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.

The Second Data Release of the Sloan Digital Sky Survey

Abstract: The Sloan Digital Sky Survey (SDSS) has validated and made publicly available its Second Data Release. This data release consists of 3324 deg2 of five-band (ugriz) imaging data with photometry for over 88 million unique objects, 367,360 spectra of galaxies, quasars, stars, and calibrating blank sky patches selected over 2627 deg2 of this area, and tables of measured parameters from these data. The imaging data reach a depth of r ≈ 22.2 (95% completeness limit for point sources) and are photometrically and astrometrically calibrated to 2% rms and 100 mas rms per coordinate, respectively. The imaging data have all been processed through a new version of the SDSS imaging pipeline, in which the most important improvement since the last data release is fixing an error in the model fits to each object. The result is that model magnitudes are now a good proxy for point-spread function magnitudes for point sources, and Petrosian magnitudes for extended sources. The spectroscopy extends from 3800 to 9200 A at a resolution of 2000. The spectroscopic software now repairs a systematic error in the radial velocities of certain types of stars and has substantially improved spectrophotometry. All data included in the SDSS Early Data Release and First Data Release are reprocessed with the improved pipelines and included in the Second Data Release. Further characteristics of the data are described, as are the data products themselves and the tools for accessing them.

Learning Curves In Manufacturing

Abstract: Large increases in productivity are typically realized as organizations gain experience in production. These "learning curves" have been found in many organizations. Organizations vary considerably in the rates at which they learn. Some organizations show remarkable productivity gains, whereas others show little or no learning. Reasons for the variation observed in organizational learning curves include organizational "forgetting," employee turnover, transfer of knowledge from other products and other organizations, and economies of scale.

Topic Detection and Tracking Pilot Study Final Report

Abstract: Topic Detection and Tracking (TDT) is a DARPA-sponsored initiative to investigate the state of the art in finding and following new events in a stream of broadcast news stories The TDT problem consists of three major tasks: (1) segmenting a stream of data, especially recognized speech, into distinct stories; (2) identifying those news stories that are the first to discuss a new event occurring in the news; and (3) given a small number of sample news stories about an event, finding all following stories in the stream

Dialogue act modeling for automatic tagging and recognition of conversational speech

Abstract: We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as STATEMENT, QUESTION, BACKCHANNEL, AGREEMENT, DISAGREEMENT, and APOLOGY. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialogue act sequence. The dialogue model is based on treating the discourse structure of a conversation as a hidden Markov model and the individual dialogue acts as observations emanating from the model states. Constraints on the likely sequence of dialogue acts are modeled via a dialogue act n-gram. The statistical dialogue grammar is combined with word n-grams, decision trees, and neural networks modeling the idiosyncratic lexical and prosodic manifestations of each dialogue act. We develop a probabilistic integration of speech recognition with dialogue modeling, to improve both speech recognition and dialogue act classification accuracy. Models are trained and evaluated using a large hand-labeled database of 1,155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We achieved good dialogue act labeling accuracy (65% based on errorful, automatically recognized words and prosody, and 71% based on word transcripts, compared to a chance baseline accuracy of 35% and human accuracy of 84%) and a small reduction in word recognition error.

Ising Model for the λ Transition and Phase Separation in He 3 - He 4 Mixtures

Abstract: A spin-1 Ising model, which simulates the thermodynamic behavior of ${\mathrm{He}}^{3}$-${\mathrm{He}}^{4}$ mixtures along the $\ensuremath{\lambda}$ line and near the critical mixing point, is introduced and solved in the mean-field approximation. For reasonable values of the parameters of the model the phase diagram is qualitatively similar to that observed experimentally and the phase separation appears as a consequence of the superfluid ordering. Changing the parameters produces many different types of phase diagram, including as features $\ensuremath{\lambda}$ lines, critical points, tricritical points, and triple points. Certain thermodynamic features which differ from the ${\mathrm{He}}^{3}$-${\mathrm{He}}^{4}$ experiments may be artifacts of the mean-field theory.