Originally published in 1985, Neil Postman’s groundbreaking polemic about the corrosive effects of television on our politics and public discourse has been hailed as a twenty-first-century book published in the twentieth century. Now, with television joined by more sophisticated electronic media—from the Internet to cell phones to DVDs—it has taken on even greater significance. Amusing Ourselves to Death is a prophetic look at what happens when politics, journalism, education, and even religion become subject to the demands of entertainment. It is also a blueprint for regaining controlof our media, so that they can serve our highest goals.

This volume systematically details both the basic principles and new developments in Data Envelopment Analysis (DEA), offering a solid understanding of the methodology, its uses, and its potential. New material in this edition includes coverage of recent developments that have greatly extended the power and scope of DEA and have lead to new directions for research and DEA uses. Each chapter accompanies its developments with simple numerical examples and discussions of actual applications. The first nine chapters cover the basic principles of DEA, while the final seven chapters provide a more advanced treatment.

Patterns of Empire comprehensively examines the two most powerful empires in modern history: the United States and Britain. Challenging the popular theory that the American empire is unique, Patterns of Empire shows how the policies, practices, forms and historical dynamics of the American empire repeat those of the British, leading up to the present climate of economic decline, treacherous intervention in the Middle East and overextended imperial confidence. A critical exercise in revisionist history and comparative social science, this book also offers a challenging theory of empire that recognizes the agency of non-Western peoples, the impact of global fields and the limits of imperial power.

If you are new to programming with Python and are looking for a solid introduction, this is the book for you. Developed by computer science instructors, books in the "for the absolute beginner" series teach the principles of programming through simple game creation. You will acquire the skills that you need for practical Python programming applications and will learn how these skills can be put to use in real-world scenarios. Throughout the chapters, you will find code samples that illustrate concepts presented. At the end of each chapter, you will find a complete game that demonstrates the key ideas in the chapter, a summary of the chapter, and a set of challenges that tests your newfound knowledge. By the time you finish this book, you?ll be well versed in Python and be able to apply the basic programming principles you?ve learned to the next programming language you tackle.

Based on the authors' market leading data structures books in Java and C++, this book offers a comprehensive, definitive introduction to data structures in Python by authoritative authors. Data Structures and Algorithms in Python is the first authoritative object-oriented book available for Python data structures. Designed to provide a comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation, the text will maintain the same general structure as Data Structures and Algorithms in Java and Data Structures and Algorithms in C++. Begins by discussing Python's conceptually simple syntax, which allows for a greater focus on concepts. Employs a consistent object-oriented viewpoint throughout the text. Presents each data structure using ADTs and their respective implementations and introduces important design patterns as a means to organize those implementations into classes, methods, and objects. Provides a thorough discussion on the analysis and design of fundamental data structures. Includes many helpful Python code examples, with source code provided on the website. Uses illustrations to present data structures and algorithms, as well as their analysis, in a clear, visual manner. Provides hundreds of exercises that promote creativity, help readers learn how to think like programmers, and reinforce important concepts. Contains many Python-code and pseudo-code fragments, and hundreds of exercises, which are divided into roughly 40% reinforcement exercises, 40% creativity exercises, and 20% programming projects.

