ON BESSEL-RIESZ OPERATORS

We consider a class of conv olution operator denoted ϕα W obtained by convolution with a generalized function expressible in terms of the Bessel function on first kind γ J with argument the distribution ( ) P ± i0 . We study some elementary properties of the operator ϕα W like the semigroup property ϕ = ϕ α β α+β W W W ; and ( +m2 ) α α−2 W ϕ = W for α > 2 where ( +m2 ) is the Klein-Gordon ultrahyperbolic operator. Moreover we prove that the operator ϕα W may be consider as a negative power of the Klein-Gordon operat

On the Inversion of Bessel Ultrahyperbolic Kernel of Marcel Riesz

We define the Bessel ultrahyperbolic Marcel Riesz operator on the function f by Uαf=RαB*f, where RαB is Bessel ultrahyperbolic kernel of Marcel Riesz, α…C, the symbol * designates as the convolution, and f∈S, S is the Schwartz space of functions. Our purpose in this paper is to obtain the operator Eα=Uα-1 such that, if Uαf=φ, then Eαφ=f

Learning Theory and Approximation

The main goal of this workshop – the third one of this type at the MFO – has been to blend mathematical results from statistical learning theory and approximation theory to strengthen both disciplines and use synergistic effects to work on current research questions. Learning theory aims at modeling unknown function relations and data structures from samples in an automatic manner. Approximation theory is naturally used for the advancement and closely connected to the further development of learning theory, in particular for the exploration of new useful algorithms, and for the theoretical understanding of existing methods. Conversely, the study of learning theory also gives rise to interesting theoretical problems for approximation theory such as the approximation and sparse representation of functions or the construction of rich kernel reproducing Hilbert spaces on general metric spaces. This workshop has concentrated on the following recent topics: Pitchfork bifurcation of dynamical systems arising from mathematical foundations of cell development; regularized kernel based learning in the Big Data situation; deep learning; convergence rates of learning and online learning algorithms; numerical refinement algorithms to learning; statistical robustness of regularized kernel based learning

Quantum Physics, Relativity, and Complex Spacetime: Towards a New Synthesis

The positivity of the energy in relativistic quantum mechanics implies that wave functions can be continued analytically to the forward tube T in complex spacetime. For Klein-Gordon particles, we interpret T as an extended (8D) classical phase space containing all 6D classical phase spaces as symplectic submanifolds. The evaluation maps $e_z: f\to f(z)$ of wave functions on T are relativistic coherent states reducing to the Gaussian coherent states in the nonrelativistic limit. It is known that no covariant probability interpretation exists for Klein-Gordon particles in real spacetime because the time component of the conserved "probability current" can attain negative values even for positive-energy solutions. We show that this problem is solved very naturally in complex spacetime, where $|f(x-iy)|^2$ is interpreted as a probability density on all 6D phase spaces in T which, when integrated over the "momentum" variables y, gives a conserved spacetime probability current whose time component is a positive regularization of the usual one. Similar results are obtained for Dirac particles, where the evaluation maps $e_z$ are spinor-valued relativistic coherent states. For free quantized Klein-Gordon and Dirac fields, the above formalism extends to n-particle/antiparticle coherent states whose scalar products are Wightman functions. The 2-point function plays the role of a reproducing kernel for the one-particle and antiparticle subspaces.Comment: 252 pages, no figures. Originally published as a book by North-Holland, 1990. Reviewed by Robert Hermann in Bulletin of the AMS Vol. 28 #1, January 1993, pp. 130-132; see http://wavelets.co