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Author Carmichael, T.W.; Quinn, S.N.; Mustill, A.J.; Huang, C.; Zhou, G.; Persson, C.M.; Nielsen, L.D.; Collins, K.A.; Ziegler, C.; Collins, K.I.; Rodriguez, J.E.; Shporer, A.; Brahm, R.; Mann, A.W.; Bouchy, F.; Fridlund, M.; Stassun, K.G.; Hellier, C.; Seidel, J.V.; Stalport, M.; Udry, S.; Pepe, F.; Ireland, M.; Zerjal, M.; Briceno, C.; Law, N.; Jordan, A.; Espinoza, N.; Henning, T.; Sarkis, P.; Latham, D.W. doi  openurl
  Title Two Intermediate-mass Transiting Brown Dwarfs from the TESS Mission Type
  Year 2020 Publication Astronomical Journal Abbreviated Journal Astron. J.  
  Volume 160 Issue 1 Pages 15 pp  
  Keywords Brown dwarfs; Radial velocity; Transit photometry; Spectroscopy; Photometry; Substellar companion stars  
  Abstract We report the discovery of two intermediate-mass transiting brown dwarfs (BDs), TOI-569b and TOI-1406b, from NASA's Transiting Exoplanet Survey Satellite mission. TOI-569b has an orbital period of P = 6.55604 0.00016 days, a mass of M-b = 64.1 1.9 , and a radius of R-b = 0.75 0.02 . Its host star, TOI-569, has a mass of M-star = 1.21 0.05, a radius of R-star = 1.47 0.03 dex, and an effective temperature of T-eff = 5768 110 K. TOI-1406b has an orbital period of P = 10.57415 0.00063 days, a mass of M-b = 46.0 2.7 , and a radius of R-b = 0.86 0.03 . The host star for this BD has a mass of M-star = 1.18 0.09 a radius of R-star = 1.35 0.03 dex, and an effective temperature of T-eff = 6290 100 K. Both BDs are in circular orbits around their host stars and are older than 3 Gyr based on stellar isochrone models of the stars. TOI-569 is one of two slightly evolved stars known to host a transiting BD (the other being KOI-415). TOI-1406b is one of three known transiting BDs to occupy the mass range of 40-50 and one of two to have a circular orbit at a period near 10 days (with the first being KOI-205b). Both BDs have reliable ages from stellar isochrones, in addition to their well-constrained masses and radii, making them particularly valuable as tests for substellar isochrones in the BD mass-radius diagram.  
  Address [Carmichael, Theron W.] Harvard Univ, Dept Astron, Cambridge, MA 02138 USA, Email: tcarmich@cfa.harvard.edu  
  Corporate Author Thesis  
  Publisher Iop Publishing Ltd Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0004-6256 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000549117200001 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 1179  
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Author Henriquez, P.A.; Ruz, G.A. pdf  doi
openurl 
  Title Noise reduction for near-infrared spectroscopy data using extreme learning machines Type
  Year 2019 Publication Engineering Applications Of Artificial Intelligence Abbreviated Journal Eng. Appl. Artif. Intell.  
  Volume 79 Issue Pages 13-22  
  Keywords Near-infrared spectroscopy; Parallel layers; Constrained optimization; Regression; Classification  
  Abstract The near infrared (NIR) spectra technique is an effective approach to predict chemical properties and it is typically applied in petrochemical, agricultural, medical, and environmental sectors. NIR spectra are usually of very high dimensions and contain huge amounts of information. Most of the information is irrelevant to the target problem and some is simply noise. Thus, it is not an easy task to discover the relationship between NIR spectra and the predictive variable. However, this kind of regression analysis is one of the main topics of machine learning. Thus machine learning techniques play a key role in NIR based analytical approaches. Pre-processing of NIR spectral data has become an integral part of chemometrics modeling. The objective of the pre-processing is to remove physical phenomena (noise) in the spectra in order to improve the regression or classification model. In this work, we propose to reduce the noise using extreme learning machines which have shown good predictive performances in regression applications as well as in large dataset classification tasks. For this, we use a novel algorithm called C-PL-ELM, which has an architecture in parallel based on a non-linear layer in parallel with another non-linear layer. Using the soft margin loss function concept, we incorporate two Lagrange multipliers with the objective of including the noise of spectral data. Six real-life dataset were analyzed to illustrate the performance of the developed models. The results for regression and classification problems confirm the advantages of using the proposed method in terms of root mean square error and accuracy.  
  Address [Henriquez, Pablo A.; Ruz, Gonzalo A.] Univ Adolfo Ibanez, Fac Ingn & Ciencias, Ave Diagonal Las Torres 2640, Santiago, Chile, Email: pabhenriquez@alumnos.uai.cl;  
  Corporate Author Thesis  
  Publisher Pergamon-Elsevier Science Ltd Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0952-1976 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000459524300002 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 984  
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