Today we are at the brink of discoveries that should soon reveal the deepest secrets of the universe.Alpha and Omega is a dispatch from the front lines of the cosmological revolution that is being waged at observatories and laboratories around the world-in Europe, in America, and even in Antarctica--where scientists are actually peering into both the cradle of the universe and its grave. Scientists--including galaxy hunters and microwave eavesdroppers, gravity theorists and atom smashers, all of whom are on the trail of dark matter, dark energy, and the growing inhabitants of the particle zoo-now know how the universe will end and are on the brink of understanding its beginning. Their findings will be among the greatest triumphs of science, even towering above the deciphering of the human genome.This is the book you need to help understand the frequent front-page headlines heralding dramatic cosmological discoveries. It makes cutting-edge science both crystal clear and wonderfully exciting.

    Some years before that, in 1977, Eco published a little book for his students, "How to Write a Thesis," in which he offered useful advice on all the steps involved in researching and writing a thesis -- from choosing a topic to organizing a work schedule to writing the final draft. Now in its twenty-third edition in Italy and translated into seventeen languages, "How to Write a Thesis "has become a classic. Remarkably, this is its first, long overdue publication in English.Eco's approach is anything but dry and academic. He not only offers practical advice but also considers larger questions about the value of the thesis-writing exercise. "How to Write a Thesis" is unlike any other writing manual. It reads like a novel. It is opinionated. It is frequently irreverent, sometimes polemical, and often hilarious. Eco advises students how to avoid "thesis neurosis" and he answers the important question "Must You Read Books?" He reminds students "You are not Proust" and "Write everything that comes into your head, but only in the first draft." Of course, there was no Internet in 1977, but Eco's index card research system offers important lessons about critical thinking and information curating for students of today who may be burdened by Big Data."How to Write a Thesis" belongs on the bookshelves of students, teachers, writers, and Eco fans everywhere. Already a classic, it would fit nicely between two other classics: "Strunk and White" and "The Name of the Rose."This MIT Press edition will be available in three different cover colors.ContentsThe Definition and Purpose of a ThesisChoosing the TopicConducting ResearchThe Work Plan and the Index CardsWriting the ThesisThe Final Draft

    This results in “visible” learning because the effect is tangible. The framework is forged out of current research in mathematics combined with John Hattie’s synthesis of more than 15 years of education research involving 300 million students. Chapter by chapter, and equipped with video clips, planning tools, rubrics, and templates, you get the inside track on which instructional strategies to use at each phase of the learning cycle: Surface learning phase: When—through carefully constructed experiences—students explore new concepts and make connections to procedural skills and vocabulary that give shape to developing conceptual understandings.Deep learning phase: When—through the solving of rich high-cognitive tasks and rigorous discussion—students make connections among conceptual ideas, form mathematical generalizations, and apply and practice procedural skills with fluency.Transfer phase: When students can independently think through more complex mathematics, and can plan, investigate, and elaborate as they apply what they know to new mathematical situations. To equip students for higher-level mathematics learning, we have to be clear about where students are, where they need to go, and what it looks like when they get there. Visible Learning for Math brings about powerful, precision teaching for K-12 through intentionally designed guided, collaborative, and independent learning.

    students, providing a practical, realistic understanding of the processes of doing research for a doctorate.New to this edition: a section on increasingly popular professional doctorates such as Ed.D., D.B.A., and D.Eng; material for supervisors of overseas, part-time, and mature students; and a diagnostic questionnaire for students to monitor progress.

    Topics covered include matrix multiplication, row reduction, matrix inverse, orthogonality and computation. The self-teaching book is loaded with examples and graphics and provides a wide array of probing problems, accompanying solutions, and a glossary. Chapter 1: Introduction to Vectors; Chapter 2: Solving Linear Equations; Chapter 3: Vector Spaces and Subspaces; Chapter 4: Orthogonality; Chapter 5: Determinants; Chapter 6: Eigenvalues and Eigenvectors; Chapter 7: Linear Transformations; Chapter 8: Applications; Chapter 9: Numerical Linear Algebra; Chapter 10: Complex Vectors and Matrices; Solutions to Selected Exercises; Final Exam. Matrix Factorizations. Conceptual Questions for Review. Glossary: A Dictionary for Linear Algebra Index Teaching Codes Linear Algebra in a Nutshell.

    Devlin offers a breathtakingly new theory of language development that describes how language evolved in two stages and how its main purpose was not communication. Devlin goes on to show that the ability to think mathematically arose out of the same symbol-manipulating ability that was so crucial to the very first emergence of true language. Why, then, can't we do math as well as we speak? The answer, says Devlin, is that we can and do -- we just don't recognize when we're using mathematical reasoning.

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

    In this richly illustrated book, accessible examples are used to show how information theory can be understood in terms of everyday games like '20 Questions', and the simple MatLab programs provided give hands-on experience of information theory in action. Written in a tutorial style, with a comprehensive glossary, this text represents an ideal primer for novices who wish to become familiar with the basic principles of information theory.Download chapter 1 from http://jim-stone.staff.shef.ac.uk/Boo...

    Even the simplest numbers can become powerful forces when manipulated by politicians or the media, but in the case of the law, your liberty -- and your life -- can depend on the right calculation. In Math on Trial, mathematicians Leila Schneps and Coralie Colmez describe ten trials spanning from the nineteenth century to today, in which mathematical arguments were used -- and disastrously misused -- as evidence. They tell the stories of Sally Clark, who was accused of murdering her children by a doctor with a faulty sense of calculation; of nineteenth-century tycoon Hetty Green, whose dispute over her aunt's will became a signal case in the forensic use of mathematics; and of the case of Amanda Knox, in which a judge's misunderstanding of probability led him to discount critical evidence -- which might have kept her in jail. Offering a fresh angle on cases from the nineteenth-century Dreyfus affair to the murder trial of Dutch nurse Lucia de Berk, Schneps and Colmez show how the improper application of mathematical concepts can mean the difference between walking free and life in prison. A colorful narrative of mathematical abuse, Math on Trial blends courtroom drama, history, and math to show that legal expertise isn't't always enough to prove a person innocent.

