David Marr's posthumously published Vision (1982) influenced a generation of brain and cognitive scientists, inspiring many to enter the field. In Vision, Marr describes a general framework for understanding visual perception and touches on broader questions about how the brain and its functions can be studied and understood. Researchers from a range of brain and cognitive sciences have long valued Marr's creativity, intellectual power, and ability to integrate insights and data from neuroscience, psychology, and computation. This MIT Press edition makes Marr's influential work available to a new generation of students and scientists. In Marr's framework, the process of vision constructs a set of representations, starting from a description of the input image and culminating with a description of three-dimensional objects in the surrounding environment. A central theme, and one that has had far-reaching influence in both neuroscience and cognitive science, is the notion of different levels of analysis--in Marr's framework, the computational level, the algorithmic level, and the hardware implementation level. Now, thirty years later, the main problems that occupied Marr remain fundamental open problems in the study of perception. Vision provides inspiration for the continuing efforts to integrate knowledge from cognition and computation to understand vision and the brain.

Mobile robots range from the Mars Pathfinder mission's teleoperated Sojourner to the cleaning robots in the Paris Metro. This text offers students and other interested readers an introduction to the fundamentals of mobile robotics, spanning the mechanical, motor, sensory, perceptual, and cognitive layers the field comprises. The text focuses on mobility itself, offering an overview of the mechanisms that allow a mobile robot to move through a real world environment to perform its tasks, including locomotion, sensing, localization, and motion planning. It synthesizes material from such fields as kinematics, control theory, signal analysis, computer vision, information theory, artificial intelligence, and probability theory. The book presents the techniques and technology that enable mobility in a series of interacting modules. Each chapter treats a different aspect of mobility, as the book moves from low-level to high-level details. It covers all aspects of mobile robotics, including software and hardware design considerations, related technologies, and algorithmic techniques.] This second edition has been revised and updated throughout, with 130 pages of new material on such topics as locomotion, perception, localization, and planning and navigation. Problem sets have been added at the end of each chapter. Bringing together all aspects of mobile robotics into one volume, Introduction to Autonomous Mobile Robots can serve as a textbook or a working tool for beginning practitioners.

The inventor of C++ presents the definitive insider's guide to the design and development of the C++ programming language. Without ommitting critical details or getting bogged down in technicalities, Stroustrup presents his unique insights into the decisions that shaped C++. Every C++ programmer will benefit from Stroustrup's explanations of the 'why's' behind C++ from the earliest features, such as the original class concept, to the latest extensions, such as new casts and explicit template instantiation. Some C++ design decisions have been universally praised, while others remain controversial, and debated vigorously; still other features have been rejected based on experimentation. In this book, Stroustrup dissects many of these decisions to present a case study in "real object- oriented language development" for the working programmer. In doing so, he presents his views on programming and design in a concrete and useful way that makes this book a must-buy for every C++ programmer. Features*Written by the inventor of C++ Stroustrup *Provides insights into the design decisions which shaped C++. *Gives technical summaries of C++.* Discusses the latest language features: templates, exceptions, run-time type information, and namespaces. *Presents Stroustrup's unique programming and design views. 0201543303B04062001

At the close of the 1980s, the independent contributions of Yann Brenier, Mike Cullen and John Mather launched a revolution in the venerable field of optimal transport founded by G. Monge in the 18th century, which has made breathtaking forays into various other domains of mathematics ever since. The author presents a broad overview of this area, supplying complete and self-contained proofs of all the fundamental results of the theory of optimal transport at the appropriate level of generality. Thus, the book encompasses the broad spectrum ranging from basic theory to the most recent research results. PhD students or researchers can read the entire book without any prior knowledge of the field. A comprehensive bibliography with notes that extensively discuss the existing literature underlines the book's value as a most welcome reference text on this subject.

The book is a collection of essays in honour of Clive Granger. The chapters are by some of the world'leading econometricians, all of whom have collaborated with or studied with (or both) Clive Granger. Central themes of Grangers work are reflected in the book with attention to tests for unit roots and cointegration, tests of misspecification, forecasting models and forecast evaluation, non-linear and non-parametric econometric techniques, and overall, a careful blend of practical empirical work and strong theory. The book shows the scope of Granger's research and the range of the profession that has been influenced by his work.

The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest. This book is aimed at both statisticians and applied researchers interested in causal inference and general effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihood estimator, including related concepts necessary to understand and apply these methods. Parts II-IX handle complex data structures and topics applied researchers will immediately recognize from their own research, including time-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data, and genomic studies.

In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.Olivier Chapelle and Alexander Zien are Research Scientists and Bernhard Scholkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in Tubingen. Scholkopf is coauthor of Learning with Kernels (MIT Press, 2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by The MIT Press.

Since its founding in 1989 by Terrence Sejnowski, Neural Computation has become the leading journal in the field. Foundations of Neural Computationcollects, by topic, the most significant papers that have appeared in the journal over the past nine years.This volume of Foundations of Neural Computation, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. The goal of unsupervised learning is to extract an efficient internal representation of the statistical structure implicit in the inputs. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. They are also of interest to engineers working in areas such as computer vision and speech recognition who seek efficient representations of raw input data.

The first comprehensive account of the growing dominance of the intangible economy
Early in the twenty-first century, a quiet revolution occurred. For the first time, the major developed economies began to invest more in intangible assets, like design, branding, R&D, and software, than in tangible assets, like machinery, buildings, and computers. For all sorts of businesses, from tech firms and pharma companies to coffee shops and gyms, the ability to deploy assets that one can neither see nor touch is increasingly the main source of long-term success.
But this is not just a familiar story of the so-called new economy. Capitalism without Capital shows that the growing importance of intangible assets has also played a role in some of the big economic changes of the last decade. The rise of intangible investment is, Jonathan Haskel and Stian Westlake argue, an underappreciated cause of phenomena from economic inequality to stagnating productivity.
Haskel and Westlake bring together a decade of research on how to measure intangible investment and its impact on national accounts, showing the amount different countries invest in intangibles, how this has changed over time, and the latest thinking on how to assess this. They explore the unusual economic characteristics of intangible investment, and discuss how these features make an intangible-rich economy fundamentally different from one based on tangibles.
Capitalism without Capital concludes by presenting three possible scenarios for what the future of an intangible world might be like, and by outlining how managers, investors, and policymakers can exploit the characteristics of an intangible age to grow their businesses, portfolios, and economies.

Radical constructivism is a theory of knowing that provides a pragmatic approach to questions about reality, truth, language and human understanding. It introduces a change to many basic ideas, and consequently has a profound influence on the general attitude towards the world we experience. By generating awareness of the thinker's active role in building up concepts, the new orientation liberates the individual from spurious tethers and shows that it is ultimately ourselves who are responsible for what we think and do. To educators, the book suggests that the most important task is not to convey ready-made knowledge but to teach the art of constructing it. In this volume, Glaserfeld offers a theoretical account of radical constructivism. It is an elegantly and thoroughly argued account of this epistemological position, providing a profound analysis of its concepts. The book traces two genealogies of the theory. The first is the constructivist strand in the history of philosophy from the pre-Socratics via Jean Piaget to the present. The second is his own intellectual biography, illustrating how a number of lines of thought became synthesised into radical constructivism. Given its diverse roots, the first full articulation of the theory is likely to have an influence that extends beyond mathematics education.