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Space–Time Conservation Element and Solution Element Method: Advances and Applications in Engineering Sciences (Engineering Applications of Computational Methods #13)
by Chih-Yung Wen Yazhong Jiang Lisong ShiThis open access book introduces the fundamentals of the space–time conservation element and solution element (CESE) method, which is a novel numerical approach for solving equations of physical conservation laws. It highlights the recent progress to establish various improved CESE schemes and its engineering applications. With attractive accuracy, efficiency, and robustness, the CESE method is particularly suitable for solving time-dependent nonlinear hyperbolic systems involving dynamical evolutions of waves and discontinuities. Therefore, it has been applied to a wide spectrum of problems, e.g., aerodynamics, aeroacoustics, magnetohydrodynamics, multi-material flows, and detonations. This book contains algorithm analysis, numerical examples, as well as demonstration codes. This book is intended for graduate students and researchers who are interested in the fields such as computational fluid dynamics (CFD), mechanical engineering, and numerical computation.
Spaghetti and Meatballs for All!: A Mathematical Story
by Debbie Tilley Marilyn BurnsKids will exercise their early math skills with this bestselling picture book--now available in Scholastic Bookshelf! Mr. and Mrs. Comfort are having a family reunion! Mr. Comfort starts cooking up his famous spaghetti and meatballs, while Mrs. Comfort carefully arranges eight tables and thirty-two chairs so that everyone will have a seat. The tables look lovely, the food is ready, and here come the guests--with their own seating plans! This delightful Marilyn Burns Brainy Day Book uses wit and humor to draw children into thinking about area and perimeter.
Spannungsfeld Flüchtlinge: Ein psychologischer Blick auf Engagierte und die Dialogkultur
by Christel KumbruckDieses Buch analysiert wissenschaftlich fundiert und zugleich allgemeinverständlich das Engagement von Menschen, die sich für die Aufnahme von Geflüchteten oder gegen die deutsche Flüchtlingspolitik engagieren, sowie ihre Motive, Emotionen, Denk- und Argumentationsweisen. Dabei werden psychologische Mechanismen, die ursächlich für die viel diskutierte Polarisierung unserer modernen Gesellschaft sind, deutlich. Erstmalig werden die tieferen Ursachen für bestehende Dialogbarrieren aufgespürt und mit psychologischen Modellen erklärt. Dabei decken die Autor*innen neben polarisierenden Dialogprozessen auch Gemeinsamkeiten beider Seiten auf und erarbeiten daraus Ansatzpunkte für Dialogchancen und eine Depolarisierung der Kommunikation. Neben der psychologischen Betrachtung erfolgt außerdem eine Einordung der beschriebenen, empirisch ermittelten Erkenntnisse in übergeordnete soziokulturelle Prozesse und gesellschaftliche Rahmenbedingungen. Auch hieraus werden Lösungsansätze, diesmal auf der Ebene von Politik und Gesellschaft, erarbeitet. Ein Buch für alle, die mehr Einblicke in das Flüchtlingsengagement haben wollen, die sich in Politik, Beratung, Coaching, Erziehung u.ä. aktiv an der Bewahrung einer Dialogkultur beteiligen, oder zumindest die (psychologischen) Mechanismen verstehen möchten, welche eine Polarisierung der Gesellschaft fördern."Dieses klare und differenzierte Arbeitsbuch ist eine großartige Hilfe zum Selbstdenken." - Prof. Aleida Assmann"Flucht und Migration werden Deutschland weiterhin vor Herausforderungen stellen. Dieses Buch zeigt nachvollziehbar, wie Wahrnehmungs-, Denk- und Handlungsweisen zu einer destruktiven Polarisierung beitragen können, aber auch welche Möglichkeiten wir alle haben, um der Falle vergifteter Kommunikation zu entgehen: ein hilfreiches Buch, um demokratischen Zusammenhalt zu stärken." - Prof. Eva Senghaas-Knobloch
Sparse Estimation with Math and R: 100 Exercises for Building Logic
