Just think of your father's sperm as a starting off point. A usual male produces about 100 million sperm per ejaculation. Only one of those sperm will survive the arduous journey to its terminal apex. How many sperm does a male produce in, say, an 80-year life span? No precise count is possible, since it varies with each individual, but one can roughly estimate the number to be around 500 billion or perhaps more impressive sounding as a 1/2 trillion. If your own father had five children, this would mean that just in terms of sperm, you are a 1 in a 100 billion winner! Couple this with the rarity of your mother's egg (of the nearly half million follicles where only about 400 or so will become viable) and the very fact that you are alive reading this essay is beyond any moneyed lottery you will ever enter.
Leading technologists, historians, and journalists reveal the stories behind the computer coding that touches all aspects of life—for better or worse Few of us give much thought to computer code or how it comes to be. The very word “code” makes it sound immutable or even inevitable. “You Are Not Expected to Understand This” demonstrates that, far from being preordained, computer code is the result of very human decisions, ones we all live with when we use social media, take photos, drive our cars, and engage in a host of other activities. Everything from law enforcement to space exploration relies on code written by people who, at the time, made choices and assumptions that would have long-lasting, profound implications for society. Torie Bosch brings together many of today’s leading technology experts to provide new perspectives on the code that shapes our lives. Contributors discuss a host of topics, such as how university databases were programmed long ago to accept only two genders, what the person who programmed the very first pop-up ad was thinking at the time, the first computer worm, the Bitcoin white paper, and perhaps the most famous seven words in Unix history: “You are not expected to understand this.” This compelling book tells the human stories behind programming, enabling those of us who don’t think much about code to recognize its importance, and those who work with it every day to better understand the long-term effects of the decisions they make. With an introduction by Ellen Ullman and contributions by Mahsa Alimardani, Elena Botella, Meredith Broussard, David Cassel, Arthur Daemmrich, Charles Duan, Quinn DuPont, Claire L. Evans, Hany Farid, James Grimmelmann, Katie Hafner, Susan C. Herring, Syeda Gulshan Ferdous Jana, Lowen Liu, John MacCormick, Brian McCullough, Charlton McIlwain, Lily Hay Newman, Margaret O’Mara, Will Oremus, Nick Partridge, Benjamin Pope, Joy Lisi Rankin, Afsaneh Rigot, Ellen R. Stofan, Lee Vinsel, Josephine Wolff, and Ethan Zuckerman.
The ACT Matrix revolutionized contextual behavioral science. Now, the creators of this pioneering new model present the first detailed, step-by-step guide to help professionals implement the ACT Matrix in clinical practice and improve clients’ psychological flexibility. If you’re a clinician, you know that acceptance and commitment therapy (ACT) is extremely effective in helping clients who are “stuck” in unhealthy thought patterns by encouraging them align their values with their thoughts and actions. However, the ACT model is complex, and it's not always easy to use. Enter the ACT Matrix, a seamless fusion of the six core processes of the ACT hexaflex—cognitive defusion, acceptance, contact with the present moment, observing the self, values, and committed action—into a simplified, easy-to-apply approach. From the editors of The ACT Matrix, The Essential Guide to the ACT Matrix offers professionals a comprehensive guide to using the innovative Matrix model in-session. With this book, you’ll learn how to help your clients break free from painful psychological traps and live more meaningful lives. You’ll also learn how client actions and behavior should be viewed as workable or unworkable, rather than good or bad. Most importantly, you'll discover how this unique approach can be used to deliver ACT more effectively in a variety of settings and contexts, even when clients are resistant or unmotivated to participate. This book is essential for any ACT clinician looking to simplify their therapeutic approach in client sessions.
Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. This has led to an increasing demand for powerful software tools to help people analyze and manage vast amounts of text data effectively and efficiently. Unlike data generated by a computer system or sensors, text data are usually generated directly by humans, and are accompanied by semantically rich content. As such, text data are especially valuable for discovering knowledge about human opinions and preferences, in addition to many other kinds of knowledge that we encode in text. In contrast to structured data, which conform to well-defined schemas (thus are relatively easy for computers to handle), text has less explicit structure, requiring computer processing toward understanding of the content encoded in text. The current technology of natural language processing has not yet reached a point to enable a computer to precisely understand natural language text, but a wide range of statistical and heuristic approaches to analysis and management of text data have been developed over the past few decades. They are usually very robust and can be applied to analyze and manage text data in any natural language, and about any topic. This book provides a systematic introduction to all these approaches, with an emphasis on covering the most useful knowledge and skills required to build a variety of practically useful text information systems. The focus is on text mining applications that can help users analyze patterns in text data to extract and reveal useful knowledge. Information retrieval systems, including search engines and recommender systems, are also covered as supporting technology for text mining applications. The book covers the major concepts, techniques, and ideas in text data mining and information retrieval from a practical viewpoint, and includes many hands-on exercises designed with a companion software toolkit (i.e., MeTA) to help readers learn how to apply techniques of text mining and information retrieval to real-world text data and how to experiment with and improve some of the algorithms for interesting application tasks. The book can be used as a textbook for a computer science undergraduate course or a reference book for practitioners working on relevant problems in analyzing and managing text data.
