Carnegie Mellon University

About: Carnegie Mellon University is a education organization based out in Pittsburgh, Pennsylvania, United States. It is known for research contribution in the topics: Computer science & Robot. The organization has 36317 authors who have published 104359 publications receiving 5975734 citations. The organization is also known as: CMU & Carnegie Mellon.

Semantic Matching of Web Services Capabilities

Abstract: The Web is moving from being a collection of pages toward a collection of services that interoperate through the Internet. The first step toward this interoperation is the location of other services that can help toward the solution of a problem. In this paper we claim that location of web services should be based on the semantic match between a declarative description of the service being sought, and a description of the service being offered. Furthermore, we claim that this match is outside the representation capabilities of registries such as UDDI and languages such as WSDL.We propose a solution based on DAML-S, a DAML-based language for service description, and we show how service capabilities are presented in the Profile section of a DAML-S description and how a semantic match between advertisements and requests is performed.

Dynamic topic models

Abstract: A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the natural parameters of the multinomial distributions that represent the topics. Variational approximations based on Kalman filters and nonparametric wavelet regression are developed to carry out approximate posterior inference over the latent topics. In addition to giving quantitative, predictive models of a sequential corpus, dynamic topic models provide a qualitative window into the contents of a large document collection. The models are demonstrated by analyzing the OCR'ed archives of the journal Science from 1880 through 2000.

Stellar masses and star formation histories for 105 galaxies from the Sloan Digital Sky Survey

Abstract: We develop a new method to constrain the star formation histories, dust attenuation and stellar masses of galaxies. It is based on two stellar absorption-line indices, the 4000-A break strength and the Balmer absorption-line index Hδ A . Together, these indices allow us to constrain the mean stellar ages of galaxies and the fractional stellar mass formed in bursts over the past few Gyr. A comparison with broad-band photometry then yields estimates of dust attenuation and of stellar mass. We generate a large library of Monte Carlo realizations of different star formation histories, including starbursts of varying strength and a range of metallicities. We use this library to generate median likelihood estimates of burst mass fractions, dust attenuation strengths, stellar masses and stellar mass-to-light ratios for a sample of 122 808 galaxies drawn from the Sloan Digital Sky Survey. The typical 95 per cent confidence range in our estimated stellar masses is ′40 per cent. We study how the stellar mass-to-light ratios of galaxies vary as a function of absolute magnitude, concentration index and photometric passband and how dust attenuation varies as a function of absolute magnitude and 4000-A break strength. We also calculate how the total stellar mass of the present Universe is distributed over galaxies as a function of their mass, size, concentration, colour, burst mass fraction and surface mass density. We find that most of the stellar mass in the local Universe resides in galaxies that have, to within a factor of approximately 2, stellar masses ∼5 x 10 1 0 M O ., half-light radii ∼3 kpc and half-light surface mass densities ∼10 9 M O .kpc - 2 . The distribution of D n (4000) is strongly bimodal, showing a clear division between galaxies dominated by old stellar populations and galaxies with more recent star formation.

Information revelation and privacy in online social networks

Abstract: Participation in social networking sites has dramatically increased in recent years. Services such as Friendster, Tribe, or the Facebook allow millions of individuals to create online profiles and share personal information with vast networks of friends - and, often, unknown numbers of strangers. In this paper we study patterns of information revelation in online social networks and their privacy implications. We analyze the online behavior of more than 4,000 Carnegie Mellon University students who have joined a popular social networking site catered to colleges. We evaluate the amount of information they disclose and study their usage of the site's privacy settings. We highlight potential attacks on various aspects of their privacy, and we show that only a minimal percentage of users changes the highly permeable privacy preferences.

Reinforcement learning in robotics: A survey

Abstract: Reinforcement learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this article, we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots. We highlight both key challenges in robot reinforcement learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our paper lies on the choice between model-based and model-free as well as between value-function-based and policy-search methods. By analyzing a simple problem in some detail we demonstrate how reinforcement learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research.