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Geniale Frauen in der Wissenschaft: Versteckte Beiträge, die die Welt verändert haben
by Lars JaegerObwohl Frauen schon früh das wissenschaftliche Denken mitgeprägt haben, sichtbar geworden sind sie fast nie. Dieses Ungleichgewicht setzt sich bis heute fort, auch wenn es aktuell weit mehr Wissenschaftlerinnen gibt als jemals zuvor. Lars Jaeger spannt einen Bogen von der Antike bis heute und porträtiert in essayartigen Einführungen das Leben und Wirken der wohl bedeutendsten weiblichen Naturwissenschaftlerinnen und Mathematikerinnen. Von Hypatia von Alexandria über Émilie du Châtelet und Emmy Noether bis hin zu Lisa Randall, sie alle haben Großes geleistet, die Wissenschaft entscheidend vorangebracht und konnten dennoch oft nicht aus dem Schatten ihrer männlichen Kollegen treten.Neben den spannenden Porträts der einzelnen Wissenschaftlerinnen sowie einer detaillierten und anschaulichen Darstellung ihrer wissenschaftlichen Leistungen beleuchtet dieses Sachbuch auch das Geschlechterverhältnis in der Wissenschaft, das sich nur quälend langsam zugunsten eines fairen Verhältnisses für die Frauen entwickelt.
Genius at Play: The Curious Mind of John Horton Conway
by Siobhan RobertsA multifaceted biography of a brilliant mathematician and iconoclastA mathematician unlike any other, John Horton Conway (1937–2020) possessed a rock star&’s charisma, a polymath&’s promiscuous curiosity, and a sly sense of humor. Conway found fame as a barefoot professor at Cambridge, where he discovered the Conway groups in mathematical symmetry and the aptly named surreal numbers. He also invented the cult classic Game of Life, a cellular automaton that demonstrates how simplicity generates complexity—and provides an analogy for mathematics and the entire universe. Moving to Princeton in 1987, Conway used ropes, dice, pennies, coat hangers, and the occasional Slinky to illustrate his winning imagination and share his nerdish delights. Genius at Play tells the story of this ambassador-at-large for the beauties and joys of mathematics, lays bare Conway&’s personal and professional idiosyncrasies, and offers an intimate look into the mind of one of the twentieth century&’s most endearing and original intellectuals.
Genocide: State Power and Mass Murder (Issues In Contemporary Civilization Ser.)
by James BaldwinThis book is dedicated to a consideration of genocide in the context of political sociology. It demonstrates that the underlining predicates of sociology give scant consideration to basic issues of life and death in favor of distinctly derivative issues of social structure and social function.
Genome Data Analysis (Learning Materials in Biosciences)
by Ju Han KimThis textbook describes recent advances in genomics and bioinformatics and provides numerous examples of genome data analysis that illustrate its relevance to real world problems and will improve the reader’s bioinformatics skills. Basic data preprocessing with normalization and filtering, primary pattern analysis, and machine learning algorithms using R and Python are demonstrated for gene-expression microarrays, genotyping microarrays, next-generation sequencing data, epigenomic data, and biological network and semantic analyses. In addition, detailed attention is devoted to integrative genomic data analysis, including multivariate data projection, gene-metabolic pathway mapping, automated biomolecular annotation, text mining of factual and literature databases, and integrated management of biomolecular databases. The textbook is primarily intended for life scientists, medical scientists, statisticians, data processing researchers, engineers, and other beginners in bioinformatics who are experiencing difficulty in approaching the field. However, it will also serve as a simple guideline for experts unfamiliar with the new, developing subfield of genomic analysis within bioinformatics.
