Mathematics Books

Book SynopsisFrom the scorching center of Earth's core to the outer limits of its atmosphere, from the gradual process of erosion that carved the Grand Canyon to the earth-shaking fury of volcanoes and earthquakes, this book tells the story of the evolution of our planet and of the science that makes it work.Trade ReviewThis companion volume to the American Museum of Natural History's award-winning Hall of Planet Earth exhibit by Mathez and Webster, the original co-curators, offers readers an accessible guided tour of how our planet works from interdisciplinary and scientific perspectives. Library Journal A handsome and well-written book, in full colour and jammed full of geological information about the earth... I commend the authors for the thoroughness, accuracy and detail they give to so many parts of the Earth Sciences.New Scientist -- Simon Lamb New Scientist This book is your engaging and scientific tour guide. Natural History This informative book... explores the major forces and factors that shaped and continue to shape our planet. Rotunda A highly recommended text for any who would wonder what climates were like in past times. Bookwatch This book...is outstanding among the many fine books on the subject available today...A book of unusually widespread appeal. Highly recommended. -- T. L. T. Gross Choice By means of well-chosen examples taken from nature as we find it, they provide the best of contemporary approaches to, and conclusions about, the major issues. Science Books & Films A thorough and yet easy-to-understand account of the origins of Earth... The authors definitely know their stuff... But true to their vocations as educators, as well as scientists, they do more than just educate in this volume; they also thoroughly entertain. -- Beth Norton Science EditorTable of ContentsPreface Part I. How Has Earth Evolved? 1. The Birth of Planet Earth From Meteorites to Earth The Formation of the Moon Early Earth Organizes Itself The Emergence of an Atmosphere and an Ocean 2. Learning the Age of Earth The Seeds of Doubt The Emergence of the Revolutionary Concept of an Old Earth Radioactivity and the Age of Earth 3. The Evolution of the Continents Earth: The Only Planet with a Continental Crust The Continents and Continental Crust The First Continental Crust The Assembly of the Continents 4. Life and Conditions on Early Earth Ancient Microbes Banded Iron Formations and Oxygenation of the Atmosphere and Ocean A Warm Early Earth? The Appearance of Animals and Explosion of Life in the Cambrian 5. Reading Rocks: The Story of the Grand Canyon How Sedimentary Rocks Describe Ancient Environments Evidence of Missing Rock How Old Is the Grand Canyon? Part II. Why Are There Ocean Basins, Continents, and Mountains? 6. Internal Earth The Core The Magnetic Field Convection in the Core and Origin of the Geomagnetic Field Earth's Internal Heat The Mantle Convection in the Mantle 7. Plate Tectonics Continental Drift: An Idea Proposed Before Its Time Wegener's Theory of Continental Drift From Continental Drift to Plate Tectonics Plate Boundaries: Where the Action Is Plate Motions and Continental Reconstruction 8. Lavas from the Depths of Earth Hawaii Kilauea Volcano Volcanoes of the Mid-Ocean Ridges Great Lava Floods and the Columbia River Basalts 9. Great Explosive Volcanoes The Nature of Explosive Volcanism How Gases Drive Explosive Eruptions Vesuvius: The Anatomy of an Explosive Eruption Tambora: Volcanoes and Climate Change Krakatau: An Explosion that Reverberated Around the World Great Prehistoric Eruptions Granite 10. Earthquakes Why Do Earthquakes Occur? How Earthquakes Are Measured How Earthquakes Destroy Fault Behavior and Calculating the Odds Short-term Predictions The San Andreas Fault Zone, California The Great Alaska Earthquake 11. Mountains The Shape of Earth and Why Mountains Are High The Importance of Erosion The Folding of Rocks The Metamorphism of Rocks 12. The Alps The Beginnings of Alpine Research The Formation and Structure of the Alps Part III. What Causes Earth's Climate and Climate Change? 13. The Atmosphere The Structure of the Atmosphere Global Atmospheric Circulation Greenhouse Earth: The Troposphere Story Ozone: The Stratosphere Story The Importance of Clouds 14. The World Ocean The Important Properties of Water Why Is the Ocean Salty? The Global Ocean Conveyor System Ocean Surface Currents Upwelling and Downwelling El Nio and La Nia 15. The Geological Record of Climate Change Climate Forcing Factors The Climate Record in Greenland Ice Some Other Indicators of Past Climate The Ice Age Part IV. Why Is Earth Habitable? 16. Conditions for Life Water: The Essential Ingredient The Carbon Cycle Earth, Venus, and Mars Earth's Intangible Shields 17. Black Smokers from the Deep How Hydrothermal Vent Fields Form Life at Hydrothermal Vents Did Life Originate in Deep-Sea Vents? Heat and Water: What Goes In, and What Goes Out 18. Some Natural Resources and How They Form What Salt, Gold, and Coal Have in Common Ore Deposits from Hot Water An Ancient Analogue of the Black Smokers Ore Deposits from Magmas Notes Glossary Bibliography Index

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Book SynopsisInsights from the history of numerical notation suggest that how humans write numbers is an active choice involving cognitive and social factors.Over the past 5,000 years, more than 100 methods of numerical notation—distinct ways of writing numbers—have been developed and used by specific communities. Most of these are barely known today; where they are known, they are often derided as cognitively cumbersome and outdated. In Reckonings, Stephen Chrisomalis considers how humans past and present use numerals, reinterpreting historical and archaeological representations of numerical notation and exploring the implications of why we write numbers with figures rather than words.Chrisomalis shows that numeration is a social practice. He argues that written numerals are conceptual tools that are transformed to fit the perceived needs of their users, and that the sorts of cognitive processes that affect decision-making around numerical activity are complex a

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Book SynopsisA commonsense, self-contained introduction to the mathematics and physics of music; essential reading for musicians, music engineers, and anyone interested in the intersection of art and science.“Mathematics can be as effortless as humming a tune, if you know the tune,” writes Gareth Loy. In Musimathics, Loy teaches us the tune, providing a friendly and spirited tour of the mathematics of music—a commonsense, self-contained introduction for the nonspecialist reader. It is designed for musicians who find their art increasingly mediated by technology, and for anyone who is interested in the intersection of art and science.In Volume 1, Loy presents the materials of music (notes, intervals, and scales); the physical properties of music (frequency, amplitude, duration, and timbre); the perception of music and sound (how we hear); and music composition. Calling himself “a composer seduced into mathematics,” Loy provides answers to foundat

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Book SynopsisSteve Lakin is an award-winning lecturer in Mathematics at the University of Glamorgan, where he specialises in teaching basic mathematics and statistics to a wide range of students across all courses and subjects.Table of ContentsPreface and acknowledgements How to use this book A note on mathematics, calculators and computer software Introductory statistics 1. Introduction to statistics and data 2. Presentation of data 3. Averages 4. Cumulative frequencies and percentiles 5. Measures of dispersion 6. Working with frequency distributions Essential mathematics 7. Factorials, permutations and combinations 8. Sigma notation Correlation and regression 9. Correlation 10. Linear regression Probability 11. An introduction to probability 12. Multiple probabilities 13. Probability trees 14. Expected values and decision criteria 15. Conditional probability Probability distributions 16. Introduction to probability distributions 17. The Poisson distribution 18. The normal distribution 19. The binormal distribution Hypothesis testing 20. Introduction to hypothesis testing. 21. z-tests 22. t-tests 23. ÷2-tests 24. F-tests. The use of F-tests for comparison of variances Statistical Tables Table A - The normal distribution N(0, 1) Table B - Confidence limits for the z-test Table C1 - The one-tailed t-test Table C2 - The two-tailed t-test Table D - Critical values for the ÷2 distribution Table E1 - Critical values for the F-test (5%) Table E2 - Critical values for the F-test (1%) Summary, glossary and appendices Summary and further work Glossary Appendix 1 - 'Use of a calculator' test Appendix 2 - The Greek alphabet Appendix 3 - Some useful Excel commands Solutions to exercises

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978-0309090384

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisLarson's COLLEGE ALGEBRA is known for its reliable, reader-friendly explanations of mathematical concepts and helpful exercises that prepare students for further study in math. The Eleventh Edition incorporates exercises with relatable, real-world scenarios, continuous review, and innovative technology. How Do You See It? exercises give you practice applying the concepts and new Summarize features and Checkpoint problems reinforce understanding of the skill sets to help you better prepare for tests. Free access to homework support websites CalcChat.com, CalcView.com and LarsonPrecalculus.com are there when you need them. If you are struggling with previously learned math skills, work on the Review & Refresh exercises and watch the Skills Review videos.Table of ContentsP. PREREQUISITES. Review of Real Numbers and Their Properties. Exponents and Radicals. Polynomials and Special Products. Factoring Polynomials. Rational Expressions. The Rectangular Coordinate System and Graphs. Summary and Study Strategies. Review Exercises. Chapter Test. Proofs in Mathematics. P.S. Problem Solving. 1. EQUATIONS, INEQUALITIES, AND MATHEMATICAL MODELING. Graphs of Equations. Linear Equations in One Variable. Modeling with Linear Equations. Quadratic Equations and Applications. Complex Numbers. Other Types of Equations. Linear Inequalities in One Variable. Other Types of Inequalities. Summary and Study Strategies. Review Exercises. Chapter Test. Proofs in Mathematics. P.S. Problem Solving. 2. FUNCTIONS AND THEIR GRAPHS. Linear Equations in Two Variables. Functions. Analyzing Graphs of Functions. A Library of Parent Functions. Transformations of Functions. Combinations of Functions: Composite Functions. Inverse Functions. Summary and Study Strategies. Review Exercises. Chapter Test. Cumulative Test for Chapters P-2. Proofs in Mathematics. P.S. Problem Solving. 3. POLYNOMIAL FUNCTIONS. Quadratic Functions and Models. Polynomial Functions of Higher Degree. Polynomial and Synthetic Division. Zeros of Polynomial Functions. Mathematical Modeling and Variation. Summary and Study Strategies. Review Exercises. Chapter Test. Proofs in Mathematics. P.S. Problem Solving. 4. RATIONAL FUNCTIONS AND CONICS. Rational Functions and Asymptotes. Graphs of Rational Functions. Conics. Translations of Conics. Summary and Study Strategies. Review Exercises. Chapter Test. Proofs in Mathematics. P.S. Problem Solving. 5. EXPONENTIAL AND LOGARITHMIC FUNCTIONS. Exponential Functions and Their Graphs. Logarithmic Functions and Their Graphs. Properties of Logarithms. Exponential and Logarithmic Equations. Exponential and Logarithmic Models. Summary and Study Strategies. Review Exercises. Chapter Test. Cumulative Test for Chapters 3-5. Proofs in Mathematics. P.S. Problem Solving. 6. SYSTEMS OF EQUATIONS AND INEQUALITIES. Linear and Nonlinear Systems of Equations. Two-Variable Linear Systems. Multivariable Linear Systems. Partial Fractions. Systems of Inequalities. Linear Programming. Summary and Study Strategies. Review Exercises. Chapter Test. Proofs in Mathematics. P.S. Problem Solving. 7. MATRICES AND DETERMINANTS. Matrices and Systems of Equations. Operations with Matrices. The Inverse of a Square Matrix. The Determinant of a Square Matrix. Applications of Matrices and Determinants. Summary and Study Strategies. Review Exercises. Chapter Test. Proofs in Mathematics. P.S. Problem Solving. 8. SEQUENCES, SERIES, AND PROBABILITY. Sequences and Series. Arithmetic Sequences and Partial Sums. Geometric Sequences and Series. Mathematical Induction. The Binomial Theorem. Counting Principles. Probability. Summary and Study Strategies. Review Exercises. Chapter Test. Cumulative Test for Chapters 6-8. Proofs in Mathematics. P.S. Problem Solving. APPENDIX A: Errors and the Algebra of Calculus. APPENDIX B: Concepts in Statistics (Web). B.1 Representing Data. B.2 Analyzing Data. B.3 Modeling Data. Alternative Version of Chapter P (Web). P.1 Operations with Real Numbers. P.2 Properties of Real Numbers. P.3 Algebraic Expressions. P.4 Operations with Polynomials. P.5 Factoring Polynomials. P.6 Factoring Trinomials.

