During the past decade, tremendous growth has occurred in the use of nutrition symbols and rating systems designed to summarize key nutritional aspects and characteristics of food products. These symbols and the systems that underlie them have become known as front-of-package (FOP) nutrition rating systems and symbols, even though the symbols themselves can be found anywhere on the front of a food package or on a retail shelf tag. Though not regulated and inconsistent in format, content, and criteria, FOP systems and symbols have the potential to provide useful guidance to consumers as well as maximize effectiveness. As a result, Congress directed the Centers for Disease Control and Prevention (CDC) to undertake a study with the Institute of Medicine (IOM) to examine and provide recommendations regarding FOP nutrition rating systems and symbols. The study was completed in two phases. Phase I focused primarily on the nutrition criteria underlying FOP systems. Phase II builds on the results of Phase I while focusing on aspects related to consumer understanding and behavior related to the development of a standardized FOP system. Front-of-Package Nutrition Rating Systems and Symbols focuses on Phase II of the study. The report addresses the potential benefits of a single, standardized front-label food guidance system regulated by the Food and Drug Administration, assesses which icons are most effective with consumer audiences, and considers the systems/icons that best promote health and how to maximize their use.

Distinguish fake news from reliable journalism with this clear and concise handbook by New York Times best-selling author Bruce Bartlett. Today’s media and political landscapes are littered with untrustworthy sources and the dangerous concept of “fake news.” This accessible guide helps you fight this deeply troubling trend and ensure that truth is not a permanent casualty. Written by Capitol Hill veteran and author Bruce Bartlett, The Truth Matters presents actionable tips and tricks for reading critically, judging sources, using fact-checking sites, avoiding confirmation bias, identifying trustworthy experts, and more.

Computer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features. This book provides necessary background to develop intuition about why interest point detectors and feature descriptors actually work, how they are designed, with observations about tuning the methods for achieving robustness and invariance targets for specific applications. The survey is broader than it is deep, with over 540 references provided to dig deeper. The taxonomy includes search methods, spectra components, descriptor representation, shape, distance functions, accuracy, efficiency, robustness and invariance attributes, and more. Rather than providing ‘how-to’ source code examples and shortcuts, this book provides a counterpoint discussion to the many fine opencv community source code resources available for hands-on practitioners.