Genome-Scale Algorithm Design: Biological Sequence Analysis in the Era of High-Throughput Sequencing
by Veli Mäkinen Djamal Belazzougui Fabio Cunial Alexandru I. TomescuHigh-throughput sequencing has revolutionised the field of biological sequence analysis. Its application has enabled researchers to address important biological questions, often for the first time. This book provides an integrated presentation of the fundamental algorithms and data structures that power modern sequence analysis workflows. The topics covered range from the foundations of biological sequence analysis (alignments and hidden Markov models), to classical index structures (k-mer indexes, suffix arrays and suffix trees), Burrows-Wheeler indexes, graph algorithms and a number of advanced omics applications. The chapters feature numerous examples, algorithm visualisations, exercises and problems, each chosen to reflect the steps of large-scale sequencing projects, including read alignment, variant calling, haplotyping, fragment assembly, alignment-free genome comparison, transcript prediction and analysis of metagenomic samples. Each biological problem is accompanied by precise formulations, providing graduate students and researchers in bioinformatics and computer science with a powerful toolkit for the emerging applications of high-throughput sequencing.
Genomic Approaches for Cross-Species Extrapolation in Toxicology
by William H. Benson Richard T. Di GiulioThe latest tools for investigating stress response in organisms, genomic technologies provide great insight into how different organisms respond to environmental conditions. However, their usefulness needs to be tested, verified, and codified. Genomic Approaches for Cross-Species Extrapolation in Toxicology provides a balanced discussion drawn from
Genomics and Bioinformatics
by Tore SamuelssonWith the arrival of genomics and genome sequencing projects, biology has been transformed into an incredibly data-rich science. The vast amount of information generated has made computational analysis critical and has increased demand for skilled bioinformaticians. Designed for biologists without previous programming experience, this textbook provides a hands-on introduction to Unix, Perl and other tools used in sequence bioinformatics. Relevant biological topics are used throughout the book and are combined with practical bioinformatics examples, leading students through the process from biological problem to computational solution. All of the Perl scripts, sequence and database files used in the book are available for download at the accompanying website, allowing the reader to easily follow each example using their own computer. Programming examples are kept at an introductory level, avoiding complex mathematics that students often find daunting. The book demonstrates that even simple programs can provide powerful solutions to many complex bioinformatics problems.
Genomics Data Analysis: False Discovery Rates and Empirical Bayes Methods
by David R. BickelStatisticians have met the need to test hundreds or thousands of genomics hypotheses simultaneously with novel empirical Bayes methods that combine advantages of traditional Bayesian and frequentist statistics. Techniques for estimating the local false discovery rate assign probabilities of differential gene expression, genetic association, etc. without requiring subjective prior distributions. This book brings these methods to scientists while keeping the mathematics at an elementary level. Readers will learn the fundamental concepts behind local false discovery rates, preparing them to analyze their own genomics data and to critically evaluate published genomics research.Key Features:* dice games and exercises, including one using interactive software, for teaching the concepts in the classroom* examples focusing on gene expression and on genetic association data and briefly covering metabolomics data and proteomics data* gradual introduction to the mathematical equations needed* how to choose between different methods of multiple hypothesis testing* how to convert the output of genomics hypothesis testing software to estimates of local false discovery rates* guidance through the minefield of current criticisms of p values* material on non-Bayesian prior p values and posterior p values not previously published
Genomics Data Analysis for Crop Improvement (Springer Protocols Handbooks)
by Priyanka Anjoy Kuldeep Kumar Girish Chandra Kishor GaikwadThis book addresses complex problems associated with crop improvement programs, using a wide range of programming solutions, for genomics data handling and sustainable agriculture. It describes important concepts in genomics data analysis and sequence-based mapping approaches along with references. The book contains 16 chapters on recent developments in several methods of genomic data analysis for crop improvements and sustainable agriculture, all authored by eminent researchers who are experts in their fields. These chapters focus on applications of a wide range of key bioinformatics topics, including assembly, annotation, and visualization of next-generation sequencing (NGS) data; expression profiles of coding and noncoding RNA; statistical and quantitative genetics; trait-based association analysis, quantitative trait loci (QTL) mapping, and artificial intelligence in genomic studies. Real examples and case studies in the book will come in handy when applying the techniques. The relative scarcity of reference materials covering bioinformatics applications as compared with the readily available books also enhances the utility of this book. The targeted readers of the book are scientists, researchers, and bioinformaticians from genomics and advanced breeding in different areas. The book will appeal to the applied researchers engaged in crop improvements and sustainable agriculture by using bioinformatics tools, students, research project leaders, and practitioners from the various marginal disciplines and interdisciplinary research.
