In this paper, we introduce the idea of generalized Ritt type and generalised Ritt weak type of entire functions represented by a vector valued Dirichlet series. Hence, we study some growth properties of two entire functions represented by a vector valued Dirichlet series on the basis of generalized Ritt type and generalised Ritt weak type.

The reconstruction of spectral function from correlation in Euclidean space is a challenging task. In this paper, we employ the Machine Learning techniques terms radial basis functions networks to reconstruct finite number data. To test our method, first generate one type data using mock by mixing several Breit-Wigner propagators. We found that compared with other traditional methods, TSVD, Tik...

Zusammenfassung Wissen ist in der modernen Sozialpädagogik besonderem Maße mit Ungewissheiten konfrontiert, die jenseits bloßer Informationen liegen. In diesem den Themenschwerpunkt einleitenden Beitrag werden Herausforderungen auch am Beispiel Pandemie veranschaulicht um dafür zu plädieren, dass eine steigende Komplexität nicht dazu führen muss und darf, als ausschließlich relativ willkürlich ...

This paper introduces the general framework, concepts, and procedures of anatomically informed basis functions (AIBF), a new method for the analysis of functional magnetic resonance imaging (fMRI) data. In contradistinction to existing voxel-based univariate or multivariate methods the approach described here can incorporate various forms of prior anatomical knowledge to specify sophisticated s...

A Bayesian framework for the analysis of radial basis functions (RBF) is proposed which readily accommodates uncertainty in the dimension of the model. A distribution is deened over the space of all RBF models of a given basis function and posteriors are computed using reversible jump Markov chain Monte Carlo samplers (Green, 1995). This alleviates the need to select one particular architecture...

Radial basis function methods are modern ways to approximate multivariate functions, especially in the absence of grid data. They have been known, tested and analysed for several years now and many positive properties have been identified. This paper gives a selective but up-to-date survey of several recent developments that explains their usefulness from the theoretical point of view and contr...

Radial basis function networks (RBF) are efficient general function approximators. They show good generalization performance and they are easy to train. Due to theoretical considerations RBFs commonly use Gaussian activation functions. It has been shown that these tight restrictions on the choice of possible activation functions can be relaxed in practical applications. As an alternative differ...

In this paper, we propose the spectral collocation method based on radial basis functions to solve the fractional Bagley-Torvik equation under uncertainty, in the fuzzy Caputo's H-differentiability sense with order ($1< nu < 2$). We define the fuzzy Caputo's H-differentiability sense with order $nu$ ($1< nu < 2$), and employ the collocation RBF method for upper and lower approximate solutions. ...

In this thesis, a method is presented that incorporates anatomical information into the statistical analysis of functional neuroimaging data. Available anatomical information is used to explicitly specify spatial components within a functional volume that are assumed to carry evidence of functional activation. After estimating the activity by fitting the same spatial model to each functional vo...