Nonlinear Assignment Problems (NAPs) are natural extensions of the classic Linear Assignment Problem, and despite the efforts of many researchers over the past three decades, they still remain some of the hardest combinatorial optimization problems to solve exactly. The purpose of this book is to provide in a single volume, major algorithmic aspects and applications of NAPs as contributed by leading international experts. The chapters included in this book are concerned with major applications and the latest algorithmic solution approaches for NAPs. Approximation algorithms, polyhedral methods, semidefinite programming approaches and heuristic procedures for NAPs are included, while applications of this problem class in the areas of multiple-target tracking in the context of military surveillance systems, of experimental high energy physics, and of parallel processing are presented. Audience: Researchers and graduate students in the areas of combinatorial optimization, mathematical programming, operations research, physics, and computer science.

This volume provides an overview of multimedia data mining and knowledge discovery and discusses the variety of hot topics in multimedia data mining research. It describes the objectives and current tendencies in multimedia data mining research and their applications. Each part contains an overview of its chapters and leads the reader with a structured approach through the diverse subjects in the field.

Automatic transformation of a sequential program into a parallel form is a subject that presents a great intellectual challenge and promises a great practical award. There is a tremendous investment in existing sequential programs, and scientists and engineers continue to write their application programs in sequential languages (primarily in Fortran). The demand for higher speedups increases. The job of a restructuring compiler is to discover the dependence structure and the characteristics of the given machine. Much attention has been focused on the Fortran do loop. This is where one expects to find major chunks of computation that need to be performed repeatedly for different values of the index variable. Many loop transformations have been designed over the years, and several of them can be found in any parallelizing compiler currently in use in industry or at a university research facility. The book series on KappaLoop Transformations for Restructuring Compilerskappa provides a rigorous theory of loop transformations and dependence analysis. We want to develop the transformations in a consistent mathematical framework using objects like directed graphs, matrices, and linear equations. Then, the algorithms that implement the transformations can be precisely described in terms of certain abstract mathematical algorithms. The first volume, Loop Transformations for Restructuring Compilers: The Foundations, provided the general mathematical background needed for loop transformations (including those basic mathematical algorithms), discussed data dependence, and introduced the major transformations. The current volume, Loop Parallelization, builds a detailed theory of iteration-level loop transformations based on the material developed in the previous book.

Understanding sequence data, and the ability to utilize this hidden knowledge, creates a significant impact on many aspects of our society. Examples of sequence data include DNA, protein, customer purchase history, web surfing history, and more. Sequence Data Mining provides balanced coverage of the existing results on sequence data mining, as well as pattern types and associated pattern mining methods. While there are several books on data mining and sequence data analysis, currently there are no books that balance both of these topics. This professional volume fills in the gap, allowing readers to access state-of-the-art results in one place. Sequence Data Mining is designed for professionals working in bioinformatics, genomics, web services, and financial data analysis. This book is also suitable for advanced-level students in computer science and bioengineering. Forward by Professor Jiawei Han, University of Illinois at Urbana-Champaign.

The modern world is awash with data. The R Project is a statistical environment and programming language that can help to make sense of it all. A huge open-source project, R has become enormously popular because of its power and flexibility. With R you can organise, analyse and visualise data. This clear and methodical book will help you learn how to use R from the ground up, giving you a start in the world of data science. Learning about data is important in many academic and business settings, and R offers a potent and adaptable programming toolbox. The book covers a range of topics, including: importing/exporting data, summarising data, visualising data, managing and manipulating data objects, data analysis (regression, ANOVA and association among others) and programming functions. Regardless of your background or specialty, you'll find this book the perfect primer on data analysis, data visualisation and data management, and a springboard for further exploration.

Data clustering is a highly interdisciplinary field, the goal of which is to divide a set of objects into homogeneous groups such that objects in the same group are similar and objects in different groups are quite distinct. Thousands of theoretical papers and a number of books on data clustering have been published over the past 50 years. However, few books exist to teach people how to implement data clustering algorithms. This book was written for anyone who wants to implement or improve their data clustering algorithms. Using object-oriented design and programming techniques, Data Clustering in C++ exploits the commonalities of all data clustering algorithms to create a flexible set of reusable classes that simplifies the implementation of any data clustering algorithm. Readers can follow the development of the base data clustering classes and several popular data clustering algorithms. Additional topics such as data pre-processing, data visualization, cluster visualization, and cluster interpretation are briefly covered. This book is divided into three parts-- * Data Clustering and C++ Preliminaries: A review of basic concepts of data clustering, the unified modeling language, object-oriented programming in C++, and design patterns * A C++ Data Clustering Framework: The development of data clustering base classes * Data Clustering Algorithms: The implementation of several popular data clustering algorithms A key to learning a clustering algorithm is to implement and experiment the clustering algorithm. Complete listings of classes, examples, unit test cases, and GNU configuration files are included in the appendices of this book as well as in the CD-ROM of the book. The only requirements to compile the code are a modern C++ compiler and the Boost C++ libraries.

