Master the robust features of R parallel programming to accelerate your data science computations About This Book Create R programs that exploit the computational capability of your cloud platforms and computers to the fullest Become an expert in writing the most efficient and highest performance parallel algorithms in R Get to grips with the concept of parallelism to accelerate your existing R programs Who This Book Is For This book is for R programmers who want to step beyond its inherent single-threaded and restricted memory limitations and learn how to implement highly accelerated and scalable algorithms that are a necessity for the performant processing of Big Data. No previous knowledge of parallelism is required. This book also provides for the more advanced technical programmer seeking to go beyond high level parallel frameworks. What You Will Learn Create and structure efficient load-balanced parallel computation in R, using R's built-in parallel package Deploy and utilize cloud-based parallel infrastructure from R, including launching a distributed computation on Hadoop running on Amazon Web Services (AWS) Get accustomed to parallel efficiency, and apply simple techniques to benchmark, measure speed and target improvement in your own code Develop complex parallel processing algorithms with the standard Message Passing Interface (MPI) using RMPI, pbdMPI, and SPRINT packages Build and extend a parallel R package (SPRINT) with your own MPI-based routines Implement accelerated numerical functions in R utilizing the vector processing capability of your Graphics Processing Unit (GPU) with OpenCL Understand parallel programming pitfalls, such as deadlock and numerical instability, and the approaches to handle and avoid them Build a task farm master-worker, spatial grid, and hybrid parallel R programs In Detail R is one of the most popular programming languages used in data science. Applying R to big data and complex analytic tasks requires the harnessing of scalable compute resources. Mastering Parallel Programming with R presents a comprehensive and practical treatise on how to build highly scalable and efficient algorithms in R. It will teach you a variety of parallelization techniques, from simple use of R's built-in parallel package versions of lapply(), to high-level AWS cloud-based Hadoop and Apache Spark frameworks. It will also teach you low level scalable parallel programming using RMPI and pbdMPI for message passing, applicable to clusters and supercomputers, and how to exploit thousand-fold simple processor GPUs through ROpenCL. By the end of the book, you will understand the factors that influence parallel efficiency, including assessing code performance and implementing load balancing; pitfalls to avoid, including deadlock and numerical instability issues; how to structure your code and data for the most appropriate type of parallelism for your problem domain; and how to extract the maximum performance from your R code running on a variety of computer systems. Style and approach This book leads you chapter by chapter from the easy to more complex forms of parallelism. The author's insights are presented through clear practical examples applied to a range of different problems, with comprehensive reference information for each of the R packages employed. The book can be read from start to finish, or by dipping in chapter by chapter, as each chapter describes a specific parallel approach and technology, so can be read as a standalone.
Master the robust features of R parallel programming to accelerate your data science computations About This Book Create R programs that exploit the computational capability of your cloud platforms and computers to the fullest Become an expert in writing the most efficient and highest performance parallel algorithms in R Get to grips with the concept of parallelism to accelerate your existing R programs Who This Book Is For This book is for R programmers who want to step beyond its inherent single-threaded and restricted memory limitations and learn how to implement highly accelerated and scalable algorithms that are a necessity for the performant processing of Big Data. No previous knowledge of parallelism is required. This book also provides for the more advanced technical programmer seeking to go beyond high level parallel frameworks. What You Will Learn Create and structure efficient load-balanced parallel computation in R, using R's built-in parallel package Deploy and utilize cloud-based parallel infrastructure from R, including launching a distributed computation on Hadoop running on Amazon Web Services (AWS) Get accustomed to parallel efficiency, and apply simple techniques to benchmark, measure speed and target improvement in your own code Develop complex parallel processing algorithms with the standard Message Passing Interface (MPI) using RMPI, pbdMPI, and SPRINT packages Build and extend a parallel R package (SPRINT) with your own MPI-based routines Implement accelerated numerical functions in R utilizing the vector processing capability of your Graphics Processing Unit (GPU) with OpenCL Understand parallel programming pitfalls, such as deadlock and numerical instability, and the approaches to handle and avoid them Build a task farm master-worker, spatial grid, and hybrid parallel R programs In Detail R is one of the most popular programming languages used in data science. Applying R to big data and complex analytic tasks requires the harnessing of scalable compute resources. Mastering Parallel Programming with R presents a comprehensive and practical treatise on how to build highly scalable and efficient algorithms in R. It will teach you a variety of parallelization techniques, from simple use of R's built-in parallel package versions of lapply(), to high-level AWS cloud-based Hadoop and Apache Spark frameworks. It will also teach you low level scalable parallel programming using RMPI and pbdMPI for message passing, applicable to clusters and supercomputers, and how to exploit thousand-fold simple processor GPUs through ROpenCL. By the end of the book, you will understand the factors that influence parallel efficiency, including assessing code performance and implementing load balancing; pitfalls to avoid, including deadlock and numerical instability issues; how to structure your code and data for the most appropriate type of parallelism for your problem domain; and how to extract the maximum performance from your R code running on a variety of computer systems. Style and approach This book leads you chapter by chapter from the easy to more complex forms of parallelism. The author's insights are presented through clear practical examples applied to a range of different problems, with comprehensive reference information for each of the R packages employed. The book can be read from start to finish, or by dipping in chapter by chapter, as each chapter describes a specific parallel approach and technology, so can be read as a standalone.
