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Hidden Figures: The American Dream And The Untold Story Of The Black Women Mathematicians Who Helped Win The Space Race
by Margot Lee ShetterlyBased on the New York Times bestselling book and the Academy Award–nominated movie, author Margot Lee Shetterly and Coretta Scott King Illustrator Honor Award winner Laura Freeman bring the incredibly inspiring true story of four black women who helped NASA launch men into space to picture book readers! Dorothy Vaughan, Mary Jackson, Katherine Johnson, and Christine Darden were good at math…really good. They participated in some of NASA's greatest successes, like providing the calculations for America's first journeys into space. And they did so during a time when being black and a woman limited what they could do. But they worked hard. They persisted. And they used their genius minds to change the world. In this beautifully illustrated picture book edition, we explore the story of four female African American mathematicians at NASA, known as "colored computers," and how they overcame gender and racial barriers to succeed in a highly challenging STEM-based career."Finally, the extraordinary lives of four African American women who helped NASA put the first men in space is available for picture book readers," proclaims Brightly in their article "18 Must-Read Picture Books of 2018." "Will inspire girls and boys alike to love math, believe in themselves, and reach for the stars."
Hidden Figures: The Untold True Story of Four African-American Women Who Helped Launch Our Nation Into Space
by Margot Lee ShetterlyNew York Times bestselling author Margot Lee Shetterly's book is now available in a new edition perfect for young readers. This is the amazing true story of four African-American female mathematicians at NASA who helped achieve some of the greatest moments in our space program. <p><p>Before John Glenn orbited the earth, or Neil Armstrong walked on the moon, a group of dedicated female mathematicians known as "human computers" used pencils, slide rules, and adding machines to calculate the numbers that would launch rockets, and astronauts, into space. <p><p>This book brings to life the stories of Dorothy Vaughan, Mary Jackson, Katherine Johnson, and Christine Darden, four African-American women who lived through the civil rights era, the Space Race, the Cold War, and the movement for gender equality, and whose work forever changed the face of NASA and the country.
Hidden Games: The Surprising Power of Game Theory to Explain Irrational Human Behavior
by Erez Yoeli Moshe HoffmanTwo MIT economists show how game theory—the ultimate theory of rationality—explains irrational behavior We like to think of ourselves as rational. This idea is the foundation for classical economic analysis of human behavior, including the awesome achievements of game theory. But as behavioral economics shows, most behavior doesn&’t seem rational at all—which, unfortunately, to cast doubt on game theory&’s real-world credibility. In Hidden Games, Moshe Hoffman and Erez Yoeli find a surprising middle ground between the hyperrationality of classical economics and the hyper-irrationality of behavioral economics. They call it hidden games. Reviving game theory, Hoffman and Yoeli use it to explain our most puzzling behavior, from the mechanics of Stockholm syndrome and internalized misogyny to why we help strangers and have a sense of fairness. Fun and powerfully insightful, Hidden Games is an eye-opening argument for using game theory to explain all the irrational things we think, feel, and do.
Hidden Harmony—Geometric Fantasies: The Rise of Complex Function Theory
by Jeremy Gray Umberto BottazziniThis book is a history of complex function theory from its origins to 1914, when the essential features of the modern theory were in place. It is the first history of mathematics devoted to complex function theory, and it draws on a wide range of published and unpublished sources. In addition to an extensive and detailed coverage of the three founders of the subject - Cauchy, Riemann, and Weierstrass - it looks at the contributions of authors from d'Alembert to Hilbert, and Laplace to Weyl. Particular chapters examine the rise and importance of elliptic function theory, differential equations in the complex domain, geometric function theory, and the early years of complex function theory in several variables. Unique emphasis has been devoted to the creation of a textbook tradition in complex analysis by considering some seventy textbooks in nine different languages. The book is not a mere sequence of disembodied results and theories, but offers a comprehensive picture of the broad cultural and social context in which the main actors lived and worked by paying attention to the rise of mathematical schools and of contrasting national traditions. The book is unrivaled for its breadth and depth, both in the core theory and its implications for other fields of mathematics. It documents the motivations for the early ideas and their gradual refinement into a rigorous theory.