Data assimilation is a hugely important mathematical technique, relevant in fields as diverse as geophysics, data science, and neuroscience. This modern book provides an authoritative treatment of the field as it relates to several scientific disciplines, with a particular emphasis on recent developments from machine learning and its role in the optimisation of data assimilation. Underlying theory from statistical physics, such as path integrals and Monte Carlo methods, are developed in the text as a basis for data assimilation, and the author then explores examples from current multidisciplinary research such as the modelling of shallow water systems, ocean dynamics, and neuronal dynamics in the avian brain. The theory of data assimilation and machine learning is introduced in an accessible and unified manner, and the book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics.

The healthcare industry produces a constant flow of data, creating a need for deep analysis of databases through data mining tools and techniques resulting in expanded medical research, diagnosis, and treatment. ""Data Mining and Medical Knowledge Management: Cases and Applications"" presents case studies on applications of various modern data mining methods in several important areas of medicine, covering classical data mining methods, elaborated approaches related to mining in electroencephalogram and electrocardiogram data, and methods related to mining in genetic data. A premier resource for those involved in data mining and medical knowledge management, this book tackles ethical issues related to cost-sensitive learning in medicine and produces theoretical contributions concerning general problems of data, information, knowledge, and ontologies.

Modern computer-based control systems are able to collect a large amount of information, display it to operators and store it in databases but the interpretation of the data and the subsequent decision making relies mainly on operators with little computer support. This book introduces developments in automatic analysis and interpretation of process-operational data both in real-time and over the operational history, and describes new concepts and methodologies for developing intelligent, state space-based systems for process monitoring, control and diagnosis. The book brings together new methods and algorithms from process monitoring and control, data mining and knowledge discovery, artificial intelligence, pattern recognition, and causal relationship discovery, as well as signal processing. It also provides a framework for integrating plant operators and supervisors into the design of process monitoring and control systems.

'Data Mining with Ontologies' examines methodologies and research for the development of ontological foundations for data mining.

Loop tiling, as one of the most important compiler optimizations, is beneficial for both parallel machines and uniprocessors with a memory hierarchy. This book explores the use of loop tiling for reducing communication cost and improving parallelism for distributed memory machines. The author provides mathematical foundations, investigates loop permutability in the framework of nonsingular loop transformations, discusses the necessary machineries required, and presents state-of-the-art results for finding communication- and time-minimal tiling choices. Throughout the book, theorems and algorithms are illustrated with numerous examples and diagrams. The techniques presented in Loop Tiling for Parallelism can be adapted to work for a cluster of workstations, and are also directly applicable to shared-memory machines once the machines are modeled as BSP (Bulk Synchronous Parallel) machines. Features and key topics: Detailed review of the mathematical foundations, including convex polyhedra and cones; Self-contained treatment of nonsingular loop transformations, code generation, and full loop permutability; Tiling loop nests by rectangles and parallelepipeds, including their mathematical definition, dependence analysis, legality test, and code generation; A complete suite of techniques for generating SPMD code for a tiled loop nest; Up-to-date results on tile size and shape selection for reducing communication and improving parallelism; End-of-chapter references for further reading. Researchers and practitioners involved in optimizing compilers and students in advanced computer architecture studies will find this a lucid and well-presented reference work with numerous citations to original sources.

This book illustrates the current work of leading multilevel modeling (MLM) researchers from around the world.

The book's goal is to critically examine the real problems that occur when trying to use MLMs in applied research, such as power, experimental design, and model violations. This presentation of cutting-edge work and statistical innovations in multilevel modeling includes topics such as growth modeling, repeated measures analysis, nonlinear modeling, outlier detection, and meta analysis.

