Objective Subclass Determination of Sloan Digital Sky Survey Unknown Spectral Objects

We analyze a portion of the SDSS photometric catalog, consisting of approximately 10,000 objects that have been spectroscopically classified into stars, galaxies, QSOs, late-type stars and unknown objects (spectroscopically unclassified objects, SUOs), in order to investigate the existence and nature of subclasses of the unclassified objects. We use a modified mixture modeling approach that makes use of both labeled and unlabeled data and performs class discovery on the data set. The modeling was done using four colors derived from the SDSS photometry: (u-g), (g-r), (r-i), and (i-z). This technique discovers putative novel classes by identifying compact clusters that largely contain objects from the spectroscopically unclassified class of objects. These clusters are of possible scientific interest because they represent structured groups of outliers, relative to the known object classes. We identify two such well defined subclasses of the SUOs. One subclass contains 58% SUOs, 40% stars, and 2% galaxies, QSOs, and late-type stars. The other contains 91% SUOs, 6% late-type stars, and 3% stars, galaxies, and QSOs. We discuss possible interpretations of these subclasses while also noting some caution must be applied to purely color-based object classifications. As a side benefit of this limited study we also find two distinct classes, consisting largely of galaxies, that coincide with the recently discussed bimodal galaxy color distribution.Comment: 31 pages; 6 figures; revised version accepted for Ap. J. Added one figure, added discussion, compared method with another approach, added appendix with algorithmic detail

Automated Classification of Sloan Digital Sky Survey (SDSS) Stellar Spectra using Artificial Neural Networks

Automated techniques have been developed to automate the process of classification of objects or their analysis. The large datasets provided by upcoming spectroscopic surveys with dedicated telescopes urges scientists to use these automated techniques for analysis of such large datasets which are now available to the community. Sloan Digital Sky Survey (SDSS) is one of such surveys releasing massive datasets. We use Probabilistic Neural Network (PNN) for automatic classification of about 5000 SDSS spectra into 158 spectral type of a reference library ranging from O type to M type stars.Comment: 27 pages, 11 figures To appear in Astrophys. Space Sci., 200

Does Foreign Direct Investment Stimulate New Firm Creation? In Search of Spillovers through Industrial and Geographical Linkages

This paper examines the spillover effects of inward foreign direct investment (FDI) on the entrepreneurial activities of new firm creation through both industrial and geographical linkages. Using a dataset of 44,434 newly created small firms in 234 regions of South Korea in 2000–2004, this study finds that while the spillover impacts of FDI in the low-tech industry are positive and significant across almost all four possible combinations of the intra-/inter-regional and intra-/inter-sectoral channels, the impacts in the high-tech industry are largely intra-sectoral within the host region and across neighboring regions. Moreover, all statistically significant spillover effects follow an inverted ‘U’-shaped curvilinear trend

MSO spent salt clean-up recovery process

An effective process has been developed to separate metals, mineral residues, and radionuclides from spent salt, a secondary waste generated by Molten Salt Oxidation (MSO). This process includes salt dissolution, pH adjustment, chemical reduction and/or sulfiding, filtration, ion exchange, and drying. The process uses dithionite to reduce soluble chromate and/or sulfiding agent to suppress solubilities of metal compounds in water. This process is capable of reducing the secondary waste to less than 5% of its original weight. It is a low temperature, aqueous process and has been demonstrated in the laboratory [1]