Université de Montréal

About: Université de Montréal is a education organization based out in Montreal, Quebec, Canada. It is known for research contribution in the topics: Population & Context (language use). The organization has 45641 authors who have published 100476 publications receiving 4004007 citations. The organization is also known as: University of Montreal & UdeM.

Multiple representations of pain in human cerebral cortex

Abstract: The representation of pain in the cerebral cortex is less well understood than that of any other sensory system. However, with the use of magnetic resonance imaging and positron emission tomography in humans, it has now been demonstrated that painful heat causes significant activation of the contralateral anterior cingulate, secondary somatosensory, and primary somatosensory cortices. This contrasts with the predominant activation of primary somatosensory cortex caused by vibrotactile stimuli in similar experiments. Furthermore, the unilateral cingulate activation indicates that this forebrain area, thought to regulate emotions, contains an unexpectedly specific representation of pain.

Vascular Anomalies Classification: Recommendations From the International Society for the Study of Vascular Anomalies

Abstract: Vascular anomalies represent a spectrum of disorders from a simple "birthmark" to life- threatening entities. Incorrect nomenclature and misdiagnoses are commonly experienced by patients with these anomalies. Accurate diagnosis is crucial for appropriate evaluation and management, often requiring multidisciplinary specialists. Classification schemes provide a consistent terminology and serve as a guide for pathologists, clinicians, and researchers. One of the goals of the International Society for the Study of Vascular Anomalies (ISSVA) is to achieve a uniform classification. The last classification (1997) stratified vascular lesions into vascular malformations and proliferative vascular lesions (tumors). However, additional disease entities have since been identified that are complex and less easily classified by generic headings, such as capillary malformation, venous malformation, lymphatic malformation, etc. We hereby present the updated official ISSVA classification of vascular anomalies. The general biological scheme of the classification is retained. The section on tumors has been expanded and lists the main recognized vascular tumors, classified as benign, locally aggressive or borderline, and malignant. A list of well-defined diseases is included under each generic heading in the "Simple Vascular Malformations" section. A short definition is added for eponyms. Two new sections were created: one dealing with the malformations of individually named vessels (previously referred to as "truncular" malformations); the second groups lesions of uncertain or debated nature (tumor versus malformation). The known genetic defects underlying vascular anomalies are included in an appendix. This classification is meant to be a framework, acknowledging that it will require modification as new scientific information becomes available.

Resolving Difficult Phylogenetic Questions: Why More Sequences Are Not Enough

Abstract: In the quest to reconstruct the Tree of Life, researchers have increasingly turned to phylogenomics, the inference of phylogenetic relationships using genome-scale data (Box 1). Mesmerized by the sustained increase in sequencing throughput, many phylogeneticists entertained the hope that the incongruence frequently observed in studies using single or a few genes [1] would come to an end with the generation of large multigene datasets. Yet, as so often happens, reality has turned out to be far more complex, as three recent large-scale analyses, one published in PLoS Biology [2]–[4], make clear. The studies, which deal with the early diversification of animals, produced highly incongruent (Box 2) findings despite the use of considerable sequence data (see Figure 1). Clearly, merely adding more sequences is not enough to resolve the inconsistencies.

On Using Very Large Target Vocabulary for Neural Machine Translation

Abstract: Neural machine translation, a recently proposed approach to machine translation based purely on neural networks, has shown promising results compared to the existing approaches such as phrase-based statistical machine translation. Despite its recent success, neural machine translation has its limitation in handling a larger vocabulary, as training complexity as well as decoding complexity increase proportionally to the number of target words. In this paper, we propose a method based on importance sampling that allows us to use a very large target vocabulary without increasing training complexity. We show that decoding can be efficiently done even with the model having a very large target vocabulary by selecting only a small subset of the whole target vocabulary. The models trained by the proposed approach are empirically found to outperform the baseline models with a small vocabulary as well as the LSTM-based neural machine translation models. Furthermore, when we use the ensemble of a few models with very large target vocabularies, we achieve the state-of-the-art translation performance (measured by BLEU) on the English!German translation and almost as high performance as state-of-the-art English!French translation system.