This volume will be beneficial for researchers with advanced statistical training and extensive experience in applying multilevel models, especially in the areas of education; clinical intervention; social, developmental and health psychology, and other behavioral sciences; or as a supplement for an introductory graduate-level course.

Language, Compilers and Run-time Systems for Scalable Computers contains 20 articles based on presentations given at the third workshop of the same title, and 13 extended abstracts from the poster session. Starting with new developments in classical problems of parallel compiler design, such as dependence analysis and an exploration of loop parallelism, the book goes on to address the issues of compiler strategy for specific architectures and programming environments. Several chapters investigate support for multi-threading, object orientation, irregular computation, locality enhancement, and communication optimization. Issues of the interface between language and operating system support are also discussed. Finally, the load balance issues are discussed in different contexts, including sparse matrix computation and iteratively balanced adaptive solvers for partial differential equations. Some additional topics are also discussed in the extended abstracts. Each chapter provides a bibliography of relevant papers and the book can thus be used as a reference to the most up-to-date research in parallel software engineering.

This book illustrates the current work of leading multilevel modeling (MLM) researchers from around the world.

The book's goal is to critically examine the real problems that occur when trying to use MLMs in applied research, such as power, experimental design, and model violations. This presentation of cutting-edge work and statistical innovations in multilevel modeling includes topics such as growth modeling, repeated measures analysis, nonlinear modeling, outlier detection, and meta analysis.

This volume will be beneficial for researchers with advanced statistical training and extensive experience in applying multilevel models, especially in the areas of education; clinical intervention; social, developmental and health psychology, and other behavioral sciences; or as a supplement for an introductory graduate-level course.

Actuarial Principles: Lifetables and Mortality Models explores the core of actuarial science: the study of mortality and other risks and applications. Including the CT4 and CT5 UK courses, but applicable to a global audience, this work lightly covers the mathematical and theoretical background of the subject to focus on real life practice. It offers a brief history of the field, why actuarial notation has become universal, and how theory can be applied to many situations. Uniquely covering both life contingency risks and survival models, the text provides numerous exercises (and their solutions), along with complete self-contained real-world assignments.

Thisbookpresentsmaterialwhichismorealgorithmicallyorientedthanmost alternatives.Italsodealswithtopicsthatareatorbeyondthestateoftheart. Examples include practical and applicable wavelet and other multiresolution transform analysis. New areas are broached like the ridgelet and curvelet transforms. The reader will ?nd in this book an engineering approach to the interpretation of scienti?c data. Compared to the 1st Edition, various additions have been made throu- out, and the topics covered have been updated. The background or en- ronment of this book's topics include continuing interest in e-science and the virtual observatory, which are based on web based and increasingly web service based science and engineering. Additional colleagues whom we would like to acknowledge in this 2nd edition include: Bedros Afeyan, Nabila Aghanim, Emmanuel Cand' es, David Donoho, Jalal Fadili, and Sandrine Pires, We would like to particularly - knowledge Olivier Forni who contributed to the discussion on compression of hyperspectral data, Yassir Moudden on multiwavelength data analysis and Vicent Mart' ?nez on the genus function. The cover image to this 2nd edition is from the Deep Impact project. It was taken approximately 8 minutes after impact on 4 July 2005 with the CLEAR6 ?lter and deconvolved using the Richardson-Lucy method. We thank Don Lindler, Ivo Busko, Mike A'Hearn and the Deep Impact team for the processing of this image and for providing it to us.

This volume explores the diverse applications of advanced tools and technologies of the emerging field of big data and their evidential value in business. It examines the role of analytics tools and methods of using big data in strengthening businesses to meet today's information challenges and shows how businesses can adapt big data for effective businesses practices. This volume shows how big data and the use of data analytics is being effectively adopted more frequently, especially in companies that are looking for new methods to develop smarter capabilities and tackle challenges in dynamic processes. Many illustrative case studies are presented that highlight how companies in every sector are now focusing on harnessing data to create a new way of doing business.