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Book SynopsisMath and Art: An Introduction to Visual Mathematics explores the potential of mathematics to generate visually appealing objects and reveals some of the beauty of mathematics. It includes numerous illustrations, computer-generated graphics, photographs, and art reproductions to demonstrate how mathematics can inspire or generate art.Focusing on accessible, visually interesting, and mathematically relevant topics, the text unifies mathematics subjects through their visual and conceptual beauty. Sequentially organized according to mathematical maturity level, each chapter covers a cross section of mathematics, from fundamental Euclidean geometry, tilings, and fractals to hyperbolic geometry, platonic solids, and topology. For art students, the book stresses an understanding of the mathematical background of relatively complicated yet intriguing visual objects. For science students, it presents various elegant mathematical theories and notions.Features Provides an accessible introduction to mathematics in art Supports the narrative with a self-contained mathematical theory, with complete proofs of the main results (including the classification theorem for similarities) Presents hundreds of figures, illustrations, computer-generated graphics, designs, photographs, and art reproductions, mainly presented in full color Includes 21 projects and approximately 280 exercises, about half of which are fully solved Covers Euclidean geometry, golden section, Fibonacci numbers, symmetries, tilings, similarities, fractals, cellular automata, inversion, hyperbolic geometry, perspective drawing, Platonic and Archimedean solids, and topology New to the Second Edition New exercises, projects and artworks Revised, reorganized and expanded chapters More use of color throughout Trade Review"A beautiful book that brings out a wide range of mathematics, ancient to modern, with rich and often unexpected connections to the visual arts."– Catherine A. Gorini, Maharishi International University"Kalajdzievski takes us on a fascinating journey through the most visual subjects in mathematics. This book has the rare quality of not only organizing topics in a sequence that reveals how geometric concepts build upon one another, but also presenting each topic in a compact and self-contained manner for readers who prefer to browse for different entry points into the text. Although verbal explanations and mathematical formulae abound here, it is the colorful diagrams and photographs that capture the attention and enchant the eye. "– James Mai, Professor of Art, Illinois State University"The book presents mathematical and geometrical topics which can be expressed as the artistic pieces and serve to inspiring the artists to explore visual beauty and power of mathematics. In comparison with the first edition (of 2008), this book is noticeably extended to 280 exercises (from 190 originally) with solutions given to a half of them, 740 figures and artworks (from 556 previously), and 21 projects suggested for students.[. . . ] The book contains various illustrations and computer-generated graphics, photographs and art reproductions almost in each page, revealing an astonishing interaction of mathematics and artistic findings in human civilization and culture. [. . . ] The book can be useful to instructors and students, and interesting to any readers wishing to extend their knowledge and understanding of the esthetics and science of the visual math and mathematical art."– Technometrics"There are many books about mathematics and art; this one distinguishes itself as an “unorthodox geometry textbook,” with exercises and fun art projects. The book is based on 20 years of offering a course to more than 10,000 students. It stops short of covering some of the mathematics (groups are mentioned but not defined), though one theorem (classification of similarities) is proved in an appendix. Topics are Euclidean geometry, transformations of the plane, similarities and fractals, hyperbolic geometry, perspective, three-dimensional objects, and topology. The book averages two figures per page, with many utterly beautiful in color. You might be surprised at the sophisticated mathematical content of some crop circles (no doubt made by aliens!), and amazed by some of the illustrations of artworks."– Mathematics Magazine, MAAPraise for the First Edition"This delightful book grew out of set of teaching notes for an interdisciplinary course called Math in Art that was co-taught by a mathematician and an artist or architect. … The mathematical ideas are presented visually in a way that seems quite natural, and it engages the reader through explorations with lots of hands-on exercises. The mathematical presentation is solid, and the choice of topics puts the focus on the visual presentation of mathematical concepts. The illustrations are beautiful! … This text is very readable. The mathematics is accessible to those with little mathematical background, and yet the presentation is still engaging for those with more background."—MAA Reviews, March 2009"All in all, this work offers an excellent account of art inspired by mathematics and art generated by mathematics, and it should interest readers in both fields. Summing Up: Highly Recommended."– R.M. Davis, emeritus, Albion College, in Choice: Current Review for Academic Libraries, February 2009, Vol. 46, No. 6Table of ContentsChapter 1. Euclidean Geometry. 1.0. Introduction. 1.1. The Five Axioms of Euclidean Geometry. 1.2. Ruler and Compass Constructions. 1.3. The Golden Ratio. 1.4. Fibonacci Numbers. Chapter 2. Plane Transformations. 2.1. Plane Symmetries. 2.2.* Plane Symmetries, Vectors, and Matrices (Optional). 2.3. Groups of Symmetries Of Planar Objects. 2.4. Frieze Patterns. 2.5. Wallpaper Designs and Tilings of the Plane. 2.6. Tilings and Art. Chapter 3. Similarities, Fractals, and Cellular Automata. 3.1. Similarities and some other Planar Transformations. 3.2.* Complex Numbers (Optional). 3.3. Fractals: Definition and Some Examples. 3.4. Julia Sets. 3.5. Cellular Automata. Chapter 4. Hyperbolic Geometry. 4.1. Non-Euclidean Geometries: Background and Some History. 4.2. Inversion. 4.3. Hyperbolic Geometry. 4.4. Some Basic Constructions in the Poincaré Model. 4.5. Tilings of the Hyperbolic Plane. Chapter 5. Perspective. 5.1. Perspective: A brief overview of the Evolution of the rules of perspective. 5.2. Perspective Drawing and Constructions of Some Two-Dimensional (Planar) Objects. 5.3. Perspective Images of Three-Dimensional Objects. 5.4.* Mathematics of Perspective Drawing: A Brief Overview (Optional). Chapter 6. Some Three-Dimensional Objects. 6.1. Regular and Other Polyhedra. 6.2. Sphere, Cylinder, Cone, and Conic Sections. 6.3. Geometry, Tilings, Fractals, and Cellular Automata in Three Dimensions. Chapter 7. Topology. 7.1. Homotopy of Spaces: An Informal Introduction. 7.2. Two-Manifolds and The Euler Characteristic. 7.3. Non-Orientable Two-Manifolds and Three-Manifolds. Appendix: Classification Theorem for Similarities. Solutions.