by Joe SuzukiThe most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of sparse estimation by considering math problems and building R programs. Each chapter introduces the notion of sparsity and provides procedures followed by mathematical derivations and source programs with examples of execution. To maximize readers’ insights into sparsity, mathematical proofs are presented for almost all propositions, and programs are described without depending on any packages. The book is carefully organized to provide the solutions to the exercises in each chapter so that readers can solve the total of 100 exercises by simply following the contents of each chapter.This textbook is suitable for an undergraduate or graduate course consisting of about 15 lectures (90 mins each). Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning by data scientists, machine learning engineers, and researchers interested in linear regression, generalized linear lasso, group lasso, fused lasso, graphical models, matrix decomposition, and multivariate analysis.This book is one of a series of textbooks in machine learning by the same author. Other titles are: - Statistical Learning with Math and R (https://www.springer.com/gp/book/9789811575679) - Statistical Learning with Math and Python (https://www.springer.com/gp/book/9789811578762) - Sparse Estimation with Math and Python
Sparse Graphical Modeling for High Dimensional Data: A Paradigm of Conditional Independence Tests (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
by Faming Liang Bochao JiaThis book provides a general framework for learning sparse graphical models with conditional independence tests. It includes complete treatments for Gaussian, Poisson, multinomial, and mixed data; unified treatments for covariate adjustments, data integration, and network comparison; unified treatments for missing data and heterogeneous data; efficient methods for joint estimation of multiple graphical models; effective methods of high-dimensional variable selection; and effective methods of high-dimensional inference. The methods possess an embarrassingly parallel structure in performing conditional independence tests, and the computation can be significantly accelerated by running in parallel on a multi-core computer or a parallel architecture. This book is intended to serve researchers and scientists interested in high-dimensional statistics, and graduate students in broad data science disciplines. Key Features: A general framework for learning sparse graphical models with conditional independence tests Complete treatments for different types of data, Gaussian, Poisson, multinomial, and mixed data Unified treatments for data integration, network comparison, and covariate adjustment Unified treatments for missing data and heterogeneous data Efficient methods for joint estimation of multiple graphical models Effective methods of high-dimensional variable selection Effective methods of high-dimensional inference
Sparse Grids and Applications - Miami 2016 (Lecture Notes in Computational Science and Engineering #123)
by Jochen Garcke Dirk Pflüger Clayton G. Webster Guannan ZhangSparse grids are a popular tool for the numerical treatment of high-dimensional problems. Where classical numerical discretization schemes fail in more than three or four dimensions, sparse grids, in their different flavors, are frequently the method of choice. This volume of LNCSE presents selected papers from the proceedings of the fourth workshop on sparse grids and applications, and demonstrates once again the importance of this numerical discretization scheme. The articles present recent advances in the numerical analysis of sparse grids in connection with a range of applications including computational chemistry, computational fluid dynamics, and big data analytics, to name but a few.
Sparse Grids and Applications - Munich 2018 (Lecture Notes in Computational Science and Engineering #144)
by Hans-Joachim Bungartz Jochen Garcke Dirk PflügerSparse grids are a popular tool for the numerical treatment of high-dimensional problems. Where classical numerical discretization schemes fail in more than three or four dimensions, sparse grids, in their different flavors, are frequently the method of choice.This volume of LNCSE presents selected papers from the proceedings of the fifth workshop on sparse grids and applications, and demonstrates once again the importance of this numerical discretization scheme. The articles present recent advances in the numerical analysis of sparse grids in connection with a range of applications including uncertainty quantification, plasma physics simulations, and computational chemistry, to name but a few.
