Little Yau

When Trau is poisoned, Little Yau and some of the other fuzzheads don clothes and shoes and venture into the world of humans, searching for the herb they need to make a cure.

Whether used for thematic story times, program and curriculum planning, readers' advisory, or collection development, this updated edition of the well-known companion makes finding the right picture books for your library a breeze. Generations of savvy librarians and educators have relied on this detailed subject guide to children's picture books for all aspects of children's services, and this new edition does not disappoint. Covering more than 18,000 books published through 2017, it empowers users to identify current and classic titles on topics ranging from apples to zebras. Organized simply, with a subject guide that categorizes subjects by theme and topic and subject headings arranged alphabetically, this reference applies more than 1,200 intuitive (as opposed to formal catalog) subject terms to children's picture books, making it both a comprehensive and user-friendly resource that is accessible to parents and teachers as well as librarians. It can be used to identify titles to fill in gaps in library collections, to find books on particular topics for young readers, to help teachers locate titles to support lessons, or to design thematic programs and story times. Title and illustrator indexes, in addition to a bibliographic guide arranged alphabetically by author name, further extend access to titles.

Can artificial intelligence learn mathematics? The question is at the heart of this original monograph bringing together theoretical physics, modern geometry, and data science. The study of Calabi–Yau manifolds lies at an exciting intersection between physics and mathematics. Recently, there has been much activity in applying machine learning to solve otherwise intractable problems, to conjecture new formulae, or to understand the underlying structure of mathematics. In this book, insights from string and quantum field theory are combined with powerful techniques from complex and algebraic geometry, then translated into algorithms with the ultimate aim of deriving new information about Calabi–Yau manifolds. While the motivation comes from mathematical physics, the techniques are purely mathematical and the theme is that of explicit calculations. The reader is guided through the theory and provided with explicit computer code in standard software such as SageMath, Python and Mathematica to gain hands-on experience in applications of artificial intelligence to geometry. Driven by data and written in an informal style, The Calabi–Yau Landscape makes cutting-edge topics in mathematical physics, geometry and machine learning readily accessible to graduate students and beyond. The overriding ambition is to introduce some modern mathematics to the physicist, some modern physics to the mathematician, and machine learning to both.

This book provides practical guidance for statisticians, clinicians, and researchers involved in clinical trials in the biopharmaceutical industry, medical and public health organisations. Academics and students needing an introduction to handling missing data will also find this book invaluable. The authors describe how missing data can affect the outcome and credibility of a clinical trial, show by examples how a clinical team can work to prevent missing data, and present the reader with approaches to address missing data effectively. The book is illustrated throughout with realistic case studies and worked examples, and presents clear and concise guidelines to enable good planning for missing data. The authors show how to handle missing data in a way that is transparent and easy to understand for clinicians, regulators and patients. New developments are presented to improve the choice and implementation of primary and sensitivity analyses for missing data. Many SAS code examples are included – the reader is given a toolbox for implementing analyses under a variety of assumptions.