Diffusion in metals is an important phenomenon, which has many applications, for example in all kinds of steel and aluminum production, in alloy formation (technical applications e.g. in superconductivity and semiconductor science). In this book the data on diffusion in metals are shown, both in graphs and in equations.
Reliable data on diffusion in metals are required by researchers who try to make sense of results from all kinds of metallurgical experiments, and they are equally needed by theorists and computer modellers. The previous compilation dates from 1990, and measurements relying on the electron microprobe and the recent Rutherford backscattering technique were hardly taken into account there.
This reference book, containing all results on self-diffusion and impurity diffusion in pure metals with an indication of their reliability, will be useful to everyone in this field for the theory, fundamental research and industrial applications covered.
- Up-to-date and complete (including EPMA and RBS investigations)
- Indication of reliability of the measurements
- Reassessment of many early results
- Data can easily be extracted from Tables and Graphs

The study of Kafka has been a respected field of research for many decades, and Gerhard Neumann is the best-known Kafka scholar. The present book documents the findings that have emerged over Neumann s more than forty years of engagement with Kafka s works. By exploring both thematic and editorial relationships, this volume provides two forms of access to one of the most important bodies of work in all of world literature."

Herman the German "This fascinating and amusing account of his life is told by a colorful and highly capable German who became one of America's most successful technical managers in the development of jet engines for aircraft." -David S. Lewis Chairman General Dynamics Corporation "Not only has Gerhard Neumann's life been filled with exotic and exciting adventures, but he was a highly successful engineer and executive-truly a superior individual and one of my favorite people." -Sanford N. McDonnell Chairman, McDonnell Douglas Corporation "Readers of Gerhard's book will probably come to the same conclusion I formed while doing business with him; there is still room in industry for free spirits." -T. A. Wilson Chairman, The Boeing Company

Many real-world problems are inherently hierarchically structured. The use of this structure in an agent's policy may well be the key to improved scalability and higher performance on motor skill tasks. However, such hierarchical structures cannot be exploited by current policy search algorithms. We concentrate on a basic, but highly relevant hierarchy - the mixed option' policy. Here, a gating network first decides which of the options to execute and, subsequently, the option-policy determines the action. Using a hierarchical setup for our learning method allows us to learn not only one solution to a problem but many. We base our algorithm on a recently proposed information theoretic policy search method, which addresses the exploitation-exploration trade-off by limiting the loss of information between policy updates.

A collection of essays examining the influence of Kant on Heinrich von Kleist. The great and eccentric German writer Heinrich von Kleist, famous for his enigmatic dramas and novellas, read the Enlightenment philosopher Immanuel Kant in 1801. A series of letters written around this time speak of the distresshe felt as he absorbed the implications of Kantian thought. This sense of distress -- long considered important to understanding Kleist's subsequent works -- has become known to Kleist scholars as the 'Kant crisis,' and marks Kleist's abandonment of the hope of gaining metaphysical certainty about his life. But it has never been established which texts of Kant Kleist actually read, how well he understood them, and why they precipitated such despair. Kleisthimself -- aside from one paraphrasing of Kant in a letter of 1801 -- was never explicit about what he called this 'sad philosophy.' Yet the distress seems never to have left him and remains an abiding preoccupation throughout his dramas and stories. This collection of essays, all in German language, represents the most recent work of prominent scholars in the field. It takes the pervasive sense of metaphysical crisis in Kleist's works as a startingpoint. In the context of Kleist's response to Kant, the essays deal with his subversive treatment of the literary motifs and genres of his day, and with the ambiguity of truth in his works -- for his characters and readers alike.In tracing the source of crisis to specific writings of Kant and to other Enlightenment thinkers such as Rousseau and Wieland, the essays show Kleist's complex dialogue with the Enlightenment to be an important new approach to understanding this notoriously difficult writer. Tim Mehigan is Professor of German in the Department of Languages and Cultures at the University of Otago, New Zealand.

As robots and other intelligent agents move from simple environments and problems to more complex, unstructured settings, manually programming their behavior has become increasingly challenging and expensive. Often, it is easier for a teacher to demonstrate a desired behavior rather than attempt to manually engineer it. This process of learning from demonstrations, and the study of algorithms to do so, is called imitation learning. An Algorithmic Perspective on Imitation Learning provides the reader with an introduction to imitation learning. It covers the underlying assumptions, approaches, and how they relate; the rich set of algorithms developed to tackle the problem; and advice on effective tools and implementation. An Algorithmic Perspective on Imitation Learning serves two audiences. First, it familiarizes machine learning experts with the challenges of imitation learning, particularly those arising in robotics, and the interesting theoretical and practical distinctions between it and more familiar frameworks like statistical supervised learning theory and reinforcement learning. Second, it provides roboticists and experts in applied artificial intelligence with a broader appreciation for the frameworks and tools available for imitation learning. It pays particular attention to the intimate connection between imitation learning approaches and those of structured prediction.