Genomics in the Cloud: Using Docker, GATK, and WDL in Terra
by Geraldine A. Van der Auwera Brian D. O'ConnorData in the genomics field is booming. In just a few years, organizations such as the National Institutes of Health (NIH) will host 50+ petabytesâ??or over 50 million gigabytesâ??of genomic data, and theyâ??re turning to cloud infrastructure to make that data available to the research community. How do you adapt analysis tools and protocols to access and analyze that volume of data in the cloud?With this practical book, researchers will learn how to work with genomics algorithms using open source tools including the Genome Analysis Toolkit (GATK), Docker, WDL, and Terra. Geraldine Van der Auwera, longtime custodian of the GATK user community, and Brian Oâ??Connor of the UC Santa Cruz Genomics Institute, guide you through the process. Youâ??ll learn by working with real data and genomics algorithms from the field.This book covers:Essential genomics and computing technology backgroundBasic cloud computing operationsGetting started with GATK, plus three major GATK Best Practices pipelinesAutomating analysis with scripted workflows using WDL and CromwellScaling up workflow execution in the cloud, including parallelization and cost optimizationInteractive analysis in the cloud using Jupyter notebooksSecure collaboration and computational reproducibility using Terra
The Gentle Art of Mathematics
by Dan PedoeMathematical games, probability, the question of infinity, topology, how the laws of algebra work, problems of irrational numbers, and more. 42 figures.
A Gentle Course in Local Class Field Theory: Local Number Fields, Brauer Groups, Galois Cohomology
by Pierre GuillotThis book offers a self-contained exposition of local class field theory, serving as a second course on Galois theory. It opens with a discussion of several fundamental topics in algebra, such as profinite groups, p-adic fields, semisimple algebras and their modules, and homological algebra with the example of group cohomology. The book culminates with the description of the abelian extensions of local number fields, as well as the celebrated Kronecker–Weber theory, in both the local and global cases. The material will find use across disciplines, including number theory, representation theory, algebraic geometry, and algebraic topology. Written for beginning graduate students and advanced undergraduates, this book can be used in the classroom or for independent study.
A Gentle Introduction to Optimization
by B. Guenin J. Könemann L. TunçelOptimization is an essential technique for solving problems in areas as diverse as accounting, computer science and engineering. Assuming only basic linear algebra and with a clear focus on the fundamental concepts, this textbook is the perfect starting point for first- and second-year undergraduate students from a wide range of backgrounds and with varying levels of ability. Modern, real-world examples motivate the theory throughout. The authors keep the text as concise and focused as possible, with more advanced material treated separately or in starred exercises. Chapters are self-contained so that instructors and students can adapt the material to suit their own needs and a wide selection of over 140 exercises gives readers the opportunity to try out the skills they gain in each section. Solutions are available for instructors. The book also provides suggestions for further reading to help students take the next step to more advanced material.
A Gentle Introduction to Scientific Computing (Chapman & Hall/CRC Numerical Analysis and Scientific Computing Series)
by Dan Stanescu Long LeeScientific Computation has established itself as a stand-alone area of knowledge in the border area between computer science and applied mathematics. Nonetheless, its interdisciplinary character cannot be denied: its methodologies are increasingly used in a wide variety of branches of science and engineering. A Gentle Introduction to Scientific Computing intends to serve a very broad audience of college students across a variety of disciplines. It aims to expose its readers to some of the basic tools and techniques used in computational science, with a view to helping them understand what happens ‘behind the scenes’ when simple tools such as solving equations, plotting and interpolation are used. To make the book as practical as possible, the authors explore their subject both from a theoretical, mathematical perspective and from an implementation-driven, programming perspective. Features Takes a middle ground approach between theoretical book and implementation Suitable reading for a broad range of students in STEM disciplines, and could be the primary text for a first course in scientific computing Introduces mathematics majors, without any prior computer science exposure, to numerical methods All mathematical knowledge needed beyond Calculus (and the more useful Calculus notation and concepts) is introduced in the text to make it self-contained.
