Coronagraphic Low Order Wave Front Sensor : post-processing sensitivity enhancer for high performance coronagraphs

Detection and characterization of exoplanets by direct imaging requires a coronagraph designed to deliver high contrast at small angular separation. To achieve this, an accurate control of low order aberrations, such as pointing and focus errors, is essential to optimize coronagraphic rejection and avoid the possible confusion between exoplanet light and coronagraphic leaks in the science image. Simulations and laboratory prototyping have shown that a Coronagraphic Low Order Wave-Front Sensor (CLOWFS), using a single defocused image of a reflective focal plane ring, can be used to control tip-tilt to an accuracy of 10^{-3} lambda/D. This paper demonstrates that the data acquired by CLOWFS can also be used in post-processing to calibrate residual coronagraphic leaks from the science image. Using both the CLOWFS camera and the science camera in the system, we quantify the accuracy of the method and its ability to successfully remove light due to low order errors from the science image. We also report the implementation and performance of the CLOWFS on the Subaru Coronagraphic Extreme AO (SCExAO) system and its expected on-sky performance. In the laboratory, with a level of disturbance similar to what is encountered in a post Adaptive Optics beam, CLOWFS post-processing has achieved speckle calibration to 1/300 of the raw speckle level. This is about 40 times better than could be done with an idealized PSF subtraction that does not rely on CLOWFS.Comment: 10 pages, 7 figures, accepted for publication in PAS

sk_p: a neural program corrector for MOOCs

We present a novel technique for automatic program correction in MOOCs, capable of fixing both syntactic and semantic errors without manual, problem specific correction strategies. Given an incorrect student program, it generates candidate programs from a distribution of likely corrections, and checks each candidate for correctness against a test suite. The key observation is that in MOOCs many programs share similar code fragments, and the seq2seq neural network model, used in the natural-language processing task of machine translation, can be modified and trained to recover these fragments. Experiment shows our scheme can correct 29% of all incorrect submissions and out-performs state of the art approach which requires manual, problem specific correction strategies

CRYSTAL: Inducing a Conceptual Dictionary

One of the central knowledge sources of an information extraction system is a dictionary of linguistic patterns that can be used to identify the conceptual content of a text. This paper describes CRYSTAL, a system which automatically induces a dictionary of "concept-node definitions" sufficient to identify relevant information from a training corpus. Each of these concept-node definitions is generalized as far as possible without producing errors, so that a minimum number of dictionary entries cover the positive training instances. Because it tests the accuracy of each proposed definition, CRYSTAL can often surpass human intuitions in creating reliable extraction rules.Comment: 6 pages, Postscript, IJCAI-95 http://ciir.cs.umass.edu/info/psfiles/tepubs/tepubs.htm

Automatically generating Feynman rules for improved lattice field theories

Deriving the Feynman rules for lattice perturbation theory from actions and operators is complicated, especially when improvement terms are present. This physically important task is, however, suitable for automation. We describe a flexible algorithm for generating Feynman rules for a wide range of lattice field theories including gluons, relativistic fermions and heavy quarks. We also present an efficient implementation of this in a freely available, multi-platform programming language (\python), optimised to deal with a wide class of lattice field theories