Sparse Grids and Applications - Stuttgart 2014
by Jochen Garcke Dirk PflügerThis volume of LNCSE is a collection of the papers from the proceedings of the third workshop on sparse grids and applications. Sparse grids are a popular approach for the numerical treatment of high-dimensional problems. Where classical numerical discretization schemes fail in more than three or four dimensions, sparse grids, in their different guises, are frequently the method of choice, be it spatially adaptive in the hierarchical basis or via the dimensionally adaptive combination technique. Demonstrating once again the importance of this numerical discretization scheme, the selected articles present recent advances on the numerical analysis of sparse grids as well as efficient data structures. The book also discusses a range of applications, including uncertainty quantification and plasma physics.
Sparse Image and Signal Processing
by Jean-Luc Starck Fionn Murtagh Jalal FadiliThis book presents the state of the art in sparse and multiscale image and signal processing, covering linear multiscale transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. Recent concepts of sparsity and morphological diversity are described and exploited for various problems such as denoising, inverse problem regularization, sparse signal decomposition, blind source separation, and compressed sensing. This book weds theory and practice in examining applications in areas such as astronomy, biology, physics, digital media, and forensics. A final chapter explores a paradigm shift in signal processing, showing that previous limits to information sampling and extraction can be overcome in very significant ways. Matlab and IDL code accompany these methods and applications to reproduce the experiments and illustrate the reasoning and methodology of the research are available for download at the associated web site.
Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity
by Jean-Luc Starck Fionn Murtagh Jalal M. FadiliThis book presents the state of the art in sparse and multiscale image and signal processing, covering linear multiscale transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. Recent concepts of sparsity and morphological diversity are described and exploited for various problems such as denoising, inverse problem regularization, sparse signal decomposition, blind source separation, and compressed sensing. This book weds theory and practice in examining applications in areas such as astronomy, biology, physics, digital media, and forensics. A final chapter explores a paradigm shift in signal processing, showing that previous limits to information sampling and extraction can be overcome in very significant ways. Matlab and IDL code accompany these methods and applications to reproduce the experiments and illustrate the reasoning and methodology of the research available for download at the associated Web site.
Sparse Optimization Theory and Methods
by Yun-Bin ZhaoSeeking sparse solutions of underdetermined linear systems is required in many areas of engineering and science such as signal and image processing. The efficient sparse representation becomes central in various big or high-dimensional data processing, yielding fruitful theoretical and realistic results in these fields. The mathematical optimization plays a fundamentally important role in the development of these results and acts as the mainstream numerical algorithms for the sparsity-seeking problems arising from big-data processing, compressed sensing, statistical learning, computer vision, and so on. This has attracted the interest of many researchers at the interface of engineering, mathematics and computer science. Sparse Optimization Theory and Methods presents the state of the art in theory and algorithms for signal recovery under the sparsity assumption. The up-to-date uniqueness conditions for the sparsest solution of underdertemined linear systems are described. The results for sparse signal recovery under the matrix property called range space property (RSP) are introduced, which is a deep and mild condition for the sparse signal to be recovered by convex optimization methods. This framework is generalized to 1-bit compressed sensing, leading to a novel sign recovery theory in this area. Two efficient sparsity-seeking algorithms, reweighted l1-minimization in primal space and the algorithm based on complementary slackness property, are presented. The theoretical efficiency of these algorithms is rigorously analysed in this book. Under the RSP assumption, the author also provides a novel and unified stability analysis for several popular optimization methods for sparse signal recovery, including l1-mininization, Dantzig selector and LASSO. This book incorporates recent development and the author’s latest research in the field that have not appeared in other books.
