Interpreting Weight
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Interpreting Weight
What is "too fat"? what is "too thin"? Interpretations of body weight vary widely across and within cultures. Meeting weight expectations is a major concern for many people because failing to do so may incur dire social consequences, such as difficulty in finding a romantic partner or even in locating adequate employment. without these social and cultural pressures, body weight would only be a health issue. while socially constructed standards of body weight may seem immutable, they are continuously recreated through social interactions that perpetuate or transform expectations about fatness and thinness. Written by sociologists, psychologists, and nutritionists, all of the chapters in this book focus on how people construct fatness and thinness, examining different strategies used to interpret body weight, such as negotiating weight identities, reinterpreting weight, and becoming involved in weight-related organizations. Together these chapters emphasize the many ways that people actively define, construct, and enact their fatness and thinness in a variety of settings and situations.
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.
Basic Skills in Interpreting Laboratory Data
This new edition of Basic Skills in Interpreting Laboratory Data, 4th Edition is acase-based learning tool that will enhance your skills in clinical lab test interpretation. It provides fundamentals of interpreting lab test results not only for pharmacy students, but also for practitioners as an aid in assessing patient drug-treatment responses. It is the only text written by and for pharmacists and provides case studies and practical information on patient therapy.Since the publication of the third edition, much has changed—in the clinical lab and in the hospital pharmacy. Consequently, the new fourth edition incorporates significant revisions and a wealth of important new information. NEW TO THIS EDITION: Three new chapters including new information on men’s health, women’s health, and pharmacogenomics and laboratory tests. Mini-cases embedded in each chapter provide therapy-related examples and reinforce important points made in the text. Quickview Charts give an overview of important clinical information including reference ranges and critical values. Learning Points focus on a clinical application of a major concept present in the chapter.