Preliminary Results from NEOWISE: An Enhancement to the Wide-field Infrared Survey Explorer for Solar System Science

The Wide-field Infrared Survey Explorer (WISE) has surveyed the entire sky at four infrared wavelengths with greatly improved sensitivity and spatial resolution compared to its predecessors, the Infrared Astronomical Satellite and the Cosmic Background Explorer. NASA's Planetary Science Division has funded an enhancement to the WISE data processing system called "NEOWISE" that allows detection and archiving of moving objects found in the WISE data. NEOWISE has mined the WISE images for a wide array of small bodies in our solar system, including near-Earth objects (NEOs), Main Belt asteroids, comets, Trojans, and Centaurs. By the end of survey operations in 2011 February, NEOWISE identified over 157,000 asteroids, including more than 500 NEOs and ~120 comets. The NEOWISE data set will enable a panoply of new scientific investigations

Analysis of spacecraft anomalies

The anomalies from 316 spacecraft covering the entire U.S. space program were analyzed to determine if there were any experimental or technological programs which could be implemented to remove the anomalies from future space activity. Thirty specific categories of anomalies were found to cover nearly 85 percent of all observed anomalies. Thirteen experiments were defined to deal with 17 of these categories; nine additional experiments were identified to deal with other classes of observed and anticipated anomalies. Preliminary analyses indicate that all 22 experimental programs are both technically feasible and economically viable

Probing the Interiors of Very Hot Jupiters Using Transit Light Curves

Accurately understanding the interior structure of extra-solar planets is critical for inferring their formation and evolution. The internal density distribution of a planet has a direct effect on the star-planet orbit through the gravitational quadrupole field created by the rotational and tidal bulges. These quadrupoles induce apsidal precession that is proportional to the planetary Love number ($k_{2p}$, twice the apsidal motion constant), a bulk physical characteristic of the planet that depends on the internal density distribution, including the presence or absence of a massive solid core. We find that the quadrupole of the planetary tidal bulge is the dominant source of apsidal precession for very hot Jupiters ($a \lesssim 0.025$ AU), exceeding the effects of general relativity and the stellar quadrupole by more than an order of magnitude. For the shortest-period planets, the planetary interior induces precession of a few degrees per year. By investigating the full photometric signal of apsidal precession, we find that changes in transit shapes are much more important than transit timing variations. With its long baseline of ultra-precise photometry, the space-based \emph{Kepler} mission can realistically detect apsidal precession with the accuracy necessary to infer the presence or absence of a massive core in very hot Jupiters with orbital eccentricities as low as $e \simeq 0.003$. The signal due to $k_{2p}$ creates unique transit light curve variations that are generally not degenerate with other parameters or phenomena. We discuss the plausibility of measuring $k_{2p}$ in an effort to directly constrain the interior properties of extra-solar planets.Comment: updated, improved, and expanded manuscript has been accepted by the Astrophysical Journal; 19 pages, 7 figure

Integrating TV/digital data spectrograph system

A 25-mm vidicon camera was previously modified to allow operation in an integration mode for low-light-level astronomical work. The camera was then mated to a low-dispersion spectrograph for obtaining spectral information in the 400 to 750 nm range. A high speed digital video image system was utilized to digitize the analog video signal, place the information directly into computer-type memory, and record data on digital magnetic tape for permanent storage and subsequent analysis

Supplement no. 1 to the January 1974 report on active and planned spacecraft and experiments

Updated information and descriptions on spacecraft and experiments are listed according to spacecraft name and principle experimental investigator. A cumulative index of active and planned spacecraft and experiments is provided; bar graph indexes for electromagnetic radiation experiments are included in table form

Towards multi-domain speech understanding with flexible and dynamic vocabulary

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (p. 201-208).In developing telephone-based conversational systems, we foresee future systems capable of supporting multiple domains and flexible vocabulary. Users can pursue several topics of interest within a single telephone call, and the system is able to switch transparently among domains within a single dialog. This system is able to detect the presence of any out-of-vocabulary (OOV) words, and automatically hypothesizes each of their pronunciation, spelling and meaning. These can be confirmed with the user and the new words are subsequently incorporated into the recognizer lexicon for future use. This thesis will describe our work towards realizing such a vision, using a multi-stage architecture. Our work is focused on organizing the application of linguistic constraints in order to accommodate multiple domain topics and dynamic vocabulary at the spoken input. The philosophy is to exclusively apply below word-level linguistic knowledge at the initial stage. Such knowledge is domain-independent and general to all of the English language. Hence, this is broad enough to support any unknown words that may appear at the input, as well as input from several topic domains. At the same time, the initial pass narrows the search space for the next stage, where domain-specific knowledge that resides at the word-level or above is applied. In the second stage, we envision several parallel recognizers, each with higher order language models tailored specifically to its domain. A final decision algorithm selects a final hypothesis from the set of parallel recognizers.(cont.) Part of our contribution is the development of a novel first stage which attempts to maximize linguistic constraints, using only below word-level information. The goals are to prevent sequences of unknown words from being pruned away prematurely while maintaining performance on in-vocabulary items, as well as reducing the search space for later stages. Our solution coordinates the application of various subword level knowledge sources. The recognizer lexicon is implemented with an inventory of linguistically motivated units called morphs, which are syllables augmented with spelling and word position. This first stage is designed to output a phonetic network so that we are not committed to the initial hypotheses. This adds robustness, as later stages can propose words directly from phones. To maximize performance on the first stage, much of our focus has centered on the integration of a set of hierarchical sublexical models into this first pass. To do this, we utilize the ANGIE framework which supports a trainable context-free grammar, and is designed to acquire subword-level and phonological information statistically. Its models can generalize knowledge about word structure, learned from in-vocabulary data, to previously unseen words. We explore methods for collapsing the ANGIE models into a finite-state transducer (FST) representation which enables these complex models to be efficiently integrated into recognition. The ANGIE-FST needs to encapsulate the hierarchical knowledge of ANGIE and replicate ANGIE's ability to support previously unobserved phonetic sequences ...by Grace Chung.Ph.D

Near-Optimal Machine Teaching via Explanatory Teaching Sets

Modern applications of machine teaching for humans often involve domain-specific, non- trivial target hypothesis classes. To facilitate understanding of the target hypothesis, it is crucial for the teaching algorithm to use examples which are interpretable to the human learner. In this paper, we propose NOTES, a principled framework for constructing interpretable teaching sets, utilizing explanations to accelerate the teaching process. Our algorithm is built upon a natural stochastic model of learners and a novel submodular surrogate objective function which greedily selects interpretable teaching examples. We prove that NOTES is competitive with the optimal explanation-based teaching strategy. We further instantiate NOTES with a specific hypothesis class, which can be viewed as an interpretable approximation of any hypothesis class, allowing us to handle complex hypothesis in practice. We demonstrate the effectiveness of NOTES on several image classification tasks, for both simulated and real human learners. Our experimental results suggest that by leveraging explanations, one can significantly speed up teaching