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Institution

Université Paris-Saclay

EducationGif-sur-Yvette, France
About: Université Paris-Saclay is a education organization based out in Gif-sur-Yvette, France. It is known for research contribution in the topics: Population & Context (language use). The organization has 29307 authors who have published 43183 publications receiving 867404 citations.


Papers
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Journal ArticleDOI
Peter A. R. Ade1, Nabila Aghanim2, M. I. R. Alves2, C. Armitage-Caplan3  +467 moreInstitutions (88)
TL;DR: The ESA's Planck satellite was launched 14 May 2009 and has been scanning the microwave and sub-millimetre sky continuously since 12 August 2009 as discussed by the authors, where it has measured gravitational lensing of CMB anisotropies at greater than 25 sigma.
Abstract: The ESA's Planck satellite, dedicated to studying the early Universe and its subsequent evolution, was launched 14 May 2009 and has been scanning the microwave and submillimetre sky continuously since 12 August 2009. This paper gives an overview of the mission and its performance, the processing, analysis, and characteristics of the data, the scientific results, and the science data products and papers in the release. The science products include maps of the CMB and diffuse extragalactic foregrounds, a catalogue of compact Galactic and extragalactic sources, and a list of sources detected through the SZ effect. The likelihood code used to assess cosmological models against the Planck data and a lensing likelihood are described. Scientific results include robust support for the standard six-parameter LCDM model of cosmology and improved measurements of its parameters, including a highly significant deviation from scale invariance of the primordial power spectrum. The Planck values for these parameters and others derived from them are significantly different from those previously determined. Several large-scale anomalies in the temperature distribution of the CMB, first detected by WMAP, are confirmed with higher confidence. Planck sets new limits on the number and mass of neutrinos, and has measured gravitational lensing of CMB anisotropies at greater than 25 sigma. Planck finds no evidence for non-Gaussianity in the CMB. Planck's results agree well with results from the measurements of baryon acoustic oscillations. Planck finds a lower Hubble constant than found in some more local measures. Some tension is also present between the amplitude of matter fluctuations derived from CMB data and that derived from SZ data. The Planck and WMAP power spectra are offset from each other by an average level of about 2% around the first acoustic peak.

367 citations

Journal ArticleDOI
09 Jul 2020-Cell
TL;DR: This panSV genome, along with 14 new reference assemblies, revealed large-scale intermixing of diverse genotypes, as well as thousands of SVs intersecting genes and cis-regulatory regions, and showed how multiple SVs that changed gene dosage and expression levels modified fruit flavor, size, and production.

367 citations

Journal ArticleDOI
TL;DR: It will be shown that the data-driven approaches should not replace, but rather complement, traditional design techniques based on mathematical models in future wireless communication networks.
Abstract: This paper deals with the use of emerging deep learning techniques in future wireless communication networks. It will be shown that the data-driven approaches should not replace, but rather complement, traditional design techniques based on mathematical models. Extensive motivation is given for why deep learning based on artificial neural networks will be an indispensable tool for the design and operation of future wireless communication networks, and our vision of how artificial neural networks should be integrated into the architecture of future wireless communication networks is presented. A thorough description of deep learning methodologies is provided, starting with the general machine learning paradigm, followed by a more in-depth discussion about deep learning and artificial neural networks, covering the most widely used artificial neural network architectures and their training methods. Deep learning will also be connected to other major learning frameworks, such as reinforcement learning and transfer learning. A thorough survey of the literature on deep learning for wireless communication networks is provided, followed by a detailed description of several novel case studies wherein the use of deep learning proves extremely useful for network design. For each case study, it will be shown how the use of (even approximate) mathematical models can significantly reduce the amount of live data that needs to be acquired/measured to implement the data-driven approaches. Finally, concluding remarks describe those that, in our opinion, are the major directions for future research in this field.