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Book SynopsisThis book presents a concise introduction to the theory underlying the analysis of the main types of spatial data. It includes examples to illustrate the topics, including R code for their implementation, as well as exercises to support course teaching and self-study. Trade Review"This book provides a concise and readable introduction to the three main areas of spatial statistics: random fields, areal data and spatial point processes. Although the focus is on the basic underlying theory, extensive analyses of real data are provided including R code. Suitable as a text or for self-study, each major chapter includes exercises and solutions. A valuable resource for students and researchers in statistics and related fields looking to learn some of the basic theory underlying spatial statistics."- Michael Stein, University of Chicago"The book is a concise introduction to spatial statistics mostly from a mathematical point of view. It devotes a chapter to each of the main classes of spatial statistics settings, point referenced data and interpolation, areal data and spatial point processes. Each chapter contain all the main definitions and theorem (with proofs) for relevant models, and some of the inference methods for each of these spatial statistics settings. The chapters end with worked through R-examples and nice and useful pointers to the literature. The book expect the reader to be both mathematical and statistical mature, and most examples are mathematical. I think this can be a nice introduction and reference book for PhD students specializing in spatial statics, and it can also work as a supporting textbook for a mathematically orientated master level course in spatial statistics."- Ingelin Steinsland, Norwegian University of Science and Technology"Theory of Spatial Statistics: A Concise Introduction is an excellent introductory resource to all three subfields in spatial statistics: geostatistics, areal data, and point processes. The book is well-organized and self-contained, covering the key knowledge of spatial statistics in a unified manner. It describes the mathematical foundations of the related statistical theory with rigorous proofs. Each chapter contains detailed illustrative examples using R packages to exemplify the methodologies applied to some well-known data sets. The book is suitable as a textbook for both graduate and advanced undergraduate students who want to learn the basis of the fast-growing areas of spatial statistics. I like the idea of providing exercises and detailed solutions so that readers can assess their learning outcomes. This book will also be of interest to practitioners of applied statistics from various disciplines as a reference book."- Yang Li, University of Minnesota Duluth"This text provides an excellent introduction to spatial statistics, including some important theoretical results, as well as practical implementation of the methodologies discussed. The modeling approaches are naturally separated into three groups depending on the type of data at hand, i.e., gridded, area unit and mapped point pattern data. The author has managed to incorporate in the text the most commonly used approaches in the literature, along with their corresponding applications. One particularly useful feature is the illustration of R packages to fit these models. Moreover, the inclusion of solutions to theoretical problems offers a nice resource to refer to and utilize in teaching graduate courses on spatial statistics and point processes. In addition, the theoretical results presented make for a nice blend between theory and application. Overall, the book is well written and will be a welcomed addition to the library of any researcher in spatial statistics."- Athanasios (Sakis) Christou Micheas, University of Missouri-Columbia"This book surveys the main topics in spatial statistics, including modeling random fields, variogram estimation, hierarchical models, and spatial point processes...It is amazing how much information van Lieshout is able to convey so concisely and compactly. She is simply masterful at explaining very difficult, intricate, and important concepts, models and statistical methods in an eloquent way...The chapters are wonderfully well organized and cover an ideal list of core topics in the statistical analysis of the most common and important forms of spatial data. Perhaps the best part of the book are the worked examples, which aid the reader new to this material and help crystalize what these statistical models and methods are prescribing...It is clear that an enormous amount of effort went into these worked examples, though as with the theoretical topics, van Lieshout explains everything so clearly and concisely that she makes the applications and R coding look easy, and in some cases almost trivial...(The book) is a remarkable fusion of the most important topics in the field, both theoretical and applied, presented beautifully and eloquently with the utmost care and precision, and so concisely that it all fits into a small handbook. I would strongly recommend this book for anyone teaching a one-semester graduate level course in spatial statistics."- Frederic P. Schoenberg, University of California at Los Angeles"This book provides a concise and readable introduction to the three main areas of spatial statistics: random fields, areal data and spatial point processes. Although the focus is on the basic underlying theory, extensive analyses of real data are provided including R code. Suitable as a text or for self-study, each major chapter includes exercises and solutions. A valuable resource for students and researchers in statistics and related fields looking to learn some of the basic theory underlying spatial statistics."- Michael Stein, University of Chicago"The book is a concise introduction to spatial statistics mostly from a mathematical point of view. It devotes a chapter to each of the main classes of spatial statistics settings, point referenced data and interpolation, areal data and spatial point processes. Each chapter contain all the main definitions and theorem (with proofs) for relevant models, and some of the inference methods for each of these spatial statistics settings. The chapters end with worked through R-examples and nice and useful pointers to the literature. The book expect the reader to be both mathematical and statistical mature, and most examples are mathematical. I think this can be a nice introduction and reference book for PhD students specializing in spatial statics, and it can also work as a supporting textbook for a mathematically orientated master level course in spatial statistics."- Ingelin Steinsland, Norwegian University of Science and Technology"Theory of Spatial Statistics: A Concise Introduction is an excellent introductory resource to all three subfields in spatial statistics: geostatistics, areal data, and point processes. The book is well-organized and self-contained, covering the key knowledge of spatial statistics in a unified manner. It describes the mathematical foundations of the related statistical theory with rigorous proofs. Each chapter contains detailed illustrative examples using R packages to exemplify the methodologies applied to some well-known data sets. The book is suitable as a textbook for both graduate and advanced undergraduate students who want to learn the basis of the fast-growing areas of spatial statistics. I like the idea of providing exercises and detailed solutions so that readers can assess their learning outcomes. This book will also be of interest to practitioners of applied statistics from various disciplines as a reference book."- Yang Li, University of Minnesota Duluth"This text provides an excellent introduction to spatial statistics, including some important theoretical results, as well as practical implementation of the methodologies discussed. The modeling approaches are naturally separated into three groups depending on the type of data at hand, i.e., gridded, area unit and mapped point pattern data. The author has managed to incorporate in the text the most commonly used approaches in the literature, along with their corresponding applications. One particularly useful feature is the illustration of R packages to fit these models. Moreover, the inclusion of solutions to theoretical problems offers a nice resource to refer to and utilize in teaching graduate courses on spatial statistics and point processes. In addition, the theoretical results presented make for a nice blend between theory and application. Overall, the book is well written and will be a welcomed addition to the library of any researcher in spatial statistics."- Athanasios (Sakis) Christou Micheas, University of Missouri-Columbia"This book surveys the main topics in spatial statistics, including modeling random fields, variogram estimation, hierarchical models, and spatial point processes...It is amazing how much information van Lieshout is able to convey so concisely and compactly. She is simply masterful at explaining very difficult, intricate, and important concepts, models and statistical methods in an eloquent way...The chapters are wonderfully well organized and cover an ideal list of core topics in the statistical analysis of the most common and important forms of spatial data. Perhaps the best part of the book are the worked examples, which aid the reader new to this material and help crystalize what these statistical models and methods are prescribing...It is clear that an enormous amount of effort went into these worked examples, though as with the theoretical topics, van Lieshout explains everything so clearly and concisely that she makes the applications and R coding look easy, and in some cases almost trivial...(The book) is a remarkable fusion of the most important topics in the field, both theoretical and applied, presented beautifully and eloquently with the utmost care and precision, and so concisely that it all fits into a small handbook. I would strongly recommend this book for anyone teaching a one-semester graduate level course in spatial statistics."- Frederic P. Schoenberg, University of California at Los AngelesTable of Contents1. Introduction. 2. Random field modelling and interpolation. 3. Models and inference for areal unit data. 4. Spatial point processes. Appendix: Solutions to theoretical exercises

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The origami introduced in this book is based on simple techniques. Some were previously known by origami artists and some were discovered by the author. Curved-Folding Origami Design shows a way to explore new area of origami composed of curved folds. Each technique is introduced in a step-by-step fashion, followed by some beautiful artwork examples. A commentary explaining the theory behind the technique is placed at the end of each chapter.Features Explains the techniques for designing curved-folding origami in seven chapters Contains many illustrations and photos (over 140 figures), with simple instructions Contains photos of 24 beautiful origami artworks, as well as their crease patterns Some basic theories behind the techniques are introduced

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Book SynopsisMultilevel modelling is a data analysis method that is frequently used to investigate hierarchal data structures in educational, behavioural, health, and social sciences disciplines. Multilevel data analysis exploits data structures that cannot be adequately investigated using single-level analytic methods such as multiple regression, path analysis, and structural modelling. This text offers a comprehensive treatment of multilevel models for univariate and multivariate outcomes. It explores their similarities and differences and demonstrates why one model may be more appropriate than another, given the research objectives. New to this edition: An expanded focus on the nature of different types of multilevel data structures (e.g., cross-sectional, longitudinal, cross-classified, etc.) for addressing specific research goals; Varied modelling methods for examining longitudinal data including random-effect and fixed-effect approaches; <Trade Review"Developing a basic modeling strategy that researchers can follow to investigate multilevel data structures can be challenging. Heck and Thomas have once again presented a must-have reference book to get the job done. This edition’s use of four different software packages and additional easy-to-follow illustrative examples enhance what was already a superb resource for both students and researchers." – George A. Marcoulides, University of California, Santa Barbara, USA Table of ContentsPreface 1. Introduction 2. Getting Started with Multilevel Analysis 3. Multilevel Regression Models 4. Extending the Two-Level Regression Model 5. Methods for Examining Individual and Organizational Change 6. Multilevel Models with Categorical Variables 7. Multilevel Structural Equation Variables 8. Multilevel Latent Growth and Mixture Models 9. Data Consideration in Examining Multilevel Models

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Book SynopsisAn Introduction to Economic Dynamics provides a framework for students to appreciate and understand the basic intuition behind economic models and to experiment with those models using simulation techniques in MATLAB. This book goes beyond the often-limited scope of other texts on economic models, which have largely focused on elucidating static equilibrium models. Comparative static analysis inhibits students from asking how the equilibrium position is achieved from an initial out-of-equilibrium position and limits their understanding of the dynamics that underlie such analysis. In this textbook, readers are introduced to ten well-established macroeconomic models including Keynesian multiplier models, Samuelson's multiplier and Solow's growth model and guided through the dynamical systems behind each model. Every chapter begins with an overview of the economic problem which the model is designed to help solve followed by an explanation of the mathematics of the moTrade Review "This book is a welcome addition to the literature on economic dynamics. Its clear writing style and the emphasis on coding using MATLAB® make it a compelling text for introducing undergraduate economics students to stability issues, cycles, and growth. The emphasis on both standard models like the Solow growth model and less standard ones such as the Goodwin growth cycle appeal to a broad spectrum of economists in the profession, and the highly competent authors have put years of experience with the material into a highly accessible textbook. I highly recommend the book to anyone willing to incorporate numerical methods into macroeconomic courses."Daniele Tavani, Associate Professor, Colorado State UniversityTable of Contents1. Introduction to Economic Dynamics 2. The Cobweb Model 3. Expectation Dynamics in the Cobweb Model 4. Keynesian Multiplier Model 5. The IS/LM Model 6. Debt, Deficit and Stabilization Policy 7. Expectation Dynamics and Hyperinflation 8. The Dornbusch Exchange Rate Overshooting Model 9. The Solow-Swan Growth Model 10. An Endogenous Growth Model 11. Business Cycles I: Samuelson’s Multiplier-Accelerator Model 12. Business Cycles II: The Real Business Cycle Model Appendix A. Difference and Differential Equations Appendix B. Matlab Codes

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Book SynopsisThis practical workbook assists school-based mentors of mathematics teachers to develop their mentoring skills, offering mentors the essential guidance needed in order to effectively support trainee or newly qualified secondary mathematics teachers, as well as in their overall career development. Offering tried and tested strategies based on research and evidence, Mentoring Mathematics Teachers in the Secondary School covers the knowledge, skills and understanding every mentor needs and offers practical tools such as lesson plans and feedback sheets, observation sheets, case studies, sample dialogues and other learning activities and exercises.Together with analytical tools for self-evaluation, this book is a vital source of support and inspiration for all those involved in developing the next generation of outstanding mathematics teachers, whether in teacher training or as newly qualified or early-career teachers. Key topics explored include:Roles and reTable of ContentsChapter 1 Models of Mentoring Chapter 2 The role of mentoring when learning through experience Chapter 3 Why Mentoring Chapter 4 Developing Reflective Practice Chapter 5 Understanding yourself as a mathematics mentor Chapter 6 Exploring the knowledge a mathematics mentor needs Chapter 7 Navigating the roles and responsibilities of a maths mentor Chapter 8 Supporting your beginning teacher Chapter 9 Observing a lesson Chapter 10 Effective professional dialogue Chapter 11 Developing a Mentoring Mathematics Department Chapter 12 The Building Blocks Chapter 13 Mathematical Misconceptions Chapter 14 Developing Lesson Planning Chapter 15 Teaching the Lesson Chapter 16 Developing understanding of assessment, marking and the use of data Chapter 17 Handing the baton on

