The Encyclopedia of Social Theory contains over 500 entries varying from concise definitions of key terms and short biographies of key theorists to comprehensive surveys of leading concepts, debates, themes and schools. The object of the Encyclopedia has been to give thorough coverage of the central topics in theoretical sociology as well as terms and concepts in the methodology and philosophy of social science. Although 106 theorists are given entries, the emphasis of the work is on the elucidation of ideas rather than intellectual biography. The Encyclopedia covers the leading contemporary domains of debate on social theory and the classical legacies of social thinkers from the nineteenth and early twentieth centuries, giving proper balance to both the European and North American traditions and to important new developments in the global self-understanding of sociology.

Social theory has become one of the most vigorous specialisms of sociology in recent years. This is in part due to the considerable overlaps of social theory with other disciplinary areas, such as cultural and media studies, anthropology, and political theory, and to the cross-disciplinary nature of theoretical approaches such as feminism and psychoanalysis, and new fields such as postcolonial studies. The editors have therefore worked to produce in the Encyclopedia of Social Theory a first-call reference for students and researchers across the social sciences and humanities with an interest in contemporary theory and the modern history of ideas.

The Encyclopedia has been authored by leading international specialists in the field under the direction of a well-balanced editorial team. It is comprehensively cross-referenced and all larger entries carry bibliographies. There is a full index.

Already popular in the analysis of medical device trials, adaptive Bayesian designs are increasingly being used in drug development for a wide variety of diseases and conditions, from Alzheimer's disease and multiple sclerosis to obesity, diabetes, hepatitis C, and HIV. Written by leading pioneers of Bayesian clinical trial designs, Bayesian Adaptive Methods for Clinical Trials explores the growing role of Bayesian thinking in the rapidly changing world of clinical trial analysis. The book first summarizes the current state of clinical trial design and analysis and introduces the main ideas and potential benefits of a Bayesian alternative. It then gives an overview of basic Bayesian methodological and computational tools needed for Bayesian clinical trials. With a focus on Bayesian designs that achieve good power and Type I error, the next chapters present Bayesian tools useful in early (Phase I) and middle (Phase II) clinical trials as well as two recent Bayesian adaptive Phase II studies: the BATTLE and ISPY-2 trials. In the following chapter on late (Phase III) studies, the authors emphasize modern adaptive methods and seamless Phase II-III trials for maximizing information usage and minimizing trial duration. They also describe a case study of a recently approved medical device to treat atrial fibrillation. The concluding chapter covers key special topics, such as the proper use of historical data, equivalence studies, and subgroup analysis. For readers involved in clinical trials research, this book significantly updates and expands their statistical toolkits. The authors provide many detailed examples drawing on real data sets. The R and WinBUGS codes used throughout are available on supporting websites. Scott Berry talks about the book on the CRC Press YouTube Channel.

The "Encyclopedia of Social Theory" contains over 500 entries varying from concise definitions of key terms and short biographies of key theorists to comprehensive surveys of leading concepts, debates, themes and schools. The object of the encyclopedia has been to give thorough coverage of the central topics in theoretical sociology as well as terms and concepts in the methodology and philosophy of social science. Although 106 theorists are given entries, the emphasis of the work is on the elucidation of ideas rather than intellectual biography. The encyclopedia covers the leading contemporary domains of debate on social theory and the classical legacies of social thinkers from the nineteenth and early twentieth centuries, giving proper balance to both the European and North American traditions and to important new developments in the global self-understanding of sociology.
Social theory has become one of the most vigorous specialisms of sociology in recent years. This is in part due to the considerable overlaps of social theory with other disciplinary areas, such as cultural and media studies, anthropology, and political theory, and to the cross-disciplinary nature of theoretical approaches such as feminism and psychoanalysis, and new fields such as postcolonial studies. The editors have therefore worked to produce in the "Encyclopedia of Social Theory "a first-call reference for students and researchers across the social sciences and humanities with an interest in contemporary theory and the modern history of ideas.
The encyclopedia has been authored by leading international specialists in the field under the direction of a well-balanced editorial team. It is comprehensivelycross-referenced and all larger entries carry bibliographies. There is a full index.

