Geometry of the Generalized Geodesic Flow and Inverse Spectral Problems

This book deals with the various aspects of stochastic dynamics, the resolution of large mechanical systems, and inverse problems. It integrates the most recent ideas from research and industry in the field of stochastic dynamics and optimization in structural mechanics over 11 chapters. These chapters provide an update on the various tools for dealing with uncertainties, stochastic dynamics, reliability and optimization of systems. The optimization–reliability coupling in structures dynamics is approached in order to take into account the uncertainties in the modeling and the resolution of the problems encountered. Accompanied by detailed examples of uncertainties, optimization, reliability, and model reduction, this book presents the newest design tools. It is intended for students and engineers and is a valuable support for practicing engineers and teacher-researchers.

This book focuses on computational methods for large-scale statistical inverse problems and provides an introduction to statistical Bayesian and frequentist methodologies. Recent research advances for approximation methods are discussed, along with Kalman filtering methods and optimization-based approaches to solving inverse problems. The aim is to cross-fertilize the perspectives of researchers in the areas of data assimilation, statistics, large-scale optimization, applied and computational mathematics, high performance computing, and cutting-edge applications. The solution to large-scale inverse problems critically depends on methods to reduce computational cost. Recent research approaches tackle this challenge in a variety of different ways. Many of the computational frameworks highlighted in this book build upon state-of-the-art methods for simulation of the forward problem, such as, fast Partial Differential Equation (PDE) solvers, reduced-order models and emulators of the forward problem, stochastic spectral approximations, and ensemble-based approximations, as well as exploiting the machinery for large-scale deterministic optimization through adjoint and other sensitivity analysis methods. Key Features: • Brings together the perspectives of researchers in areas of inverse problems and data assimilation. • Assesses the current state-of-the-art and identify needs and opportunities for future research. • Focuses on the computational methods used to analyze and simulate inverse problems. • Written by leading experts of inverse problems and uncertainty quantification. Graduate students and researchers working in statistics, mathematics and engineering will benefit from this book.

This book studies methods to concretely address inverse problems. An inverse problem arises when the causes that produced a given effect must be determined or when one seeks to indirectly estimate the parameters of a physical system. The author uses practical examples to illustrate inverse problems in physical sciences. He presents the techniques and specific methods chosen to solve inverse problems in a general domain of application, choosing to focus on a small number of methods that can be used in most applications. This book is aimed at readers with a mathematical and scientific computing background. Despite this, it is a book with a practical perspective. The methods described are applicable, have been applied, and are often illustrated by numerical examples.

This book provides researchers and engineers in the imaging field with the skills they need to effectively deal with nonlinear inverse problems associated with different imaging modalities, including impedance imaging, optical tomography, elastography, and electrical source imaging. Focusing on numerically implementable methods, the book bridges the gap between theory and applications, helping readers tackle problems in applied mathematics and engineering. Complete, self-contained coverage includes basic concepts, models, computational methods, numerical simulations, examples, and case studies. Provides a step-by-step progressive treatment of topics for ease of understanding. Discusses the underlying physical phenomena as well as implementation details of image reconstruction algorithms as prerequisites for finding solutions to non linear inverse problems with practical significance and value. Includes end of chapter problems, case studies and examples with solutions throughout the book. Companion website will provide further examples and solutions, experimental data sets, open problems, teaching material such as PowerPoint slides and software including MATLAB m files. Essential reading for Graduate students and researchers in imaging science working across the areas of applied mathematics, biomedical engineering, and electrical engineering and specifically those involved in nonlinear imaging techniques, impedance imaging, optical tomography, elastography, and electrical source imaging

A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models. The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding. The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables. Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data. Focusing on the theoretical underpinnings of these models, Foundations ofLinear and Generalized Linear Models also features: An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methods An overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problems Numerous examples that use R software for all text data analyses More than 400 exercises for readers to practice and extend the theory, methods, and data analysis A supplementary website with datasets for the examples and exercises An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, Foundations of Linear and Generalized Linear Models is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data.

The focus of this book is on «ill-posed inverse problems». These problems cannot be solved only on the basis of observed data. The building of solutions involves the recognition of other pieces of a priori information. These solutions are then specific to the pieces of information taken into account. Clarifying and taking these pieces of information into account is necessary for grasping the domain of validity and the field of application for the solutions built. For too long, the interest in these problems has remained very limited in the signal-image community. However, the community has since recognized that these matters are more interesting and they have become the subject of much greater enthusiasm. From the application field’s point of view, a significant part of the book is devoted to conventional subjects in the field of inversion: biological and medical imaging, astronomy, non-destructive evaluation, processing of video sequences, target tracking, sensor networks and digital communications. The variety of chapters is also clear, when we examine the acquisition modalities at stake: conventional modalities, such as tomography and NMR, visible or infrared optical imaging, or more recent modalities such as atomic force imaging and polarized light imaging.