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Book SynopsisRevised to reflect recent developments in the field, Phase Transformation in Metals and Alloys, Fourth Edition, continues to be the most authoritative and approachable resource on the subject. It supplies a comprehensive overview of specific types of phase transformations, supplemented by practical case studies of engineering alloys. The book's unique presentation links a basic understanding of theory with application in a gradually progressive yet exciting manner. Based on the authors' teaching notes, the text takes a pedagogical approach and provides examples for applications and problems that can be readily used for exercises.NEW IN THE FOURTH EDITION 40% of the figures and 30% of the text Insights provided by numerical modelling techniques such as ab initio, phase field, cellular automaton, and molecular dynamics Insights from the application of advanced experimental techniques, such as hTable of Contents1. Thermodynamics and Phase Diagrams 2. Diffusion 3. Crystal Interfaces and Microstructure 4. Solidification 5. Diffusional Transformations in Solids 6. Diffusionless Martensitic Transformations

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Book SynopsisThe photon, an abstract concept belonging to a global vacuum, only manifests itself duringinteraction with matter. Fundamentals of Photon Physics describes the richly faceted, basic theoryof photon-matter interaction, selecting a wide number of topics. Together with the author's bookLight -- The Physics of the Photon (CRC, 2014), both written on a scholarly level, the reader isgiven a comprehensive exposition of photon wave mechanics, quantum optics and quantumelectrodynamics (QED).Divided into 10 parts, the book begins by exploring the relation between photon wave mechanicsand quantum field theory. It then describes the theories of zero- and one-photon states andthat of bi-photons. After discussing conservation laws, Lagrangian formulations, geometricphase and topology, the author turns towards the theory of photon scattering, emphasizing adensity matrix operator approach and the role of microscopic extinction th

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Book SynopsisUnderstanding Regression Analysis unifies diverse regression applications including the classical model, ANOVA models, generalized models including Poisson, Negative binomial, logistic, and survival, neural networks, and decision trees under a common umbrella -- namely, the conditional distribution model. It explains why the conditional distribution model is the correct model, and it also explains (proves) why the assumptions of the classical regression model are wrong. Unlike other regression books, this one from the outset takes a realistic approach that all models are just approximations. Hence, the emphasis is to model Nature's processes realistically, rather than to assume (incorrectly) that Nature works in particular, constrained ways.Key features of the book include: Numerous worked examples using the R software Key points and self-study questions displayed just-in-time within chapters <Trade Review"...The authors suggest their book is suitable for those who are “research-oriented”, regardless of any prior advanced training in statistics...I particularly like the emphasis on assumptions. Rather than discuss regression in idealized terms, Westfall and Arias are upfront about why assumptions are often wrong in practice, and what an analyst can do about violations. These discussions are woven into many of the chapters, and in some cases, they are featured in stand-alone chapters...I am a fan of learning statistics by doing, so the large amount of R code woven into the book’s chapters and the hands-on exercises at the end of each chapter are valuable and a welcomed feature of the book...To me, this textbook would be most suitable for a one-semester survey course in statistical methods for students outside of biostatistics or statistics. A motivated student could even use this book for self-study...Overall, I believe this is a worthwhile addition to the literature."- Ryan Andrews, ISCB News, June 2021 Table of Contents1. Introduction to Regression Models 2. Estimating Regression Model Parameters3. The Classical Model and Its Consequences4. Evaluating Assumptions5. Transformations6. The Multiple Regression Model7. Multiple Regression from the Matrix Point of View8. R-squared, Adjusted R-Squared, the F Test, and Multicollinearity9. Polynomial Models and Interaction (Moderator) Analysis10. ANOVA, ANCOVA, and Other Applications of Indicator Variables11. Variable Selection12. Heteroscedasticity and Non-independence13. Models for Binary, Nominal, and Ordinal Response Variables14. Models for Poisson and Negative Binomial Response15. Censored Data Models16. Outliers, Identification, Problems, and Remedies (Good and Bad)17. Neural Network Regression 18. Regression Trees19. Bookend

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Book SynopsisWithout question, statistics is one of the most challenging courses for students in the social and behavioral sciences. Enrolling in their first statistics course, students are often apprehensive or extremely anxious toward the subject matter. And while IBM SPSS is one of the more easy-to-use statistical software programs available, for anxious students who realize they not only have to learn statistics but also new software, the task can seem insurmountable. Keenly aware of studentsâ anxiety with statistics (and the fact that this anxiety can affect performance), Ronald D. Yockey has written SPSS Demystified: A Simple Guide and Reference, now in its fourth edition. Through a comprehensive, step-by-step approach, this text is consistently and specifically designed to both alleviate anxiety toward the subject matter and build a successful experience analyzing data in SPSS . Topics covered in the text are appropriate for most introductory and intermediate statistics and research methods courses.Key features of the text:â Step-by-step instruction and screenshotsâ Designed to be hands-on with the user performing the analyses alongside the text on their computer as they read through each chapterâ Call-out boxes provided, highlighting important information as appropriateâ SPSS output explained, with written results provided using the popular, widely recognized APA formatâ End-of-chapter exercises included, allowing for additional practiceâ SPSS data sets available on the publisherâs websiteNew to the Fourth Edition:â Fully updated to SPSS 28â Updated screenshots in full color to reflect changes in the SPSS software system (version 28)â Exercises updated with up-to-date examplesâ Exact p-values provided (consistent with APA recommendations)Table of ContentsPart I: Introduction to SPSS, Descriptive Statistics, Graphical Procedures of Data, and Reliability Using Coefficient Alpha 1. Introduction to SPSS, 2. Descriptive Statistics: Frequencies, Measures of Central Tendency, and Measures of Variability, 3. Graphical Procedures, 4. Reliability (As Measured by Coefficient Alpha); Part II: Inferential Statistics 5. The One-Sample t Test, 6. The Independent-Samples t Test, 7. The Dependent-Samples t Test, 8. The One-Way Between Subjects Analysis of Variance (ANOVA), 9. The Two-Way Between Subjects Analysis of Variance (ANOVA), 10. The One-Way Within Subjects Analysis of Variance (ANOVA), 11. The One-Between–One-Within Subjects Analysis of Variance (ANOVA), 12. The Pearson r Correlation Coefficient, 13. Simple Linear Regression, 14. Multiple Linear Regression, 15. The Chi-Square Goodness of Fit Test, 16. The Chi-Square Test of Independence; Appendix A. Data Transformations and Other Procedures Appendix B. Solutions to Chapter Exercises

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Book SynopsisThis reference book describes the exact solutions of the following types of mathematical equations:? Algebraic and Transcendental Equations ? Ordinary Differential Equations ? Systems of Ordinary Differential Equations ? First-Order Partial Differential Equations ? Linear Equations and Problems of Mathematical Physics ? Nonlinear Equations of Mathematical Physics ? Systems of Partial Differential Equations ? Integral Equations ? Difference and Functional Equations ? Ordinary Functional Differential Equations ? Partial Functional Differential EquationsThe book delves into equations that find practical applications in a wide array of natural and engineering sciences, including the theory of heat and mass transfer, wave theory, hydrodynamics, gas dynamics, combustion theory, elasticity theory, general mechanics, theoretical physics, nonlinear optics, biology, chemical engineering sciences, ecology, and more. Most of these equations

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Book SynopsisReliability and maintenance modeling with optimization is the most fundamental and interdisciplinary research area that can be applied to every technical and management field. Reliability and Maintenance Modeling with Optimization: Advances and Applications aims at providing the most recent advances and achievements in reliability and maintenance.The book discusses replacement, repair, and inspection, offers estimation and statistical tests, covers accelerated life testing, explores warranty analysis manufacturing, and includes service reliability.The targeted readers are researchers interested in reliability and maintenance engineering. The book can serve as supplemental reading in professional seminars for engineers, designers, project managers, and graduate students.Table of Contents1. Nine Memorial Research Works. 2. Replacement First and Last Policies with Random Times for Redundant Systems. 3. Backup Policies with Random Data Updates. 4. An Optimal Age Replacement Policy for a Reparable System Consisting of Main and Auxiliary Subsystems. 5. Extended replacement policy in damage models. 6. Optimal Checking Policy for a Server System with Cyber Attack. 7. Reliability Analysis of Congestion Control Scheme with Code Error Correction Methods. 8. The Optimal Design of Consecutive-k Systems. 9. Optimal Social Infrastructure Maintenance Models. 10. Optimal Maintenance Problem with OSS-Oriented EVM for OSS Project. 11. Reliability Assessment Model Based on Wiener Process Considering Network Environment for Edge Computing. 12. Approximated Estimation of Software Target Failure Measures Conforming IEC 61508. 13. Phase-Type Expansion of Markov Regenerative Processes and Its Application to Reliability Problems. 14. A Hybrid Model Fitting Framework considering Accuracy and Performance. 15. Alternating α-Series Process. 16. Optimum Staggered Testing Strategy for Redundant Safety Instrumented Systems with Different Testing Intervals. 17. Modules Of Multi-State Systems - Introduction To Three Modules Theorem. 18. A postponed repair model for a mission-based system based on a three-stage failure process.

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Book SynopsisReliability and maintenance modeling with optimization is the most fundamental and interdisciplinary research area that can be applied to every technical and management field. Reliability and Maintenance Modeling with Optimization: Advances and Applications aims at providing the most recent advances and achievements in reliability and maintenance.The book discusses replacement, repair, and inspection, offers estimation and statistical tests, covers accelerated life testing, explores warranty analysis manufacturing, and includes service reliability.The targeted readers are researchers interested in reliability and maintenance engineering. The book can serve as supplemental reading in professional seminars for engineers, designers, project managers, and graduate students.

