Université Paris-Saclay

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.

Anharmonicity and Disorder in the Black Phases of Cesium Lead Iodide Used for Stable Inorganic Perovskite Solar Cells

Abstract: Hybrid organic–inorganic perovskites emerged as a new generation of absorber materials for high-efficiency low-cost solar cells in 2009. Very recently, fully inorganic perovskite quantum dots also led to promising efficiencies, making them a potentially stable and efficient alternative to their hybrid cousins. Currently, the record efficiency is obtained with CsPbI3, whose crystallographical characterization is still limited. Here, we show through high-resolution in situ synchrotron XRD measurements that CsPbI3 can be undercooled below its transition temperature and temporarily maintained in its perovskite structure down to room temperature, stabilizing a metastable perovskite polytype (black γ-phase) crucial for photovoltaic applications. Our analysis of the structural phase transitions reveals a highly anisotropic evolution of the individual lattice parameters versus temperature. Structural, vibrational, and electronic properties of all the experimentally observed black phases are further inspected base...

Performance of the ATLAS trigger system in 2015

Abstract: During 2015 the ATLAS experiment recorded 3.8 fb(-1) of proton-proton collision data at a centre-of-mass energy of 13 TeV. The ATLAS trigger system is a crucial component of the experiment, respons ...

Kepler Asteroseismology Program: Introduction and First Results

Abstract: .Asteroseismology involves probing the interiors of stars and quantifying their global properties, such as radius and age, through observations of normal modes of oscillation. The technical requirements for conducting asteroseismology include ultrahigh precision measured in photometry in parts per million, as well as nearly continuous time series over weeks to years, and cadences rapid enough to sample oscillations with periods as short as a few minutes. We report on results from the first 43 days of observations, in which the unique capabilities of Kepler in providing a revolutionary advance in asteroseismology are already well in evidence. The Kepler asteroseismology program holds intrinsic importance in supporting the core planetary search program through greatly enhanced knowledge of host star properties, and extends well beyond this to rich applications in stellar astrophysics.

Fully Convolutional Siamese Networks for Change Detection

Abstract: This paper presents three fully convolutional neural network architectures which perform change detection using a pair of coregistered images. Most notably, we propose two Siamese extensions of fully convolutional networks which use heuristics about the current problem to achieve the best results in our tests on two open change detection datasets, using both RGB and multispectral images. We show that our system is able to learn from scratch using annotated change detection images. Our architectures achieve better performance than previously proposed methods, while being at least 500 times faster than related systems. This work is a step towards efficient processing of data from large scale Earth observation systems such as Copernicus or Landsat.

Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning

Abstract: Imbalanced-learn is an open-source python toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced dataset frequently encountered in machine learning and pattern recognition. The implemented state-of-the-art methods can be categorized into 4 groups: (i) under-sampling, (ii) over-sampling, (iii) combination of over- and under-sampling, and (iv) ensemble learning methods. The proposed toolbox only depends on numpy, scipy, and scikit-learn and is distributed under MIT license. Furthermore, it is fully compatible with scikit-learn and is part of the scikit-learn-contrib supported project. Documentation, unit tests as well as integration tests are provided to ease usage and contribution. The toolbox is publicly available in GitHub: this https URL.