Hidden Markov Models
by David R. Westhead M. S. VijayabaskarThis volume aims to provide a new perspective on the broader usage of Hidden Markov Models (HMMs) in biology. Hidden Markov Models: Methods and Protocols guides readers through chapters on biological systems; ranging from single biomolecule, cellular level, and to organism level and the use of HMMs in unravelling the complex mechanisms that govern these complex systems. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and practical, Hidden Markov Models: Methods and Protocols aims to demonstrate the impact of HMM in biology and inspire new research.
Hidden Markov Models and Applications (Unsupervised and Semi-Supervised Learning)
by Nizar Bouguila Wentao Fan Manar AmayriThis book focuses on recent advances, approaches, theories, and applications related Hidden Markov Models (HMMs). In particular, the book presents recent inference frameworks and applications that consider HMMs. The authors discuss challenging problems that exist when considering HMMs for a specific task or application, such as estimation or selection, etc. The goal of this volume is to summarize the recent advances and modern approaches related to these problems. The book also reports advances on classic but difficult problems in HMMs such as inference and feature selection and describes real-world applications of HMMs from several domains. The book pertains to researchers and graduate students, who will gain a clear view of recent developments related to HMMs and their applications.
Hidden Markov Models for Time Series: An Introduction Using R, Second Edition (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
by Walter Zucchini Iain L. MacDonald Roland LangrockHidden Markov Models for Time Series: An Introduction Using R, Second Edition illustrates the great flexibility of hidden Markov models (HMMs) as general-purpose models for time series data. The book provides a broad understanding of the models and their uses. After presenting the basic model formulation, the book covers estimation, forecasting, decoding, prediction, model selection, and Bayesian inference for HMMs. Through examples and applications, the authors describe how to extend and generalize the basic model so that it can be applied in a rich variety of situations. The book demonstrates how HMMs can be applied to a wide range of types of time series: continuous-valued, circular, multivariate, binary, bounded and unbounded counts, and categorical observations. It also discusses how to employ the freely available computing environment R to carry out the computations. Features Presents an accessible overview of HMMs Explores a variety of applications in ecology, finance, epidemiology, climatology, and sociology Includes numerous theoretical and programming exercises Provides most of the analysed data sets online New to the second edition A total of five chapters on extensions, including HMMs for longitudinal data, hidden semi-Markov models and models with continuous-valued state process New case studies on animal movement, rainfall occurrence and capture-recapture data
Hidden Markov Models in Finance
by Rogemar S. Mamon Robert J. ElliottSince the groundbreaking research of Harry Markowitz into the application of operations research to the optimization of investment portfolios, finance has been one of the most important areas of application of operations research. The use of hidden Markov models (HMMs) has become one of the hottest areas of research for such applications to finance. This handbook offers systemic applications of different methodologies that have been used for decision making solutions to the financial problems of global markets. As the follow-up to the authors' Hidden Markov Models in Finance (2007), this offers the latest research developments and applications of HMMs to finance and other related fields. Amongst the fields of quantitative finance and actuarial science that will be covered are: interest rate theory, fixed-income instruments, currency market, annuity and insurance policies with option-embedded features, investment strategies, commodity markets, energy, high-frequency trading, credit risk, numerical algorithms, financial econometrics and operational risk. Hidden Markov Models in Finance: Further Developments and Applications, Volume II presents recent applications and case studies in finance and showcases the formulation of emerging potential applications of new research over the book's 11 chapters. This will benefit not only researchers in financial modeling, but also others in fields such as engineering, the physical sciences and social sciences. Ultimately the handbook should prove to be a valuable resource to dynamic researchers interested in taking full advantage of the power and versatility of HMMs in accurately and efficiently capturing many of the processes in the financial market.
Hidden Markov Models: Theory and Implementation using MATLAB®
by José Boaventura-Cunha João Coelho Tatiana PinhoThis book presents, in an integrated form, both the analysis and synthesis of three different types of hidden Markov models. Unlike other books on the subject, it is generic and does not focus on a specific theme, e.g. speech processing. Moreover, it presents the translation of hidden Markov models’ concepts from the domain of formal mathematics into computer codes using MATLAB®. The unique feature of this book is that the theoretical concepts are first presented using an intuition-based approach followed by the description of the fundamental algorithms behind hidden Markov models using MATLAB®. This approach, by means of analysis followed by synthesis, is suitable for those who want to study the subject using a more empirical approach. <p><P>Key Selling Points: <li>Presents a broad range of concepts related to Hidden Markov Models (HMM), from simple problems to advanced theory <li>Covers the analysis of both continuous and discrete Markov chains <li>Discusses the translation of HMM concepts from the realm of formal mathematics into computer code <li>Offers many examples to supplement mathematical notation when explaining new concepts
Hidden Markov Processes: Theory and Applications to Biology (Princeton Series in Applied Mathematics #46)
by M. VidyasagarThis book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. The book starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics.The topics examined include standard material such as the Perron-Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum-Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. The book also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.
