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Advances in Electronics and Electron Physics
Focuses on the notion that by breaking the domain of the original problem into subdomains, such an approach can, if properly implemented, lead to a considerable speedup. The methods are particularly well suited for parallel computers.
This study on multilevel analysis cuts through the confusion surrounding the development and testing of multilevel theories. It illuminates processes and effects within organisations, synthesising and updating current theory.
Multilevel convergence theory is discussed, and we adapt and apply some recent results to the Galerkin-based methods. Numerical results arepresented for several test problems, including a nonlinear PBE calculation of the electrostatic potential of Superoxide Dismutase, an enzymewhich has recently been linked to Lou Gehrig's disease. We present a collection of performance statistics and benchmarks for the linear andnonlinear methods on a number of sequential and parallel computers, and discuss the software developed in the course of the research."
A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence "Correlation is not causation." This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality -- the study of cause and effect -- on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.
This book presents the state of the art in multilevel analysis, with an emphasis on more advanced topics. These topics are discussed conceptually, analyzed mathematically, and illustrated by empirical examples. Multilevel analysis is the statistical analysis of hierarchically and non-hierarchically nested data. The simplest example is clustered data, such as a sample of students clustered within schools. Multilevel data are especially prevalent in the social and behavioral sciences and in the biomedical sciences. The chapter authors are all leading experts in the field. Given the omnipresence of multilevel data in the social, behavioral, and biomedical sciences, this book is essential for empirical researchers in these fields.