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Author: Peter C. Mayer
Publisher: Author House
ISBN: 1463420676
Category : Biography & Autobiography
Languages : en
Pages : 108
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Book Description
Their son, mostly for fun, writes this collection of vignettes about two prominent scientists. The forward includes a description of the two, summarizing their character and their careers. The summary contains an explanation of the title, Entropy Squared. The forward ends with remarks about the accuracy of the vignettes. Some vignettes include a representation of the impact on the son and some have historical significance. The first two sections concern Gttingen, Germany, from where that American, Joe, as a fellow student put it, acquired his wife. The first section of Gttingen vignettes is from the time of meeting and from visits until World War II. The second is from after the War. Marias career at Sarah Lawrence College separates sections of their supporting the World War II war effort, Joe at the Ballistics Research Laboratory of Aberdeen Proving Grounds and Maria with the Manhattan, nuclear bomb, Project. The Sarah Lawrence College section goes beyond memories because biographers have said little about Marias time at Sarah Lawrence. Sections concerning each are followed by a section on Maria receiving of the Nobel Prize. The conclusion is a memorial to Joe.
Author: Peter C. Mayer
Publisher: Author House
ISBN: 1463420676
Category : Biography & Autobiography
Languages : en
Pages : 108
Get Book Here
Book Description
Their son, mostly for fun, writes this collection of vignettes about two prominent scientists. The forward includes a description of the two, summarizing their character and their careers. The summary contains an explanation of the title, Entropy Squared. The forward ends with remarks about the accuracy of the vignettes. Some vignettes include a representation of the impact on the son and some have historical significance. The first two sections concern Gttingen, Germany, from where that American, Joe, as a fellow student put it, acquired his wife. The first section of Gttingen vignettes is from the time of meeting and from visits until World War II. The second is from after the War. Marias career at Sarah Lawrence College separates sections of their supporting the World War II war effort, Joe at the Ballistics Research Laboratory of Aberdeen Proving Grounds and Maria with the Manhattan, nuclear bomb, Project. The Sarah Lawrence College section goes beyond memories because biographers have said little about Marias time at Sarah Lawrence. Sections concerning each are followed by a section on Maria receiving of the Nobel Prize. The conclusion is a memorial to Joe.
Author: Godfried T. Toussaint
Publisher: CRC Press
ISBN: 1466512032
Category : Mathematics
Languages : en
Pages : 357
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Book Description
The Geometry of Musical Rhythm: What Makes a "Good" Rhythm Good? is the first book to provide a systematic and accessible computational geometric analysis of the musical rhythms of the world. It explains how the study of the mathematical properties of musical rhythm generates common mathematical problems that arise in a variety of seemingly dispara
Author: Magdolna Hargittai
Publisher: Oxford University Press, USA
ISBN: 0199359989
Category : Biography & Autobiography
Languages : en
Pages : 385
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Book Description
A compilation of sixty biographical sketches of influential female scientists, discussing topics like the state of the modern female scientist and the underrepresentation of women at the higher levels of academia.
Author: Jon Harrop
Publisher: John Wiley & Sons
ISBN: 1118210816
Category : Technology & Engineering
Languages : en
Pages : 241
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Book Description
"This work strikes a balance between the pure functional aspects of F# and the object-oriented and imperative features that make it so useful in practice, enable .NET integration, and make large-scale data processing possible." —Thore Graepel, PhD, Researcher, Microsoft Research Ltd. Over the next five years, F# is expected to become one of the world's most popular functional programming languages for scientists of all disciplines working on the Windows platform. F# is free and, unlike MATLAB® and other software with numerical/scientific origins, is a full-fledged programming language. Developed in consultation with Don Syme of Microsoft Research Ltd.—who wrote the language—F# for Scientists explains and demonstrates the powerful features of this important new programming language. The book assumes no prior experience and guides the reader from the basics of computer programming to the implementation of state-of-the-art algorithms. F# for Scientists begins with coverage of introductory material in the areas of functional programming, .NET, and scientific computing, and goes on to explore: Program structure Optimization Data structures Libraries Numerical analysis Databases Input and output Interoperability Visualization Screenshots of development using Visual Studio are used to illustrate compilation, debugging, and interactive use, while complete examples of a few whole programs are included to give readers a complete view of F#'s capabilities. Written in a clear and concise style, F# for Scientists is well suited for researchers, scientists, and developers who want to program under the Windows platform. It also serves as an ideal supplemental text for advanced undergraduate and graduate students with a background in science or engineering.