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Book SynopsisInterpreting Basic Statistics gives students valuable practice in interpreting statistical reporting as it actually appears in peer-reviewed journals. Features of the ninth edition: Covers a broad array of basic statistical concepts, including topics drawn from the New Statistics Up-to-date journal excerpts reflecting contemporary styles in statistical reporting Strong emphasis on data visualization Ancillary materials include data sets with almost two hours of accompanying tutorial videos, which will help students and instructors apply lessons from the book to real-life scenarios About this book Each of the 63 exercises in the book contain three central components: 1) an introduction to a statistical concept, 2) a brief excerpt from a published research article that uses the statistical concept, and 3) a set of questions (with answers) that guides students into deeper learning about the concept. The questioTrade ReviewThe 9th edition of this workbook is an engaging and invaluable tool for teaching students how to interpret statistics as they encounter them in articles written within the psychological, social, and health sciences. By choosing article excerpts that are sure to interest undergraduate readers, the authors may entice those many students who say they fear numbers into taking their first halting steps toward understanding. By providing clear and concise descriptions of key concepts and posing astute questions, the workbook demystifies the scientific enterprise and explains its importance for comprehending the social world. And by starting with the simplest ideas and gradually, step by step, moving toward a more complex understanding, the authors gently lead students on a learning journey that is sure to be deeply informative – and maybe even fun! -- Dan P. McAdams, the Henry Wade Rogers Professor of Psychology, Northwestern University, USA"This introduction to reading and understanding statistics is very basic and easy to understand, but at the same time it is scientifically oriented, contemporary in outlook and forward looking in methodology. It points students in exactly the right direction, emphasizing meaningful interpretation of scientific results over recitation of cookbook formulas. Students will come away with the tools they need for comprehending graphical analysis, effect size, and statistical power." -- Eric Turkheimer, PhD, Hugh Scott Hamilton Professor, Department of Psychology, University of Virginia, USAThe ninth edition of this workbook is an engaging and invaluable tool for teaching students how to interpret statistics as they encounter them in articles written within the psychological, social, and health sciences. By choosing article excerpts that are sure to interest undergraduate readers, the authors may entice those many students who say they fear numbers into taking their first halting steps toward understanding. By providing clear and concise descriptions of key concepts and posing astute questions, the workbook demystifies the scientific enterprise and explains its importance for comprehending the social world. And by starting with the simplest ideas and gradually, step by step, moving toward a more complex understanding, the authors gently lead students on a learning journey that is sure to be deeply informative – and maybe even fun! -- Dan P. McAdams, the Henry Wade Rogers Professor of Psychology, Northwestern University, USA"This introduction to reading and understanding statistics is very basic and easy to understand, but at the same time it is scientifically oriented, contemporary in outlook and forward looking in methodology. It points students in exactly the right direction, emphasizing meaningful interpretation of scientific results over recitation of cookbook formulas. Students will come away with the tools they need for comprehending graphical analysis, effect size, and statistical power." -- Eric Turkheimer, PhD, Hugh Scott Hamilton Professor, Department of Psychology, University of Virginia, USATable of Contents1. Basic Descriptions of the Data: Measurement and Frequency 2. Describing the Data 3. Displaying Data: Visualizing What is There 4. Finding Relationships: Association and Prediction 5. Group Differences with Normal Distributions 6. Nonparametric Tests for Group Differences 7. Test Construction

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Book SynopsisDeveloped from the authorâs course on advanced mechanics of composite materials, Finite Element Analysis of Composite Materials with Abaqus shows how powerful finite element tools tackle practical problems in the structural analysis of composites. This Second Edition includes two new chapters on Fatigue and Abaqus Programmable Features as well as a major update of chapter 10 Delaminations and significant updates throughout the remaining chapters. Furthermore, it updates all examples, sample code, and problems to Abaqus 2020. Unlike other texts, this one takes theory to a hands-on level by actually solving problems. It explains the concepts involved in the detailed analysis of composites, the mechanics needed to translate those concepts into a mathematical representation of the physical reality, and the solution of the resulting boundary value problems using Abaqus. The reader can follow a process to recreate every example using Abaqus graphical user interfacTable of Contents1. Mechanics of Orthotropic Materials. 2. Introduction to Finite Element Analysis. 3. Elasticity and Strength of Laminates. 4. Buckling. 5. Free Edge Stresses. 6. Computational Micromechanics. 7. Viscoelasticity. 8. Continuum Damage Mechanics. 9. Discrete Damage Mechanics. 10. Delaminations. 11. Fatigue. 12. Abaqus Programmable Features.

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Book SynopsisKnot theory is a fascinating mathematical subject, with multiple links to theoretical physics. This enyclopedia is filled with valuable information on a rich and fascinating subject. Ed Witten, Recipient of the Fields MedalI spent a pleasant afternoon perusing the Encyclopedia of Knot Theory. It's a comprehensive compilation of clear introductions to both classical and very modern developments in the field. It will be a terrific resource for the accomplished researcher, and will also be an excellent way to lure students, both graduate and undergraduate, into the field. Abigail Thompson, Distinguished Professor of Mathematics at University of California, Davis Knot theory has proven to be a fascinating area of mathematical research, dating back about 150 years. Encyclopedia of Knot Theory provides short, interconnected articles on a variety of active areas in knot theory, and includes beautiful pictures, deeTrade Review"Knot theory is a fascinating mathematical subject, with multiple links to theoretical physics. This enyclopedia is filled with valuable information on a rich and fascinating subject."– Ed Witten, Recipient of the Fields Medal"I spent a pleasant afternoon perusing the Encyclopedia of Knot Theory. It’s a comprehensive compilation of clear introductions to both classical and very modern developments in the field. It will be a terrific resource for the accomplished researcher, and will also be an excellent way to lure students, both graduate and undergraduate, into the field." – Abigail Thompson, Distinguished Professor of Mathematics at University of California, Davis "An encyclopedia is expected to be comprehensive, and to include independent expository articles on many topics. The Encyclopedia of Knot Theory is all this. This book will be an excellent introduction to topics in the field of knot theory for advanced undergraduates, graduate students, and researchers interested in knots from many directions."– MAA Reviews"Knot theory is an area of mathematics that requires no introduction, and while this massive tome is certainly no introductory text, it does give a panoramic — and, well, encyclopaedic — view of this vast subject.[. . . ] A book with such an ambitious remit is bound to contain omissions and oddities. [. . .] But this is a small point compared to what has been achieved by this encyclopaedia, which would make a fine addition to any personal or departmental library, or to a departmental coffee table."– London Mathematical SocietyThe Encyclopedia of Knot Theory is close to 1000 pages, and every section, article, paragraph, and sentence inspires the reader to want to learn more knot theory. A wonderful attribute of this text is the reference section at the end of each article as opposed to the end of the book. This allows readers to highlight different sources that will allow them to dive deeper into the topic of that section. [. . .] And while it is nearly impossible to include discussions of every branch of the knot theorytree, the editors made a great choice to focus on current topics showing how the area is still a living subject. [. . .] As a knot theory enthusiast, I truly enjoyed reading about topics I was more familiar with while also exploring topics that were new to me. As an educator, I am excited to share this book with my students and encourage them to read more articles on the topics. Some of the articles in the book include thoughtful open questions for researchers in the field to enjoy, while also providing background for anyone new to knot theory research to use as a foundation. All in all, I loved this text.– American Mathematical MonthlyTable of ContentsI Introduction and History of Knots. 1. Introduction to Knots. II Standard and Nonstandard Representations of Knots. 2. Link Diagrams. 3. Gauss Diagrams. 4. DT Codes. 5. Knot Mosaics. 6. Arc Presentations of Knots and Links. 7. Diagrammatic Representations of Knots and Links as Closed Braids. 8. Knots in Flows. 9. Multi-Crossing Number of Knots and Links. 10. Complementary Regions of Knot and Link Diagrams. 11. Knot Tabulation. III Tangles. 12. What Is a Tangle? 13. Rational and Non-Rational Tangles. 14. Persistent Invariants of Tangles. IV Types of Knots. 15. Torus Knots. 16. Rational Knots and Their Generalizations. 17. Arborescent Knots and Links. 18. Satellite Knots. 19. Hyperbolic Knots and Links. 20. Alternating Knots. 21. Periodic Knots. V Knots and Surfaces. 22. Seifert Surfaces and Genus. 23. Non-Orientable Spanning Surfaces for Knots. 24. State Surfaces of Links. 25. Turaev Surfaces. VI Invariants Defined in Terms of Min and Max. 26. Crossing Numbers. 27. The Bridge Number of a Knot. 28. Alternating Distances of Knots. 29. Superinvariants of Knots and Links. VII Other Knotlike Objects. 30. Virtual Knot Theory. 31. Virtual Knots and Surfaces. 32. Virtual Knots and Parity. 33. Forbidden Moves,Welded Knots and Virtual Unknotting. 34. Virtual Strings and Free Knots. 35. Abstract and Twisted Links. 36. What Is a Knotoid? 37. What Is a Braidoid? 38. What Is a Singular Knot? 39. Pseudoknots and Singular Knots. 40. An Introduction to the World of Legendrian and Transverse Knots 41. Classical Invariants of Legendrian and Transverse Knots. 42. Ruling and Augmentation Invariants of Legendrian Knots. VIII Higher Dimensional Knot Theory. 43. Broken Surface Diagrams and Roseman Moves. 44. Movies and Movie Moves. 45. Surface Braids and Braid Charts. 46. Marked Graph Diagrams and Yoshikawa Moves. 47. Knot Groups. 48. Concordance Groups. IX Spatial Graph Theory. 49. Spatial Graphs. 50. A Brief Survey on Intrinsically Knotted and Linked Graphs. 51. Chirality in Graphs. 52. Symmetries of Graphs Embedded in Sᶟ and Other 3-Manifolds. 53. Invariants of Spatial Graphs. 54. Legendrian Spatial Graphs. 55. Linear Embeddings of Spatial Graphs. 56. Abstractly Planar Spatial Graphs. X Quantum Link Invariants. 57. Quantum Link Invariants. 58. Satellite and Quantum Invariants. 59. Quantum Link Invariants: From QYBE and Braided Tensor Categories. 60. Knot Theory and Statistical Mechanics. XI Polynomial Invariants. 61. What Is the Kauffman Bracket? 62. Span of the Kauffman Bracket and the Tait Conjectures. 63. Skein Modules of 3-Manifold. 64. The Conway Polynomial. 65. Twisted Alexander Polynomials. 66. The HOMFLYPT Polynomial. 67. The Kauffman Polynomials. 68. Kauffman Polynomial on Graphs. 69. Kauffman Bracket Skein Modules of 3-Manifolds. XII Homological Invariants. 70. Khovanov Link Homology. 71. A Short Survey on Knot Floer Homolog. 72. An Introduction to Grid Homology. 73. Categorification. 74. Khovanov Homology and the Jones Polynomial. 75. Virtual Khovanov Homology. XIII Algebraic and Combinatorial Invariants. 76. Knot Colorings. 77. Quandle Cocycle Invariants. 78. Kei and Symmetric Quandles. 79. Racks, Biquandles and Biracks. 80. Quantum Invariants via Hopf Algebras and Solutions to the Yang-Baxter Equation. 81. The Temperley-Lieb Algebra and Planar Algebras. 82. Vassiliev/Finite Type Invariants. 83. Linking Number and Milnor Invariants. XIV Physical Knot Theory. 84. Stick Number for Knots and Links. 85. Random Knots. 86. Open Knots. 87. Random and Polygonal Spatial Graphs. 88. Folded Ribbon Knots in the Plane. XV Knots and Science. 89. DNA Knots and Links. 90. Protein Knots, Links, and Non-Planar Graphs. 91. Synthetic Molecular Knots and Links.