Volume I is the first of two volumes that document the three components of the CHILDES Project. It is divided into two parts which provide an introduction to the use of computational tools for studying language learning. The first part is the CHAT manual, which describes the conventions and principles of CHAT transcription and recommends specific methods for data collection and digitization. The second part is the CLAN manual, which describes the uses of the editor, sonic CHAT, and the various analytic commands. The book will be useful for both novice and experienced users of the CHILDES tools, as well as instructors and students working with transcripts of child language.
Volume II describes in detail all of the corpora included in the CHILDES database. The conversational interactions in the corpora come from monolingual children and their caregivers and siblings, as well as bilingual children, older school-aged children, adult second-language learners, children with various types of language disabilities, and aphasic recovering from language loss. The database includes transcripts in 26 different languages.
The CD-ROM that accompanies these volumes includes the transcript files described in Volume II. It runs on both Windows and Macintosh platforms. For more information or updates to the files, visit the CHILDES website at http: //childes.psy.cmu.edu

The history of the computer, and of the industry it spawned, is the latest entrant into the field of historical studies. Scholars beginning to turn their attention to the subject of computing need James Cortada's "Archives of Data Procesing History" as a brief introduction to sources immediately available for investigation. Each essay provides an overview of a major government, academic, or industrial archival collection dealing with the history of computing, the industry, and its leaders and is written by the archivist/historian who has worked with or is responsible for the collection.

The archives give practical information on hours, organization, contacts, telephone numbers, survey of contents, and assessments of the historical significance of the collections and their institutions. Reference and business librarians will definitely want to add this volume to their collections. Those interested in the history of technology, the business history of the industry, and the history of major institutions will want to consult it.

The need to electronically store, manipulate and analyze large-scale, high-dimensional data sets requires new computational methods. This book presents new intelligent data management methods and tools, including new results from the field of inference. Leading experts also map out future directions of intelligent data analysis. This book will be a valuable reference for researchers exploring the interdisciplinary area between statistics and computer science as well as for professionals applying advanced data analysis methods in industry.

It is good to mark the new Millennium by looking back as well as forward. Whatever Shines Should Be Observed looks to the nineteenth century to celebrate the achievements of five distinguished women, four of whom were born in Ireland while the fifth married into an Irish family, who made pioneering contributions to photography, microscopy, astronomy and astrophysics.

The women featured came from either aristocratic or professional families. Thus, at first sight, they had many material advantages among their peers. In the ranks of the aristocracy there was often a great passion for learning, and the mansions in which these families lived contained libraries, technical equipment (microscopes and telescopes) and collections from the world of nature. More modest professional households of the time were rich in books, while activities such as observing the stars, collecting plants etc. typically formed an integral part of the children's education.

To balance this it was the prevailing philosophy that boys could learn, in addition to basic subjects, mathematics, mechanics, physics, chemistry and classical languages, while girls were channelled into 'polite' subjects like music and needlework. This arrangement allowed boys to progress to University should they so wish, where a range of interesting career choices (including science and engineering) was open to them. Girls, on the other hand, usually received their education at home, often under the tutelage of a governess who would not herself had had any serious contact with scientific or technical subjects. In particular, progress to University was not during most of the nineteenth century an option for women, and access toscientific libraries and institutions was also prohibited.

Although those women with aristocratic and professional backgrounds were in a materially privileged position and had an opportunity to 'see' through the activities of their male friends and relatives how professional scientific life was lived, to progress from their places in society to the professions required very special determination. Firstly, they had to individually acquire scientific and technical knowledge, as well as necessary laboratory methodology, without the advantage of formal training. Then, it was necessary to carve out a niche in a particular field, despite the special difficulties attending the publication of scientific books or articles by a woman. There was no easy road to science, or even any well worn track. To achieve recognition was a pioneering activity without discernible ground rules.

With the hindsight of history, we recognise that the heroic efforts which the women featured in this volume made to overcome the social constraints that held them back from learning about, and participating in, scientific and technical subjects, had a consequence on a much broader canvas. In addition to what they each achieved professionally they contributed within society to a gradual erosion of those barriers raised against the participation of women in academic life, thereby assisting in allowing University places and professional opportunities to gradually become generally available. It is a privilege to salute and thank the wonderful women of the nineteenth century herein described for what they have contributed to the women of today. William Herschel's famous motto quicquid nitet notandum (whatever shinesshould be observed) applies in a particular way to the luminous quality of their individual lives, and those of us who presently observe their shining, as well as those who now wait in the wings of the coming centuries to emerge upon the scene, can each see a little further by their light.

First book to examine game analysis, modern didactic reflections on learning, and big data in a key topic in science and society today. Provides understanding on how to use game analysis when applied to different sports and how to use the approach for video, event and positional data. Presents translational work that has implications for academics, programmers and applied practitioners.