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MOA-2011-BLG-322Lb: a 'second generation survey' microlensing planet
Shvartzvald, Y.,Maoz, D.,Kaspi, S.,Sumi, T.,Udalski, A.,Gould, A.,Bennett, D. P.,Han, C.,Abe, F.,Bond, I. A.,Botzler, C. S.,Freeman, M.,Fukui, A.,Fukunaga, D.,Itow, Y.,Koshimoto, N.,Ling, C. H.,Masuda Oxford University Press 2014 MONTHLY NOTICES- ROYAL ASTRONOMICAL SOCIETY Vol.439 No.1
Oh, Jihoon,Yun, Kyongsik,Maoz, Uri,Kim, Tae-Suk,Chae, Jeong-Ho Elsevier 2019 Journal of affective disorders Vol.257 No.-
<P><B>Abstract</B></P> <P><B>Background</B></P> <P>As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological factors leading up to depression has remained challenging. Deep-learning algorithms can be applied to assess the factors leading up to prevalence and clinical manifestations of depression.</P> <P><B>Methods</B></P> <P>Customized deep-neural-network and machine-learning classifiers were assessed using survey data from 19,725 participants from the NHANES database (from 1999 through 2014) and 4949 from the South Korea NHANES (K-NHANES) database in 2014.</P> <P><B>Results</B></P> <P>A deep-learning algorithm showed area under the receiver operating characteristic curve (AUCs) of 0.91 and 0.89 for detecting depression in NHANES and K-NHANES, respectively. The deep-learning algorithm trained with serial datasets (NHANES, from 1999 to 2012), predicted the prevalence of depression in the following two years of data (NHANES, 2013 and 2014) with an AUC of 0.92. Machine learning classifiers trained with NHANES could further predict depression in K-NHANES. There, logistic regression had the highest performance (AUC, 0.77) followed by deep learning algorithm (AUC, 0.74).</P> <P><B>Conclusions</B></P> <P>Deep neural-networks managed to identify depression well from other health and demographic factors in both the NHANES and K-NHANES datasets. The deep-learning algorithm was also able to predict depression relatively well on new data set—cross temporally and cross nationally. Further research can delineate the clinical implications of machine learning and deep learning in detecting disease prevalence and progress as well as other risk factors for depression and other mental illnesses.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Estimating epidemiological contributors to depression and predicting the prevalence of depression are still challenging. </LI> <LI> We aimed to estimate factors affecting depression in National Health and Nutrition Examination Survey (NHANES) datasets using deep learning and machine learning algorithms. </LI> <LI> Deep-learning achieved a high performance for identifying depression on the NHANES datasets of both the United States and South Korea. </LI> <LI> Trained deep-learning and machine learning algorithms are useful for estimating the prevalence of depression. </LI> </UL> </P>
MASSES AND ORBITAL CONSTRAINTS FOR THE OGLE-2006-BLG-109Lb,c JUPITER/SATURN ANALOG PLANETARY SYSTEM
Bennett, D. P.,Rhie, S. H.,Nikolaev, S.,Gaudi, B. S.,Udalski, A.,Gould, A.,Christie, G. W.,Maoz, D.,Dong, S.,McCormick, J.,Szymań,ski, M. K.,Tristram, P. J.,Macintosh, B.,Cook, K. H.,Kubiak, M.,P IOP Publishing 2010 The Astrophysical journal Vol.713 No.2
<P>We present a new analysis of the Jupiter+Saturn analog system, OGLE-2006-BLG-109Lb,c, which was the first double planet system discovered with the gravitational microlensing method. This is the only multi-planet system discovered by any method with measured masses for the star and both planets. In addition to the signatures of two planets, this event also exhibits a microlensing parallax signature and finite source effects that provide a direct measure of the masses of the star and planets, and the expected brightness of the host star is confirmed by Keck AO imaging, yielding masses of M(*) = 0.51(-0.04)(+0.05) M(circle dot), M(b) = 231 +/- 19 M(circle plus), and M(c) = 86 +/- 7 M(circle plus). The Saturn-analog planet in this system had a planetary light-curve deviation that lasted for 11 days, and as a result, the effects of the orbital motion are visible in the microlensing light curve. We find that four of the six orbital parameters are tightly constrained and that a fifth parameter, the orbital acceleration, is weakly constrained. No orbital information is available for the Jupiter-analog planet, but its presence helps to constrain the orbital motion of the Saturn-analog planet. Assuming co-planar orbits, we find an orbital eccentricity of epsilon = 0.15(-0.10) (+0.17) and an orbital inclination of i = 64 degrees(+ 4 degrees)(-7 degrees) The 95% confidence level lower limit on the inclination of i > 49 degrees implies that this planetary system can be detected and studied via radial velocity measurements using a telescope of greater than or similar to 30 m aperture.</P>
A brown dwarf orbiting an M-dwarf: MOA 2009–BLG–411L
Bachelet, E.,Fouqué,, P.,Han, C.,Gould, A.,Albrow, M. D.,Beaulieu, J.-P.,Bertin, E.,Bond, I. A.,Christie, G. W.,Heyrovský,, D.,Horne, K.,Jørgensen, U. G.,Maoz, D.,Mathiasen, M.,Matsunaga, EDP Sciences 2012 Astronomy and astrophysics Vol.547 No.-