Institution
Aix-Marseille University
Education•Marseille, France•
About: Aix-Marseille University is a education organization based out in Marseille, France. It is known for research contribution in the topics: Population & Galaxy. The organization has 24326 authors who have published 54240 publications receiving 1455416 citations. The organization is also known as: University Aix-Marseille & université d'Aix-Marseille.
Topics: Population, Galaxy, Context (language use), Redshift, Medicine
Papers published on a yearly basis
Papers
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TL;DR: Hyper Suprime-Cam (HSC) is a wide-field imaging camera on the prime focus of the 8.2m Subaru telescope on the summit of Maunakea as mentioned in this paper.
Abstract: Hyper Suprime-Cam (HSC) is a wide-field imaging camera on the prime focus of the 8.2m Subaru telescope on the summit of Maunakea. A team of scientists from Japan, Taiwan and Princeton University is using HSC to carry out a 300-night multi-band imaging survey of the high-latitude sky. The survey includes three layers: the Wide layer will cover 1400 deg$^2$ in five broad bands ($grizy$), with a $5\,\sigma$ point-source depth of $r \approx 26$. The Deep layer covers a total of 26~deg$^2$ in four fields, going roughly a magnitude fainter, while the UltraDeep layer goes almost a magnitude fainter still in two pointings of HSC (a total of 3.5 deg$^2$). Here we describe the instrument, the science goals of the survey, and the survey strategy and data processing. This paper serves as an introduction to a special issue of the Publications of the Astronomical Society of Japan, which includes a large number of technical and scientific papers describing results from the early phases of this survey.
392 citations
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TL;DR: Administration of the HCQ+AZ combination before COVID-19 complications occur is safe and associated with very low fatality rate in patients, retrospectively report on 1061 SARS-CoV-2 positive tested patients.
392 citations
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TL;DR: This analysis suggests that users with similar interests are more likely to be friends, and therefore topical similarity measures among users based solely on their annotation metadata should be predictive of social links.
Abstract: Social media have attracted considerable attention because their open-ended nature allows users to create lightweight semantic scaffolding to organize and share content. To date, the interplay of the social and topical components of social media has been only partially explored. Here, we study the presence of homophily in three systems that combine tagging social media with online social networks. We find a substantial level of topical similarity among users who are close to each other in the social network. We introduce a null model that preserves user activity while removing local correlations, allowing us to disentangle the actual local similarity between users from statistical effects due to the assortative mixing of user activity and centrality in the social network. This analysis suggests that users with similar interests are more likely to be friends, and therefore topical similarity measures among users based solely on their annotation metadata should be predictive of social links. We test this hypothesis on several datasets, confirming that social networks constructed from topical similarity capture actual friendship accurately. When combined with topological features, topical similarity achieves a link prediction accuracy of about 92p.
390 citations
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TL;DR: The theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications are described.
Abstract: We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network simulations using biologically realistic connectivity. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including functional MRI (fMRI), EEG and MEG. Researchers from different backgrounds can benefit from an integrative software platform including a supporting framework for data management (generation, organization, storage, integration and sharing) and a simulation core written in Python. TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavorable processes. The architecture of TVB supports interaction with MATLAB packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS, and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface IDLE, enabling easy modeling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications.
389 citations
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Leibniz Institute for Astrophysics Potsdam1, Humboldt University of Berlin2, New Mexico State University3, Sternberg Astronomical Institute4, New York University5, École Polytechnique Fédérale de Lausanne6, University of Utah7, Université Paris-Saclay8, Max Planck Society9, National Autonomous University of Mexico10, Chinese Academy of Sciences11, Harvard University12, Pierre-and-Marie-Curie University13, University of California, Berkeley14, Carnegie Mellon University15, Lawrence Berkeley National Laboratory16, Russian Academy of Sciences17, University of La Laguna18, Spanish National Research Council19, Aix-Marseille University20, Ohio State University21, University of Pittsburgh22, Institut d'Astrophysique de Paris23, Autonomous University of Madrid24, Sejong University25, University of Portsmouth26, Pennsylvania State University27, Ohio University28, Brookhaven National Laboratory29, Tsinghua University30, Yale University31
TL;DR: In this paper, the Baryon Acoustic Oscillation (BAO) scale in redshift-space using clustering of quasars was measured using a sample of 147, 000 quaars from the extended Ballyon Oscillations Spectroscopic Survey (eBOSS) distributed over 2044 square degrees with redshifts 0.8 0 at 6.6s significance.
Abstract: We present measurements of the Baryon Acoustic Oscillation (BAO) scale in redshift-space using the clustering of quasars. We consider a sample of 147 000 quasars from the extended Baryon Oscillation Spectroscopic Survey (eBOSS) distributed over 2044 square degrees with redshifts 0.8 0 at 6.6s significance when testing a ΛCDM model with free curvature.
389 citations
Authors
Showing all 24784 results
Name | H-index | Papers | Citations |
---|---|---|---|
Didier Raoult | 173 | 3267 | 153016 |
Andrea Bocci | 172 | 2402 | 176461 |
Marc Humbert | 149 | 1184 | 100577 |
Carlo Rovelli | 146 | 1502 | 103550 |
Marc Besancon | 143 | 1799 | 106869 |
Jian Yang | 142 | 1818 | 111166 |
Josh Moss | 139 | 1019 | 89255 |
Maksym Titov | 139 | 1573 | 128335 |
Bernard Henrissat | 139 | 593 | 100002 |
R. D. Kass | 138 | 1920 | 107907 |
Stylianos E. Antonarakis | 138 | 746 | 93605 |
Jean-Paul Kneib | 138 | 805 | 89287 |
Brad Abbott | 137 | 1566 | 98604 |
Shu Li | 136 | 1001 | 78390 |
Georges Aad | 135 | 1121 | 88811 |