Hierarchical Archimedean Copulas (SpringerBriefs in Applied Statistics and Econometrics)
by Ostap Okhrin Jan GóreckiThis book offers a thorough understanding of Hierarchical Archimedean Copulas (HACs) and their practical applications. It covers the basics of copulas, explores the Archimedean family, and delves into the specifics of HACs, including their fundamental properties. The text also addresses sampling algorithms, HAC parameter estimation, and structure, and highlights temporal models with applications in finance and economics. The final chapter introduces R, MATLAB, and Octave toolboxes for copula modeling, enabling students, researchers, data scientists, and practitioners to model complex dependence structures and make well-informed decisions across various domains.
Hierarchical Linear Modeling: Guide and Applications
by Professor G. David GarsonThis book provides a brief, easy-to-read guide to implementing hierarchical linear modeling using three leading software platforms, followed by a set of original how-to applications articles following a standardard instructional format. The "guide" portion consists of five chapters by the editor, providing an overview of HLM, discussion of methodological assumptions, and parallel worked model examples in SPSS, SAS, and HLM software. The "applications" portion consists of ten contributions in which authors provide step by step presentations of how HLM is implemented and reported for introductory to intermediate applications.
Hierarchical Linear Models: Applications and Data Analysis Methods (2nd Edition)
by Anthony S. Bryk Stephen W. RaudenbushThe first edition of this book was a bestseller. Now the author has added four more completely new chapters to this second edition.
Hierarchical Modeling and Analysis for Spatial Data (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
by Sudipto Banerjee Bradley P. Carlin Alan E. GelfandKeep Up to Date with the Evolving Landscape of Space and Space-Time Data Analysis and ModelingSince the publication of the first edition, the statistical landscape has substantially changed for analyzing space and space-time data. More than twice the size of its predecessor, Hierarchical Modeling and Analysis for Spatial Data, Second Edition reflec
Hierarchical Type-2 Fuzzy Aggregation of Fuzzy Controllers
by Oscar Castillo Leticia CervantesThisbook focuses on the fields of fuzzy logic, granular computing and alsoconsidering the control area. These areas can work together to solve variouscontrol problems, the idea is that this combination of areas would enable evenmore complex problem solving and better results. Inthis book we test the proposed method using two benchmark problems: the totalflight control and the problem of water level control for a 3 tank system. Whenfuzzy logic is used it make it easy to performed the simulations, these fuzzysystems help to model the behavior of a real systems, using the fuzzysystems fuzzy rules are generated and with this can generate the behavior ofany variable depending on the inputs and linguistic value. For this reason thiswork considers the proposed architecture using fuzzy systems and with thisimprove the behavior of the complex control problems.
High Dimensional Probability VII
by David M. Mason Christian Houdré Patricia Reynaud-Bouret Jan RosińskiThis volume collects selected papers from the 7th High Dimensional Probability meeting held at the Institut d'Études Scientifiques de Cargèse (IESC) in Corsica, France. High Dimensional Probability (HDP) is an area of mathematics that includes the study of probability distributions and limit theorems in infinite-dimensional spaces such as Hilbert spaces and Banach spaces. The most remarkable feature of this area is that it has resulted in the creation of powerful new tools and perspectives, whose range of application has led to interactions with other subfields of mathematics, statistics, and computer science. These include random matrix theory, nonparametric statistics, empirical process theory, statistical learning theory, concentration of measure phenomena, strong and weak approximations, functional estimation, combinatorial optimization, and random graph theory. The contributions in this volume show that HDP theory continues to thrive and develop new tools, methods, techniques and perspectives to analyze random phenomena.