Spatial Analysis
by Mark R. T. Dale Marie-Josée FortinNowadays, ecologists worldwide recognize the use of spatial analysis as essential. However, because of the fast-growing range of methods available, even an expert might occasionally find it challenging to choose the most appropriate one. Providing the ecological and statistical foundations needed to make the right decision, this second edition builds and expands upon the previous one by: - Encompassing the basic methods for spatial analysis, for both complete census and sample data - Investigating updated treatments of spatial autocorrelation and spatio-temporal analysis - Introducing detailed explanations of currently developing approaches, including spatial and spatio-temporal graph theory, scan statistics, fibre process analysis, and Hierarchical Bayesian analysis - Offering practical advice for specific circumstances, such as how to analyze forest Permanent Sample Plot data and how to proceed with transect data when portions of the data series are missing. Written for graduates, researchers and professionals, this book will be a valuable source of reference for years to come.
Spatial Analysis
by Marie-Josée Fortin Mark R. T. DaleThe spatial and temporal dimensions of ecological phenomena have always been inherent in the conceptual framework of ecology, but only recently have they been incorporated explicitly into ecological theory, sampling design, experimental design and models. Statistical techniques for spatial analysis of ecological data are burgeoning and many ecologists are unfamiliar with what is available and how the techniques should be used correctly. This book gives an overview of the wide range of spatial statistics available to analyse ecological data, and provides advice and guidance for graduate students and practising researchers who are either about to embark on spatial analysis in ecological studies or who have started but are unsure how to proceed. Only a basic understanding of statistics is assumed and many schematic illustrations are given to complement or replace mathematical technicalities, making the book accessible to ecologists wishing to enter this important and fast-growing field for the first time.
Spatial Analysis Along Networks
by Kokichi Sugihara Atsuyuki OkabeIn the real world, there are numerous and various events that occur on and alongside networks, including the occurrence of traffic accidents on highways, the location of stores alongside roads, the incidence of crime on streets and the contamination along rivers. In order to carry out analyses of those events, the researcher needs to be familiar with a range of specific techniques. Spatial Analysis Along Networks provides a practical guide to the necessary statistical techniques and their computational implementation.Each chapter illustrates a specific technique, from Stochastic Point Processes on a Network and Network Voronoi Diagrams, to Network K-function and Point Density Estimation Methods, and the Network Huff Model. The authors also discuss and illustrate the undertaking of the statistical tests described in a Geographical Information System (GIS) environment as well as demonstrating the user-friendly free software package SANET.Spatial Analysis Along Networks:Presents a much-needed practical guide to statistical spatial analysis of events on and alongside a network, in a logical, user-friendly order.Introduces the preliminary methods involved, before detailing the advanced, computational methods, enabling the readers a complete understanding of the advanced topics.Dedicates a separate chapter to each of the major techniques involved.Demonstrates the practicalities of undertaking the tests described in the book, using a GIS.Is supported by a supplementary website, providing readers with a link to the free software package SANET, so they can execute the statistical methods described in the book.Students and researchers studying spatial statistics, spatial analysis, geography, GIS, OR, traffic accident analysis, criminology, retail marketing, facility management and ecology will benefit from this book.
Spatial Analysis and Location Modeling in Urban and Regional Systems
by Jean-Claude ThillThe contributed volume collects cutting-edge research in Geographic Information Science & Technologies, Location Modeling, and Spatial Analysis of Urban and Regional Systems. The contributions emphasize methodological innovations or substantive breakthroughs on many facets of the socio-economic and environmental reality of urban and regional contexts.
Spatial Analysis for the Social Sciences
by David DarmofalMany theories in the social sciences predict spatial dependence or the similarity of behaviors at neighboring locations. Spatial Analysis for the Social Sciences demonstrates how researchers can diagnose and model this spatial dependence and draw more valid inferences as a result. The book is structured around the well-known Galton's problem and presents a step-by-step guide to the application of spatial analysis. The book examines a variety of spatial diagnostics and models through a series of applied examples drawn from the social sciences. These include spatial lag models that capture behavioral diffusion between actors, spatial error models that account for spatial dependence in errors, and models that incorporate spatial heterogeneity in the effects of covariates. Spatial Analysis for the Social Sciences also examines advanced spatial models for time-series cross-sectional data, categorical and limited dependent variables, count data, and survival data.
