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This highly original work presents laboratory science in a deliberately skeptical way: as an anthropological approach to the culture of the scientist. Drawing on recent work in literary criticism, the authors study how the social world of the laboratory produces papers and other "texts,"' and how the scientific vision of reality becomes that set of statements considered, for the time being, too expensive to change. The book is based on field work done by Bruno Latour in Roger Guillemin's laboratory at the Salk Institute and provides an important link between the sociology of modern sciences and laboratory studies in the history of science.
An extensive survey of available information from the literature on heat transfer to boiling hydrogen, nitrogen, and oxygen is presented. Included are a bibliography of available experimental data, a summary of boiling theory, graphical comparisons of the experimental data, and compilations of pertinent physical properties for each of the three cryogenic fluids. Based upon the results of the survey, recommendations are made for a test program to provide critical information there data are lacking.
For Stirling engines to enjoy widespread application and acceptance, not only must the fundamental operation of such engines be widely understood, but the requisite analytic tools for the stimulation, design, evaluation and optimization of Stirling engine hardware must be readily available. The purpose of this design manual is to provide an introduction to Stirling cycle heat engines, to organize and identify the available Stirling engine literature, and to identify, organize, evaluate and, in so far as possible, compare non-proprietary Stirling engine design methodologies. This report was originally prepared for the National Aeronautics and Space Administration and the U. S. Department of Energy.
Fusion research started over half a century ago. Although the task remains unfinished, the end of the road could be in sight if society makes the right decisions. Nuclear Fusion: Half a Century of Magnetic Confinement Fusion Research is a careful, scholarly account of the course of fusion energy research over the past fifty years. The authors outline the different paths followed by fusion research from initial ignorance to present understanding. They explore why a particular scheme would not work and why it was more profitable to concentrate on the mainstream tokamak development. The book features descriptive sections, in-depth explanations of certain physical and technical issues, scientific terms, and an extensive glossary that explains relevant abbreviations and acronyms.
The First Conference on the History of Nordic Computing (HiNC1) was organized in Trondheim, in June 2003. The HiNC1 event focused on the early years of computing, that is the years from the 1940s through the 1960s, although it formally extended to year 1985. In the preface of the proceedings of HiNC1, Janis Bubenko, Jr. , John Impagliazzo, and Arne Sølvberg describe well the peculiarities of early Nordic c- puting [1]. While developing hardware was a necessity for the first professionals, quite soon the computer became an industrial product. Computer scientists, among others, grew increasingly interested in programming and application software. P- gress in these areas from the 1960s to the 1980s was experienced as astonishing. The developments during these decades were taken as the focus of HiNC2. During those decades computers arrived to every branch of large and medium-sized businesses and the users of the computer systems were no longer only computer s- cialists but also people with other main duties. Compared to the early years of comp- ing before 1960, where the number of computer projects and applications was small, capturing a holistic view of the history between the 1960s and the 1980s is conside- bly more difficult. The HiNC2 conference attempted to help in this endeavor.
This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: · Evaluation and Generalization in Interpretable Machine Learning · Explanation Methods in Deep Learning · Learning Functional Causal Models with Generative Neural Networks · Learning Interpreatable Rules for Multi-Label Classification · Structuring Neural Networks for More Explainable Predictions · Generating Post Hoc Rationales of Deep Visual Classification Decisions · Ensembling Visual Explanations · Explainable Deep Driving by Visualizing Causal Attention · Interdisciplinary Perspective on Algorithmic Job Candidate Search · Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions · Inherent Explainability Pattern Theory-based Video Event Interpretations
Millions have seen the movie and thousands have read the book but few have fully appreciated the mathematics developed by John Nash's beautiful mind. Today Nash's beautiful math has become a universal language for research in the social sciences and has infiltrated the realms of evolutionary biology, neuroscience, and even quantum physics. John Nash won the 1994 Nobel Prize in economics for pioneering research published in the 1950s on a new branch of mathematics known as game theory. At the time of Nash's early work, game theory was briefly popular among some mathematicians and Cold War analysts. But it remained obscure until the 1970s when evolutionary biologists began applying it to their work. In the 1980s economists began to embrace game theory. Since then it has found an ever expanding repertoire of applications among a wide range of scientific disciplines. Today neuroscientists peer into game players' brains, anthropologists play games with people from primitive cultures, biologists use games to explain the evolution of human language, and mathematicians exploit games to better understand social networks. A common thread connecting much of this research is its relevance to the ancient quest for a science of human social behavior, or a Code of Nature, in the spirit of the fictional science of psychohistory described in the famous Foundation novels by the late Isaac Asimov. In A Beautiful Math, acclaimed science writer Tom Siegfried describes how game theory links the life sciences, social sciences, and physical sciences in a way that may bring Asimov's dream closer to reality.