High Dimensional Probability VIII: The Oaxaca Volume (Progress in Probability #74)
by Mokshay Madiman Nathael Gozlan Rafał Latała Karim LouniciThis volume collects selected papers from the 8th High Dimensional Probability meeting held at Casa Matemática Oaxaca (CMO), Mexico. High Dimensional Probability (HDP) is an area of mathematics that includes the study of probability distributions and limit theorems in infinite-dimensional spaces such as Hilbert spaces and Banach spaces. The most remarkable feature of this area is that it has resulted in the creation of powerful new tools and perspectives, whose range of application has led to interactions with other subfields of mathematics, statistics, and computer science. These include random matrices, nonparametric statistics, empirical processes, statistical learning theory, concentration of measure phenomena, strong and weak approximations, functional estimation, combinatorial optimization, random graphs, information theory and convex geometry. The contributions in this volume show that HDP theory continues to thrive and develop new tools, methods, techniques and perspectives to analyze random phenomena.
High Dimensional Space to Formulate Marriage and Birth Functions
by Shuichirou IkeWith the collapse of Demographic Transition Theory, new theories of population must not just be explanations, but should be falsifiable theories which can compute the number of occurrences of marriages and births. This book reviews computable marriage and birth function using dynamic properties. To do that, the functions are defined in high dimensional space. The reaction-diffusion equation of the number of children in a space is applied to these phenomena, providing solutions to many problems concerning a decline in fertility. The functions are developed as stochastic maps based on the present behaviors of successive behaviors in a geographical space. As we assume that there is an inter-dependence of human behaviors, we use the law of dynamics concerning the function of marriage and birth. The exact mathematical definition of interactions in a space naturally implies a causal relation. For the function concerning the number of children of parents, two geographical-dimensional spaces are required. The decline in fertility in Belgium due to different languages is explained, and the longer fertility period in Brittany is explained by the Laplacian of the diffusion equation. Depending on the degree of symbolic control over behaviors, we need to add the degree of the dimension of the space. For the marriage function, we add age as a biological dimension to the geographical space. In this higher dimensional space, the mapping from neighboring present marriages to neighboring successive marriages is no less than that of the marriage function. These chain reactions caused the baby boom as an exothermal reaction-diffusion. Birth functions require one to add the marriage-age dimension to two geographical and age dimensions so that it is a five dimensional hypersurface. It can, thus, determine birth probabilities of a female who married at a certain age. The phenomenon of modern fertility decline may only be the result of these chain reactions. These processes are solely dependent upon time-space, and not on socioeconomic conditions. This is the very reason why we are able to predict it mathematically. The book provides a new thinking in fertility decline for demographic research. Readers need to be aware that the fertility decline experienced throughout the modern era is a spatial pattern formation (as a reaction-diffusion). The author hopes new mathematical applications in human activities are developed through these new models.
High Order Nonlinear Numerical Schemes for Evolutionary PDEs
by Rémi Abgrall Héloïse Beaugendre Pietro Marco Congedo Cécile Dobrzynski Vincent Perrier Mario RicchiutoThis book collects papers presented during the European Workshop on High Order Nonlinear Numerical Methods for Evolutionary PDEs (HONOM 2013) that was held at INRIA Bordeaux Sud-Ouest, Talence, France in March, 2013. The central topic is high order methods for compressible fluid dynamics. In the workshop, and in this proceedings, greater emphasis is placed on the numerical than the theoretical aspects of this scientific field. The range of topics is broad, extending through algorithm design, accuracy, large scale computing, complex geometries, discontinuous Galerkin, finite element methods, Lagrangian hydrodynamics, finite difference methods and applications and uncertainty quantification. These techniques find practical applications in such fields as fluid mechanics, magnetohydrodynamics, nonlinear solid mechanics, and others for which genuinely nonlinear methods are needed.
High Performance Computing for Big Data: Methodologies and Applications (Chapman & Hall/CRC Big Data Series)
by Chao WangHigh-Performance Computing for Big Data: Methodologies and Applications explores emerging high-performance architectures for data-intensive applications, novel efficient analytical strategies to boost data processing, and cutting-edge applications in diverse fields, such as machine learning, life science, neural networks, and neuromorphic engineering. The book is organized into two main sections. The first section covers Big Data architectures, including cloud computing systems, and heterogeneous accelerators. It also covers emerging 3D IC design principles for memory architectures and devices. The second section of the book illustrates emerging and practical applications of Big Data across several domains, including bioinformatics, deep learning, and neuromorphic engineering. Features Covers a wide range of Big Data architectures, including distributed systems like Hadoop/Spark Includes accelerator-based approaches for big data applications such as GPU-based acceleration techniques, and hardware acceleration such as FPGA/CGRA/ASICs Presents emerging memory architectures and devices such as NVM, STT- RAM, 3D IC design principles Describes advanced algorithms for different big data application domains Illustrates novel analytics techniques for Big Data applications, scheduling, mapping, and partitioning methodologies Featuring contributions from leading experts, this book presents state-of-the-art research on the methodologies and applications of high-performance computing for big data applications. About the Editor Dr. Chao Wang is an Associate Professor in the School of Computer Science at the University of Science and Technology of China. He is the Associate Editor of ACM Transactions on Design Automations for Electronics Systems (TODAES), Applied Soft Computing, Microprocessors and Microsystems, IET Computers & Digital Techniques, and International Journal of Electronics. Dr. Chao Wang was the recipient of Youth Innovation Promotion Association, CAS, ACM China Rising Star Honorable Mention (2016), and best IP nomination of DATE 2015. He is now on the CCF Technical Committee on Computer Architecture, CCF Task Force on Formal Methods. He is a Senior Member of IEEE, Senior Member of CCF, and a Senior Member of ACM.
