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arXiv 2022年

作者： Rodriguez, Joseph E. Quinn, Samuel N. Vanderburg, Andrew Zhou, George Eastman, Jason D. Thygesen, Erica Cale, Bryson Ciardi, David R. Reed, Phillip A. Oelkers, Ryan J. Collins, Karen A. Bieryla, Allyson Latham, David W. Gonzales, Erica J. Gaudi, B. Scott Hellier, Coel Jones, Matías I. Brahm, Rafael Sokolovsky, Kirill Schulte, Jack Srdoc, Gregor Kielkopf, John Horta, Ferran Grau Massey, Bob Evans, Phil Stephens, Denise C. McLeod, Kim K. Chazov, Nikita Krushinsky, Vadim Ghachoui, Mourad Safonov, Boris S. Dedrick, Cayla M. Conti, Dennis Laloum, Didier Giacalone, Steven Ziegler, Carl Serra, Pere Guerra Nogues, Ramon Naves Murgas, Felipe Michaels, Edward J. Ricker, George R. Vanderspek, Roland K. Seager, Sara Winn, Joshua N. Jenkins, Jon M. Addison, Brett Alfaro, Owen Anderson, D.R. Aydi, Elias Beatty, Thomas G. Bedding, Timothy R. Belinski, Alexander A. Benkhaldoun, Zouhair Berlind, Perry Blake, Cullen H. Bowen, Michael J. Bowler, Brendan P. Boyle, Andrew W. Branson, Dalton Briceño, César Calkins, Michael L. Campbell, Emma Christiansen, Jessie L. Chomiuk, Laura Collins, Kevin I. Cornachione, Matthew A. Daassou, Ahmed Dressing, Courtney D. Esquerdo, Gilbert A. Feliz, Dax L. Fong, William Fukui, Akihiko Gan, Tianjun Gill, Holden Goliguzova, Maria V. Hansen, Jarrod Henning, Thomas Hintz, Eric G. Hobson, Melissa J. Horner, Jonathan Huang, Chelsea X. James, David J. Jensen, Jacob S. Johnson, Samson A. Jordán, Andrés Kane, Stephen R. Barkaoui, Khalid Kim, Myung-Jin Kim, Kingsley Kuhn, Rudolf B. Law, Nicholas Lewin, Pablo Liu, Hui-Gen Lund, Michael B. Mann, Andrew W. McCrady, Nate Mengel, Matthew W. Mink, Jessica Murphy, Lauren G. Narita, Norio Newman, Patrick Okumura, Jack Osborn, Hugh P. Paegert, Martin Palle, Enric Pepper, Joshua Plavchan, Peter Popov, Alexander A. Rabus, Markus Ranshaw, Jessica Rodriguez, Jennifer A. Roh, Dong-Goo Reefe, Michael A. Savel, Arjun B. Schwarz, Richard P. Shporer, Avi Siverd, Robert J. Sliski, David H. Stassun, Keivan G. Stevens, Daniel J. Soubkiou, Abderahmane Ting, Eric B. Tinney, C.G. Vow Center for Data Intensive and Time Domain Astronomy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Center for Astrophysics | Harvard & Smithsonian 60 Garden St CambridgeMA02138 United States Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Centre for Astrophysics University of Southern Queensland West Street ToowoombaQLD4350 Australia Caltech IPAC NASA Exoplanet Science Institute 1200 E. California Ave PasadenaCA91125 United States Department of Physical Sciences Kutztown University KutztownPA19530 United States George P. and Cynthia Woods Mitchell Institute for Fundamental Physics and Astronomy Texas A&M University College StationTX77843 United States Charles R. & Judith G. Munnerlyn Astronomical Laboratory Department of Physics & Astronomy Texas A&M University College StationTX77843 United States Center for Gravitational Wave Astronomy The University of Texas Rio Grande Valley BrownsvilleTX78520 United States Department of Astronomy and Astrophysics University of California Santa CruzCA95064 United States Department of Astronomy The Ohio State University 140 West 18th Avenue ColumbusOH43210 United States Astrophysics Group Keele University StaffordshireST5 5BG United Kingdom European Southern Observatory Alonso de Córdova 3107 Vitacura Casilla Santiago19001 Chile Facultad de Ingeniería y Ciencias Universidad Adolfo Ibáñez Av. Diagonal las Torres 2640 Peñalolén Santiago Chile Millennium Institute for Astrophysics Chile Data Observatory Foundation Chile Kotizarovci Observatory Sarsoni 90 Viskovo51216 Croatia Department of Physics and Astronomy University of Louisville LouisvilleKY40292 United States Observatri de Ca l'Ou United States Villa'39 Observatory LandersCA92285 United States El Sauce Observatory Coquimbo Province Chile Department of Physics and Astronomy Brigham Young Un

