Explanatory debugging: Supporting end-user debugging of machine-learned programs

Many machine-learning algorithms learn rules of behavior from individual end users, such as task-oriented desktop organizers and handwriting recognizers. These rules form a “program” that tells the computer what to do when future inputs arrive. Little research has explored how an end user can debug these programs when they make mistakes. We present our progress toward enabling end users to debug these learned programs via a Natural Programming methodology. We began with a formative study exploring how users reason about and correct a text-classification program. From the results, we derived and prototyped a concept based on “explanatory debugging”, then empirically evaluated it. Our results contribute methods for exposing a learned program's logic to end users and for eliciting user corrections to improve the program's predictions

Phobos DTM and Coordinate Refinement for Phobos-Grunt Mission Support.

Images obtained by the High Resolution Stereo Camera (HRSC) during recent Phobos flybys were used to study the proposed new landing site area of the Russian Phobos-Grunt mission, scheduled for launch in 2011 [1]. From the stereo images (resolution of up to 4.4 m/pixel), a digital terrain model (DTM) with a lateral resolution of 100 m per pixel and a relative point accuracy of ±15 m, was determined. Images and DTM were registered to the established Phobos control point network [7]. A map of the landing site area was produced enabling mission planers and scientists to extract accurate body-fixed coordinates of features in the Phobos Grunt landing site area

End-user feature labeling: a locally-weighted regression approach

When intelligent interfaces, such as intelligent desktop assistants, email classifiers, and recommender systems, customize themselves to a particular end user, such customizations can decrease productivity and increase frustration due to inaccurate predictions - especially in early stages, when training data is limited. The end user can improve the learning algorithm by tediously labeling a substantial amount of additional training data, but this takes time and is too ad hoc to target a particular area of inaccuracy. To solve this problem, we propose a new learning algorithm based on locally weighted regression for feature labeling by end users, enabling them to point out which features are important for a class, rather than provide new training instances. In our user study, the first allowing ordinary end users to freely choose features to label directly from text documents, our algorithm was both more effective than others at leveraging end users' feature labels to improve the learning algorithm, and more robust to real users' noisy feature labels. These results strongly suggest that allowing users to freely choose features to label is a promising method for allowing end users to improve learning algorithms effectively

End-User Feature Labeling via Locally Weighted Logistic Regression

Applications that adapt to a particular end user often make inaccurate predictions during the early stages when training data is limited. Although an end user can improve the learning algorithm by labeling more training data, this process is time consuming and too ad hoc to target a particular area of inaccuracy. To solve this problem, we propose a new learning algorithm based on Locally Weighted Logistic Regression for feature labeling by end users, enabling them to point out which features are important for a class, rather than provide new training instances. In our user study, the first allowing ordinary end users to freely choose features to label directly from text documents, our algorithm was more effective than others at leveraging end users’ feature labels to improve the learning algorithm. Our results strongly suggest that allowing users to freely choose features to label is a promising method for allowing end users to improve learning algorithms effectively

Detection of the 13CO(J=6-5) Transition in the Starburst Galaxy NGC 253

We report the detection of 13CO(J=6-5) emission from the nucleus of the starburst galaxy NGC 253 with the redshift (z) and Early Universe Spectrometer (ZEUS), a new submillimeter grating spectrometer. This is the first extragalactic detection of the 13CO(J=6-5) transition, which traces warm, dense molecular gas. We employ a multi-line LVG analysis and find ~ 35% - 60% of the molecular ISM is both warm (T ~ 110 K) and dense (n(H2) ~ 10^4 cm^-3). We analyze the potential heat sources, and conclude that UV and X-ray photons are unlikely to be energetically important. Instead, the molecular gas is most likely heated by an elevated density of cosmic rays or by the decay of supersonic turbulence through shocks. If the cosmic rays and turbulence are created by stellar feedback within the starburst, then our analysis suggests the starburst may be self-limiting.Comment: 4 pages, 2 figures, accepted by ApJ Letter

Motional sidebands and direct measurement of the cooling rate in the resonance fluorescence of a single trapped ion

Resonance fluorescence of a single trapped ion is spectrally analyzed using a heterodyne technique. Motional sidebands due to the oscillation of the ion in the harmonic trap potential are observed in the fluorescence spectrum. From the width of the sidebands the cooling rate is obtained and found to be in agreement with the theoretical prediction.Comment: 4 pages, 4 figures. Final version after minor changes, 1 figure replaced; to be published in PRL, July 10, 200

