This book presents recent results on positivity and optimization of polynomials in non-commuting variables. Researchers in non-commutative algebraic geometry, control theory, system engineering, optimization, quantum physics and information science will find the unified notation and mixture of algebraic geometry and mathematical programming useful. Theoretical results are matched with algorithmic considerations; several examples and information on how to use NCSOStools open source package to obtain the results provided. Results are presented on detecting the eigenvalue and trace positivity of polynomials in non-commuting variables using Newton chip method and Newton cyclic chip method, relaxations for constrained and unconstrained optimization problems, semidefinite programming formulations of the relaxations and finite convergence of the hierarchies of these relaxations, and the practical efficiency of algorithms.

MATLAB is a high-level language and environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java. MATLAB Matrix Algebra introduces you to the MATLAB language with practical hands-on instructions and results, allowing you to quickly achieve your goals. Starting with a look at symbolic and numeric variables, with an emphasis on vector and matrix variables, you will go on to examine functions and operations that support vectors and matrices as arguments, including those based on analytic parent functions. Computational methods for finding eigenvalues and eigenvectors of matrices are detailed, leading to various matrix decompositions. Applications such as change of bases, the classification of quadratic forms and how to solve systems of linear equations are described, with numerous examples. A section is dedicated to sparse matrices and other types of special matrices. In addition to its treatment of matrices, you will also learn how MATLAB can be used to work with arrays, lists, tables, sequences and sets.

This book provides a modern introductory tutorial on specialized theoretical aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter provides up-to-date coverage of particle association measures that underpin the theoretical properties of recently developed random set methods in space and time otherwise known as the class of probability hypothesis density framework (PHD filters). The second chapter gives an overview of recent advances in Monte Carlo methods for Bayesian filtering in high-dimensional spaces. In particular, the chapter explains how one may extend classical sequential Monte Carlo methods for filtering and static inference problems to high dimensions and big-data applications. The third chapter presents an overview of generalized families of processes that extend the class of Gaussian process models to heavy-tailed families known as alpha-stable processes. In particular, it covers aspects of characterization via the spectral measure of heavy-tailed distributions and then provides an overview of their applications in wireless communications channel modeling. The final chapter concludes with an overview of analysis for probabilistic spatial percolation methods that are relevant in the modeling of graphical networks and connectivity applications in sensor networks, which also incorporate stochastic geometry features.

Learn how to form and execute an enterprise information strategy: topics include data governance strategy, data architecture strategy, information security strategy, big data strategy, and cloud strategy. Manage information like a pro, to achieve much better financial results for the enterprise, more efficient processes, and multiple advantages over competitors. As you'll discover in Enterprise Information Management in Practice, EIM deals with both structured data (e.g. sales data and customer data) as well as unstructured data (like customer satisfaction forms, emails, documents, social network sentiments, and so forth). With the deluge of information that enterprises face given their global operations and complex business models, as well as the advent of big data technology, it is not surprising that making sense of the large piles of data is of paramount importance. Enterprises must therefore put much greater emphasis on managing and monetizing both structured and unstructured data. As Saumya Chaki-an information management expert and consultant with IBM-explains in Enterprise Information Management in Practice, it is now more important than ever before to have an enterprise information strategy that covers the entire life cycle of information and its consumption while providing security controls. With Fortune 100 consultant Saumya Chaki as your guide, Enterprise Information Management in Practice covers each of these and the other pillars of EIM in depth, which provide readers with a comprehensive view of the building blocks for EIM. Enterprises today deal with complex business environments where information demands take place in real time, are complex, and often serve as the differentiator among competitors. The effective management of information is thus crucial in managing enterprises. EIM has evolved as a specialized discipline in the business intelligence and enterprise data warehousing space to address the complex needs of information processing and delivery-and to ensure the enterprise is making the most of its information assets.

