!--StartFragment--What is the difference between choking and panicking? Why are there dozens of varieties of mustard-but only one variety of ketchup? What do football players teach us about how to hire teachers? What does hair dye tell us about the history of the 20th century? In the past decade, Malcolm Gladwell has written three books that have radically changed how we understand our world and ourselves: The Tipping Point; Blink; and Outliers. Now, in What the Dog Saw, he brings together, for the first time, the best of his writing from TheNew Yorker over the same period. Here is the bittersweet tale of the inventor of the birth control pill, and the dazzling inventions of the pasta sauce pioneer Howard Moscowitz. Gladwell sits with Ron Popeil, the king of the American kitchen, as he sells rotisserie ovens, and divines the secrets of Cesar Millan, the "dog whisperer" who can calm savage animals with the touch of his hand. He explores intelligence tests and ethnic profiling and "hindsight bias" and why it was that everyone in Silicon Valley once tripped over themselves to hire the same college graduate. "Good writing," Gladwell says in his preface, "does not succeed or fail on the strength of its ability to persuade. It succeeds or fails on the strength of its ability to engage you, to make you think, to give you a glimpse into someone else's head." What the Dog Saw is yet another example of the buoyant spirit and unflagging curiosity that have made Malcolm Gladwell our most brilliant investigator of the hidden extraordinary. !--EndFragment--

A collection of refereed papers from a six-week workshop on statistics in the health sciences, that brought together theoretical and applied statisticians from universities, medical and public health schools, government and private research institutions, and pharmaceutical companies involved in prediction problems in the life and social sciences and in diagnostic and screening tests. A number of papers with applications were presented and particularly lively discussions ensued involving the critical issues and difficulties in using and interpreting diagnostic tests and implementing mass screening programs. The prediction or controlling future events, such as survival, comparative survival and survival post intervention for a disease or even for certain biological or natural events was also represented by participants who presented work that devised predictive methodology for a variety of problems mainly from a Bayesian perspective.

Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery provides a comprehensive introduction of intelligent fault diagnosis and RUL prediction based on the current achievements of the author's research group. The main contents include multi-domain signal processing and feature extraction, intelligent diagnosis models, clustering algorithms, hybrid intelligent diagnosis strategies, and RUL prediction approaches, etc. This book presents fundamental theories and advanced methods of identifying the occurrence, locations, and degrees of faults, and also includes information on how to predict the RUL of rotating machinery. Besides experimental demonstrations, many application cases are presented and illustrated to test the methods mentioned in the book. This valuable reference provides an essential guide on machinery fault diagnosis that helps readers understand basic concepts and fundamental theories. Academic researchers with mechanical engineering or computer science backgrounds, and engineers or practitioners who are in charge of machine safety, operation, and maintenance will find this book very useful. - Provides a detailed background and roadmap of intelligent diagnosis and RUL prediction of rotating machinery, involving fault mechanisms, vibration characteristics, health indicators, and diagnosis and prognostics - Presents basic theories, advanced methods, and the latest contributions in the field of intelligent fault diagnosis and RUL prediction - Includes numerous application cases, and the methods, algorithms, and models introduced in the book are demonstrated by industrial experiences

Control theories have dominated criminological theory and research since the 1969 publication of Hirschi's seminal work on the social bond. Social control and self-control theorists are unique in suggesting that patterns in criminal behaviors are better explained by variations in social constraints rather than by individual motivational impulses, thus indicating that their main concerns are the explication and clarification of the techniques, processes, and institutions of informal social control. The four major sections of this volume focus on: the similarities and differences among the major contributors to the early developmental stage of social control theory; the central importance of parents, peers, and schools in the creation of informal control mechanisms and their link to crime and delinquency; the theoretical underpinnings of self-control theory, including empirical tests and criticisms; and theoretical integrations of social control and self-control theories with various motivational theories of crime and delinquency.

The Practice of Organizational Diagnosis: Theory & Methods presents a new paradigm for examining the intergroup dynamics of organizations by combining the procedures of organizational diagnosis with the theory of embedded intergroup relations. In this volume, Alderfer explains the relevance of the paradigm concept for the present work, shows the importance of intergroup relations in the formative organization studies, reviews extant modes of organizational diagnosis, and demonstrates the limitations of interpersonal and intra-group theories. He then presents the five laws of embedded intergroup relations as a response to the problems associated with the earlier work. After comparing and contrasting alterative group level theories and explaining the several meanings of empirical support, the author describes the empirical basis of the five laws. Based on examining alternative codes of professional conduct and applying the five laws, he provides his prescriptions for the ethical basis of sound diagnostic practice. With the theory and ethical position in place, he then explains procedures for conducting each phase of organizational diagnosis: entry, data collection, data analysis, and feedback. He follows that by reporting the empirical bases for the methods used in the four phases. The volume concludes by describing the courses and educational processes essential for educating people to conduct organizational diagnoses. A recurring theme from beginning to end is that the lawfulness of human behavior in relation to organizations is as applicable to diagnosticians, whether working alone or in teams, as it is to their clients. By addressing theory, method, data, and values, the volume presents a complete paradigm for organizational diagnosis.

The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems. - Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learning - Be able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clustering - Learn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection

Intelligent Modeling, Prediction, and Diagnosis from Epidemiological Data: COVID-19 and Beyond is a handy treatise to elicit and elaborate possible intelligent mechanisms for modeling, prediction, diagnosis, and early detection of diseases arising from outbreaks of different epidemics with special reference to COVID-19. Starting with a formal introduction of the human immune systems, this book focuses on the epidemiological aspects with due cognizance to modeling, prevention, and diagnosis of epidemics. In addition, it also deals with evolving decisions on post-pandemic socio-economic structure. The book offers a comprehensive coverage of the most essential topics, including: A general overview of pandemics and their outbreak behavior A detailed overview of CI techniques Intelligent modeling, prediction, and diagnostic measures for pandemics Prognostic models Post-pandemic socio-economic structure The accompanying case studies are based on available real-world data sets. While other books may deal with this COVID-19 pandemic, none features topics covering the human immune system as well as influences on the environmental disorder due to the ongoing pandemic. The book is primarily intended to benefit medical professionals and healthcare workers as well as the virologists who are essentially the frontline fighters of this pandemic. In addition, it also serves as a vital resource for relevant researchers in this interdisciplinary field as well as for tutors and postgraduate and undergraduate students of information sciences.