Casting cultural controversies in a whole new light, an eminent philosopher presents bold, new theories that take into account scientific advances in physics, evolutionary biology, economics, and cognitive neurosience.

Invariances in Human Information Processing examines and identifies processing universals and how they are implemented in elementary judgemental processes. This edited collection offers evidence that these universals can be extracted and identified from observing law-like principles in perception, cognition, and action. Addressing memory operations, development, and conceptual learning, this book considers basic and complex meso- and makro-stages of information processing. Chapter authors provide theoretical accounts of cognitive processing that may offer tools for identification of functional components in brain activity in cognitive neuroscience

ISBN 9042003030 (paperback) NLG 45.00 Main headings: I. Philosophical and methodological problems of the process of cognition.- II. The structure of ideal learning process.- III. Control processes in memory. disillusion.

This book originated in the Workshop on "Hadronic Matter at Extreme Energy Density," held at the Ettore Majorana Center in Erice, October 13-21, 1978. The lectures have been expanded to their present size, and the contributions of seven seminars have been represented by abstracts which should stimulate the reader's interest and guide him to the original literature. The title of the book perhaps does not fully represent its content but still is a good indication of the conceptual motiva tion of our Workshop. The development of physics in recent years has filled in the first details of the grand design which was initiated with the theory of general relativity and aspires to a synthesis of all the different interactions. However, this development has not been a linear one but .has followed a divided pattern: general relativity had its phenomenological domain in cosmology and had little to do with high-energy elementary particle physics. It was progress in the knowledge of symmetries in particle physics that fueled the advance toward the present formulation of supergravity, thus help ing to heal this historical separation. The great program would not have advanced so far if our attention had all the time stayed focused at infinity, where the great issues are.

The developmental psychology text covers such topics as Darwinian dichotomies and their dissolution, dynamic systems theories, the creation and origins of knowledge, and coupled primal and plastic interactions in humans.

This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence. Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.

"This scholarly discussion of managerial challenges details the most recent research on how organizations can better create, share, and exploit knowledge. Spanning the business and public service context, the information provided covers practical issues such as measuring returns, establishing trust, and integrating technology. Also discussed are knowledge management systems, Internet support, and information systems development."

Covariance matrices play important roles in many areas of mathematics, statistics, and machine learning, as well as their applications. In computer vision and image processing, they give rise to a powerful data representation, namely the covariance descriptor, with numerous practical applications. In this book, we begin by presenting an overview of the {\it finite-dimensional covariance matrix} representation approach of images, along with its statistical interpretation. In particular, we discuss the various distances and divergences that arise from the intrinsic geometrical structures of the set of Symmetric Positive Definite (SPD) matrices, namely Riemannian manifold and convex cone structures. Computationally, we focus on kernel methods on covariance matrices, especially using the Log-Euclidean distance. We then show some of the latest developments in the generalization of the finite-dimensional covariance matrix representation to the {\it infinite-dimensional covariance operator} representation via positive definite kernels. We present the generalization of the affine-invariant Riemannian metric and the Log-Hilbert-Schmidt metric, which generalizes the Log-Euclidean distance. Computationally, we focus on kernel methods on covariance operators, especially using the Log-Hilbert-Schmidt distance. Specifically, we present a two-layer kernel machine, using the Log-Hilbert-Schmidt distance and its finite-dimensional approximation, which reduces the computational complexity of the exact formulation while largely preserving its capability. Theoretical analysis shows that, mathematically, the approximate Log-Hilbert-Schmidt distance should be preferred over the approximate Log-Hilbert-Schmidt inner product and, computationally, it should be preferred over the approximate affine-invariant Riemannian distance. Numerical experiments on image classification demonstrate significant improvements of the infinite-dimensional formulation over the finite-dimensional counterpart. Given the numerous applications of covariance matrices in many areas of mathematics, statistics, and machine learning, just to name a few, we expect that the infinite-dimensional covariance operator formulation presented here will have many more applications beyond those in computer vision.