Presenting a comprehensive resource for the mastery of network analysis in R, the goal of Network Analysis with R is to introduce modern network analysis techniques in R to social, physical, and health scientists. The mathematical foundations of network analysis are emphasized in an accessible way and readers are guided through the basic steps of network studies: network conceptualization, data collection and management, network description, visualization, and building and testing statistical models of networks. As with all of the books in the Use R! series, each chapter contains extensive R code and detailed visualizations of datasets. Appendices will describe the R network packages and the datasets used in the book. An R package developed specifically for the book, available to readers on GitHub, contains relevant code and real-world network datasets as well.

This comprehensive and stimulating introduction to Matlab, a computer language now widely used for technical computing, is based on an introductory course held at Qian Weichang College, Shanghai University, in the fall of 2014. Teaching and learning a substantial programming language aren't always straightforward tasks. Accordingly, this textbook is not meant to cover the whole range of this high-performance technical programming environment, but to motivate first- and second-year undergraduate students in mathematics and computer science to learn Matlab by studying representative problems, developing algorithms and programming them in Matlab. While several topics are taken from the field of scientific computing, the main emphasis is on programming. A wealth of examples are completely discussed and solved, allowing students to learn Matlab by doing: by solving problems, comparing approaches and assessing the proposed solutions.

"Logistic Core Operations with SAP" not only provides an overview of core logistics processes and functionality-it also shows how SAP's Business Suite covers logistic core operations, what features are supported, and which systems can be used to implement end-to-end processes in the following logistic core disciplines: Procurement, Distribution, Transportation, Warehouse Logistics and Inventory Management, and Compliance and Reporting. In this context the authors not only explain their integration, the organizational set-up, and master data, but also which solution fits best for a particular business need. This book serves as a solid foundation for understanding SAP software. No matter whether you are a student or a manager involved in an SAP implementation, the authors go far beyond traditional function and feature descriptions, helping you ask the right questions, providing answers, and making recommendations. The book assists you in understanding SAP terminology, concepts and technological components as well as their closed-loop integration. Written in a clear, straight-forward style and using practical examples, it contains valuable tips, illustrative screenshots and flowcharts, as well as best practices-showing how business requirements are mapped into software functionality.

Master the SAP product ecosystem, the client environment, and the feasibility of implementing critical business process with the required technical and functional configuration. SAP Project Management Pitfalls is the first book to provide you with real examples of the pitfalls that you can avoid, providing you with a road-map to a successful implementation. Jay Kay, a SAP Program Manager for Capgemini, first takes a deep dive into common pitfalls in implementing SAP ERP projects in a complex IT landscape. You will learn about the potential causes of failures, study a selection of relevant project implementation case studies in the area, and see a range of possible countermeasures. Jay Kay also provides background on each - the significance of each implementation area, its relevance to a service company that implements SAP projects, and the current state of research. Key highlights of the book: Tools and techniques for project planning and templates for allocating resources Industry standards and innovations in SAP implementation projects in the form of standard solutions aimed at successful implementation Managing SAP system ECC upgrades, EHP updates and project patches Learn effective ways to implement robust SAP release management practices (change management, BAU) Wearing a practitioner's insight, Jay Kay explores the relevance of each failed implementation scenario and how to support your company or clients to succeed in a SAP implementation. There are many considerations when implementing SAP, but as you will learn, knowledge, insight, and effective tools to mitigate risks can take you to a successful implementation project.

