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Institution

Aix-Marseille University

EducationMarseille, 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.


Papers
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Journal ArticleDOI
Hiroaki Aihara1, Nobuo Arimoto2, Nobuo Arimoto3, Robert Armstrong4  +167 moreInstitutions (41)
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

Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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

NameH-indexPapersCitations
Didier Raoult1733267153016
Andrea Bocci1722402176461
Marc Humbert1491184100577
Carlo Rovelli1461502103550
Marc Besancon1431799106869
Jian Yang1421818111166
Josh Moss139101989255
Maksym Titov1391573128335
Bernard Henrissat139593100002
R. D. Kass1381920107907
Stylianos E. Antonarakis13874693605
Jean-Paul Kneib13880589287
Brad Abbott137156698604
Shu Li136100178390
Georges Aad135112188811
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023170
2022748
20215,607
20205,697
20195,288
20185,125