Today, anyone in a scientific or technical discipline needs programming skills. Python is an ideal first programming language, and Introduction to Programming in Python is the best guide to learning it. Princeton University’s Robert Sedgewick, Kevin Wayne, and Robert Dondero have crafted an accessible, interdisciplinary introduction to programming in Python that emphasizes important and engaging applications, not toy problems. The authors supply the tools needed for students to learn that programming is a natural, satisfying, and creative experience. This example-driven guide focuses on Python’s most useful features and brings programming to life for every student in the sciences, engineering, and computer science. Coverage includes Basic elements of programming: variables, assignment statements, built-in data types, conditionals, loops, arrays, and I/O, including graphics and sound Functions, modules, and libraries: organizing programs into components that can be independently debugged, maintained, and reused Object-oriented programming and data abstraction: objects, modularity, encapsulation, and more Algorithms and data structures: sort/search algorithms, stacks, queues, and symbol tables Examples from applied math, physics, chemistry, biology, and computer science—all compatible with Python 2 and 3 Drawing on their extensive classroom experience, the authors provide Q&As, exercises, and opportunities for creative practice throughout. An extensive amount of supplementary information is available at introcs.cs.princeton.edu/python. With source code, I/O libraries, solutions to selected exercises, and much more, this companion website empowers people to use their own computers to teach and learn the material.
Economics is an integral aspect to every successful society, yet basic financial practices have gone unchanged for decades. Analyzing unconventional finance methods can provide new ways to ensure personal financial futures on an individual level, as well as boosting international economies. Alternative Decision-Making Models for Financial Portfolio Management: Emerging Research and Opportunities is an essential reference source that discusses methods and techniques that make financial administration more efficient for professionals in economic fields. Featuring relevant topics such as mean-variance portfolio theory, decision tree analysis, risk protection strategies, and asset-liability management, this publication is ideal for academicians, students, economists, and researchers that would like to stay current on new and innovative methods to transform the financial realm.
This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. Programming skills are indispensable in today’s world, not just for computer science students, but also for anyone in any scientific or technical discipline. Introduction to Programming in Java, Second Edition, by Robert Sedgewick and Kevin Wayne is an accessible, interdisciplinary treatment that emphasizes important and engaging applications, not toy problems. The authors supply the tools needed for students and professionals to learn that programming is a natural, satisfying, and creative experience, and to become conversant with one of the world’s most widely used languages. This example-driven guide focuses on Java’s most useful features and brings programming to life for every student in the sciences, engineering, and computer science. Coverage includes Basic elements of programming: variables, assignment statements, built-in data types, conditionals, loops, arrays, and I/O, including graphics and sound Functions, modules, and libraries: organizing programs into components that can be independently debugged, maintained, and reused Algorithms and data structures: sort/search algorithms, stacks, queues, and symbol tables Applications from applied math, physics, chemistry, biology, and computer science Drawing on their extensive classroom experience, throughout the text the authors provide Q&As, exercises, and opportunities for creative engagement with the material. Together with the companion materials described below, this book empowers people to pursue a modern approach to teaching and learning programming. Companion web site (introcs.cs.princeton.edu/java) contains Chapter summaries Supplementary exercises, some with solutions Detailed instructions for installing a Java programming environment Program code and test data suitable for easy download Detailed creative exercises, projects, and other supplementary materials Companion studio-produced online videos (informit.com/sedgewick) are available for purchase and provide students and professionals with the opportunity to engage with the material at their own pace and give instructors the opportunity to spend their time with students helping them to succeed on assignments and exams. Register your product at informit.com/register for convenient access to downloads, updates, and corrections as they become available.
This book demonstrates the importance of computer-generated statistical analyses in behavioral science research, particularly those using the R software environment. Statistical methods are being increasingly developed and refined by computer scientists, with expertise in writing efficient and elegant computer code. Unfortunately, many researchers lack this programming background, leaving them to accept on faith the black-box output that emerges from the sophisticated statistical models they frequently use. Building on the author’s previous volume, Linear Models in Matrix Form, this text bridges the gap between computer science and research application, providing easy-to-follow computer code for many statistical analyses using the R software environment. The text opens with a foundational section on linear algebra, then covers a variety of advanced topics, including robust regression, model selection based on bias and efficiency, nonlinear models and optimization routines, generalized linear models, and survival and time-series analysis. Each section concludes with a presentation of the computer code used to illuminate the analysis, as well as pointers to packages in R that can be used for similar analyses and nonstandard cases. The accessible code and breadth of topics make this book an ideal tool for graduate students or researchers in the behavioral sciences who are interested in performing advanced statistical analyses without having a sophisticated background in computer science and mathematics.
Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, medicine, computer science, and information theory. The print book version includes a code that provides free access to an eBook version. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment.