A Gentle Introduction To Stata
by Alan C. AcockAcock assumes that the user is not familiar with any statistical software. This assumption of a blank slate is central to the structure and contents of the book. Acock starts with the basics; for example, the part of the book that deals with data management begins with a careful and detailed example of turning survey data on paper into a Stata-ready dataset on the computer. When explaining how to go about basic exploratory statistical procedures, Acock includes notes that will help the reader develop good work habits. This mixture of explaining good Stata habits and good statistical habits continues throughout the book.
A Gentle Introduction To Stata (Fifth Edition)
by Alan C. AcockAlan C. Acock's A Gentle Introduction to Stata, Fifth Edition, is aimed at new Stata users who want to become proficient in Stata. After reading this introductory text, new users will be able not only to use Stata well but also to learn new aspects of Stata. Acock assumes that the user is not familiar with any statistical software. This assumption of a blank slate is central to the structure and contents of the book. Acock starts with the basics; for example, the portion of the book that deals with data management begins with a careful and detailed example of turning survey data on paper into a Stata-ready dataset on the computer. When explaining how to go about basic exploratory statistical procedures, Acock includes notes that will help the reader develop good work habits. This mixture of explaining good Stata habits and good statistical habits continues throughout the book. Acock is quite careful to teach the reader all aspects of using Stata. He covers data management, good work habits (including the use of basic do-files), basic exploratory statistics (including graphical displays), and analyses using the standard array of basic statistical tools (correlation, linear and logistic regression, and parametric and nonparametric tests of location and dispersion). He also successfully introduces some more advanced topics such as multiple imputation and structural equation modeling in a very approachable manner. Acock teaches Stata commands by using the menus and dialog boxes while still stressing the value of do-files. In this way, he ensures that all types of users can build good work habits. Each chapter has exercises that the motivated reader can use to reinforce the material. The tone of the book is friendly and conversational without ever being glib or condescending. Important asides and notes about terminology are set off in boxes, which makes the text easy to read without any convoluted twists or forward-referencing. Rather than splitting topics by their Stata implementation, Acock arranges the topics as they would appear in a basic statistics textbook; graphics and postestimation are woven into the material in a natural fashion. Real datasets, such as the General Social Surveysfrom 2002 and 2006, are used throughout the book. The focus of the book is especially helpful for those in the behavioral and social sciences because the presentation of basic statistical modeling is supplemented with discussions of effect sizes and standardized coefficients. Various selection criteria, such as semipartial correlations, are discussed for model selection. Acock also covers a variety of commands available for evaluating reliability and validity of measurements. The fifth edition of the book includes two new chapters that cover multilevel modeling and item response theory (IRT) models. The multilevel modeling chapter demonstrates how to fit linear multilevel models using the mixedcommand. Acock discusses models with both random intercepts and random coefficients, and he provides a variety of examples that apply these models to longitudinal data. The IRT chapter introduces the use of IRT models for evaluating a set of items designed to measure a specific trait such as an attitude, value, or a belief. Acock shows how to use the irt suite of commands, which are new in Stata 14, to fit IRT models and to graph the results. In addition, he presents a measure of reliability that can be computed when using IRT.
