English
Related papers

Related papers: Quadrature domains and kernel function zipping

200 papers

Spectral kernel methods are techniques for transforming data into a coordinate system that efficiently reveals the geometric structure - in particular, the "connectivity" - of the data. These methods depend on certain tuning parameters. We…

Methodology · Statistics 2008-11-04 Ann B. Lee , Larry Wasserman

Quantum algorithms are sequences of abstract operations, performed on non-existent computers. They are in obvious need of categorical semantics. We present some steps in this direction, following earlier contributions of Abramsky, Coecke…

Quantum Physics · Physics 2016-11-09 Dusko Pavlovic

We explore the relationship between the Bergman kernel of a Hartogs domain and weighted Bergman kernels over its base domain. In particular we develop a representation of the Bergman kernel of a Hartogs domain as a series involving weighted…

Complex Variables · Mathematics 2024-06-25 Blake J. Boudreaux

We prove the boundedness of Bergman type projections in two different analytic function spaces in bounded strongly pseudoconvex domains with the smooth boundary. Our results were previously well-known in the case of the unit disk.

Complex Variables · Mathematics 2025-08-28 R. F. Shamoyan , E. B. Tomashevskaya

A high-order quadrature algorithm is presented for computing integrals over curved surfaces and volumes whose geometry is implicitly defined by the level sets of (one or more) multivariate polynomials. The algorithm recasts the implicitly…

Numerical Analysis · Mathematics 2021-11-24 Robert I. Saye

In this article we continue the study of properties of squeezing functions and geometry of bounded domains. The limit of squeezing functions of a sequence of bounded domains is studied. We give comparisons of intrinsic positive forms and…

Complex Variables · Mathematics 2013-02-25 Fusheng Deng , Qi'an Guan , Liyou Zhang

On a two dimensional Stein space with isolated, normal singularities, smooth finite type boundary, and locally algebraic Bergman kernel, we establish an estimate on the type of the boundary in terms of the local algebraic degree of the…

Complex Variables · Mathematics 2025-03-17 Peter Ebenfelt , Soumya Ganguly , Ming Xiao

The past few years have seen a revived interest in quantum geometrical characterizations of band structures due to the rapid development of topological insulators and semi-metals. Although the metric tensor has been connected to many…

Mesoscale and Nanoscale Physics · Physics 2023-03-07 Adrien Bouhon , Abigail Timmel , Robert-Jan Slager

Metaplectic operators form a relevant class of operators appearing in different applications, in the present work we study their Schwartz kernels. Namely, diagonality of a kernel is defined by imposing rapid off-diagonal decay conditions,…

Analysis of PDEs · Mathematics 2025-10-16 Gianluca Giacchi , Luigi Rodino

The cosmic web is one of the most complex systems in nature, consisting of galaxies and clusters of galaxies joined by filaments and walls, leaving large empty regions called cosmic voids. The most common method of describing the web is a…

Cosmology and Nongalactic Astrophysics · Physics 2025-09-05 Jaan Einasto

Given a spherical spacelike three-geometry, there exists a very simple algebraic condition which tells us whether, and in which, Schwarzschild solution this geometry can be smoothly embedded. One can use this result to show that any given…

General Relativity and Quantum Cosmology · Physics 2009-11-10 Niall Ó Murchadha , Krzysztof Roszkowski

The Minkowski function is a crucial tool used in the study of balanced domains and, more generally, quasi-balanced domains in several complex variables. If a quasi-balanced domain is bounded and pseudoconvex then it is well-known that its…

Complex Variables · Mathematics 2018-05-29 Pranav Haridas , Jaikrishnan Janardhanan

This work concerns the construction and characterization of product kernels for multivariate approximation from a finite set of discrete samples. To this end, we consider composing different component kernels, each acting on a…

Numerical Analysis · Mathematics 2024-11-27 Kristof Albrecht , Juliane Entzian , Armin Iske

Solving numerically the equations of motion for the effective lagrangian describing supersymmetric QCD with the SU(2) gauge group, we find a menagerie of complex domain wall solutions connecting different chirally asymmetric vacua. Some of…

High Energy Physics - Theory · Physics 2008-11-26 A. V. Smilga , A. I. Veselov

For certain real quadratic number fields, we prove density results concerning 4-ranks of tame kernels. We also discuss a relationship between 4-ranks of tame kernels and 4-class ranks of narrow ideal class groups. Additionally, we give a…

Number Theory · Mathematics 2021-02-03 Robert Osburn

We consider the problem of positive-semidefinite continuation: extending a partially specified covariance kernel from a subdomain $\Omega$ of a rectangular domain $I\times I$ to a covariance kernel on the entire domain $I\times I$. For a…

Statistics Theory · Mathematics 2022-05-13 Kartik G. Waghmare , Victor M. Panaretos

This paper presents new quadrature rules for functions in a reproducing kernel Hilbert space using nodes drawn by a sampling algorithm known as randomly pivoted Cholesky. The resulting computational procedure compares favorably to previous…

Numerical Analysis · Mathematics 2023-12-08 Ethan N. Epperly , Elvira Moreno

Two transparent layers are introduced at the boundaries of the fifth dimension for the optimal domain-wall fermions. For the quark fields defined in terms of these two transparent layers, they obey the usual chiral projection rule in the…

High Energy Physics - Lattice · Physics 2012-09-14 Ting-Wai Chiu

This paper presents a framework for computing random operator-valued feature maps for operator-valued positive definite kernels. This is a generalization of the random Fourier features for scalar-valued kernels to the operator-valued case.…

Machine Learning · Computer Science 2016-08-22 Ha Quang Minh

This survey is an introduction to positive definite kernels and the set of methods they have inspired in the machine learning literature, namely kernel methods. We first discuss some properties of positive definite kernels as well as…

Machine Learning · Statistics 2009-12-04 Marco Cuturi