Institution
Université de Sherbrooke
Education•Sherbrooke, Quebec, Canada•
About: Université de Sherbrooke is a education organization based out in Sherbrooke, Quebec, Canada. It is known for research contribution in the topics: Population & Receptor. The organization has 14922 authors who have published 28783 publications receiving 792511 citations. The organization is also known as: Universite de Sherbrooke & Sherbrooke University.
Papers published on a yearly basis
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TL;DR: The first machine learning method for ground truthing a video is proposed, based on a multi-resolution convolutional neural network with a cascaded architecture, for segmenting foreground moving objects pictured in surveillance videos.
288 citations
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06 Jul 2015TL;DR: This work introduces a simple modification for autoencoder neural networks that yields powerful generative models and proves that this approach is competitive with state-of-the-art tractable distribution estimators.
Abstract: There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples. We introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our method masks the autoencoder's parameters to respect autoregressive constraints: each input is reconstructed only from previous inputs in a given ordering. Constrained this way, the autoencoder outputs can be interpreted as a set of conditional probabilities, and their product, the full joint probability. We can also train a single network that can decompose the joint probability in multiple different orderings. Our simple framework can be applied to multiple architectures, including deep ones. Vectorized implementations, such as on GPUs, are simple and fast. Experiments demonstrate that this approach is competitive with state-of-the-art tractable distribution estimators. At test time, the method is significantly faster and scales better than other autoregressive estimators.
288 citations
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TL;DR: The development of the 3D version of CASINO is presented, which has an improved energy range for scanning electron microscopy and scanning transmission electron microscopeopy applications and is available freely to the scientific community.
Abstract: Monte Carlo softwares are widely used to understand the capabilities of electron microscopes. To study more realistic applications with complex samples, 3D Monte Carlo softwares are needed. In this article, the development of the 3D version of CASINO is presented. The software feature a graphical user interface, an efficient (in relation to simulation time and memory use) 3D simulation model, accurate physic models for electron microscopy applications, and it is available freely to the scientific community at this website: www.gel.usherbrooke.ca/casino/index.html. It can be used to model backscattered, secondary, and transmitted electron signals as well as absorbed energy. The software features like scan points and shot noise allow the simulation and study of realistic experimental conditions. This software has an improved energy range for scanning electron microscopy and scanning transmission electron microscopy applications.
287 citations
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Erasmus University Rotterdam1, University Hospital Southampton NHS Foundation Trust2, University of Southampton3, University of Porto4, Paris Descartes University5, Sorbonne6, University of Crete7, University of Southern California8, Maastricht University9, National and Kapodistrian University of Athens10, University Medical Center Groningen11, Université de Sherbrooke12, Norwegian Institute of Public Health13, University of Bologna14, Nofer Institute of Occupational Medicine15, University of California, Davis16, Harvard University17, University of Illinois at Chicago18, University of Valencia19, National Institutes of Health20, University of Turku21, University of Bristol22, Helmholtz Centre for Environmental Research - UFZ23, Jagiellonian University Medical College24, Åbo Akademi University25, Harokopio University26, Public Health Research Institute27, University of Copenhagen28, University of Southern Denmark29, La Trobe University30, University of Helsinki31, University of Turin32, Radboud University Nijmegen33, University of Trieste34, University of Bergen35, Ludwig Maximilian University of Munich36, Slovak Medical University37, Utrecht University38, Pompeu Fabra University39
TL;DR: In this meta-analysis of pooled individual participant data from 25 cohort studies, the risk for adverse maternal and infant outcomes varied by gestational weight gain and across the range of prepregnancy weights, however, the optimal gestations weight gain ranges had limited predictive value for the outcomes assessed.
Abstract: Importance Both low and high gestational weight gain have been associated with adverse maternal and infant outcomes, but optimal gestational weight gain remains uncertain and not well defined for all prepregnancy weight ranges. Objectives To examine the association of ranges of gestational weight gain with risk of adverse maternal and infant outcomes and estimate optimal gestational weight gain ranges across prepregnancy body mass index categories. Design, Setting, and Participants Individual participant-level meta-analysis using data from 196 670 participants within 25 cohort studies from Europe and North America (main study sample). Optimal gestational weight gain ranges were estimated for each prepregnancy body mass index (BMI) category by selecting the range of gestational weight gain that was associated with lower risk for any adverse outcome. Individual participant-level data from 3505 participants within 4 separate hospital-based cohorts were used as a validation sample. Data were collected between 1989 and 2015. The final date of follow-up was December 2015. Exposures Gestational weight gain. Main Outcomes and Measures The main outcome termedany adverse outcomewas defined as the presence of 1 or more of the following outcomes: preeclampsia, gestational hypertension, gestational diabetes, cesarean delivery, preterm birth, and small or large size for gestational age at birth. Results Of the 196 670 women (median age, 30.0 years [quartile 1 and 3, 27.0 and 33.0 years] and 40 937 were white) included in the main sample, 7809 (4.0%) were categorized at baseline as underweight (BMI Conclusions and Relevance In this meta-analysis of pooled individual participant data from 25 cohort studies, the risk for adverse maternal and infant outcomes varied by gestational weight gain and across the range of prepregnancy weights. The estimates of optimal gestational weight gain may inform prenatal counseling; however, the optimal gestational weight gain ranges had limited predictive value for the outcomes assessed.
286 citations
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TL;DR: This paper demonstrates how to implement a quantum version of the Metropolis algorithm, a method that has basically acquired a monopoly on the simulation of interacting particles and permits sampling directly from the eigenstates of the Hamiltonian, and thus avoids the sign problem present in classical simulations.
Abstract: The original motivation to build a quantum computer came from Feynman, who imagined a machine capable of simulating generic quantum mechanical systems--a task that is believed to be intractable for classical computers. Such a machine could have far-reaching applications in the simulation of many-body quantum physics in condensed-matter, chemical and high-energy systems. Part of Feynman's challenge was met by Lloyd, who showed how to approximately decompose the time evolution operator of interacting quantum particles into a short sequence of elementary gates, suitable for operation on a quantum computer. However, this left open the problem of how to simulate the equilibrium and static properties of quantum systems. This requires the preparation of ground and Gibbs states on a quantum computer. For classical systems, this problem is solved by the ubiquitous Metropolis algorithm, a method that has basically acquired a monopoly on the simulation of interacting particles. Here we demonstrate how to implement a quantum version of the Metropolis algorithm. This algorithm permits sampling directly from the eigenstates of the Hamiltonian, and thus evades the sign problem present in classical simulations. A small-scale implementation of this algorithm should be achievable with today's technology.
285 citations
Authors
Showing all 15051 results
Name | H-index | Papers | Citations |
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Masashi Yanagisawa | 130 | 524 | 83631 |
Joseph V. Bonventre | 126 | 596 | 61009 |
Jeffrey L. Benovic | 99 | 264 | 30041 |
Alessio Fasano | 96 | 478 | 34580 |
Graham Pawelec | 89 | 572 | 27373 |
Simon C. Robson | 88 | 552 | 29808 |
Paul B. Corkum | 88 | 576 | 37200 |
Mario Leclerc | 88 | 374 | 35961 |
Stephen M. Collins | 86 | 320 | 25646 |
Ed Harlow | 86 | 190 | 61008 |
William D. Fraser | 85 | 827 | 30155 |
Jean Cadet | 83 | 372 | 24000 |
Vincent Giguère | 82 | 227 | 27481 |
Robert Gurny | 81 | 396 | 28391 |
Jean-Michel Gaillard | 81 | 410 | 26780 |