Atomic matter wave scanner

We report on the experimental realization of an atom optical device, that allows scanning of an atomic beam. We used a time-modulated evanescent wave field above a glass surface to diffract a continuous beam of metastable Neon atoms at grazing incidence. The diffraction angles and efficiencies were controlled by the frequency and form of modulation, respectively. With an optimized shape, obtained from a numerical simulation, we were able to transfer more than 50% of the atoms into the first order beam, which we were able to move over a range of 8 mrad.Comment: 4 pages, 4 figure

New Morphometric Measurements of Peak-Ring Basins on Mercury and the Moon: Results from the Mercury Laser Altimeter and Lunar Orbiter Laser Altimeter

Peak-ring basins (large impact craters exhibiting a single interior ring) are important to understanding the processes controlling the morphological transition from craters to large basins on planetary bodies. New image and topography data from the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) and Lunar Reconnaissance Orbiter (LRO) spacecraft have helped to update the catalogs of peak-ring basins on Mercury and the Moon [1,2] and are enabling improved calculations of the morphometric properties of these basins. We use current orbital altimeter measurements from the Mercury Laser Altimeter (MLA) [3] and the Lunar Orbiter Laser Altimeter (LOLA) [4], as well as stereo-derived topography [5], to calculate the floor depths and peak-ring heights of peak-ring basins on Mercury and the Moon. We present trends in these parameters as functions of rim-crest diameter, which are likely to be related to processes controlling the onset of peak rings in these basins

KELT-12b: A P ~ 5 day, Highly Inflated Hot Jupiter Transiting A Mildly Evolved Hot Star

We announce the discovery of KELT-12b, a highly inflated Jupiter-mass planet transiting the mildly evolved, V = 10.64 host star TYC 2619-1057-1. We followed up the initial transit signal in the KELT-North survey data with precise ground-based photometry, high-resolution spectroscopy, precise radial velocity measurements, and high-resolution adaptive optics imaging. Our preferred best-fit model indicates that the host star has = 6279 ± 51 K, = 3.89 ± 0.05,[Fe/H] = , = , and = 2.37 ± 0.17 . The planetary companion has = 0.95 ± 0.14 , = , = , and density = g cm −3 , making it one of the most inflated giant planets known. Furthermore, for future follow-up, we report a high-precision time of inferior conjunction in of 2,457,083.660459 ± 0.000894 and period of days. Despite the relatively large separation of ∼0.07 au implied by its ∼5.03-day orbital period, KELT-12b receives significant flux of erg s −1 cm −2 from its host. We compare the radii and insolations of transiting gas giant planets around hot ( K) and cool stars, noting that the observed paucity of known transiting giants around hot stars with low insolation is likely due to selection effects. We underscore the significance of long-term ground-based monitoring of hot stars and space-based targeting of hot stars with the Transiting Exoplanet Survey Satellite to search for inflated gas giants in longer-period orbits

KELT-18b: Puffy Planet, Hot Host, Probably Perturbed

We report the discovery of KELT-18b, a transiting hot Jupiter in a 2.87-day orbit around the bright ( V = 10.1), hot, F4V star BD+60 1538 (TYC 3865-1173-1). We present follow-up photometry, spectroscopy, and adaptive optics imaging that allow a detailed characterization of the system. Our preferred model fits yield a host stellar temperature of K and a mass of , situating it as one of only a handful of known transiting planets with hosts that are as hot, massive, and bright. The planet has a mass of , a radius of , and a density of , making it one of the most inflated planets known around a hot star. We argue that KELT-18b’s high temperature and low surface gravity, which yield an estimated ∼600 km atmospheric scale height, combined with its hot, bright host, make it an excellent candidate for observations aimed at atmospheric characterization. We also present evidence for a bound stellar companion at a projected separation of ∼1100 au, and speculate that it may have contributed to the strong misalignment we suspect between KELT-18\u27s spin axis and its planet’s orbital axis. The inferior conjunction time is 2457542.524998 ± 0.000416 (BJD TDB ) and the orbital period is 2.8717510 ± 0.0000029 days. We encourage Rossiter–McLaughlin measurements in the near future to confirm the suspected spin–orbit misalignment of this system