High Performance Computing for Computational Science -- VECPAR 2014
by Michel Daydé Osni Marques Kengo NakajimaThis book constitutes the thoroughly refereed post-conference proceedings of the 11th International Conference on High Performance Computing for Computational Science, VECPAR 2014, held in Eugene, OR, USA, in June/July 2014. The 25 papers presented were carefully reviewed and selected of numerous submissions. The papers are organized in topical sections on algorithms for GPU and manycores, large-scale applications, numerical algorithms, direct/hybrid methods for solving sparse matrices, performance tuning. The volume also contains the papers presented at the 9th International Workshop on Automatic Performance Tuning.
High Performance Computing in Biomimetics: Modeling, Architecture and Applications (Series in BioEngineering)
by Mohammad Jawaid Tabrej Khan Kamarul Arifin Ahmad Balbir Singh Nor Asilah Wati Abdul HamidThis book gives a complete overview of current developments in the implementation of high performance computing (HPC) in various biomimetic technologies. The book presents various topics that are subdivided into the following parts: A) biomimetic models and mechanics; B) locomotion and computational methods; C) distributed computing and its evolution; D) distributed and parallel computing architecture; E) high performance computing and biomimetics; F) big data, management, and visualization; and G) future of high performance computing in biomimetics. This book presents diverse computational technologies to model and replicate biologically inspired design for the purpose of solving complex human problems. The content of this book is presented in a simple and lucid style which can also be used by professionals, non-professionals, scientists, and students who are interested in the research area of high performance computing applications in the development of biomimetics technologies.
High Performance Computing in Remote Sensing
by Chein-I Chang Antonio J. PlazaSolutions for Time-Critical Remote Sensing ApplicationsThe recent use of latest-generation sensors in airborne and satellite platforms is producing a nearly continual stream of high-dimensional data, which, in turn, is creating new processing challenges. To address the computational requirements of time-critical applications, researchers
High Performance Computing in Science and Engineering ' 17
by Wolfgang E. Nagel Michael M. Resch Dietmar H. KrönerThis book presents the state-of-the-art in supercomputer simulation. It includes the latest findings from leading researchers using systems from the High Performance Computing Center Stuttgart (HLRS) in 2017. The reports cover all fields of computational science and engineering ranging from CFD to computational physics and from chemistry to computer science with a special emphasis on industrially relevant applications. Presenting findings of one of Europe’s leading systems, this volume covers a wide variety of applications that deliver a high level of sustained performance.The book covers the main methods in high-performance computing. Its outstanding results in achieving the best performance for production codes are of particular interest for both scientists and engineers. The book comes with a wealth of color illustrations and tables of results.
High Performance Computing in Science and Engineering ' 18: Transactions of the High Performance Computing Center, Stuttgart (HLRS) 2018
by Wolfgang E. Nagel Michael M. Resch Dietmar H. KrönerThis book presents the state-of-the-art in supercomputer simulation. It includes the latest findings from leading researchers using systems from the High Performance Computing Center Stuttgart (HLRS) in 2018. The reports cover all fields of computational science and engineering ranging from CFD to computational physics and from chemistry to computer science with a special emphasis on industrially relevant applications. Presenting findings of one of Europe’s leading systems, this volume covers a wide variety of applications that deliver a high level of sustained performance.The book covers the main methods in high-performance computing. Its outstanding results in achieving the best performance for production codes are of particular interest for both scientists and engineers. The book comes with a wealth of color illustrations and tables of results.