Hadrian, a Roman emperor, the builder of Hadrian's Wall in the north of England, a restless and ambitious man who was interested in architecture and was passionate about Greece and Greek culture. Is this the common image today of the ruler of one of the greatest powers of the ancient world?" "Published to complement a major exhibition at the British Museum, this wide-ranging book rediscovers Hadrian. The sharp contradictions in his personality are examined, previous concepts are questioned and myths that surround him are exploded." --Book Jacket.
Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications. Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning.
The primary focus of this study is the question of the extent and impact of Old Testament traditions in Ephesians. A close examination of the range of quotations, allusions and echoes found in the epistle shows that the Old Testament influence was greater and more deliberate than has hitherto been assumed. The main part of the book is a thorough exegetical study of various aspects of the question, ranging from identification of the relevant Old Testament texts to an examination of the ways in which they are appropriated and applied in the New Testament context. A number of implications emerge for our understanding of the letter's intended readership, and these are illuminating for the assessment of the epistle's relationship to the letter to the Colossians.
Managing Negotiations is a collection of seven global, real-life case studies on prominent negotiations in the realm of international business and politics. The book combines the rigorously researched frameworks of academia with the real-world challenges of negotiations. The cases combine scientific negotiation management practices as well as theories with real-world examples that demonstrate how to conduct successful negotiations and which prominent pitfalls to avoid. The topics discussed reach from mergers & acquisitions, collective bargaining, international diplomatic treaties to international free trade agreements. Each case study starts with an overview comprising three key objectives and ends with the key learnings as well as reflective questions for class discussion. This casebook can be used as recommended reading on Negotiation and Strategic Management courses at postgraduate, MBA and Executive Education level and serves as a guide for practitioners responsible for contract management, negotiation and procurement.
Thesis (M.A.) from the year 2006 in the subject Film Science, grade: 1,3, University of Cologne (Englisches Seminar), 42 entries in the bibliography, language: English, abstract: In this paper, I want to examine Kubrick's work for the notion of man interacting with machines and relate it to various theoretical models that also deal with the relation of man and machine. I chose the term 'machine' as a generic term for any theory applying technological, mechanical or machinic ideas, most of which using the machine as a metaphor for sociological, philosophical or psychoanalytic approaches. At the same time, I want to illustrate on the basis of Kubrick's work how the theoretical discourse on this topic has changed in the course of time. Being initially cut down to a very literal understanding of machines as actual physical devices, the 20th century discourse about technology has shown that the demarcation line between what is nature and what is technology is not as easily drawn as it might appear. Man is inseparably bound up with his tools and culture as a whole could be regarded as some kind of machinery. Thus, a great part of both this paper and Kubrick's work deals with the notion of a cultural machine. Another part, however, will leave the narrow view of the machine as a strictly cultural metaphor. Recent philosophical currents like the work of Deleuze, Maturana and the academic gender discourse try to evolve a new coining of the term 'machinic' that goes beyond rigid dualistic notions. I will try to show that these ideas can be found in Kubrick's films as well.
The purpose of this book is to give a sound economic foundation of finance. Finance is a coherent branch of applied economics that is designed to understand financial markets in order to give advice for practical financial decisions. This book argues that for a sound economic foundation of finance the famous general equilibrium model which in its modern form emphasizes the incompleteness of financial markets is well suited. The aim of the book is to demonstrate that financial markets can be meaningfully embedded into a more general system of markets including, for example, commodity markets. The interaction of these markets can be described via the well known notion of a competitive equilibrium. We argue that for a sound foundation this competitive equilibrium should be unique. In a first step we demonstrate that this essential goal cannot of be achieved based only on the rationality principle, i. e. on the assumption utility maximization of some utility function subject to the budget constraint. In particular we show that this important lack of structure is disturbing as well for the case of mean-variance utility functions which are the basis of the Capital Asset Pricing Model, one of the cornerstones of finance. The final goal of our book is to give reasonable restrictions on the agents' utility functions which lead to a well determined financial markets model.