The book is written for students of business economics and tax law. It focuses on investment and financing decisions in cross-border situations.

In particular, the book deals with: Legal structures of international company taxation, International double taxation, Source-based and residence-based income taxation, International investment and profit shifting, International corporate tax planning, International tax planning and European law, Harmonization of corporate taxation in the European Union, International tax planning and tax accounting.

International tax law is designed to avoid international double taxation and to combat international tax avoidance. Nevertheless, companies investing in foreign countries may suffer from international double taxation of profits. On the other hand, these companies may also be able to exploit an international tax rate differential by means of cross-border tax planning.

Ulrich Schreiber holds the chair of Business Administration and Business Taxation at the University of Mannheim. He serves as co-editor of Schmalenbachs Zeitschrift fur betriebswirtschaftliche Forschung (zfbf) and Schmalenbach Business Review (sbr) and is affiliated with the Centrefor European Economic Research (ZEW) as a research associate. Ulrich Schreiber is a member of the Academic Advisory Board of the Federal Ministry of Finance.

This book presents a collection of research papers that address the challenge of how to develop software in a principled way that, in particular, enables reasoning. The individual papers approach this challenge from various perspectives including programming languages, program verification, and the systematic variation of software. Topics covered include programming abstractions for concurrent and distributed software, specification and verification techniques for imperative programs, and development techniques for software product lines. With this book the editors and authors wish to acknowledge - on the occasion of his 60th birthday - the work of Arnd Poetzsch-Heffter, who has made major contributions to software technology throughout his career. It features articles on Arnd's broad research interests including, among others, the implementation of programming languages, formal semantics, specification and verification of object-oriented and concurrent programs, programming language design, distributed systems, software modeling, and software product lines. All contributing authors are leading experts in programming languages and software engineering who have collaborated with Arnd in the course of his career. Overall, the book offers a collection of high-quality articles, presenting original research results, major case studies, and inspiring visions. Some of the work included here was presented at a symposium in honor of Arnd Poetzsch-Heffter, held in Kaiserslautern, Germany, in November 2018.

This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book's structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in online software pages.

Modeling and prediction of oceanographic phenomena and climate is based on the integration of dynamic equations. The Equations of Oceanic Motions derives and systematically classifies the most common dynamic equations used in physical oceanography, from large-scale thermohaline circulations to those governing small scale motions and turbulence. After establishing the basic dynamical equations that describe all oceanic motions, Muller then derives approximate equations, emphasizing the assumptions made and physical processes eliminated. He distinguishes between geometric, thermodynamic and dynamic approximations and between the acoustic, gravity, vortical and temperature-salinity modes of motion. Basic concepts and formulae of equilibrium thermodynamics, vector and tensor calculus, curvilinear coordinate systems, and the kinematics of fluid motion and wave propagation are covered in appendices. Providing the basic theoretical background for graduate students and researchers of physical oceanography and climate science, this book will serve as both a comprehensive text and an essential reference.

The interdisciplinary nature of bioinformatics presents a research challenge in integrating concepts, methods, software and multiplatform data. Although there have been rapid developments in new technology and an inundation of statistical methods for addressing other types of high-throughput data, such as proteomic profiles that arise from mass spectrometry experiments. This book discusses the development and application of Bayesian methods in the analysis of high-throughput bioinformatics data that arise from medical, in particular, cancer research, as well as molecular and structural biology. The Bayesian approach has the advantage that evidence can be easily and flexibly incorporated into statistical methods. A basic overview of the biological and technical principles behind multi-platform high-throughput experimentation is followed by expert reviews of Bayesian methodology, tools and software for single group inference, group comparisons, classification and clustering, motif discovery and regulatory networks, and Bayesian networks and gene interactions.

Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. This book provides a review of current research challenges and opportunities. While the book can not exhaustively cover all current research areas, it does include some exemplary discussion of most research frontiers. Topics include objective Bayesian inference, shrinkage estimation and other decision based estimation, model selection and testing, nonparametric Bayes, the interface of Bayesian and frequentist inference, data mining and machine learning, methods for categorical and spatio-temporal data analysis and posterior simulation methods. Several major application areas are covered: computer models, Bayesian clinical trial design, epidemiology, phylogenetics, bioinformatics, climate modeling and applications in political science, finance and marketing. As a review of current research in Bayesian analysis the book presents a balance between theory and applications. The lack of a clear demarcation between theoretical and applied research is a reflection of the highly interdisciplinary and often applied nature of research in Bayesian statistics. The book is intended as an update for researchers in Bayesian statistics, including non-statisticians who make use of Bayesian inference to address substantive research questions in other fields. It would also be useful for graduate students and research scholars in statistics or biostatistics who wish to acquaint themselves with current research frontiers.

Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the text are the extensive discussion of available software packages combined with a brief but complete and mathematically rigorous introduction to Bayesian inference. The text introduces Monte Carlo methods, Markov chain Monte Carlo methods, and Bayesian software, with additional material on model validation and comparison, transdimensional MCMC, and conditionally Gaussian models. The inclusion of problems makes the book suitable as a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion of Bayesian software - R/R-INLA, OpenBUGS, JAGS, STAN, and BayesX - makes it useful also for researchers and graduate students from beyond statistics.

The primary objective of this source edition is to provide readers with original documents, allowing them to form their own impression of the development, manufacture and operation of the Heinkel He 162. By linking this accumulated data with the historical situation of those times, we hope to have facilitated a more objective assessment of the "Volksjager" project. For the purposes of this book, around 200 file documents were selected from a multitude of material on the Heinkel He 162 and reproduced in their original state. Complementing them are some 140 photographs, 50 scale drawings, 30 colour plates and innumerable citations from contemporary documents, teleprints, orders, lectures, reviews and intelligence reports. To make orientation easy, documents and illustrations are arranged in chapters that follow the historic pass off: Project, devlopment of the Heinkel He 162, manufacturing of the"Volksjaeger", in action within the Luftwaffe and allied war booty are the most important topics. For the first time the production and delivery numbers of the Heinkel He 162 have been broken down. Most of the original documents are not translated, but discussed in the text. A German-English glossary helps to understand the content of those original files. Rounding off the publication is a comprehensive annex giving details of propulsion units, camouflage schemes, armament, the cockpit, plus the various versions of the Heinkel He 162. An index of individals, place names and subjects and a list of sources and literature, both covering several pages, completes this supremely detailed retrospective of this famous German aircraft. About the Author The author, Peter Muller, was a Lieutenant Colonel in the Swiss Army and is now a military historian. For the past quarter of a century, he has researched armament from 1914-1945.

The study of the ocean is almost as old as the history of mankind itself. When the first seafarers set out in their primitive ships they had to understand, as best they could, tides and currents, eddies and vortices, for lack of understanding often led to loss of live. These primitive oceanographers were, of course, primarily statisticians. They collected what empirical data they could, and passed it down, ini tially by word of mouth, to their descendants. Data collection continued throughout the millenia, and although data bases became larger, more re liable, and better codified, it was not really until surprisingly recently that mankind began to try to understand the physics behind these data, and, shortly afterwards, to attempt to model it. The basic modelling tool of physical oceanography is, today, the partial differential equation. Somehow, we all 'know" that if only we could find the right set of equations, with the right initial and boundary conditions, then we could solve the mysteries of ocean dynamics once and for all.

Modeling and prediction of oceanographic phenomena and climate is based on the integration of dynamic equations. The Equations of Oceanic Motions derives and systematically classifies the most common dynamic equations used in physical oceanography, from large scale thermohaline circulations to those governing small scale motions and turbulence. After establishing the basic dynamical equations that describe all oceanic motions, M??ller then derives approximate equations, emphasizing the assumptions made and physical processes eliminated. He distinguishes between geometric, thermodynamic and dynamic approximations and between the acoustic, gravity, vortical and temperature-salinity modes of motion. Basic concepts and formulae of equilibrium thermodynamics, vector and tensor calculus, curvilinear coordinate systems, and the kinematics of fluid motion and wave propagation are covered in appendices. Providing the basic theoretical background for graduate students and researchers of physical oceanography and climate science, this book will serve as both a comprehensive text and an essential reference.

Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. This book provides a review of current research challenges and opportunities. While the book can not exhaustively cover all current research areas, it does include some exemplary discussion of most research frontiers. Topics include objective Bayesian inference, shrinkage estimation and other decision based estimation, model selection and testing, nonparametric Bayes, the interface of Bayesian and frequentist inference, data mining and machine learning, methods for categorical and spatio-temporal data analysis and posterior simulation methods. Several major application areas are covered: computer models, Bayesian clinical trial design, epidemiology, phylogenetics, bioinformatics, climate modeling and applications in political science, finance and marketing. As a review of current research in Bayesian analysis the book presents a balance between theory and applications. The lack of a clear demarcation between theoretical and applied research is a reflection of the highly interdisciplinary and often applied nature of research in Bayesian statistics. The book is intended as an update for researchers in Bayesian statistics, including non-statisticians who make use of Bayesian inference to address substantive research questions in other fields. It would also be useful for graduate students and research scholars in statistics or biostatistics who wish to acquaint themselves with current research frontiers.

The field of brain imaging is developing at a rapid pace and has greatly advanced the areas of cognitive and clinical neuroscience. The availability of neuroimaging techniques, especially magnetic resonance imaging (MRI), functional MRI (fMRI), diffusion tensor imaging (DTI) and magnetoencephalography (MEG) and magnetic source imaging (MSI) has brought about breakthroughs in neuroscience. To obtain comprehensive information about the activity of the human brain, different analytical approaches should be complemented. Thus, in "intermodal multimodality" imaging, great efforts have been made to combine the highest spatial resolution (MRI, fMRI) with the best temporal resolution (MEG or EEG). "Intramodal multimodality" imaging combines various functional MRI techniques (e.g., fMRI, DTI, and/or morphometric/volumetric analysis). The multimodal approach is conceptually based on the combination of different noninvasive functional neuroimaging tools, their registration and cointegration. In particular, the combination of imaging applications that map different functional systems is useful, such as fMRI as a technique for the localization of cortical function and DTI as a technique for mapping of white matter fiber bundles or tracts. This booklet gives an insight into the wide field of multimodal imaging with respect to concepts, data acquisition, and postprocessing. Examples for intermodal and intramodal multimodality imaging are also demonstrated. Table of Contents: Introduction / Neurological Measurement Techniques and First Steps of Postprocessing / Coordinate Transformation / Examples for Multimodal Imaging / Clinical Aspects of Multimodal Imaging / References / Biography

This book presents new techniques for the formal specification and verification of object-oriented software. Since modularity is of critical importance for reuse and component-based programming, special emphasis is given to the completeness of the presented specification techniques to allow module verification based on the specification of the imported modules. A formal framework developed for a Java subset illustrates these new techniques.

This volume provides a thorough introduction and reference for any researcher who is interested in Bayesian inference for wavelet-based models, but is not necessarily an expert in either. To achieve this goal the book starts with an extensive introductory chapter providing a self-contained introduction to the use of wavelet decompositions and the relation to Bayesian inference. The remaining papers in this volume are divided into six parts: independent prior modeling; decision theoretic aspects; dependent prior modeling; spatial models using bivariate wavelet bases; empirical Bayes approaches; and case studies. Chapters are written by experts who published the original research papers establishing the use of wavelet-based models in Bayesian inference. Peter Müller is Associate Professor and Brani Vidakovic is Assistant Professor of Statistics at Duke University.