Take a deep dive into SAP Fiori and discover Fiori architecture, Fiori landscape installation, Fiori standard applications, Fiori Launchpad configuration, tools for developing Fiori applications and extending standard Fiori applications. You will learn: Fiori architecture and its applications Setting up a Fiori landscape and Fiori Launchpad Configuring, customizing and enhancing standard Fiori applications Developing Fiori native applications for mobile Internet of Things-based custom Fiori applications with the HANA cloud platform Bince Mathew, a SAP mobility expert working for an MNC in Germany, shows you how SAP Fiori, based on HTML5 technology, addresses the most widely and frequently used SAP transactions like purchase order approvals, sales order creation, information lookup, and self-service tasks. This set of HTML5 apps provides a very simple and accessible experience across desktops, tablets, and smartphones. Prerequisites and steps for setting up a Fiori landscape and Launchpad Fiori standard application configuration Extending and customizing standard Fiori applications Developing custom Fiori applications from scratch Building custom Fiori applications for Internet Of Things using HANA cloud Fiori apps with cordova and kapsel plugins

Gesch ftsprozesse in Unternehmen sind h ufig ereignisgesteuert. Denn im Gesch ftsumfeld treten Ereignisse auf, auf die angemessen und m glichst in Echtzeit reagiert werden muss, etwa in Sensornetzwerken oder im automatischen Wertpapierhandel. Event-Driven Architecture (EDA) ist ein neues Paradigma der Softwarearchitektur, das auf der Verarbeitung von Ereignissen beruht. Das Buch diskutiert die Grundprinzipien von EDA, f hrt in die wichtigsten Konzepte der Ereignisverarbeitung ein und veranschaulicht deren Umsetzung anhand einer Fallstudie.

In der deutschen Volkswirtschaft entstehen ca. 90% der Wertschopfung durch Informationsverarbeitung und Kommunikation an elektronisch unterstutzten Arbeitsplatzen. Aspekte wie Arbeitsorganisation, Kommunikationsprozessgestaltung, Ergonomie, Buroraumgestaltung, Motivation, Fuhrung, Strategie und I&K-Ausstattung beeinflussen das komplexe System Arbeitsplatz und damit die mogliche Wertschopfung.

In diesem Buch werden die wesentlichen Aspekte des Themas Arbeitsplatzgestaltung betrachtet: Nach einer Klarung der Bedeutung der Arbeits(platz)gestaltung fur die Wertschopfung eines Unternehmens werden die kurz- und mittelfristig relevanten optionalen Konzepte elektronischer Kommunikation und Zusammenarbeit vorgestellt. Alle diskutierten Konzepte werden mit Fallstudien abgeschlossen, die dem Leser aufzeigen, welche Bedeutung das jeweilige Konzept fur sein Unternehmen haben konnte. Abschliessend werden die Themen diskutiert, mit denen sich jeder zwangslaufig beschaftigen muss, der zum Thema Arbeits(platz)gestaltung Verantwortung tragt. Dabei werden Empfehlungen zu Knowledge Management und Prozessoptimierung gegeben."

Explaining how graph theory and social network analysis can be applied to team sports analysis, This book presents useful approaches, models and methods that can be used to characterise the overall properties of team networks and identify the prominence of each team player. Exploring the different possible network metrics that can be utilised in sports analysis, their possible applications and variances from situation to situation, the respective chapters present an array of illustrative case studies. Identifying the general concepts of social network analysis and network centrality metrics, readers are shown how to generate a methodological protocol for data collection. As such, the book provides a valuable resource for students of the sport sciences, sports engineering, applied computation and the social sciences.

Beginning R, Second Edition is a hands-on book showing how to use the R language, write and save R scripts, read in data files, and write custom statistical functions as well as use built in functions. This book shows the use of R in specific cases such as one-way ANOVA analysis, linear and logistic regression, data visualization, parallel processing, bootstrapping, and more. It takes a hands-on, example-based approach incorporating best practices with clear explanations of the statistics being done. It has been completely re-written since the first edition to make use of the latest packages and features in R version 3. R is a powerful open-source language and programming environment for statistics and has become the de facto standard for doing, teaching, and learning computational statistics. R is both an object-oriented language and a functional language that is easy to learn, easy to use, and completely free. A large community of dedicated R users and programmers provides an excellent source of R code, functions, and data sets, with a constantly evolving ecosystem of packages providing new functionality for data analysis. R has also become popular in commercial use at companies such as Microsoft, Google, and Oracle. Your investment in learning R is sure to pay off in the long term as R continues to grow into the go to language for data analysis and research.What You Will Learn: How to acquire and install R Hot to import and export data and scripts How to analyze data and generate graphics How to program in R to write custom functions Hot to use R for interactive statistical explorations How to conduct bootstrapping and other advanced techniques

