similarity functions and interactively labeling a small number of record pairs. We describe how active learning is useful in selecting informative examples of duplicates and non-duplicates that can be used to train a deduplication function.

of how problems considered individually in the classroom or laboratory interact through several influential forces in a real, complex system. This new lens is a characteristic of transformative learning. Influences of Field Immersion, Disciplinary Projects, and Extra-Disciplinary Activities

in motor control as much as in as the rest of science; we need reduced systems in order to build up from the simple to the complex. Sherrington's studies of reflexes across single joints in cats and dogs were predicated on just this kind of reasoning Psychophysical studies, in which learning

Abstract not found

I hereby declare that I have created this work completely on my own and used no other sources or tools than the ones listed, and that I have marked any citations accordingly. Hiermit versichere ich, dass ich die vorliegende Arbeit selbständig verfasst und keine anderen als die angegebenen Quellen und Hilfsmittel benutzt sowie Zitate kenntlich gemacht habe.

the designer specify entire behaviors by demonstration, instead of using low-level programming or trial and error by the robot. In biological systems, imitation appears to be a complex learning mechanism that involves an intricate interaction between visual perception and motor control, both of which

systems In the particular area of mobile learning, "context" has been defined in an abstract way, as an artifact that is continuously created by people interacting with other people in their surroundings (establishing the aforementioned conversation and relationships of trust) and using

This thesis presents CraneTracker, a multi-modal sensing and communication system for monitoring migratory species at the continental level. By exploiting the robust and extensive cellular infrastructure across the continent, traditional mobile wireless sensor networks can be extended to enable

Paper prepared for the NBER project on “Risks of Financial Institutions”. We benefited from suggestions and criticism by many participants in the NBER project on “Risks of financial institutions”, in particular by the organizers Mark Carey (also involving Dean Amel and Allen Berger) and Rene Stulz, by our discussant Tony Saunders and by Patrick de Fontnouvelle, Gary Gorton, Andy Lo, Jim O’Brien and Eric Rosengren. Furthermore, we are grateful for comments we received at the 2004 European Finance Association Meetings in Maastricht, in particular by our discussant Marco da Rin and by Christian Upper, at the 2004 Ottobeuren seminar in economics, notably the thoughts of our discussant Ernst Baltensberger, of Friedrich Heinemann and of Gerhard Illing, as well as at seminars of the Max Planck Institute for Research on Collective Goods, the Federal Reserve Bank of St. Louis, the ECB and the University of Frankfurt. Gabe de Bondt and David Marques Ibanez supported us enormously in finding yield spread data, Lieven Baele and Richard Stehle kindly made us aware of pitfalls in Datastream equity data. Very helpful research assistance by Sandrine Corvoisier, Peter Galos and Marco Lo Duca as well as editorial support by Sabine Wiedemann are gratefully acknowledged. Any views expressed only reflect those of the authors and should not be interpreted as the ones of the ECB or the Eurosystem. The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National Bureau of Economic Research.

the target system; Hughes refers to this step as 'interpretation'. It is the latter two notions that are at stake here. Learning about the model: experiments, thought experiments and simulation 10 Learning about a model happens at two places, in the construction and the manipulation of the model