Our time is characterized by an explosive growth in the use of ever more complicated and sophisticated (computer) models. These models rely on dynamical systems theory for the interpretation of their results and on probability theory for the quantification of their uncertainties. A conscientious and intelligent use of these models requires that both these theories are properly understood. This book is to provide such understanding. It gives a unifying treatment of dynamical systems theory and probability theory. It covers the basic concepts and statements of these theories, their interrelations, and their applications to scientific reasoning and physics. The book stresses the underlying concepts and mathematical structures but is written in a simple and illuminating manner without sacrificing too much mathematical rigor. The book is aimed at students, post-docs, and researchers in the applied sciences who aspire to better understand the conceptual and mathematical underpinnings of the models that they use. Despite the peculiarities of any applied science, dynamics and probability are the common and indispensable tools in any modeling effort. The book is self-contained, with many technical aspects covered in appendices, but does require some basic knowledge in analysis, linear algebra, and physics. Peter Muller, now a professor emeritus at the University of Hawaii, has worked extensively on ocean and climate models and the foundations of complex system theories.

When the SCION project started in 2009, the goal was to create an architecture offering high availability and security for basic point-to-point communication. In the five years since the publication of SCION: A Secure Internet Architecture, this next-generation Internet architecture has evolved in terms of both design and deployment. On the one hand, there has been development of exciting new concepts and systems, including a new global time-synchronization system, an inter-domain approach for bandwidth reservations called COLIBRI, and Green Networking, which allows combating global climate change on three fronts. On the other hand, SCION is now also in production use by the Swiss financial ecosystem, and enables participants such as the Swiss National Bank, the Swiss provider of clearing services (SIX), and all Swiss financial institutes to communicate securely and reliably with each other via the Secure Swiss Finance Network. This unique guidebook provides an updated description of SCION's main components, covering new research topics and the most recent deployments. In particular, it presents in-depth discussion of formal verification efforts. Importantly, it offers a comprehensive, thorough description of the current SCION system: Describes the principles that guided SCION's design as a secure and robust Internet architecture Provides a comprehensive description of the next evolution in the way data finds its way through the Internet Explains how SCION can contribute to reducing carbon emissions, by introducing SCION Green Networking Demonstrates how SCION not only functions in academic settings but also works in production deployments Discusses additional use cases for driving SCION's adoption Presents the approaches for formal verification of protocols and code Illustrated with many colorful figures, pictures, and diagrams, allowing easy access to the concepts and use cases Assembled by a team with extensive experience in the fields of computer networks and security, this text/reference is suitable for researchers, practitioners, and graduate students interested in network security. Also, readers with limited background in computer networking but with a desire to know more about SCION will benefit from an overview of relevant chapters in the beginning of the book.

Our time is characterized by an explosive growth in the use of ever more complicated and sophisticated (computer) models. These models rely on dynamical systems theory for the interpretation of their results and on probability theory for the quantification of their uncertainties. A conscientious and intelligent use of these models requires that both these theories are properly understood. This book is to provide such understanding. It gives a unifying treatment of dynamical systems theory and probability theory. It covers the basic concepts and statements of these theories, their interrelations, and their applications to scientific reasoning and physics. The book stresses the underlying concepts and mathematical structures but is written in a simple and illuminating manner without sacrificing too much mathematical rigor. The book is aimed at students, post-docs, and researchers in the applied sciences who aspire to better understand the conceptual and mathematical underpinnings of the models that they use. Despite the peculiarities of any applied science, dynamics and probability are the common and indispensable tools in any modeling effort. The book is self-contained, with many technical aspects covered in appendices, but does require some basic knowledge in analysis, linear algebra, and physics. Peter Muller, now a professor emeritus at the University of Hawaii, has worked extensively on ocean and climate models and the foundations of complex system theories.

Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Prunster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics.

Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the text are the extensive discussion of available software packages combined with a brief but complete and mathematically rigorous introduction to Bayesian inference. The text introduces Monte Carlo methods, Markov chain Monte Carlo methods, and Bayesian software, with additional material on model validation and comparison, transdimensional MCMC, and conditionally Gaussian models. The inclusion of problems makes the book suitable as a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion of Bayesian software - R/R-INLA, OpenBUGS, JAGS, STAN, and BayesX - makes it useful also for researchers and graduate students from beyond statistics.

This open access book constitutes the proceedings of the 29th European Symposium on Programming, ESOP 2020, which was planned to take place in Dublin, Ireland, in April 2020, as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The actual ETAPS 2020 meeting was postponed due to the Corona pandemic. The papers deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems.