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This book integrates the fundamentals of asymmetric multidimensional scaling, spectral graph theory, graph embedding theory, and various dynamical systems theories, that deal with the static and dynamic aspects of asymmetric phenomena. In this way, it provides a comprehensive introduction to theories and methods for analyzing phenomena observed universally in social, behavioral, economical, geographical, biological, neural, chemical reaction and other networks. The topics addressed in here include the notions of asymmetric similarity matrices, graph spectra, dimension reduction, and difference and differential equations to describe the dynamics of networks, bifurcation of vector fields, Mandelbrot sets, fractals and chaos, and Hilbert spaces. Illustrated by carefully chosen examples and supported by extensive simulation studies, the book is highly recommended to readers who seek to discover static asymmetric structures among members or nodes. It also appeals to those who want to understand the kinds of dynamics that are theoretically possible in their research domains.

This book constitutes revised selected papers from the thoroughly refereed proceedings of the 11th International Conference on Analysis of Images, Social Networks and Texts, AIST 2023, held in Yerevan, Armenia, during September 28-30, 2023. The 24 full papers included in this book were carefully reviewed and selected from 93 submissions. They were organized in topical sections as follows: natural language processing; computer vision; data analysis and machine learning; network analysis; and theoretical machine learning and optimization. The book also contains one invited talk in full paper length.

Applied Probability presents a unique blend of theory and applications, with special emphasis on mathematical modeling, computational techniques, and examples from the biological sciences. Chapter 1 reviews elementary probability and provides a brief survey of relevant results from measure theory. Chapter 2 is an extended essay on calculating expectations. Chapter 3 deals with probabilistic applications of convexity, inequalities, and optimization theory. Chapters 4 and 5 touch on combinatorics and combinatorial optimization. Chapters 6 through 11 present core material on stochastic processes. If supplemented with appropriate sections from Chapters 1 and 2, there is sufficient material for a traditional semester-long course in stochastic processes covering the basics of Poisson processes, Markov chains, branching processes, martingales, and diffusion processes. This third edition includes new topics and many worked exercises. The new chapter on entropy stresses Shannon entropy and its mathematical applications. New sections in existing chapters explain the Chinese restaurant problem, the infinite alleles model, saddlepoint approximations, and recurrence relations. The extensive list of new problems pursues topics such as random graph theory omitted in the previous editions. Computational probability receives even greater emphasis than earlier. Some of the solved problems are coding exercises, and Julia code is provided. Mathematical scientists from a variety of backgrounds will find Applied Probability appealing as a reference. This updated edition can serve as a textbook for graduate students in applied mathematics, biostatistics, computational biology, computer science, physics, and statistics. Readers should have a working knowledge of multivariate calculus, linear algebra, ordinary differential equations, and elementary probability theory.

This book gathers outstanding research papers presented at the 6th International Joint Conference on Advances in Computational Intelligence (IJCACI 2022), held in hybrid mode during October 15 – 16, 2022. IJCACI 2022 is jointly organized by Jahangirnagar University (JU), Bangladesh and South Asian University (SAU), India. The book presents the novel contributions in areas of computational intelligence and it serves as a reference material for advance research. The topics covered are collective intelligence, soft computing, optimization, cloud computing, machine learning, intelligent software, robotics, data science, data security, big data analytics, and signal and natural language processing.

This book grew out of the Random Transformations and Invariance in Stochastic Dynamics conference held in Verona from the 25th to the 28th of March 2019 in honour of Sergio Albeverio. It presents the new area of studies concerning invariance and symmetry properties of finite and infinite dimensional stochastic differential equations.This area constitutes a natural, much needed, extension of the theory of classical ordinary and partial differential equations, where the reduction theory based on symmetry and invariance of such classical equations has historically proved to be very important both for theoretical and numerical studies and has given rise to important applications.The purpose of the present book is to present the state of the art of the studies on stochastic systems from this point of view, present some of the underlying fundamental ideas and methods involved, and to outline the main lines for future developments. The main focus is on bridging the gap between deterministic and stochastic approaches, with the goal of contributing to the elaboration of a unified theory that will have a great impact both from the theoretical point of view and the point of view of applications. The reader is a mathematician or a theoretical physicist. The main discipline is stochastic analysis with profound ideas coming from Mathematical Physics and Lie's Group Geometry. While the audience consists essentially of academicians, the reader can also be a practitioner with Ph.D., who is interested in efficient stochastic modelling.

This book provides the foundations for geometric applications of convex cones and presents selected examples from a wide range of topics, including polytope theory, stochastic geometry, and Brunn–Minkowski theory. Giving an introduction to convex cones, it describes their most important geometric functionals, such as conic intrinsic volumes and Grassmann angles, and develops general versions of the relevant formulas, namely the Steiner formula and kinematic formula. In recent years questions related to convex cones have arisen in applied mathematics, involving, for example, properties of random cones and their non-trivial intersections. The prerequisites for this work, such as integral geometric formulas and results on conic intrinsic volumes, were previously scattered throughout the literature, but no coherent presentation was available. The present book closes this gap. It includes several pearls from the theory of convex cones, which should be better known.