Results 31 to 40 of about 9,375 (294)
Generalized Ritt type and generalized Ritt weak type connected growth properties of entire functions represented by vector valued Dirichlet series [PDF]
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.
Sanjib Kumar Datta +2 more
doaj
Joint weighted limit theorems for general dirichlet series
In the paper,two joint weighted limit theorems in the sense of weak convergence of probability measures on the complex plane for general Dirichlet series are obtained.
Jonas Genys, Antanas Laurinčikas
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On Dirichlet series similar to Hadamard compositions in half-plane
Let $F(s)=\sum\limits_{n=1}^{\infty}a_n\exp\{s\lambda_n\}$ and $F_j(s)=\sum\limits_{n=1}^{\infty}a_{n,j}\exp\{s\lambda_n\},$ $j=\overline{1,p},$ be Dirichlet series with exponents $0\le\lambda_n\uparrow+\infty,$ $n\to\infty,$ and the abscissas of ...
A.I. Bandura +2 more
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ABSTRACT This paper examines the determinants of generative AI (GenAI) knowledge and usage among agricultural extension professionals. Drawing on survey data from agricultural extension personnel in Tennessee, we employ regression analyses and latent Dirichlet allocation (LDA) for topic modeling of open‐ended responses to study the knowledge and usage ...
Abdelaziz Lawani +3 more
wiley +1 more source
Some Results on Harmonic Type Sums
In this study, we consider the summatory function of convolutions of theMöbius function with harmonic numbers, and we show that these summatoryfunctions are linked to the distribution of prime numbers.
Haydar Göral, Doğa Can Sertbaş
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On the non-randomness of modular arithmetic progressions: a solution to a problem by V. I. Arnold [PDF]
We solve a problem by V. I. Arnold dealing with "how random" modular arithmetic progressions can be. After making precise how Arnold proposes to measure the randomness of a modular sequence, we show that this measure of randomness takes a simplified form
Eda Cesaratto +2 more
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We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang +4 more
wiley +1 more source
Spatial metrics in fire ecology: seeking consistency amidst complexity
ABSTRACT Technological advances, including remote sensing, have led to a proliferation of metrics used in ecological studies to examine spatial patterns of fire regimes and their ecological effects. Researchers can use many different metrics to analyse spatial variation in both fire events and resulting fire regimes, including fire size, shape ...
Alexander R. Carey +5 more
wiley +1 more source
Bayesian clustering of multivariate extremes
Abstract The asymptotic dependence structure between multivariate extreme values is fully characterized by their projections on the unit simplex. Under mild conditions, the only constraint on the resulting distributions is that their marginal means must be equal, which results in a nonparametric model that can be difficult to use in applications ...
Sonia Alouini, Anthony C. Davison
wiley +1 more source
Iteration of Composition Operators on small Bergman spaces of Dirichlet series
The Hilbert spaces ℋw consisiting of Dirichlet series F(s)=∑n=1∞ann-s$F(s) = \sum\nolimits_{n = 1}^\infty {{a_n}{n^{ - s}}}$ that satisfty ∑n=1∞|an|2/wn 0 and {wn}n having average order (logj+n)α${(\log _j^ + n)^\alpha }$, that the composition operators ...
Zhao Jing
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