The development of software system with acceptable level of reliability and quality within available time frame and budget becomes a challenging objective. This objective could be achieved to some extent through early prediction of number of faults present in the software, which reduces the cost of development as it provides an opportunity to make early corrections during development process. The book presents an early software reliability prediction model that will help to grow the reliability of the software systems by monitoring it in each development phase, i.e. from requirement phase to testing phase. Different approaches are discussed in this book to tackle this challenging issue. An important approach presented in this book is a model to classify the modules into two categories (a) fault-prone and (b) not fault-prone. The methods presented in this book for assessing expected number of faults present in the software, assessing expected number of faults present at the end of each phase and classification of software modules in fault-prone or no fault-prone category are easy to understand, develop and use for any practitioner. The practitioners are expected to gain more information about their development process and product reliability, which can help to optimize the resources used.

This book presents multivariate time series methods for the analysis and optimal control of feedback systems. Although ships' autopilot systems are considered through the entire book, the methods set forth in this book can be applied to many other complicated, large, or noisy feedback control systems for which it is difficult to derive a model of the entire system based on theory in that subject area. The basic models used in this method are the multivariate autoregressive model with exogenous variables (ARX) model and the radial bases function net-type coefficients ARX model. The noise contribution analysis can then be performed through the estimated autoregressive (AR) model and various types of autopilot systems can be designed through the state-space representation of the models. The marine autopilot systems addressed in this book include optimal controllers for course-keeping motion, rolling reduction controllers with rudder motion, engine governor controllers, noise adaptive autopilots, route-tracking controllers by direct steering, and the reference course-setting approach. The methods presented here are exemplified with real data analysis and experiments on real ships. This book is highly recommended to readers who are interested in designing optimal or adaptive controllers not only of ships but also of any other complicated systems under noisy disturbance conditions.

SAP ERP modules are notoriously hard to configure and use effectively without a lot of practice and experience. But as SAP ERP Financial Accounting and Controlling: Configuration and Use Management shows, it doesn't have to be so difficult. The book takes a systematic approach that leads SAP Financial Accounting and Controlling (FICO) users step by step through configuring and using all the program's facets. This approach makes configuration complexities manageable. The book's author-SAP expert, trainer, and accountant Andrew Okungbowa-ensures that both you and your end users are up and running quickly and confidently with FICO. He also provides sound and tested procedures that ensure your implementation works without error. SAP ERP Financial Accounting and Controlling: Configuration and Use Management is in fact the most comprehensive and easy-to-follow SAP FICO configuration book in the market. It incorporates a hands-on approach, with hundreds of screen shots and practical examples, that allows a person without prior configuration training to make SAP FICO ready for use in the enterprise. You'll find that you don't need to be a rocket scientist to grasp the concepts explained and apply them to your work-even when the finances are complicated, such as with the ins and outs of taxes, currency conversions, or special general ledger entries such as down payments or bills of exchange. Providing an in-depth coverage of both configuration and end user procedures, the book covers most aspects of the SAP FICO certification syllabus-SAP's view of the module's key tasks and procedures-including: Configuring and using the general ledger and accounts payable and receivable screens Configuring and completing closing procedures, asset accounting, and financial reporting Configuring global settings and enterprise variables Accounting for both profit and cost centers Creating a house bank Integrating FICO with other SAP modules Taking a jargon-free tone and providing an abundance of examples, Andrew Okungbowa provides a clear understanding of configuration techniques and the breadth of functionalities encompassed by SAP FICO. And as an accountant, Okungbowa understands the needs of end users as well as of those answering to the CIO.

