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Symmetric kernel matrices are a well-researched topic in the literature of kernel based approximation. In particular stability properties in terms of lower bounds on the smallest eigenvalue of such symmetric kernel matrices are thoroughly…

Numerical Analysis · Mathematics 2025-12-16 Tizian Wenzel , Armin Iske

Contragenic functions are defined to be reduced-quaternion-valued harmonic functions which are orthogonal to all monogenic and antimonogenic functions in the $L^2$ norm of a given domain. The parallelism between the spaces of contragenic…

Complex Variables · Mathematics 2024-10-07 R. García-Ancona , J. Morais , R. Michael Porter

This paper uses the convolution theorem of the Laplace transform to derive new inverse Laplace transforms for the product of two parabolic cylinder functions in which the arguments may have opposite sign. These transforms are subsequently…

Classical Analysis and ODEs · Mathematics 2019-08-02 Dirk Veestraeten

A covariant scattering kernel is a core component in any self-consistent general relativistic radiative transfer formulation in scattering media. An explicit closed-form expression for a covariant Compton scattering kernel with a good…

High Energy Astrophysical Phenomena · Physics 2015-06-16 Ziri Younsi , Kinwah Wu

We survey many of the important properties of spherically symmetric spacetimes as follows. We present several different ways of describing a spherically symmetric spacetime and the resulting metrics. We then focus our discussion on an…

General Relativity and Quantum Cosmology · Physics 2014-10-10 Alan R. Parry

Covariance functions and variograms play a fundamental role in exploratory analysis and statistical modelling of spatial and spatio-temporal datasets. In this paper, we construct a new class of spatial covariance functions using the Fourier…

Methodology · Statistics 2021-11-16 Mohammad Ghorbani , Jorge Mateu

New solutions for second-order intertwining relations in two-dimensional SUSY QM are found via the repeated use of the first order supersymmetrical transformations with intermediate constant unitary rotation. Potentials obtained by this…

High Energy Physics - Theory · Physics 2009-11-10 M. V. Ioffe , P. A. Valinevich

The standard Radon transform of holomorphic functions is not always well defined, as the integration of such functions over planes may not converge. In this paper, we introduce new Radon-type transforms of co-(real)dimension $2$ for…

Complex Variables · Mathematics 2025-09-10 Ren Hu , Pan Lian

The notion of spherically symmetric superfunctions as functions invariant under the orthosymplectic group is introduced. This leads to dimensional reduction theorems for differentiation and integration in superspace. These spherically…

Mathematical Physics · Physics 2015-05-19 Kevin Coulembier , Hendrik De Bie , Frank Sommen

In this paper, we describe the irreducible spherical functions of fundamental $K$-types associated with the pair $(G,K)=({\mathrm{SO}}(n+1),{\mathrm{SO}}(n))$ in terms of matrix hypergeometric functions. The output of this description is…

Representation Theory · Mathematics 2014-07-08 Juan Alfredo Tirao , Ignacio Nahuel Zurrián

We introduce a new concept of the so-called {\it composite wavelet transforms}. These transforms are generated by two components, namely, a kernel function and a wavelet function (or a measure). The composite wavelet transforms and the…

Functional Analysis · Mathematics 2007-11-12 Ilham A. Aliev , Boris Rubin , Sinem Sezer , Simten B. Uyhan

This paper constructs weight-shifting integral operators for Maass forms on the full modular group SL(2,Z). Under the weight parity condition t = k (mod 2), the operator utilizes an automorphic kernel constructed via Poincare series from a…

Number Theory · Mathematics 2025-12-01 Seung Ju Lee

We consider the problem of learning regression functions from pairwise data when there exists prior knowledge that the relation to be learned is symmetric or anti-symmetric. Such prior knowledge is commonly enforced by symmetrizing or…

Machine Learning · Computer Science 2015-06-22 Tapio Pahikkala , Markus Viljanen , Antti Airola , Willem Waegeman

In this paper, we first give a direct proof for two recurrence relations of the heat kernels for hyperbolic spaces in \cite{DM}. Then, by similar computation, we give two similar recurrence relations of the heat kernels for spheres.…

Differential Geometry · Mathematics 2018-07-17 Chengjie Yu , Feifei Zhao

The rapid and accurate evaluation of convolutions with singular kernels plays crucial roles in a wide range of scientific and engineering applications. Building on the recently introduced Truncated Fourier Filtering method for smooth…

Numerical Analysis · Mathematics 2025-11-27 Oscar Bruno , Jinghao Cao

Regularity estimates for an integral operator with a symmetric continuous kernel on a convex bounded domain are derived. The covariance of a mean-square continuous random field on the domain is an example of such an operator. The estimates…

Probability · Mathematics 2022-04-25 Mihály Kovács , Annika Lang , Andreas Petersson

This article is devoted to developing a theory for effective kernel interpolation and approximation in a general setting. For a wide class of compact, connected $C^\infty$ Riemannian manifolds, including the important cases of spheres and…

Classical Analysis and ODEs · Mathematics 2015-03-17 T. Hangelbroek , F. J. Narcowich , J. D. Ward

The paper introduces new sufficient conditions of strict positive definiteness for kernels on d-dimensional spheres which are not radially symmetric but possess specific coefficient structures. The results use the series expansion of the…

Numerical Analysis · Mathematics 2021-05-07 Martin Buhmann , Janin Jäger

A novel spherical convolution is defined through the sifting property of the Dirac delta on the sphere. The so-called sifting convolution is defined by the inner product of one function with a translated version of another, but with the…

Information Theory · Computer Science 2023-04-24 Patrick J. Roddy , Jason D. McEwen

We introduce a regularization loss based on kernel mean embeddings with rotation-invariant kernels on the hypersphere (also known as dot-product kernels) for self-supervised learning of image representations. Besides being fully competitive…

Computer Vision and Pattern Recognition · Computer Science 2023-03-09 Léon Zheng , Gilles Puy , Elisa Riccietti , Patrick Pérez , Rémi Gribonval