We present the discovery and characterization of six short-period, transiting giant planets from NASA's Transiting Exoplanet Survey Satellite (TESS) - TOI-1811 (TIC 376524552), TOI-2025 (TIC 394050135), TOI-2145 (TIC 88992642), TOI-2152 (TIC 395393265), TOI-2154 (TIC 428787891), & TOI-2497 (TIC 97568467). All six planets orbit bright host stars (8.9 P = 0.99-1.45 RJ), have masses ranging from 0.92 to 5.26 MJ, and orbit F, G, and K stars (4766 ≤ Teff ≤ 7360 K). We detect a significant orbital eccentricity for the three longest-period systems in our sample: TOI-2025 b (P = 8.872 days, 0.394+0.035..0.038, TOI-2145 b (P = 10.261 days, e = 0.208+0.034-0.047, and TOI-2497 b (P = 10.656 days, e = 0.195+0.043-0.040. TOI-2145 b and TOI-2497 b both orbit subgiant host stars (3.8 +0.38-0.37 MJ (TOI-2145 b) and 4.82 ± 0.41 MJ (TOI-2497 b). These six new discoveries contribute to the larger community effort to use TESS to create a magnitude-complete, self-consistent sample of giant planets with well-determined parameters for future detailed studies. Copyright © 2022, The Authors. All rights reserved.

关键词： Satellites

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Unsupervised clustering and active learning of hyperspectral images with nonlinear diffusion

arXiv

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arXiv 2017年

作者： Murphy, James M. Maggioni, Mauro Department of Mathematics Tufts University United States Department of Mathematics Department of Applied Mathematics and Statistics Institute of Data Intensive Engineering and Science Mathematical Institute of Data Sciences Johns Hopkins University United States

The problem of unsupervised learning and segmentation of hyperspectral images is a significant challenge in remote sensing. The high dimensionality of hyperspectral data, presence of substantial noise, and overlap of classes all contribute to the difficulty of automatically clustering and segmenting hyperspectral images. We propose an unsupervised learning technique called spectral-spatial diffusion learning (DLSS) that combines a geometric estimation of class modes with a diffusion-inspired labeling that incorporates both spectral and spatial information. The mode estimation incorporates the geometry of the hyperspectral data by using diffusion distance to promote learning a unique mode from each class. These class modes are then used to label all points by a joint spectral-spatial nonlinear diffusion process. A related variation of DLSS is also discussed, which enables active learning by requesting labels for a very small number of well-chosen pixels, dramatically boosting overall clustering results. Extensive experimental analysis demonstrates the efficacy of the proposed methods against benchmark and state-of-the-art hyperspectral analysis techniques on a variety of real datasets, their robustness to choices of parameters, and their low computational complexity. Copyright © 2017, The Authors. All rights reserved.

关键词： Spectroscopy

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AA-ICP: Iterative closest point with anderson acceleration

arXiv

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arXiv 2017年

作者： Pavlov, A.L. Ovchinnikov, G.V. Derbyshev, D.Yu. Tsetserukou, D. Oseledets, I.V. Space Center Skolkovo Institute of Science and Technology Skolkovo Innovation Center Moscow143026 Russia Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow Institute of Physics and Technology Space Center Skolkovo Institute of Science and Technology

Iterative Closest Point (ICP) is a widely used method for performing scan-matching and registration. Being simple and robust method, it is still computationally expensive and may be challenging to use in real-time applications with limited resources on mobile platforms. In this paper we propose novel effective method for acceleration of ICP which does not require substantial modifications to the existing code. This method is based on an idea of Anderson acceleration which is an iterative procedure for finding a fixed point of contractive mapping. The latter is often faster than a standard Picard iteration, usually used in ICP implementations. We show that ICP, being a fixed point problem, can be significantly accelerated by this method enhanced by heuristics to improve overall robustness. We implement proposed approach into Point Cloud Library (PCL) and make it available online. Benchmarking on real-world data fully supports our claims. Copyright © 2017, The Authors. All rights reserved.

关键词： Heuristic methods

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Multiscale strategies for computing optimal transport

arXiv

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arXiv 2017年

作者： Gerber, Samuel Maggioni, Mauro Kitware NC United States Department of Mathematics Department of Applied Mathematics Institute for Data Intensive Engineering and Science Johns Hopkins University BaltimoreMD United States

This paper presents a multiscale approach to efficiently compute approximate optimal transport plans between point sets. It is particularly well-suited for point sets that are in high-dimensions, but are close to being intrinsically low-dimensional. The approach is based on an adaptive multiscale decomposition of the point sets. The multiscale decomposition yields a sequence of optimal transport problems, that are solved in a top-to-bottom fashion from the coarsest to the finest scale. We provide numerical evidence that this multiscale approach scales approximately linearly, in time and memory, in the number of nodes, instead of quadratically or worse for a direct solution. Empirically, the multiscale approach results in less than one percent relative error in the objective function. Furthermore, the multiscale plans constructed are of interest by themselves as they may be used to introduce novel features and notions of distances between point sets. An analysis of sets of brain MRI based on optimal transport distances illustrates the effectiveness of the proposed method on a real world data set. The application demonstrates that multiscale optimal transport distances have the potential to improve on state-of-the-art metrics currently used in computational anatomy. Copyright © 2017, The Authors. All rights reserved.

关键词： Geometry

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Field heterogeneity detection based on the modified FastICA RGB-image processing

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Journal of Physics: Conference Series 2018年第1期1117卷

作者： L Mirvakhabova M Pukalchik S Matveev P Tregubova I Oseledets Skoltech Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology 1 Nobel Street Moscow 121205 Russia Marchuk Institute of Numerical Mathematics RAS 8 Gubkin Street 119333 Moscow Russia

In the light of the increasing demand for food production, climate change challenges, and economic pressure, precision farming is becoming an ever-growing market. Nowadays using satellite images for field zoning has become a widely spread technique employed in precision agriculture. Usually satellite images are used to evaluate Normalized Difference Vegetation Index (NDVI) as a common yield assessment. At the same time it is rather a challenging task to designing NDVI maps as compared to ordinary RGB images. We propose a new algorithm for site-specific zoning based on the performance of a well-known and widely used ICA algorithm (FastICA). We analyse high resolution RGB satellite images for 3 arable fields located in Kursk region, Russia, (2017) provided by the Planet Labs service. The main outcomes from this study are (i) the algorithm creates site-specific zoning maps with a relative accuracy of yield distribution maps between 0.78 and 0.89; (ii) the algorithm requires a relatively small dataset – from 8 to 10 RGB images. The obtained results indicate that the algorithm in question can be used not only to identify management zones but also to map the variations in yield within the fields.

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Low-density star cluster formation: discovery of a young faint fuzzy on the outskirts of the low-mass spiral galaxy NGC 247

arXiv

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arXiv 2022年

作者： Romanowsky, Aaron J. Larsen, Søren S. Villaume, Alexa Carlin, Jeffrey L. Janz, Joachim Sand, David J. Strader, Jay Brodie, Jean P. Chakrabarti, Sukanya Cheng, Chloe M. Crnojević, Denija Forbes, Duncan A. Garling, Christopher T. Hargis, Jonathan R. Karunakaran, Ananthan Martín-Navarro, Ignacio Olsen, Knut A.G. Rider, Nicole Salimkumar, Bitha Santhanakrishnan, Vakini Spekkens, Kristine Tang, Yimeng van Dokkum, Pieter G. Willman, Beth Department of Physics & Astronomy San José State University One Washington Square San JoseCA95192 United States University of California Observatories 1156 High Street Santa CruzCA95064 United States Department of Astronomy & Astrophysics University of California Santa Cruz 1156 High Street Santa CruzCA95064 United States Department of Astrophysics IMAPP Radboud University PO Box 9010 Nijmegen6500 GL Netherlands Waterloo Centre for Astrophysics Department of Physics & Astronomy University of Waterloo 200 University Ave. W. WaterlooONN2L 3G1 Canada Rubin Observatory Project Office 950 N. Cherry Ave. TucsonAZ85719 United States University of Turku Vesilinnantie 5 FI-20014 Finland Space Physics and Astronomy Research Unit University of Oulu P.O. Box 3000 Yliopisto OulunFI-90014 Finland Specim Spectral Imaging Ltd. Elektroniikkatie 13 OuluFI-90590 Finland Steward Observatory University of Arizona 933 North Cherry Avenue TucsonAZ85721 United States Center for Data Intensive and Time Domain Astronomy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Centre for Astrophysics and Supercomputing Swinburne University John Street HawthornVIC3122 Australia School of Physics and Astronomy RIT RochesterNY14623 United States Institute for Advanced Study 1 Einstein Drive PrincetonNJ08540 United States University of Tampa 401 West Kennedy Boulevard TampaFL33606 United States CCAPP Department of Astronomy The Ohio State University 140 W. 18th Ave. ColumbusOH43210 United States Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Department of Physics Engineering Physics and Astronomy Queen’s University KingstonONK7L 3N6 Canada Instituto de Astrofísica de Canarias Tenerife La LagunaE-38205 Spain Departamento de Astrofísica Universidad de La Laguna Tenerife La LagunaE-38205 Spain NSF’s National Optical-Infrared Astronomy Research Laboratory 950

The classical globular clusters found in all galaxy types have half-light radii of rh ∼ 2–4 pc, which have been tied to formation in the dense cores of giant molecular clouds. Some old star clusters have larger sizes, and it is unclear if these represent a fundamentally different mode of low-density star cluster formation. We report the discovery of a rare, young "faint fuzzy" star cluster, NGC 247-SC1, on the outskirts of the low-mass spiral galaxy NGC 247 in the nearby Sculptor group, and measure its radial velocity using Keck spectroscopy. We use Hubble Space Telescope imaging to measure the cluster half-light radius of rh ' 12 pc and a luminosity of Lv ' 4 × 105L . We produce a colour–magnitude diagram of cluster stars and compare to theoretical isochrones, finding an age of '300 Myr, a metallicity of [Z/H] ∼ −0.6 and an inferred mass of M★ ' 9 × 104M . The narrow width of blue-loop star magnitudes implies an age spread of . 50 Myr, while no old red-giant branch stars are found, so SC1 is consistent with hosting a single stellar population, modulo several unexplained bright "red straggler" stars. SC1 appears to be surrounded by tidal debris, at the end of a ∼ 2 kpc long stellar filament that also hosts two low-mass, low-density clusters of a similar age. We explore a link between the formation of these unusual clusters and an external perturbation of their host galaxy, illuminating a possible channel by which some clusters are born with large sizes. © 2022, CC BY-NC-SA.

关键词： Galaxies

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The meerKAT galaxy cluster legacy survey I. survey overview and highlights

arXiv

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arXiv 2021年

作者： Knowles, Kenda Cotton, W.D. Rudnick, L. Camilo, F. Goedhart, S. Deane, R. Ramatsoku, M. Bietenholz, M.F. Brüggen, M. Button, C. Chen, H. Chibueze, J.O. Clarke, T.E. de Gasperin, F. Ianjamasimanana, R. Józsa, G.I.G. Hilton, M. Kesebonye, K.C. Kolokythas, K. Kraan-Korteweg, R.C. Lawrie, G. Lochner, M. Loubser, S.I. Marchegiani, P. Mhlahlo, N. Moodley, K. Murphy, E. Namumba, B. Oozeer, N. Parekh, V. Pillay, D.S. Passmoor, S.S. Ramaila, A.J.T. Ranchod, S. Retana-Montenegro, E. Sebokolodi, L. Sikhosana, S.P. Smirnov, O. Thorat, K. Venturi, T. Abbott, T.D. Adam, R.M. Adams, G. Aldera, M.A. Bauermeister, E.F. Bennett, T.G.H. Bode, W.A. Botha, D.H. Botha, A.G. Brederode, L.R.S. Buchner, S. Burger, J.P. Cheetham, T. de Villiers, D.I.L. Dikgale-Mahlakoana, M.A. du Toit, L.J. Esterhuyse, S.W.P. Fadana, G. Fanaroff, B.L. Fataar, S. Foley, A.R. Fourie, D.J. Frank, B.S. Gamatham, R.R.G. Gatsi, T.G. Geyer, M. Gouws, M. Gumede, S.C. Heywood, I. Hlakola, M.J. Hokwana, A. Hoosen, S.W. Horn, D.M. Horrell, J.M.G. Hugo, B.V. Isaacson, A.R. Jonas, J.L. Jordaan, J.D.B. Joubert, A.F. Julie, R.P.M. Kapp, F.B. Kasper, V.A. Kenyon, J.S. Kotzé, P.P.A. Kotze, A.G. Kriek, N. Kriel, H. Krishnan, V.K. Kusel, T.W. Legodi, L.S. Lehmensiek, R. Liebenberg, D. Lord, R.T. Lunsky, B.M. Madisa, K. Magnus, L.G. Main, J.P.L. Makhaba, A. Makhathini, S. Malan, J.A. Manley, J.R. Marais, S.J. Maree, M.D.J. Martens, A. Mauch, T. McAlpine, K. Merry, B.C. Millenaar, R.P. Mokone, O.J. Monama, T.E. Mphego, M.C. New, W.S. Ngcebetsha, B. Ngoasheng, K.J. Ockards, M.T. Otto, A.J. Patel, A.A. Peens-Hough, A. Perkins, S.J. Ramanujam, N.M. Ramudzuli, Z.R. Ratcliffe, S.M. Renil, R. Robyntjies, A. Rust, A.N. Salie, S. Sambu, N. Schollar, C.T.G. Schwardt, L.C. Schwartz, R.L. Serylak, M. Siebrits, R. Sirothia, S.K. Slabber, M. Sofeya, L. Taljaard, B. Tasse, C. Tiplady, A.J. Toruvanda, O. Twum, S.N. van Balla, T.J. van der Byl, A. van der Merwe, C. van Dyk, C.L. van Tonder, V. van Wyk, R. Venter, A.J. Venter, M. Welz, M.G. Williams, L.P. Xaia, B. Astrophysics Research Centre University of KwaZulu-Natal Durban4041 South Africa Centre for Radio Astronomy Techniques and Technologies Department of Physics and Electronics Rhodes University P.O. Box 94 Makhanda6140 South Africa South African Radio Astronomy Observatory 2 Fir Street Observatory7925 South Africa National Radio Astronomy Observatory CharlottesvilleVA22903 United States Minnesota Institute for Astrophysics University of Minnesota 116 Church St SE MinneapolisMN55455 United States Wits Centre for Astrophysics School of Physics University of the Witwatersrand 1 Jan Smuts Avenue Johannesburg2050 South Africa Department of Physics University of Pretoria Hatfield0028 South Africa I-09047 Italy Hartebeesthoek Radio Astronomy Observatory SARAO Krugersdorp1740 South Africa York University TorontoON Canada Hamburger Sternwarte Universität Hamburg Gojenbergsweg 112 Hamburg21029 Germany Department of Astronomy University of Cape Town Rondebosch7700 South Africa Centre for Space Research North-West University Potchefstroom2520 South Africa Department of Physics and Astronomy Faculty of Physical Sciences University of Nigeria Carver Building 1 University Road Nsukka Nigeria Naval Research Laboratory WashingtonDC20375 United States INAF – Istituto di Radioastronomia via Gobetti 101 BolognaI–40129 Italy Argelander-Institut für Astronomie Universität Bonn Auf dem Hügel 71 BonnD-53121 Germany School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Westville 3696 South Africa Department of Physics and Astronomy University of the Western Cape Bellville7535 South Africa Dipartimento di Fisica Universitá Sapienza P.le Aldo Moro 2 Roma00185 Italy African Institute for Mathematical Sciences 6-8 Melrose Road Muizenberg7945 South Africa Inter-University Institute for Data Intensive Astronomy University of Cape Town Rondebosch7700 South Africa Ltd. 64-74 White Road Retreat 7945 South Africa EMSS Ant

