Titlebook: Probability-2; Albert N. Shiryaev Textbook 20193rd edition Springer Science+Business Media, LLC, part of Springer Nature 2019 probability

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Albert N. Shiryaev extinction algorithms and two approaches have been compared. The first one is based on the damage model Gurson-Tvergard-Needleman (GTN) (see [12]), the second on the cohesive zone model with the exponential traction separation law. The bulk of the paper is concentrated on the cohesive zone modellin

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Albert N. Shiryaev assumed that on the microscale inertia effects can be neglected. Here, contrary to these approaches, inertia effects are taken into account, leading to a frequency dependent microscopic behavior. Additionally to this effect, non-convex microstructures are considered..It is assumed that the microstr

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Albert N. Shiryaevlytic-function theory. The process invests old theorems with new mean­ ings, and bestows upon functional analysis an intriguing class of concrete linear operators. Best of all, the subject can be appreciated by anyone with an interest in function theory or functional analysis, and a background rough

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Absorption Spectroscopy from Selectively Excited Atomic Singlet Levelsorption series .P.. — .D. have been measured and extended. Series perturbations by .D. levels of Ba I have shown lines of the 6s 6p .P.. — 6s (n + 1)s .S. series and terms of the auto-ionization series 6s 6p .P.. — 5d ns .D. have also been observed and assigned. A strong auto-ionization resonance in

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Microscopic Analysis: Morphotypes and Cellular Appendagesype can be imaged by light microscopy, morphotypes of microbial cells are normally viewed by transmission electron microscopy (TEM). The specific location of proteins on microbial cell surface or inside the cells is normally detected using immunoelectron microscopy (IEM). Here we describe the techni

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Coupled Semi-supervised Clustering: Exploring Attribute Correlations in Heterogeneous Information Ne additional attributes of nodes in HIN, the Attributed Heterogeneous Information Network (AHIN) was brought forward. Recently, clustering on HIN becomes a hot topic, since it is useful in many applications. Although existing semi-supervised clustering methods in HIN have achieved performance improve

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