This title now updated: the definitive neuroscience resource-from Eric R. Kandel, MD (winner of the Nobel Prize in 2000); James H. Schwartz, MD, PhD; Thomas M. Jessell, PhD; Steven A. Siegelbaum, PhD; and A. J. Hudspeth, PhD 900 full-color illustrations. Deciphering the link between the human brain and behavior has always been one of the most intriguing - and often challenging-aspects of scientific endeavor. The sequencing of the human genome, and advances in molecular biology, have illuminated the pathogenesis of many neurological diseases and have propelled our knowledge of how the brain controls behavior. To grasp the wider implications of these developments and gain a fundamental understanding of this dynamic, fast-moving field, Principles of Neuroscience stands alone as the most authoritative and indispensible resource of its kind. In this classic text, prominent researchers in the field expertly survey the entire spectrum of neural science, giving an up-to-date, unparalleled view of the discipline for anyone who studies brain and mind. Here, in one remarkable volume, is the current state of neural science knowledge - ranging from molecules and cells, to anatomic structures and systems, to the senses and cognitive functions-all supported by more than 900 precise, full-color illustrations. In addition to clarifying complex topics, the book also benefits from a cohesive organization, beginning with an insightful overview of the interrelationships between the brain, nervous system, genes, and behavior. Principles of Neural Science then proceeds with an in-depth examination of the molecular and cellular biology of nerve cells, synaptic transmission, and the neural basis of cognition. The remaining sections illuminate how cells, molecules, and systems give us sight, hearing, touch, movement, thought, learning, memories, and emotions. The new fifth edition of Principles of Neural Science is thoroughly updated to reflect the tremendous amount of research, and the very latest clinical perspectives, that have significantly transformed the field within the last decade. Ultimately, Principles of Neural Science affirms that all behavior is an expression of neural activity, and that the future of clinical neurology and psychiatry hinges on the progress of neural science. Far exceeding the scope and scholarship of similar texts, this unmatched guide offers a commanding, scientifically rigorous perspective on the molecular mechanisms of neural function and disease-one that you'll continually rely on to advance your comprehension of brain, mind, and behavior. Features: the cornerstone reference in the field of neuroscience that explains how the nerves, brain, and mind function; clear emphasis on how behavior can be examined through the electrical activity of both individual neurons and systems of nerve cells; current focus on molecular biology as a tool for probing the pathogenesis of many neurological diseases, including muscular dystrophy, Huntington disease, and certain forms of Alzheimer's disease; more than 900 engaging full-color illustrations - including line drawings, radiographs, micrographs, and medical photographs clarify often-complex neuroscience concepts; outstanding section on the development and emergence of behavior, including important coverage of brain damage repair, the sexual differentiation of the nervous system, and the aging brain. Features: more detailed discussions of cognitive and behavioral functions, and an expanded review of cognitive processes; a focus on the increasing importance of computational neural science, which enhances our ability to record the brain's electrical activity and study cognitive processes more directly; and chapter-opening. Key concepts: provides a convenient, study-enhancing introduction to the material covered in each chapter; selected readings and full reference citations at the close of each chapter facilitate further study and research; and helpful appendices highlight basic circuit theory; the neurological examination of the patient; circulation of the brain; the blood-brain barrier, choroid plexus, and cerebrospinal fluid; neural networks; and theoretical approaches to neuroscience.

Causality is central to the understanding and use of data. Without an understanding of cause effect relationships, we cannot use data to answer questions as basic as, “Does this treatment harm or help patients?” But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data.
Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest.
This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding.

This is a book about how we see: the environment around us (its surfaces, their layout, and their colors and textures); where we are in the environment; whether or not we are moving and, if we are, where we are going; what things are good for; how to do things (to thread a needle or drive an automobile); or why things look as they do. The basic assumption is that vision depends on the eye which is connected to the brain. The author suggests that natural vision depends on the eyes in the head on a body supported by the ground, the brain being only the central organ of a complete visual system. When no constraints are put on the visual system, people look around, walk up to something interesting and move around it so as to see it from all sides, and go from one vista to another. That is natural vision -- and what this book is about.

First published in 2001, Causality in Macroeconomics addresses the long-standing problems of causality while taking macroeconomics seriously. The practical concerns of the macroeconomist and abstract concerns of the philosopher inform each other. Grounded in pragmatic realism, the book rejects the popular idea that macroeconomics requires microfoundations, and argues that the macroeconomy is a set of structures that are best analyzed causally. Ideas originally due to Herbert Simon and the Cowles Commission are refined and generalized to non-linear systems, particularly to the non-linear systems with cross-equation restrictions that are ubiquitous in modern macroeconomic models with rational expectations (with and without regime-switching). These ideas help to clarify philosophical as well as economic issues. The structural approach to causality is then used to evaluate more familiar approaches to causality due to Granger, LeRoy and Glymour, Spirtes, Scheines and Kelly, as well as vector autoregressions, the Lucas critique, and the exogeneity concepts of Engle, Hendry and Richard.