MeerKAT’s large number (64) of 13.5 m-diameter antennas, spanning 8 km with a densely packed 1 km core, create a powerful instrument for wide-area surveys, with high sensitivity over a wide range of angular scales. The MeerKAT Galaxy Cluster Legacy Survey (MGCLS) is a programme of long-track MeerKAT L-band (900–1670 MHz) observations of 115 galaxy clusters, observed for ∼ 6–10 hours each in full polarisation. The first legacy product data release (DR1), made available with this paper, includes the MeerKAT visibilities, basic image cubes at ∼ 800 resolution, and enhanced spectral and polarisation image cubes at ∼ 800 and 1500 resolutions. Typical sensitivities for the full-resolution MGCLS image products range over ∼ 3–5 µJy beam−1. The basic cubes are full-field and span 2◦ × 2◦. The enhanced products consist of the inner 1.2◦ × 1.2◦ field of view, corrected for the primary beam. The survey is fully sensitive to structures up to ∼ 100 scales and the wide bandwidth allows spectral and Faraday rotation mapping. Relatively narrow frequency channels (209 kHz) are also used to provide H i mapping in windows of 0 200. We find no dependence of the star formation rate on distance from the cluster centre, and observe a small excess of the radio-to-100 µm flux ratio towards the centre of Abell 209 that may reflect a ram pressure enhancement in the denser environment. We detect diffuse cluster radio emission in 62 of the surveyed systems and present a catalogue of the 99 diffuse cluster emission structures, of which 56 are new. These include mini-halos, halos, relics, and other diffuse structures for which no suitable characterization currently exists. We highlight some of the radio galaxies which challenge current paradigms such as trident-shaped structures, jets that remain well-collimated far beyond their bending radius, and filamentary features linked to radio galaxies that likely illuminate magnetic flux tubes in the intracluster medium. We also present early results from

关键词： Surveys

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Fink, a new generation of broker for the LSST community

