Open University

About: Open University is a education organization based out in Milton Keynes, United Kingdom. It is known for research contribution in the topics: Context (language use) & Population. The organization has 11702 authors who have published 35020 publications receiving 1110835 citations. The organization is also known as: Open University, The & Open University.

The SCUBA HAlf Degree Extragalactic Survey - III. Identification of radio and mid-infrared counterparts to submillimetre galaxies

Abstract: Determining an accurate position for a submillimetre (submm) galaxy (SMG) is the crucial step that enables us to move from the basic properties of an SMG sample – source counts and 2D clustering – to an assessment of their detailed, multiwavelength properties, their contribution to the history of cosmic star formation and their links with present-day galaxy populations. In this paper, we identify robust radio and/or infrared (IR) counterparts, and hence accurate positions, for over two-thirds of the SCUBA HAlf-Degree Extragalactic Survey (SHADES) Source Catalogue, presenting optical, 24-μm and radio images of each SMG. Observed trends in identification rate have given no strong rationale for pruning the sample. Uncertainties in submm position are found to be consistent with theoretical expectations, with no evidence for significant additional sources of error. Employing the submm/radio redshift indicator, via a parametrization appropriate for radio-identified SMGs with spectroscopic redshifts, yields a median redshift of 2.8 for the radio-identified subset of SHADES, somewhat higher than the median spectroscopic redshift. We present a diagnostic colour–colour plot, exploiting Spitzer photometry, in which we identify regions commensurate with SMGs at very high redshift. Finally, we find that significantly more SMGs have multiple robust counterparts than would be expected by chance, indicative of physical associations. These multiple systems are most common amongst the brightest SMGs and are typically separated by 2–6 arcsec, ~15–50/ sin i kpc at z∼ 2, consistent with early bursts seen in merger simulations.

Weakly Supervised Joint Sentiment-Topic Detection from Text

Abstract: Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework called joint sentiment-topic (JST) model based on latent Dirichlet allocation (LDA), which detects sentiment and topic simultaneously from text. A reparameterized version of the JST model called Reverse-JST, obtained by reversing the sequence of sentiment and topic generation in the modeling process, is also studied. Although JST is equivalent to Reverse-JST without a hierarchical prior, extensive experiments show that when sentiment priors are added, JST performs consistently better than Reverse-JST. Besides, unlike supervised approaches to sentiment classification which often fail to produce satisfactory performance when shifting to other domains, the weakly supervised nature of JST makes it highly portable to other domains. This is verified by the experimental results on data sets from five different domains where the JST model even outperforms existing semi-supervised approaches in some of the data sets despite using no labeled documents. Moreover, the topics and topic sentiment detected by JST are indeed coherent and informative. We hypothesize that the JST model can readily meet the demand of large-scale sentiment analysis from the web in an open-ended fashion.

The fifteenth data release of the Sloan Digital Sky Surveys: First release of MaNGA-derived quantities, data visualization tools, and stellar library

Abstract: Twenty years have passed since first light for the Sloan Digital Sky Survey (SDSS). Here, we release data taken by the fourth phase of SDSS (SDSS-IV) across its first three years of operation (2014 July–2017 July). This is the third data release for SDSS-IV, and the 15th from SDSS (Data Release Fifteen; DR15). New data come from MaNGA—we release 4824 data cubes, as well as the first stellar spectra in the MaNGA Stellar Library (MaStar), the first set of survey-supported analysis products (e.g., stellar and gas kinematics, emission-line and other maps) from the MaNGA Data Analysis Pipeline, and a new data visualization and access tool we call "Marvin." The next data release, DR16, will include new data from both APOGEE-2 and eBOSS; those surveys release no new data here, but we document updates and corrections to their data processing pipelines. The release is cumulative; it also includes the most recent reductions and calibrations of all data taken by SDSS since first light. In this paper, we describe the location and format of the data and tools and cite technical references describing how it was obtained and processed. The SDSS website (www.sdss.org) has also been updated, providing links to data downloads, tutorials, and examples of data use. Although SDSS-IV will continue to collect astronomical data until 2020, and will be followed by SDSS-V (2020–2025), we end this paper by describing plans to ensure the sustainability of the SDSS data archive for many years beyond the collection of data.

Observing Strategy for the SDSS-IV/MaNGA IFU Galaxy Survey

Abstract: Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) is an integral-field spectroscopic survey that is one of three core programs in the fourth-generation Sloan Digital Sky Survey (SDSS-IV). MaNGA's 17 pluggable optical fiber-bundle integral field units (IFUs) will observe a sample of 10,000 nearby galaxies distributed throughout the SDSS imaging footprint (focusing particularly on the North Galactic Cap). In each pointing these IFUs are deployed across a 3° field; they yield spectral coverage 3600−10300 A at a typical resolution R ~ 2000, and sample the sky with 2'' diameter fiber apertures with a total bundle fill factor of 56%. Observing over such a large field and range of wavelengths is particularly challenging for obtaining uniform and integral spatial coverage and resolution at all wavelengths and across each entire fiber array. Data quality is affected by the IFU construction technique, chromatic and field differential refraction, the adopted dithering strategy, and many other effects. We use numerical simulations to constrain the hardware design and observing strategy for the survey with the aim of ensuring consistent data quality that meets the survey science requirements while permitting maximum observational flexibility. We find that MaNGA science goals are best achieved with IFUs composed of a regular hexagonal grid of optical fibers with rms displacement of 5 μm or less from their nominal packing position; this goal is met by the MaNGA hardware, which achieves 3 μm rms fiber placement. We further show that MaNGA observations are best obtained in sets of three 15 minute exposures dithered along the vertices of a 1.44 arcsec equilateral triangle; these sets form the minimum observational unit, and are repeated as needed to achieve a combined signal-to-noise ratio of 5 A-1 per fiber in the r-band continuum at a surface brightness of 23 AB arcsec-2. In order to ensure uniform coverage and delivered image quality, we require that the exposures in a given set be obtained within a 60 minute interval of each other in hour angle, and that all exposures be obtained at airmass ≲ 1.2 (i.e., within 1–3 hr of transit depending on the declination of a given field).

Uncovering the Problem-Solving Process: Cued Retrospective Reporting Versus Concurrent and Retrospective Reporting.

Abstract: This study investigated the amounts of problem-solving process information ("action," "why," "how," and "metacognitive") elicited by means of concurrent, retrospective, and cued retrospective reporting. In a within-participants design, 26 participants completed electrical circuit troubleshooting tasks under different reporting conditions. The method of cued retrospective reporting used the original computer-based task and a superimposed record of the participant's eye fixations and mouse-keyboard operations as a cue for retrospection. Cued retrospective reporting (with the exception of why information) and concurrent reporting (with the exception of metacognitive information) resulted in a higher number of codes on the different types of information than did retrospective reporting.