Astrometry.net: Blind astrometric calibration of arbitrary astronomical images

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 background 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.Comment: submitted to A

Mining student cvs repositories for performance indicators

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. 1

Open Source 3D Reconstruction

A new open source 3D reconstruction and evaluation pipeline is described, with a thorough description of the algorithms employed. A new evaluation framework is introduced, which is easy to use for comparison of state-of-the-art multiview reconstruction algorithms. The evaluation framework also includes tools for creating data sets which have ground truth. The source code is available under the GPL; a first for a complete end-to-end reconstruction system.MAS

Jit instrumentation: a novel approach to dynamically instrument operating systems

As modern operating systems become more complex, understanding their inner workings is increasingly difficult. Dynamic kernel instrumentation is a well established method of obtaining insight into the workings of an OS, with applications including debugging, profiling and monitoring, and security auditing. To date, all dynamic instrumentation systems for operating systems follow the probe-based instrumentation paradigm. While efficient on fixed-length instruction set architectures, probes are extremely expensive on variable-length ISAs such as the popular Intel x86 and AMD x86-64. We propose using just-in-time (JIT) instrumentation to overcome this problem. While common in user space, JIT instrumentation has not until now been attempted in kernel space. In this work, we show the feasibility and desirability of kernel-based JIT instrumentation for operating systems with our novel prototype, implemented as a Linux kernel module. The prototype is fully SMP capable. We evaluate our prototype against the popular Kprobes Linux instrumentation tool. Our prototype outperforms Kprobes, at both micro and macro levels, by orders of magnitude when applying medium- and fine-grained instrumentation