Author: Linda M. Collins
Publisher: John Wiley & Sons
ISBN: 111821076X
Category : Mathematics
Languages : en
Pages : 273
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Book Description
A modern, comprehensive treatment of latent class and latent transition analysis for categorical data On a daily basis, researchers in the social, behavioral, and health sciences collect information and fit statistical models to the gathered empirical data with the goal of making significant advances in these fields. In many cases, it can be useful to identify latent, or unobserved, subgroups in a population, where individuals' subgroup membership is inferred from their responses on a set of observed variables. Latent Class and Latent Transition Analysis provides a comprehensive and unified introduction to this topic through one-of-a-kind, step-by-step presentations and coverage of theoretical, technical, and practical issues in categorical latent variable modeling for both cross-sectional and longitudinal data. The book begins with an introduction to latent class and latent transition analysis for categorical data. Subsequent chapters delve into more in-depth material, featuring: A complete treatment of longitudinal latent class models Focused coverage of the conceptual underpinnings of interpretation and evaluationof a latent class solution Use of parameter restrictions and detection of identification problems Advanced topics such as multi-group analysis and the modeling and interpretation of interactions between covariates The authors present the topic in a style that is accessible yet rigorous. Each method is presented with both a theoretical background and the practical information that is useful for any data analyst. Empirical examples showcase the real-world applications of the discussed concepts and models, and each chapter concludes with a "Points to Remember" section that contains a brief summary of key ideas. All of the analyses in the book are performed using Proc LCA and Proc LTA, the authors' own software packages that can be run within the SAS® environment. A related Web site houses information on these freely available programs and the book's data sets, encouraging readers to reproduce the analyses and also try their own variations. Latent Class and Latent Transition Analysis is an excellent book for courses on categorical data analysis and latent variable models at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners in the social, behavioral, and health sciences who conduct latent class and latent transition analysis in their everyday work.
Author: Andon Dimitrov Lazarov
Publisher: John Wiley & Sons
ISBN: 1848215746
Category : Computers
Languages : en
Pages : 194
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Book Description
Bistatic radar consists of a radar system which comprises a transmitter and receiver which are separated by a distance comparable to the expected target distance. This book provides a general theoretical description of such bistatic technology in the context of synthetic aperture, inverse synthetic aperture and forward scattering radars from the point of view of analytical geometrical and signal formation as well as processing theory. Signal formation and image reconstruction algorithms are developed with the application of high informative linear frequency and phase code modulating techniques, and numerical experiments that confirm theoretical models are carried out. The authors suggest the program implementation of developed algorithms. A theoretical summary of the latest results in the field of bistatic radars is provided, before applying an analytical geometrical description of scenarios of bistatic synthetic aperture, inverse synthetic aperture and forward scattering radars with cooperative and non-cooperative transmitters. Signal models with linear frequency and phase code modulation are developed, and special phase modulations with C/A (coarse acquisition) and P (precision) of GPS satellite transmitters are considered. The authors suggest Matlab implementations of all geometrical models and signal formation and processing algorithms. Contents 1. Bistatic Synthetic Aperture Radar (BSAR) Survey. 2. BSAR Geometry. 3. BSAR Waveforms and Signal Models. 4. BSAR Image Reconstruction Algorithms. 5. Analytical Geometrical Determination of BSAR Resolution. 6. BSAR Experimental Results. 7. BSAR Matlab Implementation. A general theoretical description of bistatic technology within the scope of synthetic aperture, inverse synthetic aperture and forward scattering radars from the point of view of analytical geometrical and signal formation and processing theory. Signal formation and image reconstruction algorithms are developed in this title, with application of high informative linear frequency and phase code modulating techniques. Numerical experiments that confirm theoretical models are carried out and the authors suggest program implementation for the algorithms developed.