arXiv

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arXiv 2020年

作者： Möller, Anais Peloton, Julien Ishida, Emille E.O. Arnault, Chris Bachelet, Etienne Blaineau, Tristan Boutigny, Dominique Chauhan, Abhishek Gangler, Emmanuel Hernandez, Fabio Hrivnac, Julius Leoni, Marco Leroy, Nicolas Moniez, Marc Pateyron, Sacha Ramparison, Adrien Turpin, Damien Ansari, Réza Allam, Tarek Bajat, Armelle Biswas, Biswajit Boucaud, Alexandre Bregeon, Johan Campagne, Jean-Eric Cohen-Tanugi, Johann Coleiro, Alexis Dornic, Damien Fouchez, Dominique Godet, Olivier Gris, Philippe Karpov, Sergey Gomez-Moran, Ada Nebot Neveu, Jérémy Plaszczynski, Stephane Savchenko, Volodymyr Webb, Natalie Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-FerrandF-63000 France Université Paris-Saclay CNRS/IN2P3 IJCLab Orsay France Las Cumbres Observatory 6740 Cortona Drive suite 102 GoletaCA93117 United States Université Grenoble-Alpes Université Savoie Mont Blanc CNRS/IN2P3 Laboratoire d’Annecy-le-Vieux de Physique des Particules France Department of Ocean Engineering and Naval Architecture Indian Institute of Technology Kharagpur West Bengal721302 India CNRS CC-IN2P3 21 avenue Pierre de Coubertin CS70202 Villeurbanne cedex69627 France Direction des Systèmes d’Information Université Paris Sud Orsay Cedex91405 France Université Paris-Saclay CNRS CEA Département d’Astrophysique Instrumentation et Modélisation de Paris-Saclay Gif-sur-Yvette France Centre for Data Intensive Science Department of Physics and Astronomy University College London LondonWC1E 6BT United Kingdom Department of Space and Climate Physics University College London SurreyRH5 6NT United Kingdom CEICO Institute of Physics Czech Academy of Sciences Prague Czech Republic Department of Physics Birla Institute of Technology and Science Pilani Pilani Campus Rajasthan333031 India Université de Paris CNRS AstroParticule et Cosmologie ParisF-75013 France Grenoble France LUPM Université de Montpellier et CNRS Montpellier Cedex 0534095 France Aix Marseille Université CNRS/IN2P3 CPPM Marseille France IRAP UPS CNRS/CNES 9 avenue du colonel Roche 3028 Toulouse Cedex 04 France Observatoire Astronomique de Strasbourg Université de Strasbourg CNRS UMR 7550 11 rue de l’Université Strasbourg67000 France ISDC Department of Astronomy University of Geneva Chemin d’Ecogia 16 VersoixCH-1290 Switzerland

Fink is a broker designed to enable science with large time-domain alert streams such as the one from the upcoming Legacy Survey of Space and Time (LSST). It exhibits traditional astronomy broker features such as automatised ingestion, enrichment, selection and redistribution of promising alerts for transient science. It is also designed to go beyond traditional broker features by providing real-time transient classification which is continuously improved by using state-of-the-art Deep Learning and Adaptive Learning techniques. These evolving added values will enable more accurate scientific output from LSST photometric data for diverse science cases while also leading to a higher incidence of new discoveries which shall accompany the evolution of the survey. In this paper we introduce Fink, its science motivation, architecture and current status including first science verification cases using the Zwicky Transient Facility alert stream. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

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The Lancet 2020年第10258期396卷 1204-1222页

