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Related papers: Kenyon's identities for the height function and co…

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As is known, the free heat-kernel on the integers (a modified Bessel function) is turned into the periodic free heat-kernel on the discrete circle by factoring, giving a pre-image sum. I generalise existing treatments by making the…

Mathematical Physics · Physics 2012-09-03 J. S. Dowker

We study numerically the two-point correlation functions of height functions in the six-vertex model with domain wall boundary conditions. The correlation functions and the height functions are computed by the Markov chain Monte-Carlo…

Statistical Mechanics · Physics 2020-12-10 Pavel Belov , Nicolai Reshetikhin

Correlation matrices are fundamental summaries of functional brain networks, yet standard analyses often treat entries independently, ignoring the curved geometry of correlation space. Existing geometric methods frequently lack closed-form…

Machine Learning · Computer Science 2026-05-22 Mario Severino , Manuela Moretto , Robert A. McCutcheon , Mattia Veronese

The goal of this paper is to provide estimates leading to a direct proof of the exponential decay of the n-point correlation functions for certain unbounded models of Kac type. The methods are based on estimating higher order derivatives of…

Mathematical Physics · Physics 2009-11-13 Assane Lo

We define a new theory of discrete Riemann surfaces and present its basic results. The key idea is to consider not only a cellular decomposition of a surface, but the union with its dual. Discrete holomorphy is defined by a straightforward…

Differential Geometry · Mathematics 2016-11-25 Christian Mercat

We consider a Hamiltonian $H$ which is the sum of a deterministic part $H_0$ and of a random potential $V$. For finite $N \times N$ matrices, following a method introduced by Kazakov, we derive a representation of the correlation functions…

Condensed Matter · Physics 2009-10-28 E. Brézin , S. Hikami

At the classical level, redefinitions of the field content of a Lagrangian allow to rewrite an interacting model on a flat target space, in the form of a free field model (no potential term) on a curved target space. In the present work we…

High Energy Physics - Theory · Physics 2015-09-03 Victor I. Afonso , Diego J. Cirilo-Lombardo

We consider the problem of identifying universal low-dimensional features from high-dimensional data for inference tasks in settings involving learning. For such problems, we introduce natural notions of universality and we show a local…

Machine Learning · Computer Science 2019-11-22 Shao-Lun Huang , Anuran Makur , Gregory W. Wornell , Lizhong Zheng

I have made an ample study of one dimensional quantum oscillators, ranging from logarithmic to exponential potentials. I have found that the eigenvalues of the hamiltonian of the oscillator with the limiting (approachissimo) harmonic…

Quantum Physics · Physics 2007-05-23 N. Garcia

We develop a field-theoretical approach to determination of the background target space fields corresponding to general $G/H$ coset conformal theories described by gauged WZW models. The basic idea is to identify the effective action of a…

High Energy Physics - Theory · Physics 2009-09-17 A. A. Tseytlin

Classical isomorphism theorems due to Dynkin, Eisenbaum, Le Jan, and Sznitman establish equalities between the correlation functions or distributions of occupation times of random paths or ensembles of paths and Markovian fields, such as…

Probability · Mathematics 2021-11-03 Adrien Kassel , Thierry Lévy

In this work we study the variational problem associated to dimer models, a class of models from integrable probability and statistical mechanics in dimension two which have been the focus of intense research efforts over the last decades.…

Analysis of PDEs · Mathematics 2025-12-09 Kari Astala , Erik Duse , István Prause , Xiao Zhong

We develop the convergence theory for a well-known method for the interpolation of functions on the real axis with rational functions. Precise new error estimates for the interpolant are de- rived using existing theory for trigonometric…

Numerical Analysis · Mathematics 2014-03-12 Thomas Trogdon

The first part of this thesis proposes a general approach to infinite dimensional non-Gaussian analysis, including the Poissonian case. In particular distribution theory is developed. Using appropriate integral transformations, generalized…

Mathematical Physics · Physics 2007-05-23 Werner Westerkamp

The expressive power of Bayesian kernel-based methods has led them to become an important tool across many different facets of artificial intelligence, and useful to a plethora of modern application domains, providing both power and…

Machine Learning · Computer Science 2021-07-13 Luca Martino , Jesse Read

We study the asymptotics of certain measures on partitions (the so-called z-measures and their relatives) in two different regimes: near the diagonal of the corresponding Young diagram and in the intermediate zone between the diagonal and…

Mathematical Physics · Physics 2007-05-23 Alexei Borodin , Grigori Olshanski

Isoradial dimer models were introduced in \cite{Kenyon3} - they consist of dimer models whose underlying graph satisfies a simple geometric condition, and whose weight function is chosen accordingly. In this paper, we prove a conjecture of…

Probability · Mathematics 2009-02-11 Béatrice de Tilière

The Dirac Hamiltonian formalism is applied to a system in $(2+1)$-dimensions consisting of a Dirac field $\psi$ minimally coupled to Chern-Simons $U(1)$ and $SO(2,1)$ connections, $A$ and $\omega$, respectively. This theory is connected to…

High Energy Physics - Theory · Physics 2016-08-22 Alfredo Guevara , Pablo Pais , Jorge Zanelli

Functional linear and single-index models are core regression methods in functional data analysis and are widely used for performing regression in a wide range of applications when the covariates are random functions coupled with scalar…

Statistics Theory · Mathematics 2024-03-28 Krishnakumar Balasubramanian , Hans-Georg Müller , Bharath K. Sriperumbudur

This paper proposes a generalized framework with joint normalization which learns lower-dimensional subspaces with maximum discriminative power by making use of the Riemannian geometry. In particular, we model the similarity/dissimilarity…

Computer Vision and Pattern Recognition · Computer Science 2017-11-20 Tianci Liu , Zelin Shi , Yunpeng Liu