Author: Yuxi (Hayden) Liu
Publisher: Packt Publishing Ltd
ISBN: 1800203861
Category : Computers
Languages : en
Pages : 527
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Book Description
A comprehensive guide to get you up to speed with the latest developments of practical machine learning with Python and upgrade your understanding of machine learning (ML) algorithms and techniques Key FeaturesDive into machine learning algorithms to solve the complex challenges faced by data scientists todayExplore cutting edge content reflecting deep learning and reinforcement learning developmentsUse updated Python libraries such as TensorFlow, PyTorch, and scikit-learn to track machine learning projects end-to-endBook Description Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems. What you will learnUnderstand the important concepts in ML and data scienceUse Python to explore the world of data mining and analyticsScale up model training using varied data complexities with Apache SparkDelve deep into text analysis and NLP using Python libraries such NLTK and GensimSelect and build an ML model and evaluate and optimize its performanceImplement ML algorithms from scratch in Python, TensorFlow 2, PyTorch, and scikit-learnWho this book is for If you’re a machine learning enthusiast, data analyst, or data engineer highly passionate about machine learning and want to begin working on machine learning assignments, this book is for you. Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial, although this is not necessary.
Author: Zi-Kui Liu
Publisher: Cambridge University Press
ISBN: 0521198968
Category : Science
Languages : en
Pages : 261
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Book Description
Integrates fundamental concepts with experimental data and practical applications, including worked examples and end-of-chapter problems.
Author: Huijuan Cui
Publisher: MDPI
ISBN: 3038972223
Category : Technology & Engineering
Languages : en
Pages : 512
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Book Description
Entropy theory has wide applications to a range of problems in the fields of environmental and water engineering, including river hydraulic geometry, fluvial hydraulics, water monitoring network design, river flow forecasting, floods and droughts, river network analysis, infiltration, soil moisture, sediment transport, surface water and groundwater quality modeling, ecosystems modeling, water distribution networks, environmental and water resources management, and parameter estimation. Such applications have used several different entropy formulations, such as Shannon, Tsallis, Rényi, Burg, Kolmogorov, Kapur, configurational, and relative entropies, which can be derived in time, space, or frequency domains. More recently, entropy-based concepts have been coupled with other theories, including copula and wavelets, to study various issues associated with environmental and water resources systems. Recent studies indicate the enormous scope and potential of entropy theory in advancing research in the fields of environmental and water engineering, including establishing and explaining physical connections between theory and reality. The objective of this Special Issue is to provide a platform for compiling important recent and current research on the applications of entropy theory in environmental and water engineering. The contributions to this Special Issue have addressed many aspects associated with entropy theory applications and have shown the enormous scope and potential of entropy theory in advancing research in the fields of environmental and water engineering.
Author: Marcia Fampa
Publisher: Springer Nature
ISBN: 3031130782
Category : Mathematics
Languages : en
Pages : 206
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Book Description
This monograph presents a comprehensive treatment of the maximum-entropy sampling problem (MESP), which is a fascinating topic at the intersection of mathematical optimization and data science. The text situates MESP in information theory, as the algorithmic problem of calculating a sub-vector of pre-specificed size from a multivariate Gaussian random vector, so as to maximize Shannon's differential entropy. The text collects and expands on state-of-the-art algorithms for MESP, and addresses its application in the field of environmental monitoring. While MESP is a central optimization problem in the theory of statistical designs (particularly in the area of spatial monitoring), this book largely focuses on the unique challenges of its algorithmic side. From the perspective of mathematical-optimization methodology, MESP is rather unique (a 0/1 nonlinear program having a nonseparable objective function), and the algorithmic techniques employed are highly non-standard. In particular, successful techniques come from several disparate areas within the field of mathematical optimization; for example: convex optimization and duality, semidefinite programming, Lagrangian relaxation, dynamic programming, approximation algorithms, 0/1 optimization (e.g., branch-and-bound), extended formulation, and many aspects of matrix theory. The book is mainly aimed at graduate students and researchers in mathematical optimization and data analytics.