    R is packed with powerful programming capabilities, but learning to use R in the real world can be overwhelming for even the most seasoned statisticians. This easy-to-follow guide explains how to use R for data processing and statistical analysis, and then, shows you how to present your data using compelling and informative graphics. You'll gain practical experience using R in a variety of settings and delve deeper into R's feature-rich toolset.Includes tips for the initial installation of RDemonstrates how to easily perform calculations on vectors, arrays, and lists of dataShows how to effectively visualize data using R's powerful graphics packagesGives pointers on how to find, install, and use add-on packages created by the R communityProvides tips on getting additional help from R mailing lists and websitesWhether you're just starting out with statistical analysis or are a procedural programming pro, "R For Dummies" is the book you need to get the most out of R.

    Being and Event is the greatest work of Alain Badiou, France's most important living philosopher. Long-awaited in translation, Being and Event makes available to an English-speaking readership Badiou's groundbreaking work on set theory - the cornerstone of his whole philosophy. The book makes the scope and aim of Badiou's whole philosophical project clear, enabling full comprehension of Badiou's significance for contemporary philosophy. Badiou draws upon and is fully engaged with the European philosophical tradition from Plato onwards; Being and Event deals with such key figures as Descartes, Spinoza, Leibniz, Hegel, Rousseau, Heidegger and Lacan.

    On different continents, under pressure from historical events and social conditions, people of faith have glimpsed the living God in fresh ways. It is not that a wholly different God is discovered from the One believed in by previous generations. Christian faith does not believe in a new God but, finding itself in new situations, seeks the presence of God there. Aspects long-forgotten are brought into new relationships with current events, and the depths of divine compassion are appreciated in ways not previously imagined.' This book sets out the fruit of these discoveries. The first chapter describes Johnson's point of departure and the rules of engagement, with each succeeding chapter distilling a discrete idea of God. Featured are transcendental, political, liberation, feminist, black, Hispanic, interreligious, and ecological theologies, ending with the particular Christian idea of the one God as Trinity.

    Switching from thin tailed to fat tailed distributions requires more than "changing the color of the dress." Traditional asymptotics deal mainly with either n=1 or n=∞, and the real world is in between, under the "laws of the medium numbers"-which vary widely across specific distributions. Both the law of large numbers and the generalized central limit mechanisms operate in highly idiosyncratic ways outside the standard Gaussian or Levy-Stable basins of convergence. A few examples: - The sample mean is rarely in line with the population mean, with effect on "na�ve empiricism," but can be sometimes be estimated via parametric methods. - The "empirical distribution" is rarely empirical. - Parameter uncertainty has compounding effects on statistical metrics. - Dimension reduction (principal components) fails. - Inequality estimators (Gini or quantile contributions) are not additive and produce wrong results. - Many "biases" found in psychology become entirely rational under more sophisticated probability distributions. - Most of the failures of financial economics, econometrics, and behavioral economics can be attributed to using the wrong distributions. This book, the first volume of the Technical Incerto, weaves a narrative around published journal articles.

    While such visualizations can better inform us, they can also deceive by displaying incomplete or inaccurate data, suggesting misleading patterns—or simply misinform us by being poorly designed, such as the confusing “eye of the storm” maps shown on TV every hurricane season.Many of us are ill equipped to interpret the visuals that politicians, journalists, advertisers, and even employers present each day, enabling bad actors to easily manipulate visuals to promote their own agendas. Public conversations are increasingly driven by numbers, and to make sense of them we must be able to decode and use visual information. By examining contemporary examples ranging from election-result infographics to global GDP maps and box-office record charts, How Charts Lie teaches us how to do just that.

    Unfortunately, people tend to be terrible forecasters. As Wharton professor Philip Tetlock showed in a landmark 2005 study, even experts’ predictions are only slightly better than chance. However, an important and underreported conclusion of that study was that some experts do have real foresight, and Tetlock has spent the past decade trying to figure out why. What makes some people so good? And can this talent be taught?   In Superforecasting, Tetlock and coauthor Dan Gardner offer a masterwork on prediction, drawing on decades of research and the results of a massive, government-funded forecasting tournament. The Good Judgment Project involves tens of thousands of ordinary people—including a Brooklyn filmmaker, a retired pipe installer, and a former ballroom dancer—who set out to forecast global events. Some of the volunteers have turned out to be astonishingly good. They’ve beaten other benchmarks, competitors, and prediction markets. They’ve even beaten the collective judgment of intelligence analysts with access to classified information. They are "superforecasters."   In this groundbreaking and accessible book, Tetlock and Gardner show us how we can learn from this elite group. Weaving together stories of forecasting successes (the raid on Osama bin Laden’s compound) and failures (the Bay of Pigs) and interviews with a range of high-level decision makers, from David Petraeus to Robert Rubin, they show that good forecasting doesn’t require powerful computers or arcane methods. It involves gathering evidence from a variety of sources, thinking probabilistically, working in teams, keeping score, and being willing to admit error and change course. Superforecasting offers the first demonstrably effective way to improve our ability to predict the future—whether in business, finance, politics, international affairs, or daily life—and is destined to become a modern classic.