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Book SynopsisMachine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers, notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon, as the jargon and coding requirements may seem out-of-reach. Machine learning for factor investing: Python version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics.The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns, Bayesian additivTrade Review"Machine learning is considered promising for investment management applications, yet the associated low signal to noise ratio presents a high bar for improving on the incumbent quant asset management tooling. The book of Coqueret and Guida is a treat for those who do not want to lose sight of the machine learning forest for the trees. Whether you are an academic scholar or a finance practitioner, you will learn just what you need to rigorously investigate machine learning techniques for factor investing applications, along with plenty of useful code snippets." -Harald Lohre, Executive Director of Research at Robeco and Honorary Researcher at Lancaster University Management School"Written by two experts on quantitative finance, this book covers everything from basic materials to advanced techniques in the field of quantitative investment strategies: data processing, alpha signal generation, portfolio optimization, backtesting and performance evaluation. Concrete examples related to asset management problems illustrate each machine learning technique, such as neural network, lasso regression, autoencoder or reinforcement learning. With more than 20 coding exercises and solutions provided in Python, this publication is a must for both students, academics and professionals who are looking for an up-to-date technical exposition on quantitative asset management from basic smart beta portfolios to enhanced alpha strategies including factor investing."-Thierry Roncalli, Head of Quantitative Portfolio Strategy at Amundi Institute, Amundi Asset ManagementTable of ContentsPart 1. Introduction 1. Notations and data 2. Introduction 3. Factor investing and asset pricing anomalies 4. Data preprocessing Part 2. Common supervised algorithms 5. Penalized regressions and sparse hedging for minimum variance portfolios 6. Tree-based methods 7. Neural networks 8. Support vector machines 9. Bayesian methods Part 3. From predictions to portfolios 10. Validating and tuning 11. Ensemble models 12. Portfolio backtesting Part 4. Further important topics 13. Interpretability 14. Two key concepts: causality and non-stationarity 15. Unsupervised learning 16. Reinforcement learning Part 5. Appendix 17. Data description 18. Solutions to exercises

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Book SynopsisHigh-Performance Computing (HPC) delivers higher computational performance to solve problems in science, engineering and finance. There are various HPC resources available for different needs, ranging from cloud computingâ that can be used without much expertise and expense â to more tailored hardware, such as Field-Programmable Gate Arrays (FPGAs) or D-Waveâs quantum computer systems. High-Performance Computing in Finance is the first book that provides a state-of-the-art introduction to HPC for finance, capturing both academically and practically relevant problems. Table of ContentsPart I: Computationally Expensive Problems in the Financial Industry 1. Computationally Expensive Problems in Investment Banking 2. Using Market Sentiment to Enhance Second-Order Stochastic Dominance Trading Models 3. The Alpha Engine: Designing an Automated Trading Algorithm 4. Portfolio Liquidation and Ambiguity Aversion 5. Challenges in Scenario Generation: Modeling Market and Non-Market Risks in Insurance Part II: Numerical Methods in Financial High-Performance Computing (HPC) 6. Finite Difference Methods for Medium- and High-Dimensional Derivative Pricing PDEs 7. Multilevel Monte Carlo Methods for Applications in Finance 8. Fourier and Wavelet Option Pricing Methods 9. A Practical Robust Long-Term Yield Curve Model 10. Algorithmic Differentiation 11. Case Studies of Real-Time Risk Management via Adjoint Algorithmic Differentiation (AAD) 12. Tackling Reinsurance Contract Optimization by Means of Evolutionary Algorithms and HPC 13. Evaluating Blockchain Implementation of Clearing and Settlement at the IATA Clearing House Part III: HPC Systems: Hardware, Software, and Data with Financial Applications 14. Supercomputers 15. Multiscale Dataflow Computing in Finance 16. Manycore Parallel Computation 17. Practitioner’s Guide on the Use of Cloud Computing in Finance 18. Blockchains and Distributed Ledgers in Retrospective and Perspective 19. Optimal Feature Selection Using a Quantum Annealer

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Book SynopsisThis book is devoted to efficient pairing computations and implementations, useful tools for cryptographers working on topics like identity-based cryptography and the simplification of existing protocols like signature schemes.As well as exploring the basic mathematical background of finite fields and elliptic curves, Guide to Pairing-Based Cryptography offers an overview of the most recent developments in optimizations for pairing implementation. Each chapter includes a presentation of the problem it discusses, the mathematical formulation, a discussion of implementation issues, solutions accompanied by code or pseudocode, several numerical results, and references to further reading and notes. Intended as a self-contained handbook, this book is an invaluable resource for computer scientists, applied mathematicians and security professionals interested in cryptography.Table of ContentsIntroduction. Mathematical Background. Pairings. Pairing-Friendly Elliptic Curves. Miller's Algorithm. Arithmetic of Finite Fields. Final Exponentiation. Algorithms. Software Implementation. Hardware Implementation.

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Book SynopsisMedical Risk Prediction Models: With Ties to Machine Learning is a hands-on book for clinicians, epidemiologists, and professional statisticians who need to make or evaluate a statistical prediction model based on data. The subject of the book is the patientâs individualized probability of a medical event within a given time horizon. Gerds and Kattan describe the mathematical details of making and evaluating a statistical prediction model in a highly pedagogical manner while avoiding mathematical notation. Read this book when you are in doubt about whether a Cox regression model predicts better than a random survival forest.Features: All you need to know to correctly make an online risk calculator from scratch. Discrimination, calibration, and predictive performance with censored data and competing risks. R-code and illustrative examples. InterpreTrade Review"Two of the top researchers in the field of clinical prediction models have produced a highly innovative book that brings a very technical topic to public grasp by throwing out the formulas and just talking straight from the heart of practical experience. While clinicians and medical residents can now learn how to build, diagnose and validate risk models themselves, all public health researchers, old and new, will reap the benefits and enjoyment from reading this book."~Donna Ankerst, Technical University of Munich "Two of the top researchers in the field of clinical prediction models have produced a highly innovative book that brings a very technical topic to public grasp by throwing out the formulas and just talking straight from the heart of practical experience. While clinicians and medical residents can now learn how to build, diagnose and validate risk models themselves, all public health researchers, old and new, will reap the benefits and enjoyment from reading this book."~Donna Ankerst, Technical University of Munich "Overall, the book offers a well-written, complete and illustrative overview of clinical prediction models with clear stances and directions on the modelling methods, choices and strategies. I find this a very welcome and much needed addition to the literature because prediction is the backbone of medical decision-making; few books are dedicated to modelling strategies and artificial intelligence is ascending in medical research. I thereby highly recommend this book for anyone who would be interested in performing predictive modelling for prognostic or diagnostic research." -Evangelos I. Kritsotakis, International Society for Clinical Biostatistics, 72, 2021 Table of Contents Software. 2. I am going to make a prediction model. What do I need to know? 3. Regression model. 4. How should I prepare for modeling? 5. I am ready to build a prediction model. 7. Does my model predict accurately? 7. How do I decide between rival models? 8. Can't the computer just take care of all of this? 9. Things you might have expected in our book.

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Book SynopsisThis book addresses the topics related to artificial intelligence, the Internet of Things, blockchain technology, and machine learning. It brings together researchers, developers, practitioners, and users interested in cybersecurity and forensics. The first objective is to learn and understand the need for and impact of advanced cybersecurity and forensics and its implementation with multiple smart computational technologies. This objective answers why and how cybersecurity and forensics have evolved as one of the most promising and widely-accepted technologies globally and has widely-accepted applications. The second objective is to learn how to use advanced cybersecurity and forensics practices to answer computational problems where confidentiality, integrity, and availability are essential aspects to handle and answer. This book is structured in such a way so that the field of study is relevant to each readerâs major or interests. It aims to help each reader see the relevance of cybersecurity and forensics to their career or interests. This book intends to encourage researchers to develop novel theories to enrich their scholarly knowledge to achieve sustainable development and foster sustainability. Readers will gain valuable knowledge and insights about smart computing technologies using this exciting book.This book:â Includes detailed applications of cybersecurity and forensics for real-life problemsâ Addresses the challenges and solutions related to implementing cybersecurity in multiple domains of smart computational technologies â Includes the latest trends and areas of research in cybersecurity and forensicsâ Offers both quantitative and qualitative assessments of the topics Includes case studies that will be helpful for the researchersProf. Keshav Kaushik is Assistant Professor in the Department of Systemics, School of Computer Science at the University of Petroleum and Energy Studies, Dehradun, India.Dr. Shubham Tayal is Assistant Professor at SR University, Warangal, India.Dr. Akashdeep Bhardwaj is Professor (Cyber Security & Digital Forensics) at the University of Petroleum & Energy Studies (UPES), Dehradun, India.Dr. Manoj Kumar is Assistant Professor (SG) (SoCS) at the University of Petroleum and Energy Studies, Dehradun, India.Table of Contents1. Detection of Cross-Site Scripting and Phishing Website Vulnerabilities Using Machine Learning. 2. A Review: Security and Privacy Defensive Techniques for Cyber Security Using Deep Neural Networks (DNNs). 3. DNA-Based Cryptosystem for Connected Objects and IoT Security. 4. A Role of Digital Evidence: Mobile Forensics Data. 5. Analysis of Kernel Vulnerabilities Using Machine Learning. 6. Cyber Threat Exploitation and Growth during COVID-19 Times. 7. An Overview of the Cybersecurity in Smart Cities in the Modern Digital Age. 8. The Fundamentals and Potential for Cyber Security of Machine Learning in the Modern World. 9. Qualitative and Quantitative Evaluation of Encryption Algorithms. 10. Analysis and Investigation of Advanced Malware Forensics. 11. Network Intrusion Detection System Using Naïve Bayes Classification Technique for Anomaly Detection. 12. Data Security Analysis in Mobile Cloud Computing for Cyber Security. 13. A Comprehensive Review of Investigations of Suspects of Cyber Crimes. 14. Fault Analysis Techniques in Lightweight Ciphers for IoT Devices.