Gentrification Trends in the United States
by Richard W. MartinGentrification Trends in the United States is the first book to quantify the changes that take place when a neighborhood’s income level, educational attainment, or occupational makeup outpace the city as a whole – the much-debated yet poorly understood phenomenon of gentrification. Applying a novel method to four decades of U.S. Census data, this resource for students and scholars provides a quantitative basis for the nuanced demographic trends uncovered through ethnography and other forms of qualitative research. This analysis of a rich data source characterized by a broad regional and chronological scope provides new insight into larger questions about the nature and prevalence of gentrification across the United States. Has gentrification become more common over time? Which cities have experienced the most gentrification? Is gentrification widespread, or does it tend to be concentrated in a small number of cities? Has the nature of gentrification changed over time? Ideal reading for courses in real estate, urban planning, urban economics, sociology, geography, econometrics, and GIS, this pathbreaking addition to the urban studies literature will enrich the perspective of any scholar of U.S. cities.
Gentzen Calculi for Modal Propositional Logic
by Francesca PoggiolesiThe book is about Gentzen calculi for (the main systems of) modal logic. It is divided into three parts. In the first part we introduce and discuss the main philosophical ideas related to proof theory, and we try to identify criteria for distinguishing good sequent calculi. In the second part we present the several attempts made from the 50's until today to provide modal logic with Gentzen calculi. In the third and and final part we analyse new calculi for modal logics, called tree-hypersequent calculi, which were recently introduced by the author. We show in a precise and clear way the main results that can be proved with and about them.
Gentzen's Centenary
by Reinhard Kahle Michael RathjenGerhard Gentzen has been described as logic's lost genius, whom Gödel called a better logician than himself. This work comprises articles by leading proof theorists, attesting to Gentzen's enduring legacy to mathematical logic and beyond. The contributions range from philosophical reflections and re-evaluations of Gentzen's original consistency proofs to the most recent developments in proof theory. Gentzen founded modern proof theory. His sequent calculus and natural deduction system beautifully explain the deep symmetries of logic. They underlie modern developments in computer science such as automated theorem proving and type theory.
Geo-Environmental Hazards using AI-enabled Geospatial Techniques and Earth Observation Systems (Advances in Geographic Information Science)
by Jung-Sup Um Tanupriya Choudhury Bappaditya Koley Anindita Nath Atul Kumar PatidarThis edited collection provides a comprehensive exploration of cutting-edge ideas, approaches, simulations, evaluations of risk, and systems that enhance the practicality of current geospatial technologies for reducing hazard risks. The various sections within this book delve into subjects such as the foundational principles of Earth Observation Systems (EOS) and geospatial methodologies. Additionally, the text serves as an advisory resource on the collaborative use of satellite-derived data and artificial intelligence to track and alleviate geo-environmental threats. The volume imparts extensive understanding regarding geo-environmental dangers and their analysis via EOS along with geospatial strategies. It encompasses key hazard-related themes including coastal degradation, predisposition to landslides, mapping vegetation coverages, tropical storm patterns, soil depletion due to erosion processes, vulnerability to rapid or extended flooding events, variations in oceansurface temperatures alongside chlorophyll-a levels; it also addresses assessments related to groundwater reserves and quality measures as well as sustainable management practices for watersheds that support community livelihoods—all through leveraging AI-integrated geospatial tools in conjunction with earth observation technologies. Furthermore, this work engages in discourse about systems designed for mitigating these ecological challenges sustainably. Scholars engaged in research activities; educational professionals; those involved in landscape design; engineers working at ground level; individuals responsible for policy-making—all who are concerned with geo-environmental hazards or associated domains—will find valuable insights within these pages.
Geo-intelligence for Sustainable Development (Advances in Geographical and Environmental Sciences)
by T. P. Singh Dharmaveer Singh R. B. SinghGlobally, concerns for the environment and human well-being have increased as results of threats imposed by climate change and disasters, environmental degradation, pollution of natural resources, water scarcity and proliferation of slums. Finding appropriate solutions to these threats and challenges is not simple, as these are generally complex and require state-of-the-art technology to collect, measure, handle and analyse large volumes of varying data sets. However, the recent advances in sensor technology, coupled with the rapid development of computational power, have greatly enhanced our abilities to capture, store and analyse the surrounding physical environment. This book explores diverse dimensions of geo-intelligence (GI) technology in developing a computing framework for location-based, data-integrating earth observation and predictive modelling to address these issues at all levels and scales. The book provides insight into the applications of GI technology in several fields of spatial and social sciences and attempts to bridge the gap between them.
