Université de Sherbrooke

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.

Two-step Coulomb explosions of diatoms in intense laser fields

Abstract: A two-step model of Coulomb explosions of diatoms in intense laser fields is presented. In this model the molecule loses several electrons when the atoms are at the equilibrium internuclear distance and then fast Coulomb explosions occur until the products reach a critical distance Rc approximately=9 Bohr, at which several additional electrons are lost due to a recently discovered maxima of ionization rates occurring at R=Rc. Then the subsequent Coulomb explosion for the higher-charged ions takes place. The total combined Coulomb explosion energy agrees well with experimental results, showing striking regularities. The origin and intensity dependence of unexpectedly high ionization rates of dissociating nuclei at preferential, large internuclear distances R=Rc is also discussed and an analytic expression for Rc is derived.

Correlations, indistinguishability and entanglement in Hong-Ou-Mandel experiments at microwave frequencies

Abstract: Two indistinguishable single photons that simultaneously enter a beam splitter will always leave together, and this Hong–Ou–Mandel effect is now observed with microwave photons for the first time. Coherence between the beam-splitter output arms is demonstrated, enabling two-mode entanglement, which is useful for quantum communication processing at microwave frequencies.

Elimination of endocrine disrupting chemicals using white rot fungi and their lignin modifying enzymes: A review

Abstract: The ability of white rot fungi (WRF) and their lignin modifying enzymes (LMEs), i.e. laccase and lignin- and manganese-dependent peroxidase, to treat endocrine disrupting chemicals (EDCs) is extensively reviewed in this paper. These chemicals cause adverse health effects by mimicking endogenous hormones in receiving organisms. The alkylphenolic EDCs nonylphenol, bisphenol A and triclosan, the phthalic acid esters dibutylphthalate, diethylphthalate and di-(2-ethylhexyl)phthalate, the natural estrogens estrone, 17 beta-estradiol, estriol and 17 alpha-ethynylestradiol and the phytoestrogens genistein and P-sitosterol have been shown to be eliminated by several fungi and LMEs. WRF have manifested a highly efficient removal of EDCs in aqueous media and soil matrices using both LME and non LME-systems. The ligninolytic system of WRF could also be used for the elimination of several EDCs and the associated hormone-mimicking activity. The transformation of EDCs by LMEs and WRF is supported by emerging knowledge on the physiology and biochemistry of these organisms and the biocatalytic properties of their enzymes. Due to field reaction conditions, which drastically differ from laboratory conditions, further efforts will have to be directed towards developing robust and reliable biotechnological processes for the treatment of EDC-contaminated environmental matrices.

A Meta-Learning Perspective on Cold-Start Recommendations for Items

Abstract: Matrix factorization (MF) is one of the most popular techniques for product recommendation, but is known to suffer from serious cold-start problems. Item cold-start problems are particularly acute in settings such as Tweet recommendation where new items arrive continuously. In this paper, we present a meta-learning strategy to address item cold-start when new items arrive continuously. We propose two deep neural network architectures that implement our meta-learning strategy. The first architecture learns a linear classifier whose weights are determined by the item history while the second architecture learns a neural network whose biases are instead adjusted. We evaluate our techniques on the real-world problem of Tweet recommendation. On production data at Twitter, we demonstrate that our proposed techniques significantly beat the MF baseline and also outperform production models for Tweet recommendation.