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Book SynopsisIntroduction to the Maths and Physics of Quantum Mechanics details the mathematics and physics that are needed to learn the principles of quantum mechanics.It provides an accessible treatment of how to use quantum mechanics and why it is so successful in explaining natural phenomena. This book clarifies various aspects of quantum physics such as why quantum mechanics equations contain I, the imaginary number?', Is it possible to make a transition from classical mechanics to quantum physics without using postulates?' and What is the origin of the uncertainty principle?'. A significant proportion of discussion is dedicated to the issue of why the wave function must be complex to properly describe our real world.The book also addresses the different formulations of quantum mechanics. A relatively simple introductory treatment is given for the standard Heisenberg matrix formulation and Schrodinger wave-function formulation and Feynman path integrals and second quantTable of ContentsChapter 1: Classical Physics. Chapter 2: The Crisis of Classical Mechanics. Chapter 3: From Classical to Quantum Physics. Chapter 4: Early Quantum Theory: Bohr's Atom. Chapter 5: Schrodinger Equation. Chapter 6: Matrices in Quantum Mechanics. References. Index.

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Book SynopsisProof techniques in cryptography are very difficult to understand, even for students or researchers who major in cryptography. In addition, in contrast to the excessive emphases on the security proofs of the cryptographic schemes, practical aspects of them have received comparatively less attention. This book addresses these two issues by providing detailed, structured proofs and demonstrating examples, applications and implementations of the schemes, so that students and practitioners may obtain a practical view of the schemes.Seong Oun Hwang is a professor in the Department of Computer Engineering and director of Artificial Intelligence Security Research Center, Gachon University, Korea. He received the Ph.D. degree in computer science from the Korea Advanced Institute of Science and Technology (KAIST), Korea. His research interests include cryptography, cybersecurity, networks, and machine learning.Intae Kim is an associate research fellow at the Institute of CybersTable of ContentsPart 1: Foundations of Cryptography. 1. History of Cryptography. 2. Overview of Provable Security. 3. Private Key Encryption. 4. Message Authentication Code. 5. Hash Function. 6. Public Key Cryptography. 7. Digital Signature. 8. Protocols. Part 2: Fundamentals of IBE. 9. First Provable IBE (Boneh-Franklin). 10. Selective Security IBE (Boneh-Boyen). 11. Tight Security IBE (Gentry IBE). 12. Dual System Encryption (Waters). 13. Hierarchical IBE (Allison-Waters). 14. Broadcast Encryption. 15. Identity Based Signature. 16. Transformation Techniques. Part 3: Advanced IBE. 17. Key Policy-Attribute Based Encryption. 18. Ciphertext Policy-Attribute Based Encryption. 19. Predicate Encryption. 20. Functional Encryption. 21. Searchable Encryption. Part 4: Future Cryptography. 22. Certificate Based Cryptography. 23. Certificate-Less Cryptography. 24. Homomorphic Encryption. 25. Post Quantum Cryptography. 26. Blockchain Applications.

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Book SynopsisPractical introduction to crime mapping and spatial data analysis using R and R Studio. Crime mapping and analysis of crime problems using spatially explicit data has become a central feature of law enforcement agencies across the world. Criminology degrees have begun to adapt their curriculums to foster the skills required for these jobs.Trade Review"I think overall the book is pitched perfectly and the step by step approach with code will act as an excellent training resources as well as reference guide.”-Ruth Weir, City, University of London"Overall, this is a great book! It is written in an accessible style, is up to date and covers the foundational material one would want a student to understand. As an experienced R user, I was delighted to learn something. Staying abreast of the fast-developing packages is nearly a full-time job, so I see this book as highly useful to many readers. The authors do a great job illustrating the main concepts of import but also pointing readers to places to follow up for more detailed treatments.”-Michael Townsley, Professor of Criminology and Criminal Justice, Griffith UniversityTable of Contents1. Producing your First Crime Map 2. Basic Geospatial Operations in R 3. Mapping Rates and Counts 4. Variations of Thematic Mapping 5. Basics of Cartographic Design: Elements of a Map 6. Time Matters 7. Spatial Point Patterns of Crime Events 8. Crime Along Spatial Networks 9. Spatial Dependence and Autocorrelation 10. Detecting Hot Spots and Repeats 11. Spatial Regression Models 12. Spatial Heterogeneity and Regression 13. Appendix: A Quick Intro to R and RStudio 14. Appendix B: Regression Analysis (A Refresher) 15. Appendix C: Sourcing Geographical Data for Crime Analysis

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Book SynopsisBig Data Systems encompass massive challenges related to data diversity, storage mechanisms, and requirements of massive computational power. Further, capabilities of big data systems also vary with respect to type of problems. For instance, distributed memory systems are not recommended for iterative algorithms. Similarly, variations in big data systems also exist related to consistency and fault tolerance. The purpose of this book is to provide a detailed explanation of big data systems. The book covers various topics including Networking, Security, Privacy, Storage, Computation, Cloud Computing, NoSQL and NewSQL systems, High Performance Computing, and Deep Learning. An illustrative and practical approach has been adopted in which theoretical topics have been aided by well-explained programming and illustrative examples.Key Features: Introduces concepts and evolution of Big Data technology. Illustrates examples Table of ContentsPreface Author Bios Acknowledgements List of Figures List of Tables Introduction to Big Data Systems 1.1 INTRODUCTION: REVIEW OF BIG DATA SYSTEMS1.2 UNDERSTANDING BIG DATA 1.3 TYPE OF DATA: TRANSACTIONAL OR ANALYTICAL1.4 REQUIREMENTS AND CHALLENGES OF BIG DATA 1.5 CONCLUDING REMARKS 1.6 FURTHER READING 1.7 EXERCISE QUESTIONS Architecture and Organization of Big Data Systems 2.1 ARCHITECTURE FOR BIG DATA SYSTEMS 2.2 ORGANIZATION OF BIG DATA SYSTEMS: CLUSTERS2.3 CLASSIFICATION OF CLUSTERS: DISTRIBUTED MEMORY VS. SHARED MEMORY2.4 CONCLUDING REMARKS 2.5 FURTHER READING 2.6 EXERCISE QUESTIONS Cloud Computing for Big Data 3.1 CLOUD COMPUTING 3.2 VIRTUALIZATION 3.3 PROCESSOR VIRTUALIZATION 3.4 CONTAINERIZATION 3.5 VIRTUALIZATION OR CONTAINERIZATION 3.6 FOG COMPUTING 3.7 EXAMPLES 3.8 CONCLUDING REMARKS 3.9 FURTHER READING 3.10 EXERCISE QUESTIONS HADOOP: An Efficient Platform for Storing and Processing Big Data 4.1 REQUIREMENTS FOR PROCESSING AND STORING BIG DATA 4.2 HADOOP - THE BIG PICTURE 4.3 HADOOP DISTRIBUTED FILE SYSTEM 4.4 MAPREDUCE 4.5 HBASE 4.6 CONCLUDING REMARKS 4.7 FURTHER READING 4.8 EXERCISE QUESTIONS Enhancements in Hadoop 5.1 ISSUES WITH HADOOP 5.2 YARN 5.3 PIG 5.4 HIVE 5.5 DREMEL 5.6 IMPALA 5.7 DRILL 5.8 DATA TRANSFER 5.9 AMBARI 5.10 CONCLUDING REMARKS 5.11 FURTHER READING 5.12 EXERCISE QUESTIONS Spark 6.1 LIMITATIONS OF MAPREDUCE 6.2 INTRODUCTION TO SPARK 6.3 SPARK CONCEPTS 6.4 SPARK SQL 6.5 SPARK MLLIB 6.6 STREAM BASED SYSTEM 6.7 SPARK STREAMING 6.8 CONCLUDING REMARKS 6.9 FURTHER READING 6.10 EXERCISE QUESTIONS NoSQL Systems 7.1 INTRODUCTION 7.2 HANDLING BIG DATA SYSTEMS - PARALLEL RDBMS 7.3 EMERGENCE OF NOSQL SYSTEMS 7.4 KEY-VALUE DATABASE 7.5 DOCUMENT-ORIENTED DATABASE 7.6 COLUMN-ORIENTED DATABASE 7.7 GRAPH DATABASE 7.8 CONCLUDING REMARKS 7.9 FURTHER READING 7.10 EXERCISE QUESTIONS NewSQL Systems 8.1 INTRODUCTION8.2 TYPES OF NEWSQL SYSTEMS 8.3 FEATURES 8.4 NEWSQL SYSTEMS: CASE STUDIES 8.5 CONCLUDING REMARKS 8.6 FURTHER READING8.7 EXERCISE QUESTIONS Networking for Big Data 9.1 NETWORK ARCHITECTURE FOR BIG DATA SYSTEMS9.2 CHALLENGES AND REQUIREMENTS 9.3 NETWORK PROGRAMMABILITY AND SOFTWARE DEFINED NETWORKING 9.4 LOW LATENCY AND HIGH SPEED DATA TRANSFER9.5 AVOIDING TCP INCAST - ACHIEVING LOW LATENCYAND HIGH THROUGHPUT 9.6 FAULT TOLERANCE9.7 CONCLUDING REMARKS 9.8 FURTHER READING 9.9 EXERCISE QUESTIONS Security for Big Data 10.1 INTRODUCTION 10.2 SECURITY REQUIREMENTS 10.3 SECURITY: ATTACK TYPES AND MECHANISMS 10.4 ATTACK DETECTION AND PREVENTION 10.5 CONCLUDING REMARKS 10.6 FURTHER READING 10.7 EXERCISE QUESTIONS Privacy for Big Data 11.1 INTRODUCTION 11.2 UNDERSTANDING BIG DATA AND PRIVACY 11.3 PRIVACY VIOLATIONS AND THEIR IMPACT 11.4 TYPES OF PRIVACY VIOLATIONS 11.5 PRIVACY PROTECTION SOLUTIONS AND THEIR LIMITATIONS 11.6 CONCLUDING REMARKS 11.7 FURTHER READING 11.8 EXERCISE QUESTIONS High Performance Computing for Big Data 12.1 INTRODUCTION 12.2 SCALABILITY: NEED FOR HPC 12.3 GRAPHIC PROCESSING UNIT 12.4 TENSOR PROCESSING UNIT 12.5 HIGH SPEED INTERCONNECTS 12.6 MESSAGE PASSING INTERFACE 12.7 OPENMP 12.8 OTHER FRAMEWORKS 12.9 CONCLUDING REMARKS 12.10 FURTHER READING 12.11 EXERCISE QUESTIONS Deep Learning with Big Data 13.1 INTRODUCTION 13.2 FUNDAMENTALS 13.3 NEURAL NETWORK 13.4 TYPES OF DEEP NEURAL NETWORK 13.5 BIG DATA APPLICATIONS USING DEEP LEARNING13.6 CONCLUDING REMARKS 13.7 FURTHER READING 13.8 EXERCISE QUESTIONS Big Data Case Studies 14.1 GOOGLE EARTH ENGINE 14.2 FACEBOOK MESSAGES APPLICATION 14.3 HADOOP FOR REAL-TIME ANALYTICS 14.4 BIG DATA PROCESSING AT UBER 14.5 BIG DATA PROCESSING AT LINKEDIN 14.6 DISTRIBUTED GRAPH PROCESSING AT GOOGLE 14.7 FUTURE TRENDS 14.8 CONCLUDING REMARKS 14.9 FURTHER READING 14.10 EXERCISE QUESTIONS Bibliography Index