Geochemical Mechanics and Deep Neural Network Modeling: Applications to Earthquake Prediction (Advances in Geological Science)
by Mitsuhiro ToriumiThe recent understandings about global earth mechanics are widely based on huge amounts of monitoring data accumulated using global networks of precise seismic stations, satellite monitoring of gravity, very large baseline interferometry, and the Global Positioning System. New discoveries in materials sciences of rocks and minerals and of rock deformation with fluid water in the earth also provide essential information. This book presents recent work on natural geometry, spatial and temporal distribution patterns of various cracks sealed by minerals, and time scales of their crack sealing in the plate boundary. Furthermore, the book includes a challenging investigation of stochastic earthquake prediction testing by means of the updated deep machine learning of a convolutional neural network with multi-labeling of large earthquakes and of the generative autoencoder modeling of global correlated seismicity. Their manifestation in this book contributes to the development of human society resilient from natural hazards. Presented here are (1) mechanics of natural crack sealing and fluid flow in the plate boundary regions, (2) large-scale permeable convection of the plate boundary, (3) the rapid process of massive extrusion of plate boundary rocks, (4) synchronous satellite gravity and global correlated seismicity, (5) Gaussian network dynamics of global correlated seismicity, and (6) prediction testing of plate boundary earthquakes by machine learning and generative autoencoders.
Geocoding Health Data: The Use of Geographic Codes in Cancer Prevention and Control, Research and Practice
by Gerard Rushton Marc P. Armstrong Josephine Gittler Barry R. Greene Claire E. Pavlik Michele M. West Dale L. ZimmermanIn the past, disease pattern mapping depended on census tracts based on political units, such as states and counties. However, with the advent of geographic information systems (GIS), researchers can now achieve a new level of precision and flexibility in geographic locating. This emerging technology allows the mapping of many different kinds of ge
GeoComputation
by Robert J. Abrahart Linda SeeA revision of Openshaw and Abrahart's seminal work, GeoComputation, Second Edition retains influences of its originators while also providing updated, state-of-the-art information on changes in the computational environment. In keeping with the field's development, this new edition takes a broader view and provides comprehensive coverage across the
Geocomputation with Python (Chapman & Hall/CRC The Python Series)
by Michael Dorman Anita Graser Jakub Nowosad Robin LovelaceGeocomputation with Python is a comprehensive resource for working with geographic data with the most popular programming language in the world. The book gives an overview of Python's capabilities for spatial data analysis, as well as dozens of worked-through examples covering the entire range of standard GIS operations. A unique selling point of the book is its cohesive and joined-up coverage of both vector and raster geographic data models and consistent learning curve. This book is an excellent starting point for those new to working with geographic data with Python, making it ideal for students and practitioners beginning their journey with Python.Key features: Showcases the integration of vector and raster datasets operations. Provides explanation of each line of code in the book to minimize surprises. Includes example datasets and meaningful operations to illustrate the applied nature of geographic research. Another unique feature is that this book is part of a wider community. Geocomputation with Python is a sister project of Geocomputation with R (Lovelace, Nowosad, and Muenchow 2019), a book on geographic data analysis, visualization, and modeling using the R programming language that has numerous contributors and an active community.The book teaches how to import, process, examine, transform, compute, and export spatial vector and raster datasets with Python, the most widely used language for data science and many other domains. Reading the book and running the reproducible code chunks within will make you a proficient user of key packages in the ecosystem, including shapely, geopandas, and rasterio. The book also demonstrates how to make use of dozens of additional packages for a wide range of tasks, from interactive map making to terrain modeling. Geocomputation with Python provides a firm foundation for more advanced topics, including spatial statistics, machine learning involving spatial data, and spatial network analysis, and a gateway into the vibrant and supportive community developing geographic tools in Python and beyond.