We have built a reliable and robust system that takes as input an astronomical image, and returns as output the pointing, scale, and orientation of that image (the astrometric calibration or WCS information). The system requires no first guess, and works with the information in the image pixels alone; that is, the problem is a generalization of the "lost in space" problem in which nothing-not even the image scale-is known. After robust source detection is performed in the input image, asterisms (sets of four or five stars) are geometrically hashed and compared to pre-indexed hashes to generate hypotheses about the astrometric calibration. A hypothesis is only accepted as true if it passes a Bayesian decision theory test against a null hypothesis. With indices built from the USNO-B Catalog and designed for uniformity of coverage and redundancy, the success rate is > 99.9 % for contemporary near-ultraviolet and visual imaging survey data, with no false positives. The failure rate is consistent with the incompleteness of the USNO-B Catalog; augmentation with indices built from the 2MASS Catalog brings the completeness to 100 % with no false positives. We are using this system to generate consistent and standards-compliant meta-data for digital and digitized imaging from plate repositories, automated observatories, individual scientific investigators, and hobbyists. This is the first step in a program of making it possible to trust calibration meta-data for astronomical data of arbitrary provenance.

We present a novel recurrent neural network (RNN) model for voice activity detection. Our multi-layer RNN model, in which nodes compute quadratic polynomials, outperforms a much larger baseline system composed of Gaussian mixture models (GMMs) and a hand-tuned state machine (SM) for temporal smoothing. All parameters of our RNN model are optimized together, so that it properly weights its preference for temporal continuity against the acoustic features in each frame. Our RNN uses one tenth the parameters and outperforms the GMM+SM baseline system by 26% reduction in false alarms, reducing overall speech recognition computation time by 17% while reducing word error rate by 1% relative.Index Terms-Voice activity detection (VAD), endpointing, recurrent neural networks (RNNs)

Over 200 CVS repositories representing the assignments of students in a second year undergraduate computer science course have been assembled. This unique data set represents many individuals working separately on identical projects, presenting the opportunity to evaluate the effects of the work habits captured by CVS on performance. This paper outlines our experiences mining and analyzing these repositories. We extracted various quantitative measures of student behaviour and code quality, and attempted to correlate these features with grades. Despite examining 166 features, we find that grade performance cannot be accurately predicted; certainly no predictors stronger than simple lines-of-code were found.