Spatial Analysis in Geology Using R (Chapman & Hall/CRC The R Series)
by Pedro M. NogueiraThe integration of geology with data science disciplines, such as spatial statistics, remote sensing, and geographic information systems (GIS), has given rise to a shift in many natural sciences schools, pushing the boundaries of knowledge and enabling new discoveries in geological processes and earth systems. Spatial analysis of geological data can be used to identify patterns and trends in data, to map spatial relationships, and to model spatial processes. R is a consolidated and yet growing statistical programming language with increasing value in spatial analysis often replacing, with advantage, GIS tools. By providing a comprehensive guide for geologists to harness the power of spatial analysis in R, Spatial Analysis in Geology Using R serves as a tool in addressing real-world problems, such as natural resource management, environmental conservation, and hazard prediction and mitigation.Features: Provides a practical and accessible overview of spatial analysis in geology using R Organised in three independent and complementary parts: Introduction to R, Spatial Analysis with R, and Spatial Statistics and Modelling Applied approach with many detailed examples and case studies using real geological data Presents a collection of R packages that are useful in many geological situations Does not assume any prior knowledge of R; all code are explained in detail Supplemented by a website with all data, code, and examples Spatial Analysis in Geology Using R will be useful to any geological researcher who has acquired basic spatial analysis skills, often using GIS, and is interested in deepening those skills through the use of R. It could be used as a reference by applied researchers and analysts in public, private, or third-sector industries. It could also be used to teach a course on the topic to graduate students or for self-study.
Spatial Analysis Methods of Road Traffic Collisions
by Becky P. Loo Tessa Kate AndersonExamine the Prevalence and Geography of Road CollisionsSpatial Analysis Methods of Road Traffic Collisions centers on the geographical nature of road crashes, and uses spatial methods to provide a greater understanding of the patterns and processes that cause them. Written by internationally known experts in the field of transport geography, the bo
Spatial Analysis with R: Statistics, Visualization, and Computational Methods
by Tonny J. OyanaIn the five years since the publication of the first edition of Spatial Analysis: Statistics, Visualization, and Computational Methods, many new developments have taken shape regarding the implementation of new tools and methods for spatial analysis with R. The use and growth of artificial intelligence, machine learning and deep learning algorithms with a spatial perspective, and the interdisciplinary use of spatial analysis are all covered in this second edition along with traditional statistical methods and algorithms to provide a concept-based problem-solving learning approach to mastering practical spatial analysis. Spatial Analysis with R: Statistics, Visualization, and Computational Methods, Second Edition provides a balance between concepts and practicums of spatial statistics with a comprehensive coverage of the most important approaches to understand spatial data, analyze spatial relationships and patterns, and predict spatial processes. New in the Second Edition: Includes new practical exercises and worked-out examples using R Presents a wide range of hands-on spatial analysis worktables and lab exercises All chapters are revised and include new illustrations of different concepts using data from environmental and social sciences Expanded material on spatiotemporal methods, visual analytics methods, data science, and computational methods Explains big data, data management, and data mining This second edition of an established textbook, with new datasets, insights, excellent illustrations, and numerous examples with R, is perfect for senior undergraduate and first-year graduate students in geography and the geosciences.
Spatial and Social Disparities
by Claudia Thomas John Stillwell Paul Norman Paula SurridgeInequality is one of the major problems of the contemporary world. Significant geographical disparities exist within nations of the developed world, as well as between these countries and those referred to as the 'South' in the Bruntland Report. Issues of equity and deprivation must be addressed in view of sustainable development. However, before policymakers can remove the obstacles to a fairer world, it is essential to understand the nature of inequality, both in terms of its spatial and socio-demographic characteristics. This second volume in the series contains population studies that examine the disparities evident across geographical space in the UK and between different individuals or groups. Topics include demographic and social change, deprivation, happiness, cultural consumption, ethnicity, gender, employment, health, religion, education and social values. These topics and the relationships between them are explored using secondary data from censuses, surveys or administrative records. In volume 1 the findings of research on fertility, living arrangements, care and mobility are examined. Volume 3 will focus on ethnicity and integration.
