Martin Milanic, Brigitte Servatius and Herman Servatius
July 20, 2023
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Discrete Mathematics provides key concepts and a solid, rigorous foundation in mathematical reasoning. Appropriate for undergraduate as well as a starting point for more advanced class, the resource offers a logical progression through key topics without assuming any background in algebra or computational skills and without duplicating what they will learn in higher level courses. The book is designed as an accessible introduction for students in mathematics or computer science as it explores questions that test the understanding of proof strategies, such as mathematical induction. For students interested to dive into this subject, the text offers a rigorous introduction to mathematical thought through useful examples and exercises.
Introduction to Probability Models: Thirteenth Edition is available in two manageable volumes: an Elementary edition appropriate for undergraduate use and an Advanced edition for graduate use. Together, and through their hallmark exercises and real examples, both versions offer a comprehensive foundation of this key subject with applications across engineering, computer science, management science, the physical and social sciences and operations research. Users will find comprehensive information that introduces them to the foundations of probability modeling and stochastic processes from Random Variables, to Markov Chains and Renewal Theory.
Partial Differential Equations and Applications: A Bridge for Students and Researchers in Applied Sciences offers a unique approach to this key subject by connecting mathematical principles to the latest research advances in select topics. Beginning with very elementary PDEs, such as classical heat equations, wave equations and Laplace equations, the book focuses on concrete examples. It gives students basic skills and techniques to find explicit solutions for partial differential equations. As it progresses, the book covers more advanced topics such as the maximum principle and applications, Green’s representation, Schauder’s theory, finite-time blowup, and shock waves. By exploring these topics, students gain the necessary tools to deal with research topics in their own fields, whether proceeding in math or engineering areas.
Automata Theory and Formal Languages presents the difficult concepts of automata theory in a straightforward manner, including discussions on diverse concepts and tools that play major roles in developing computing machines, algorithms and code. Automata theory includes numerous concepts such as finite automata, regular grammar, formal languages, context free and context sensitive grammar, push down automata, Turing machine, and decidability, which constitute the backbone of computing machines. This book enables readers to gain sufficient knowledge and experience to construct and solve complex machines. Each chapter begins with key concepts followed by a number of important examples that demonstrate the solution. The book explains concepts and simultaneously helps readers develop an understanding of their application with real-world examples, including application of Context Free Grammars in programming languages and Artificial Intelligence, and cellular automata in biomedical problems.
Reachable Sets of Dynamic Systems: Uncertainty, Sensitivity, and Complex Dynamics introduces differential inclusions, providing an overview as well as multiple examples of its interdisciplinary applications. The design of dynamic systems of any type is an important issue as is the influence of uncertainty in model parameters and model sensitivity. The possibility of calculating the reachable sets may be a powerful additional tool in such tasks. This book can help graduate students, researchers, and engineers working in the field of computer simulation and model building, in the calculation of reachable sets of dynamic models.
In Spectral Properties of Certain Operators on a Free Hilbert Space and the Semicircular Law, the authors consider the so-called free Hilbert spaces, which are the Hilbert spaces induced by the usual l2 Hilbert spaces and operators acting on them. The construction of these operators itself is interesting and provides new types of Hilbert-space operators. Also, by considering spectral-theoretic properties of these operators, the authors illustrate how “free-Hilbert-space” Operator Theory is different from the classical Operator Theory. More interestingly, the authors demonstrate how such operators affect the semicircular law induced by the ONB-vectors of a fixed free Hilbert space. Different from the usual approaches, this book shows how “inside” actions of operator algebra deform the free-probabilistic information—in particular, the semicircular law.
Esteban A. Hernandez-Vargas, Edgar N. Sanchez + 1 more
Esteban A. Hernandez-Vargas, Edgar N. Sanchez and Jorge X. Velasco-Hernandez
March 21, 2023
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Mathematical Modeling, Simulations, and Artificial Intelligence for Emergent Pandemic Diseases: Lessons Learned from COVID-19 includes new research, models and simulations developed during the COVID-19 pandemic into how mathematical methods and practice can impact future response. Chapters go beyond forecasting COVID-19, bringing different scale angles and mathematical techniques (e.g., ordinary differential and difference equations, agent-based models, artificial intelligence, and complex networks) which could have potential use in modeling other emergent pandemic diseases. A major part of the book focuses on preparing the scientific community for the next pandemic, particularly the application of mathematical modeling in ecology, economics and epidemiology. Readers will benefit from learning how to apply advanced mathematical modeling to a variety of topics of practical interest, including optimal allocations of masks and vaccines but also more theoretical problems such as the evolution of viral variants.
Machine Learning: A Constraint-Based Approach, Second Edition provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that include neural networks and kernel machines. The book presents the information in a truly unified manner that is based on the notion of learning from environmental constraints. It draws a path towards deep integration with machine learning that relies on the idea of adopting multivalued logic formalisms, such as in fuzzy systems. Special attention is given to deep learning, which nicely fits the constrained-based approach followed in this book.The book presents a simpler unified notion of regularization, which is strictly connected with the parsimony principle, including many solved exercises that are classified according to the Donald Knuth ranking of difficulty, which essentially consists of a mix of warm-up exercises that lead to deeper research problems. A software simulator is also included.
Arni S.R. Srinivasa Rao, Venu Govindaraju + 1 more
Arni S.R. Srinivasa Rao, Venu Govindaraju and C.R. Rao
February 28, 2023
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Deep Learning, Volume 48 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics, including Generative Adversarial Networks for Biometric Synthesis, Data Science and Pattern Recognition, Facial Data Analysis, Deep Learning in Electronics, Pattern Recognition, Computer Vision and Image Processing, Mechanical Systems, Crop Technology and Weather, Manipulating Faces for Identity Theft via Morphing and Deepfake, Biomedical Engineering, and more.