作者： Abbafati, Cristiana Abbas, Kaja M. Abbasi, Mohammad Abbasifard, Mitra Abbasi-Kangevari, Mohsen Abbastabar, Hedayat Abd-Allah, Foad Abdelalim, Ahmed Abdollahi, Mohammad Abdollahpour, Ibrahim Abedi, Aidin Abedi, Parisa Abegaz, Kedir Hussein Abolhassani, Hassan Abosetugn, Akine Eshete Aboyans, Victor Abrams, Elissa M. Abreu, Lucas Guimarães Abrigo, Michael R.M. Abu Haimed, Abdulaziz Khalid Abualhasan, Ahmed Abu-Gharbieh, Eman Abu-Raddad, Laith Jamal Abushouk, Abdelrahman I. Acebedo, Alyssa Ackerman, Ilana N. Adabi, Maryam Adair, Tim Adamu, Abdu A. Adebayo, Oladimeji M. Adedeji, Isaac Akinkunmi Adekanmbi, Victor Adelson, Jaimie D. Adeoye, Abiodun Moshood Adetokunboh, Olatunji O. Adham, Davoud Advani, Shailesh M. Afarideh, Mohsen Afshari, Mahdi Afshin, Ashkan Agardh, Emilie E. Agarwal, Gina Agasthi, Pradyumna Agesa, Kareha M. Aghaali, Mohammad Aghamir, Seyed Mohammad Kazem Agrawal, Anurag Ahmad, Tauseef Ahmadi, Alireza Ahmadi, Keivan Ahmadi, Mehdi Ahmadieh, Hamid Ahmadpour, Ehsan Ahmed, Muktar Beshir Aji, Budi Akalu, Temesgen Yihunie Akinyemi, Rufus Olusola Akinyemiju, Tomi Akombi, Blessing Akunna, Chisom Joyqueenet Alahdab, Fares Al-Aly, Ziyad Alam, Khurshid Alam, Noore Alam, Samiah Alam, Tahiya Alanezi, Fahad Mashhour Alanzi, Turki M. Albertson, Samuel B. Alcalde-Rabanal, Jacqueline Elizabeth Alema, Niguse Meles Alemu, Biresaw Wassihun Alemu, Yihun Mulugeta Alhabib, Khalid F. Alhassan, Robert Kaba Ali, Muhammad Ali, Saqib Alicandro, Gianfranco Alijanzadeh, Mehran Alinia, Cyrus Alipour, Vahid Alizade, Hesam Aljunid, Syed Mohamed Alla, François Allebeck, Peter Almadi, Majid Abdulrahman Hamad Almasi, Ali Almasi-Hashiani, Amir Almasri, Nihad A. Al-Mekhlafi, Hesham M. Almulhim, Abdulaziz M. Alonso, Jordi Al-Raddadi, Rajaa M. Altirkawi, Khalid A. Alumran, Arwa Khalid Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Amare, Bekalu Amini, Saeed Amini-Rarani, Mostafa Aminorroaya, Arya Amiri, Fatemeh Amit, Arianna Maever L. Amugsi, Dickson A. Amul, Gianna Gayle Herrera Anbesu, Etsay Woldu Ancuceanu, Robert Anderl Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Infectious Disease Epidemiology London School of Hygiene and Tropical Medicine London United Kingdom Social Determinants of Health Research Center Tehran Iran Department of Neurology Cairo University Cairo Egypt School of Pharmacy Tehran Iran The Institute of Pharmaceutical Sciences (TIPS) Tehran Iran Neuroscience Research Center Isfahan University of Medical Sciences Isfahan Iran Biostatistics Department Near East University Nicosia Cyprus Biostatistics and Health Informatics Madda Walabu University Bale Robe Ethiopia Research Center for Immunodeficiencies Tehran Iran Karolinska University Hospital Huddinge Sweden Department of Cardiology Dupuytren University Hospital Limoges France University of Limoges Limoges France Department of Pediatric Dentistry Federal University of Minas Gerais Belo Horizonte Brazil Department of Research Philippine Institute for Development Studies Quezon City Philippines Department of Healthcare Policy and Research Weill Cornell Medical College in Qatar Doha Qatar Harvard Medical School Boston MA United States Department of Medicine Ain Shams University Cairo Egypt Hamadan University of Medical Sciences Hamadan Iran School of Medicine Cardiff University Cardiff United Kingdom School of Laboratory Medicine and Science University College Hospital Ibadan Ibadan Nigeria Institute of Cardiovascular Diseases University of Ibadan Ibadan Nigeria Centre of Excellence for Epidemiological Modelling and Analysis Stellenbosch University Cape Town South Africa Department of Global Health Stellenbosch University Cape Town South Africa School of Health Ardabil University of Medical Science Ardabil Iran Social Behavioral Research Branch National Institute of Health Bethesda MD United States Department of Oncology Georgetown University Washington DC DC United States Department of Health Metrics Sciences School of Medicine Seattle WA

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw value

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data management at scale

Data management at scale

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作者： Choudhury, G. Sayeed Sheridan Libraries Institute for Data Intensive Engineering and Science Johns Hopkins University 3400 N. Charles Street BaltimoreMD21218 United States

This paper offers a high-level, non-scientific assessment regarding the impact of research library based data management services, particularly related to predictive data analytics - an area where the corporate sector has arguably made more tangible impact. The fundamental premise rests upon the assertion that research libraries should consider the potential consequences, need for interpretation and degree of control to better allocate and optimize scarce resources for data management. By doing so, it may be possible to support broader goals of data management at scale, identify network effects through linked data and highlight possibilities for partnerships, including with the corporate sector. © 2016 IOS Press and the authors.

关键词： Information management

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