Proceedings Of The Twenty Sixth General Assembly Prague 2006

IAU Transactions XXVIB contains the Proceedings of the IAU XXVII General Assembly held in Prague, 14-25 August 2006, hosting a total of 2412 participants from 73 countries. The Assembly featured a rich scientific program, comprising 6 Symposia, 17 Joint Discussions and 7 Special Sessions. During the program about 650 papers were presented and more than 1550 posters displayed. The Proceedings of the 6 Symposia have been published in the Proceedings of the IAU Symposia Series, and the proceedings of the Joint Discussions and Special Sessions feature in IAU Highlights of Astronomy, 14. Together with those 7 volumes, these Transactions cover the entire General Assembly. In addition to the scientific program, the XXVI General Assembly hosted the regular Business Meetings of the EC, the 12 Divisions, 40 Commissions and 75 Working Groups. This volume records the organizational and administrative business of the XXVI General Assembly and the status of the IAU membership.

This accessible handbook demonstrates how reflected light can be measured and used to investigate the properties of Solar System objects.

Astronomical discovery involves more than detecting something previously unseen. The reclassification of Pluto as a dwarf planet in 2006, and the controversy it generated, shows that discovery is a complex and ongoing process – one comprising various stages of research, interpretation and understanding. Ranging from Galileo's observation of Jupiter's satellites, Saturn's rings and star clusters, to Herschel's nebulae and the modern discovery of quasars and pulsars, Steven J. Dick's comprehensive history identifies the concept of 'extended discovery' as the engine of progress in astronomy. The text traces more than 400 years of telescopic observation, exploring how the signal discoveries of new astronomical objects relate to and inform one another, and why controversies such as Pluto's reclassification are commonplace in the field. The volume is complete with a detailed classification system for known classes of astronomical objects, offering students, researchers and amateur observers a valuable reference and guide.

How social scientists' disagreements about their key words and distinctions have been misconceived, and what to do about it Social scientists do research on a variety of topics—gender, capitalism, populism, and race and ethnicity, among others. They make descriptive and explanatory claims about empathy, intelligence, neoliberalism, and power. They advise policymakers on diversity, digitalization, work, and religion. And yet, as Gabriel Abend points out in this provocative book, they can’t agree on what these things are and how to identify them. How to tell if something is a religion or a cult or a sect? What is empathy? What makes this society a capitalist one? Disputes of this sort arise again and again in the social sciences. Abend argues that these disagreements have been doubly misconceived. First, they conflate two questions: how a social science community should use its most important words, and what distinctions it should accept and work with. Second, there’s no fact of the matter about either. Instead, they’re practical reason questions for a community, which aim at epistemically and morally good outcomes. Abend calls on social science communities to work together on their words, distinctions, and classifications. They must make collective decisions about the uses of words, the acceptability of distinctions, and the criteria for assessing both. These decisions aren’t up to individual scholars; the community gets the last word. According to Abend, the common good, justice, and equality should play a significant role in the logic of scientific research. Gabriel Abend is professor of sociology at University of Lucerne and the author of The Moral Background: An Inquiry into the History of Business Ethics (Princeton).

A complete record of the formal organisational and administrative proceedings of the XXVII General Assembly of the International Astronomical Union.

Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success (e.g., the Internet in networking, the relational model in databases, etc.). So far this interface layer has been missing in AI. First-order logic and probabilistic graphical models each have some of the necessary features, but a viable interface layer requires combining both. Markov logic is a powerful new language that accomplishes this by attaching weights to first-order formulas and treating them as templates for features of Markov random fields. Most statistical models in wide use are special cases of Markov logic, and first-order logic is its infinite-weight limit. Inference algorithms for Markov logic combine ideas from satisfiability, Markov chain Monte Carlo, belief propagation, and resolution. Learning algorithms make use of conditional likelihood, convex optimization, and inductive logic programming. Markov logic has been successfully applied to problems in information extraction and integration, natural language processing, robot mapping, social networks, computational biology, and others, and is the basis of the open-source Alchemy system. Table of Contents: Introduction / Markov Logic / Inference / Learning / Extensions / Applications / Conclusion

This book constitutes the joint refereed proceedings of six workshops held in conjunction with the 8th International Conference on Web Information Systems Engineering, WISE 2007 in Nancy, France, in December 2007. The 44 revised full papers presented were carefully reviewed and selected from numerous submissions for presentation in the six workshops. The workshops discuss a broad range of subjects.