Relationship Marketing provides a comprehensive overview of the fundamentals and important recent developments in this fast-growing field. "This book makes a landmark contribution in assembling some of the best contemporary thinking about relationship marketing illustrated with concrete descriptions of companies in the automobile industry, consumer electronics, public utilities and so on, which are implementing relationship marketing. I highly recommend this to all companies who want to see what their future success will require." PROF. PHILIP KOTLER, NORTHWESTERN UNIVERSITY, ILLINOIS
For decades, the debate about the tension between IP and antitrust law has revolved around the question to what extent antitrust should accept that IP laws may bar competition in order to stimulate innovation. The rise of IP rights in recent years has highlighted the problem that IP may also impede innovation, if research for new technologies or the marketing of new products requires access to protected prior innovation. How this 'cumulative innovation' is actually accounted for under IP and antitrust laws in the EU and the US, and how it could alternatively be dealt with, are the central questions addressed in this unique study by lawyer and economist Thorsten Käseberg. Taking an integrated view of both IP and antitrust rules – in particular on refusals to deal based on IP – the book assesses policy levers under European and US patent, copyright and trade secrecy laws, such as the bar for and scope of protection as well as research exemptions, compulsory licensing regimes and misuse doctrines. It analyses what the allocation of tasks is and should be between these IP levers and antitrust rules, in particular the law on abuse of dominance (Article 102 TFEU) and monopolisation (Section 2 Sherman Act), while particular attention is paid to the essential facilities doctrine, including pricing methodologies for access to IP. Many recent decisions and judgments are put into a coherent analytical framework, such as IMS Health, AstraZeneca, GlaxoSmithKline (in the EU), Apple (France), Orange Book Standard (Germany), Trinko, Rambus, NYMEX, eBay (US), Microsoft and IBM/T3 (both EU and US). Further topics covered include: IP protection for software, interoperability information and databases; industry-specific tailoring of IP; antitrust innovation market analysis; and the WTO law on the IP/antitrust interface.
The concept of increasing power density is a successful approach to improving the conflict between efficiency and emission behavior of spark-ignition engine drive units for light-duty vehicles. This leads to highly charged gasoline engines with direct injection and high specific torque and power densities, promoting a not yet fully understood combustion anomaly known as low-speed pre-ignition (LSPI). This unpredictable, multicyclic phenomenon limits the depictable in-cylinder pressures, further efficiency gains and engine reliability. Only with a holistic understanding of the LSPI root cause mechanisms and processes can targeted countermeasures be taken and further efficiency gains achieved. A novel methodology pathway for LSPI root cause analysis was developed to accompany the entire LSPI event emergence process path by means of a multi-experimental approach on a modern high efficiency engine. This includes the identification of key LSPI activity – engine parameter specification relations, minimally invasive high-speed endoscopic imaging and further LSPI key experiments. Only the accumulation of inorganic substances originating from lubricating oil additives enables specific deposits/particles to ignite the surrounding mixture over a multicyclic process due to the resulting increased oxidation reactivity. Through a final synthesis step of all results, a multi-cycle oxidation-reactivity-enhanced deposit/particle-driven LSPI root cause mechanism is established.
Understand How to Analyze and Interpret Information in Ecological Point PatternsAlthough numerous statistical methods for analyzing spatial point patterns have been available for several decades, they haven't been extensively applied in an ecological context. Addressing this gap, Handbook of Spatial Point-Pattern Analysis in Ecology shows how the t
Most of the matter in our universe is in a gaseous or plasma state. Yet, most textbooks on quantum statistics focus on examples from and applications in condensed matter systems, due to the prevalence of solids and liquids in our day-to-day lives. In an attempt to remedy that oversight, this book consciously focuses on teaching the subject matter in the context of (dilute) gases and plasmas, while aiming primarily at graduate students and young researchers in the field of quantum gases and plasmas for some of the more advanced topics. The majority of the material is based on a two-semester course held jointly by the authors over many years, and has benefited from extensive feedback provided by countless students and co-workers. The book also includes many historical remarks on the roots of quantum statistics: firstly because students appreciate and are strongly motivated by looking back at the history of a given field of research, and secondly because the spirit permeating this book has been deeply influenced by meetings and discussions with several pioneers of quantum statistics over the past few decades.
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