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Book SynopsisYour Essential Guide to Quantitative Hedge Fund Investing provides a conceptual framework for understanding effective hedge fund investment strategies. The book offers a mathematically rigorous exploration of different topics, framed in an easy to digest set of examples and analogies, including stories from some legendary hedge fund investors. Readers will be guided from the historical to the cutting edge, while building a framework of understanding that encompasses it all. Features Filled with novel examples and analogies from within and beyond the world of finance Suitable for practitioners and graduate-level students with a passion for understanding the complexities that lie behind the raw mechanics of quantitative hedge fund investment A unique insight from an author with experience of both the practical and academic spheres. Trade ReviewThis wise and lucid treatise explaining the promise and the hazards of alternative hedge fund strategies should be required reading for both investors and students of financial engineering.-Burton G. Malkiel, author of A Random Walk Down Wall Street, 50th anniversary edition.The book covers both the classical works and recent advances in quantitative aspects of hedge fund investing. Broad in scope and commendable in erudition, the book easily earns a prime spot on hedge fund allocator’s proverbial book shelf. Particular focus is rightfully paid to the portfolio construction aspects of hedge fund investing, alpha/beta separation of hedge fund returns, and persistence of those returns more generally. Personal stories covered among other material are a nice touch and make the whole book even more fun to read. In all, a welcome and long overdue addition to the professional literature on this thorny but relevant subject matter.-Alexander Rudin, Ph.D., Global Head of Multi-Asset and Fixed Income Research as State Street Global Advisors. Molyboga and Swedroe have produced a comprehensive guide to the theory and practice of hedge fund investing. From strategy and manager selection to portfolio construction, the book offers rigorous and pragmatic advice. I wish this book existed when I was starting out.-Tobias Carlisle, Managing Director, Acquirers Funds. This book covers a vast range, from classic topics of hedge fund performance sources, biases, and persistence, to smartly constructing hedge fund portfolios, to newer topics like diversity. Wonderful practitioner and expert interviews bring further flesh and color to the subject. I wish I had read this before I wrote my own books.-Antti Ilmanen, Principal, AQR Capital.Molyboga and Swedroe provide a comprehensive and insightful guide to quantitative investing in hedge funds. They explain carefully and lucidly all the technical details of this important area of investing. For each topic, they provide an excellent account of both the empirical evidence and the theory, based on results from the most recent academic research. This book provides the definitive cutting-edge guide for graduate students and investment professionals who wish to acquire a broader and deeper understanding of hedge fund investing. If you are going to read one book on hedge funds, you should read this one.-Raman Uppal, Professor of Finance, EDHEC Business School.The title doesn’t do this book justice. It’s about much more than quantitative hedge fund investing. It also discusses general manager and factor selection, whether performance persists, risk parity vs. traditional investing, even cutting edge topics like machine learning and important less quantitative topics like inclusion and diversity. In particular the interview section of the book was exceptionally informative save the one negative being that I was not an interviewee :) We will correct that in the next edition. The authors tackle this wide range of topics with their typical thoroughness and insight and I recommend this book whole-heartily if you’re interested in the titled subject or just good investing in general.-Cliff Asness, Managing and Founding Principal, AQR Capital Management.A very thorough guide on hedge funds from a refreshing allocator’s perspective. Unlike the many books on hedge funds that take the fund manager’s point of view and inevitably fall short on details for obvious secrecy reasons, this book gets deep into the data and modeling. The allocator’s perspective, and this book in particular, would be my choice for a business school course on hedge funds.-Michael W. Brandt, Kalman J. Cohen Professor of Finance, Fuqua School of Business, Duke University.Finally, a comprehensive guidebook to systematic hedge fund investing that bridges the gap between academic research and the real-world practice of asset management. The authors do a great job de-mystifying popular trading strategies and the statistical tools behind them, zeroing in on the key problem: distinguishing investing skill from luck is fiendishly hard. The "human" side of the book in the form of interviews with practitioners exhibiting a wide diversity of backgrounds and experiences brings a refreshing new perspective on the opaque world of hedge fund investing.-Nikolai Roussanov, Moise Y. Safra Professor of Finance, Wharton Business School, University of Pennsylvania.I learned a lot from this book. There are cautionary histories of hedge funds’ successes and failures, an impressively thorough review of the research on hedge fund performance, and engaging personal stories of hedge fund managers. Each chapter is followed by succinct "key takeaways." My grand takeaway is that investing in hedge funds is complex enough that I would not attempt to find a select set of them for my portfolio without the help of a wise, knowledgeable, and trusted advisor.-Edward Tower, Professor of Economics, Duke University.Hedge funds benefit from a mystique supported by perceptions of exclusivity and outsize performance available only to institutions and wealthy investors. This book takes a much-needed clear eyed approach to evaluating the portfolio value of hedge funds. Backed by dozens of academic studies, the authors provide a realistic evaluation of the headwinds faced by hedge funds hoping to provide value in a market filled with smart traders and barriers to persistence. I know of no other book that provides an equally exhaustive evaluation of the methodologies used to evaluate whether hedge funds are able to improve performance, and whether skilled advisors can select managers that provide value.-Michael Finke, Frank M. Engle Chair of Economic Security at The American College.As someone who teaches quantitative investing, this book is a wonderful resource for practical insight on quantitative methods in investing. Covering a wide range of topics that include portfolio construction, performance evaluation (and its biases), discretionary versus systematic funds, and newer topics on diversity, this book offers a wealth of information and tools for applying quant methods in finance. A wonderful resource for students and practitioners.-Toby Moskowitz, Dean Takahashi Professor of Finance at Yale University and AQR Principal.Well-researched and easy to read, this book is a must-read for all investors considering alternative investments. Grab a copy!-Wesley R. Gray, PhD, CEO of Alpha Architect.Table of Contents1. Introduction To Hedge Funds. 2. Hedge Fund Research And Data. 3. Manager Selection and Hedge Fund Factors. 4. Performance Persistence. 5. From Mean-Variance to Risk Parity. 6. Advanced Portfolio Construction. 7. Expert Hedge Fund Managers. 8. Expert Hedge Fund Investors. 9. Inclusion and Diversity. 10. Conclusion.

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Book SynopsisThis book elucidates the important role of conduction, convection, and radiation heat transfer, mass transport in solids and fluids, and internal and external fluid flow in the behavior of materials processes. These phenomena are critical in materials engineering because of the connection of transport to the evolution and distribution of microstructural properties during processing. From making choices in the derivation of fundamental conservation equations, to using scaling (order-of-magnitude) analysis showing relationships among different phenomena, to giving examples of how to represent real systems by simple models, the book takes the reader through the fundamentals of transport phenomena applied to materials processing. Fully updated, this third edition of a classic textbook offers a significant shift from the previous editions in the approach to this subject, representing an evolution incorporating the original ideas and extending them to a more comprehensive approach to the Table of Contents1. Introduction to Transport Phenomena in Materials Processing. 2. Steady State Conduction Heat Transfer. 3. Transient Conduction Heat Transfer. 4. Mass Diffusion in the Solid State. 5. Fluid Statics. 6. Mechanical Energy Balance in Fluid Flow. 7. Equations of Fluid Motion. 8. Internal Flows. 9. External Flows. 10. Convection Heat Transfer. 11. Mass Transfer in Fluids. 12. Radiation Heat Transfer.

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Book SynopsisThis book covers the main models within the GLM (i.e., logistic, Poisson, negative binomial, ordinal, and multinomial). For each model, estimations, interpretations, model fit, diagnostics, and how to convey results graphically are provided. There is a focus on graphic displays of results as these are a core strength of using R for statistical analysis. Many in the social sciences are transitioning away from using Stata, SPSS and SAS, to using R, and this book uses statistical models which are relevant to the social sciences. Social Science Applications of Regression for Categorical Outcomes Using R will be useful for graduate students in the social sciences who are looking to expand their statistical knowledge, and for Quantitative social scientists due to it's ability to act as a practitioners guide. Key Features: Applied- in the sense that we will provide code that others can easily adapt Flexible- R is basically just a fancy Table of Contents1. Introduction 2. Introduction to R Studio and Packages 3. Overview of OLS Regression and Introduction to the General Linear Model 4. Describing Categorical Variables and Some Useful Tests of Association 5. Regression for Binary Outcomes 6. Regression for Binary Outcomes – Moderation and Squared Terms 7. Regression for Ordinal Outcomes 8. Regression for Nominal Outcomes 9. Regression for Count Outcomes 10. Additional Outcome Types 11. Special Topics: Comparing Between Models and Missing Data

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