M
Michaël Aupetit
Researcher at Qatar Computing Research Institute
Publications - 89
Citations - 1469
Michaël Aupetit is an academic researcher from Qatar Computing Research Institute. The author has contributed to research in topics: Voronoi diagram & Computer science. The author has an hindex of 18, co-authored 82 publications receiving 1073 citations. Previous affiliations of Michaël Aupetit include French Alternative Energies and Atomic Energy Commission & Qatar Airways.
Papers
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Journal ArticleDOI
Multidimensional Projection for Visual Analytics: Linking Techniques with Distortions, Tasks, and Layout Enrichment
TL;DR: This survey provides detailed analysis and taxonomies as to the organization of MDP techniques according to their main properties and traits, discussing the impact of such properties for visual perception and other human factors and providing future research axes to fill discovered gaps in this domain.
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Visualizing distortions and recovering topology in continuous projection techniques
TL;DR: This work proposes to visualize any measure associated to a projected datum or to a pair of projected data, by coloring the corresponding Voronoi cell in the projection space, by defining specific measures and showing how they allow estimating visually whether some part of the projection is or is not a reliable image of the original manifolds.
Journal ArticleDOI
The future of sleep health: a data-driven revolution in sleep science and medicine.
Ignacio Perez-Pozuelo,Ignacio Perez-Pozuelo,Bing Zhai,Joao Palotti,Joao Palotti,Raghvendra Mall,Michaël Aupetit,Juan M. García-Gómez,Shahrad Taheri,Yu Guan,Luis Fernandez-Luque +10 more
TL;DR: The state-of-the-art in sleep-monitoring technologies are introduced, the opportunities and challenges from data acquisition to the eventual application of insights in clinical and consumer settings are discussed, and the strengths and limitations of current and emerging sensing methods are explored.
Proceedings Article
Unsupervised User Stance Detection on Twitter
TL;DR: This paper proposed an unsupervised framework for detecting the stance of prolific Twitter users with respect to controversial topics using dimensionality reduction to project users onto a low-dimensional space, followed by clustering, which allows to find core users that are representative of the different stances.
Journal ArticleDOI
CheckViz: Sanity Check and Topological Clues for Linear and Non-Linear Mappings
TL;DR: A two‐dimensional perceptually uniform colour coding which allows visualizing tears and false neighbourhoods, the two elementary and complementary types of geometrical mapping distortions, straight onto the map at the location where they occur is defined.