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The Lincoln Highway
#1 NEW YORK TIMES BESTSELLER More than ONE MILLION copies sold A TODAY Show Read with Jenna Book Club Pick A New York Times Notable Book, a New York Times Readers’ Choice Best Book of the Century, and Chosen by Oprah Daily, Time, NPR, The Washington Post, Bill Gates and Barack Obama as a Best Book of the Year “Wise and wildly entertaining . . . permeated with light, wit, youth.” —The New York Times Book Review “A classic that we will read for years to come.” —Jenna Bush Hager, Read with Jenna book club “Fantastic. Set in 1954, Towles uses the story of two brothers to show that our personal journeys are never as linear or predictable as we might hope.” —Bill Gates “A real joyride . . . elegantly constructed and compulsively readable.” —NPR The bestselling author of A Gentleman in Moscow and Rules of Civility and master of absorbing, sophisticated fiction returns with a stylish and propulsive novel set in 1950s America In June, 1954, eighteen-year-old Emmett Watson is driven home to Nebraska by the warden of the juvenile work farm where he has just served fifteen months for involuntary manslaughter. His mother long gone, his father recently deceased, and the family farm foreclosed upon by the bank, Emmett's intention is to pick up his eight-year-old brother, Billy, and head to California where they can start their lives anew. But when the warden drives away, Emmett discovers that two friends from the work farm have hidden themselves in the trunk of the warden's car. Together, they have hatched an altogether different plan for Emmett's future, one that will take them all on a fateful journey in the opposite direction—to the City of New York. Spanning just ten days and told from multiple points of view, Towles's third novel will satisfy fans of his multi-layered literary styling while providing them an array of new and richly imagined settings, characters, and themes. “Once again, I was wowed by Towles’s writing—especially because The Lincoln Highway is so different from A Gentleman in Moscow in terms of setting, plot, and themes. Towles is not a one-trick pony. Like all the best storytellers, he has range. He takes inspiration from famous hero’s journeys, including The Iliad, The Odyssey, Hamlet, Huckleberry Finn, and Of Mice and Men. He seems to be saying that our personal journeys are never as linear or predictable as an interstate highway. But, he suggests, when something (or someone) tries to steer us off course, it is possible to take the wheel.” – Bill Gates
Research Synthesis and Meta-Analysis
The Fifth Edition of Harris Cooper′s bestselling text offers practical advice on how to conduct a synthesis of research in the social, behavioral, and health sciences. The book is written in plain language with four running examples drawn from psychology, education, and health science. With ample coverage of literature searching and the technical aspects of meta-analysis, this one-of-a-kind book applies the basic principles of sound data gathering to the task of producing a comprehensive assessment of existing research.
Metric Learning
Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. In this book, we provide a thorough review of the metric learning literature that covers algorithms, theory and applications for both numerical and structured data. We first introduce relevant definitions and classic metric functions, as well as examples of their use in machine learning and data mining. We then review a wide range of metric learning algorithms, starting with the simple setting of linear distance and similarity learning. We show how one may scale-up these methods to very large amounts of training data. To go beyond the linear case, we discuss methods that learn nonlinear metrics or multiple linear metrics throughout the feature space, and review methods for more complex settings such as multi-task and semi-supervised learning. Although most of the existing work has focused on numerical data, we cover the literature on metric learning for structured data like strings, trees, graphs and time series. In the more technical part of the book, we present some recent statistical frameworks for analyzing the generalization performance in metric learning and derive results for some of the algorithms presented earlier. Finally, we illustrate the relevance of metric learning in real-world problems through a series of successful applications to computer vision, bioinformatics and information retrieval. Table of Contents: Introduction / Metrics / Properties of Metric Learning Algorithms / Linear Metric Learning / Nonlinear and Local Metric Learning / Metric Learning for Special Settings / Metric Learning for Structured Data / Generalization Guarantees for Metric Learning / Applications / Conclusion / Bibliography / Authors' Biographies