Spatial and Spatio-temporal Bayesian Models with R - INLA
by Marta Blangiardo Michela CamelettiSpatial and Spatio-Temporal Bayesian Models with R-INLA provides a much needed, practically oriented & innovative presentation of the combination of Bayesian methodology and spatial statistics. The authors combine an introduction to Bayesian theory and methodology with a focus on the spatial and spatio-temporal models used within the Bayesian framework and a series of practical examples which allow the reader to link the statistical theory presented to real data problems. The numerous examples from the fields of epidemiology, biostatistics and social science all are coded in the R package R-INLA, which has proven to be a valid alternative to the commonly used Markov Chain Monte Carlo simulations
Spatial and Spatio-Temporal Geostatistical Modeling and Kriging (Wiley Series in Probability and Statistics #998)
by José-María Montero Gema Fernández-Avilés Jorge MateuStatistical Methods for Spatial and Spatio-Temporal Data Analysis provides a complete range of spatio-temporal covariance functions and discusses ways of constructing them. This book is a unified approach to modeling spatial and spatio-temporal data together with significant developments in statistical methodology with applications in R. This book includes: Methods for selecting valid covariance functions from the empirical counterparts that overcome the existing limitations of the traditional methods. The most innovative developments in the different steps of the kriging process. An up-to-date account of strategies for dealing with data evolving in space and time. An accompanying website featuring R code and examples
Spatial Behavior in Haredi Jewish Communities in Great Britain (The Urban Book Series)
by Shlomit Flint AsheryThis book focuses on the strict orthodox Jewish (Haredi) community, which comprises many sects whose communal identity plays a central role in everyday life and spatial organization. The research reveals and analyses powerful mechanisms of residential segregation acting at the apartment-, building- and near-neighbourhood levels. Identifying the main engines of spontaneous and organised neighbourhood change and evaluating the difficulties of liberalism dealing with non-autonomous individuals in the housing market sheds light on similar processes occurring in other city centres with diverse population groups. Highlighting the impact of various organisational levels on the spatial structure of the urban enclave, the book focuses on the internal dynamics of ethno-religious enclaves that emerge from three levels of action: (1) individuals' relationships with their own and other groups; (2) the community leadership's powers within the group and in respect of other groups; and (3) government directives and tools (e.g planning). The study examines how different levels of communal organisation are reflected in the residential patterns of four British communities: the Litvish communities of Golders Green and Gateshead, and the Hassidic communities of Stamford Hill and Canvey Island.
Spatial Complexity: Theory, Mathematical Methods and Applications
by Fivos PapadimitriouThis book delivers stimulating input for a broad range of researchers, from geographers and ecologists to psychologists interested in spatial perception and physicists researching in complex systems. How can one decide whether one surface or spatial object is more complex than another? What does it require to measure the spatial complexity of small maps, and why does this matter for nature, science and technology? Drawing from algorithmics, geometry, topology, probability and informatics, and with examples from everyday life, the reader is invited to cross the borders into the bewildering realm of spatial complexity, as it emerges from the study of geographic maps, landscapes, surfaces, knots, 3D and 4D objects. The mathematical and cartographic experiments described in this book lead to hypotheses and enigmas with ramifications in aesthetics and epistemology.
Spatial Context: An Introduction to Fundamental Computer Algorithms for Spatial Analysis (ISPRS Book Series #12)
by Christopher GoldMany disciplines are concerned with manipulating geometric (or spatial) objects in the computer – such as geology, cartography, computer aided design (CAD), etc. – and each of these have developed their own data structures and techniques, often independently. Nevertheless, in many cases the object types and the spatial queries are similar, and this book attempts to find a common theme.