What assumptions and methods allow us to turn observations into causal knowledge, and how can even incomplete causal knowledge be used in planning and prediction to influence and control our environment? In this book Peter Spirtes, Clark Glymour, and Richard Scheines address these questions using the formalism of Bayes networks, with results that have been applied in diverse areas of research in the social, behavioral, and physical sciences.The authors show that although experimental and observational study designs may not always permit the same inferences, they are subject to uniform principles. They axiomatize the connection between causal structure and probabilistic independence, explore several varieties of causal indistinguishability, formulate a theory of manipulation, and develop asymptotically reliable procedures for searching over equivalence classes of causal models, including models of categorical data and structural equation models with and without latent variables.The authors show that the relationship between causality and probability can also help to clarify such diverse topics in statistics as the comparative power of experimentation versus observation, Simpson's paradox, errors in regression models, retrospective versus prospective sampling, and variable selection. The second edition contains a new introduction and an extensive survey of advances and applications that have appeared since the first edition was published in 1993.

One of the most valuable skills in our economy is becoming increasingly rare. If you master this skill, you'll achieve extraordinary results.
Deep work is the ability to focus without distraction on a cognitively demanding task. It's a skill that allows you to quickly master complicated information and produce better results in less time. Deep work will make you better at what you do and provide the sense of true fulfillment that comes from craftsmanship. In short, deep work is like a super power in our increasingly competitive twenty-first century economy. And yet, most people have lost the ability to go deep-spending their days instead in a frantic blur of e-mail and social media, not even realizing there's a better way.
In DEEP WORK, author and professor Cal Newport flips the narrative on impact in a connected age. Instead of arguing distraction is bad, he instead celebrates the power of its opposite. Dividing this book into two parts, he first makes the case that in almost any profession, cultivating a deep work ethic will produce massive benefits. He then presents a rigorous training regimen, presented as a series of four "rules," for transforming your mind and habits to support this skill.
A mix of cultural criticism and actionable advice, DEEP WORK takes the reader on a journey through memorable stories-from Carl Jung building a stone tower in the woods to focus his mind, to a social media pioneer buying a round-trip business class ticket to Tokyo to write a book free from distraction in the air-and no-nonsense advice, such as the claim that most serious professionals should quit social media and that you should practice being bored. DEEP WORK is an indispensable guide to anyone seeking focused success in a distracted world.

Git is the version control system developed by Linus Torvalds for Linux kernel development. It took the open source world by storm since its inception in 2005, and is used by small development shops and giants like Google, Red Hat, and IBM, and of course many open source projects.
* A book by Git experts to turn you into a Git expert
* Introduces the world of distributed version control
* Shows how to build a Git development workflow
What you’ll learn
* Use Git as a programmer or a project leader.
* Become a fluent Git user.
* Use distributed features of Git to the full.
* Acquire the ability to insert Git in the development workflow.
* Migrate programming projects from other SCMs to Git.
* Learn how to extend Git.
This book is for all open source developers: you are bound to encounter it somewhere in the course of your working life. Proprietary software developers will appreciate Git’s enormous scalability, since it is used for the Linux project, which comprises thousands of developers and testers.

You've experienced the shiny, point-and-click surface of your Linux computer-now dive below and explore its depths with the power of the command line. The Linux Command Line takes you from your very first terminal keystrokes to writing full programs in Bash, the most popular Linux shell. Along the way you'll learn the timeless skills handed down by generations of gray-bearded, mouse-shunning gurus: file navigation, environment configuration, command chaining, pattern matching with regular expressions, and more. In addition to that practical knowledge, author William Shotts reveals the philosophy behind these tools and the rich heritage that your desktop Linux machine has inherited from Unix supercomputers of yore. As you make your way through the book's short, easily-digestible chapters, you'll learn how to: * Create and delete files, directories, and symlinks * Administer your system, including networking, package installation, and process management * Use standard input and output, redirection, and pipelines * Edit files with Vi, the world's most popular text editor * Write shell scripts to automate common or boring tasks * Slice and dice text files with cut, paste, grep, patch, and sed Once you overcome your initial "shell shock," you'll find that the command line is a natural and expressive way to communicate with your computer. Just don't be surprised if your mouse starts to gather dust.

Rigorously updated, with a new modular format, the second edition of Learning and Memory brings a modern perspective to the study of this key topic. Reflecting the growing importance of neuroscience in the field, it compares brain studies and behavioural approaches in human and other animal species, and is available in full-color throughout.