The last three decades have seen an explosion of social, psychological and clinical research to identify effective strategies to prevent and treat alcohol-related problems. This “Essential Handbook” contains an updated selection of reviews of “what works” drawn from the critically acclaimed International Handbook of Alcohol Dependence and Problems. Selected specifically for health and other professionals, who need to provide effective responses in their work, these authoritative, science-based reviews are a distillation of the more practical elements, designed to save time for the busy practitioner.

This book introduces state-of-the-art verification techniques for real-time embedded systems, based on the inverse method for parametric timed automata. It reviews popular formalisms for the specification and verification of timed concurrent systems and, in particular, timed automata as well as several extensions such as timed automata equipped with stopwatches, linear hybrid automata and affine hybrid automata. The inverse method is introduced, and its benefits for guaranteeing robustness in real-time systems are shown. Then, it is shown how an iteration of the inverse method can solve the good parameters problem for parametric timed automata by computing a behavioral cartography of the system. Different extensions are proposed particularly for hybrid systems and applications to scheduling problems using timed automata with stopwatches. Various examples, both from the literature and industry, illustrate the techniques throughout the book. Various parametric verifications are performed, in particular of abstractions of a memory circuit sold by the chipset manufacturer ST-Microelectronics, as well as of the prospective flight control system of the next generation of spacecraft designed by ASTRIUM Space Transportation. Contents: 1. Parametric Timed Automata. 2. The Inverse Method for Parametric Timed Automata. 3. The Inverse Method in Practice: Application to Case Studies. 4. Behavioral Cartography of Timed Automata. 5. Parameter Synthesis for Hybrid Automata. 6. Application to the Robustness Analysis of Scheduling Problems. 7. Conclusion and Perspectives. About the Authors ?tienne Andr? is Associate Professor in the Laboratoire d’Informatique de Paris Nord, in the University of Paris 13 (Sorbonne Paris Cit?) in France. His current research interests focus on the verification of real-time systems. Romain Soulat is currently completing his PhD at the LSV laboratory at ENS-Cachan in France, focusing on the modeling and verification of hybrid temporal systems.

This book provides a practical guide to, and critical review of, community and individual professional interventions that could ease the lives of children with developmental disorders and mental health problems. A critical review of, and practical guide to, the interventions that could ease the lives of children with developmental disorders and mental health problems. Structured around the stages and developmental tasks in a child’s life span, from conception to teenage years. Discusses inherited disorders, intrauterine problems, neonatal and perinatal problems, early childhood attachment and physical disorders, disabilities at school, and problems associated with socialisation. Also covers problems that affect children at all ages, such as learning disabilities, abuse and various psychological and psychiatric disorders. Consistently considers the role of parents, the family, and the community in interventions. Written by a leading expert in clinical child and adolescent psychology and social work. Bridges the gap between psychosocial interventions and medically-based treatments. Can be used alongside Herbert’s Typical and Atypical Development: From Conception to Adolescence (BPS Blackwell, 2003).

Professor Emanuel uses clear presentation to compare and facilitate understanding of two seminal standards, The IEEE Std. 1459 and The DIN 40110-2:2002-11. Through critical analysis of the most important and recent theories and review of basic concepts, a highly accessible guide to the essence of the standards is presented. Key features: Explains the physical mechanism of energy flow under different conditions: single- and three-phase, sinusoidal and nonsinusoidal, balanced and unbalanced systems Starts at an elementary level and becomes more complex, with six core chapters and six appendices to clarify the mathematical aspects Discusses and recommends power definitions that played a significant historical role in paving the road for the two standards Provides a number of original unsolved problems at the end of each chapter Introduces a new nonactive power; the Randomness power. Power Definitions and the Physical Mechanism of Power Flow is useful for electrical engineers and consultants involved in energy and power quality. It is also helpful to engineers dealing with energy flow quantification, design and manufacturing of metering instrumentation; consultants working with regulations related to renewable energy courses and the smart grid; and electric utility planning and operation engineers dealing with energy bill structure. The text is also relevant to university researchers, professors, and advanced students in power systems, power quality and energy related courses.