Through this book, researchers and students will learn to use R for analysis of large-scale genomic data and how to create routines to automate analytical steps. The philosophy behind the book is to start with real world raw datasets and perform all the analytical steps needed to reach final results. Though theory plays an important role, this is a practical book for graduate and undergraduate courses in bioinformatics and genomic analysis or for use in lab sessions. How to handle and manage high-throughput genomic data, create automated workflows and speed up analyses in R is also taught. A wide range of R packages useful for working with genomic data are illustrated with practical examples. The key topics covered are association studies, genomic prediction, estimation of population genetic parameters and diversity, gene expression analysis, functional annotation of results using publically available databases and how to work efficiently in R with large genomic datasets. Important principles are demonstrated and illustrated through engaging examples which invite the reader to work with the provided datasets. Some methods that are discussed in this volume include: signatures of selection, population parameters (LD, FST, FIS, etc); use of a genomic relationship matrix for population diversity studies; use of SNP data for parentage testing; snpBLUP and gBLUP for genomic prediction. Step-by-step, all the R code required for a genome-wide association study is shown: starting from raw SNP data, how to build databases to handle and manage the data, quality control and filtering measures, association testing and evaluation of results, through to identification and functional annotation of candidate genes. Similarly, gene expression analyses are shown using microarray and RNAseq data. At a time when genomic data is decidedly big, the skills from this book are critical. In recent years R has become the de facto< tool for analysis of gene expression data, in addition to its prominent role in analysis of genomic data. Benefits to using R include the integrated development environment for analysis, flexibility and control of the analytic workflow. Included topics are core components of advanced undergraduate and graduate classes in bioinformatics, genomics and statistical genetics. This book is also designed to be used by students in computer science and statistics who want to learn the practical aspects of genomic analysis without delving into algorithmic details. The datasets used throughout the book may be downloaded from the publisher's website.

This Festschrift in honour of Ursula Gather's 60th birthday deals with modern topics in the field of robust statistical methods, especially for time series and regression analysis, and with statistical methods for complex data structures. The individual contributions of leading experts provide a textbook-style overview of the topic, supplemented by current research results and questions. The statistical theory and methods in this volume aim at the analysis of data which deviate from classical stringent model assumptions, which contain outlying values and/or have a complex structure. Written for researchers as well as master and PhD students with a good knowledge of statistics.

MATLAB is a high-level language and environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java. MATLAB Differential Equations introduces you to the MATLAB language with practical hands-on instructions and results, allowing you to quickly achieve your goals. In addition to giving an introduction to the MATLAB environment and MATLAB programming, this book provides all the material needed to work on differential equations using MATLAB. It includes techniques for solving ordinary and partial differential equations of various kinds, and systems of such equations, either symbolically or using numerical methods (Euler's method, Heun's method, the Taylor series method, the Runge-Kutta method,...). It also describes how to implement mathematical tools such as the Laplace transform, orthogonal polynomials, and special functions (Airy and Bessel functions), and find solutions of finite difference equations.

This book focuses on the applications of convex optimization and highlights several topics, including support vector machines, parameter estimation, norm approximation and regularization, semi-definite programming problems, convex relaxation, and geometric problems. All derivation processes are presented in detail to aid in comprehension. The book offers concrete guidance, helping readers recognize and formulate convex optimization problems they might encounter in practice.

MATLAB is a high-level language and environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java. MATLAB Linear Algebra introduces you to the MATLAB language with practical hands-on instructions and results, allowing you to quickly achieve your goals. In addition to giving an introduction to the MATLAB environment and MATLAB programming, this book provides all the material needed to work in linear algebra with ease. In addition to exploring MATLAB's matrix algebra capabilities, it describes the MATLAB commands that are used to create two- and three-dimensional graphics, including explicit, implicit and parametric curve and surface plotting, and various methods of data representation. Methods for solving systems of equations are detailed.