection of the most…

Machine Learning · Computer Science 2025-08-05 Fábio Mendonça , Sheikh Shanawaz Mostafa , Fernando Morgado-Dias , Antonio G. Ravelo-García

We present a novel attention-based model for discrete event data to capture complex non-linear temporal dependence structures. We borrow the idea from the attention mechanism and incorporate it into the point processes' conditional…

Machine Learning · Statistics 2021-02-23 Shixiang Zhu , Minghe Zhang , Ruyi Ding , Yao Xie

Intertwining relations for $N$-particle Calogero-like models with internal degrees of freedom are investigated. Starting from the well known Dunkl-Polychronakos operators, we construct new kind of local (without exchange operation)…

High Energy Physics - Theory · Physics 2008-11-26 M. V. Ioffe , A. I. Neelov

In this paper we consider the kernel of the radially deformed Fourier transform introduced in the context of Clifford analysis in [10]. By adapting the Laplace transform method from [4], we obtain the Laplace domain expressions of the…

Classical Analysis and ODEs · Mathematics 2024-08-09 Hendrik De Bie , Ze Yang

A model of interacting one--dimensional fermions confined to a harmonic trap is proposed. The model is treated analytically to all orders of the coupling constant by a method analogous to that used for the Luttinger model. As a first…

Quantum Physics · Physics 2009-11-06 W. Wonneberger

The purpose of this paper is to investigate the distribution of zeros of entire functions which can be represented as the Fourier transforms of certain admissible kernels. The principal results bring to light the intimate connection between…

Complex Variables · Mathematics 2014-02-24 George Csordas

We investigate the capabilities of neural inverse procedural modeling to infer high-quality procedural yarn models with fiber-level details from single images of depicted yarn samples. While directly inferring all parameters of the…

Computer Vision and Pattern Recognition · Computer Science 2023-03-02 Elena Trunz , Jonathan Klein , Jan Müller , Lukas Bode , Ralf Sarlette , Michael Weinmann , Reinhard Klein