366 citations

Journal ArticleDOI
I. Pĝris1, I. Pĝris2, Patrick Petitjean2, Éric Aubourg, Stephen Bailey3, Nicholas P. Ross3, Adam D. Myers4, Adam D. Myers5, Michael A. Strauss6, Scott F. Anderson7, Eduard Arnau, Julian E. Bautista, D. V. Bizyaev8, Adam S. Bolton9, Jo Bovy, W. N. Brandt10, Howard Brewington8, J. R. Browstein9, Nicolás G. Busca, Daniel M. Capellupo11, Daniel M. Capellupo12, William Carithers3, Rupert A. C. Croft13, Kyle S. Dawson9, T. Delubac14, Garrett Ebelke8, Daniel J. Eisenstein15, P. Engelke16, Xiaohui Fan17, N. Filiz Ak10, N. Filiz Ak18, Hayley Finley2, Andreu Font-Ribera19, Andreu Font-Ribera3, Jian Ge12, R. R. Gibson7, Patrick B. Hall20, Fred Hamann12, Joseph F. Hennawi4, Shirley Ho13, David W. Hogg21, Å Ivezić7, Linhua Jiang17, Amy Kimball22, Amy Kimball7, D. Kirkby23, Jessica A. Kirkpatrick3, Khee-Gan Lee4, Khee-Gan Lee6, J. M. Le Goff14, Britt Lundgren16, Chelsea L. MacLeod, Elena Malanushenko8, Viktor Malanushenko8, Claudia Maraston24, Ian D. McGreer17, Richard G. McMahon25, Jordi Miralda-Escudé, Demitri Muna21, Pasquier Noterdaeme2, Daniel Oravetz8, Nathalie Palanque-Delabrouille14, Kaike Pan8, Ismael Perez-Fournon26, Ismael Perez-Fournon27, Matthew M. Pieri24, Gordon T. Richards28, Emmanuel Rollinde2, Erin Sheldon29, David J. Schlegel3, Donald P. Schneider10, Anže Slosar29, Alaina Shelden8, Yue Shen15, A. Simmons8, S. A. Snedden8, Nao Suzuki30, Nao Suzuki3, Jeremy L. Tinker21, M. Viel, Benjamin A. Weaver21, David H. Weinberg31, Martin White3, W. M. Wood-Vasey32, C. Yeche14 
TL;DR: The Data Release 9 Quasar (DR9Q) catalog from the Baryon Oscillation Spectroscopic Survey (BOSS) of the Sloan Digital Sky Survey (III) is presented in this article.
Abstract: We present the Data Release 9 Quasar (DR9Q) catalog from the Baryon Oscillation Spectroscopic Survey (BOSS) of the Sloan Digital Sky Survey III. The catalog includes all BOSS objects that were targeted as quasar candidates during the survey, are spectrocopically confirmed as quasars via visual inspection, have luminosities M i [z = 2] 0 = 70 km s-1 Mpc-1 , ΩM = 0.3, and ΩΛ = 0.7) and either display at least one emission line with full width at half maximum (FWHM) larger than 500 km s-1 or, if not, have interesting/complex absorption features. It includes as well, known quasars (mostly from SDSS-I and II) that were reobserved by BOSS. This catalog contains 87 822 quasars (78 086 are new discoveries) detected over 3275 deg2 with robust identification and redshift measured by a combination of principal component eigenspectra newly derived from a training set of 8632 spectra from SDSS-DR7. The number of quasars with z > 2.15 (61 931) is ~2.8 times larger than the number of z > 2.15 quasars previously known. Redshifts and FWHMs are provided for the strongest emission lines (C iv, C iii], Mg ii). The catalog identifies 7533 broad absorption line quasars and gives their characteristics. For each object the catalog presents five-band (u , g , r , i , z ) CCD-based photometry with typical accuracy of 0.03 mag, and information on the morphology and selection method. The catalog also contains X-ray, ultraviolet, near-infrared, and radio emission properties of the quasars, when available, from other large-area surveys. The calibrated digital spectra cover the wavelength region 3600−10 500 A at a spectral resolution in the range 1300 < 2500; the spectra can be retrieved from the SDSS Catalog Archive Server. We also provide a supplemental list of an additional 949 quasars that have been identified, among galaxy targets of the BOSS or among quasar targets after DR9 was frozen.

365 citations

Journal ArticleDOI
Georges Aad1, Alexander Kupco2, Peter Davison2, Samuel Webb3  +2918 moreInstitutions (81)
TL;DR: In this paper, the ATLAS detector at the Large Hadron Collider is used to search for high-mass resonances decaying to dielectron or dimuon final states.
Abstract: The ATLAS detector at the Large Hadron Collider is used to search for high-mass resonances decaying to dielectron or dimuon final states. Results are presented from an analysis of proton-proton (pp) collisions at a center-of-mass energy of 8 TeV corresponding to an integrated luminosity of 20.3 fb(-1) in the dimuon channel. A narrow resonance with Standard Model Z couplings to fermions is excluded at 95% confidence level for masses less than 2.79 TeV in the dielectron channel, 2.53 TeV in the dimuon channel, and 2.90 TeV in the two channels combined. Limits on other model interpretations are also presented, including a grand-unification model based on the E-6 gauge group, Z* bosons, minimal Z' models, a spin-2 graviton excitation from Randall-Sundrum models, quantum black holes, and a minimal walking technicolor model with a composite Higgs boson.

364 citations


Authors

Showing all 29679 results

NameH-indexPapersCitations
Guido Kroemer2361404246571
Patrick O. Brown183755200985
Didier Raoult1733267153016
Sophie Henrot-Versille171957157040
Philippe Ciais149965114503
Stanislas Dehaene14945686539
Marc Humbert1491184100577
Jean Bousquet145128896769
Jean-François Cardoso145373115144
Marc Besancon1431799106869
Maksym Titov1391573128335
W. Kozanecki138149899758
Nabila Aghanim137416100914
Yves Sirois137133495714
Patrick Janot136148593626
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023214
2022735
20218,412
20208,032
20197,008
20186,458