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From a February 1991 conference in Santa Monica, California, 22 papers by particle, theoretical, and accelerator physicists discuss flavor physics and novel flavor factories (aspects of the subatomic world). CP violation and the search for flavor-changing weak neutral currents are covered extensivel
Reducing the intake of sodium is an important public health goal for Americans. Since the 1970s, an array of public health interventions and national dietary guidelines has sought to reduce sodium intake. However, the U.S. population still consumes more sodium than is recommended, placing individuals at risk for diseases related to elevated blood pressure. Strategies to Reduce Sodium Intake in the United States evaluates and makes recommendations about strategies that could be implemented to reduce dietary sodium intake to levels recommended by the Dietary Guidelines for Americans. The book reviews past and ongoing efforts to reduce the sodium content of the food supply and to motivate consumers to change behavior. Based on past lessons learned, the book makes recommendations for future initiatives. It is an excellent resource for federal and state public health officials, the processed food and food service industries, health care professionals, consumer advocacy groups, and academic researchers.
In this timeless and profound inquiry, Aristotle presents a view of the psyche that avoids the simplifications both of the materialists and those who believe in the soul as something quite distinct from body. On the Soul also includes Aristotle's idiosyncratic and influential account of light and colors. On Memory and Recollection continues the investigation of some of the topics introduced in On the Soul. Sachs's fresh and jargon-free approach to the translation of Aristotle, his lively and insightful introduction, and his notes and glossaries, all bring out the continuing relevance of Aristotle's thought to biological and philosophical questions.
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.