École Polytechnique de Montréal

About: École Polytechnique de Montréal is a education organization based out in Montreal, Quebec, Canada. It is known for research contribution in the topics: Finite element method & Computer science. The organization has 8015 authors who have published 18390 publications receiving 494372 citations.

Analysis of row nucleated lamellar morphology of polypropylene obtained from the cast film process: Effect of melt rheology and process conditions

Abstract: Five different polypropylene resins were characterized by rheology to study the effect of melt rheology on the row-nucleated lamellar structure development during the cast film process. The arrangement and orientation of the crystalline and amorphous phases were examined by WAXD (wide angle X-ray diffraction) and FTIR (Fourier transform infrared) methods. Tensile tests were carried out to examine the effect of orientation on the behavior of the samples. It was found that the molecular weight evaluated from rheology and the processing conditions played a crucial role on the orientation of the crystalline and amorphous phases and, in turn, affected significantly the tensile response. The molecular weight was the main parameter that controlled the orientation and it was found that the resin with a higher molecular weight had a tendency to form a planar crystalline morphology as the draw ratio increased. It was also observed that a planar morphology was associated with a suppression of the yield behavior in the tensile measurements. POLYM. ENG. SCI., 47:1170–1178, 2007. © 2007 Society of Plastics Engineers

Recent progress on magnetic nanoparticles for magnetic hyperthermia.

Abstract: Recent advances in nanomaterials science contributed to develop new micro- and nano-devices as potential diagnostic and therapeutic tools in the field of oncology. The synthesis of superparamagnetic nanoparticles (SPMNPs) has been intensively studied, and the use of these particles in magnetic hyperthermia therapy has demonstrated successes in treatment of cancer. However, some physical limitations have been found to impact the heating efficiency required to kill cancer cells. Moreover, the bio-safety of NPs remains largely unexplored. The primary goals of this review are to summarize the recent progress in the development of magnetic nanoparticles (MNPs) for hyperthermia, and discuss the limitations and advances in the synthesis of these particles. Based on this knowledge, new perspectives on development of new biocompatible and biofunctional nanomaterials for magnetic hyperthermia are discussed.

Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning

Abstract: A lot of the recent success in natural language processing (NLP) has been driven by distributed vector representations of words trained on large amounts of text in an unsupervised manner These representations are typically used as general purpose features for words across a range of NLP problems However, extending this success to learning representations of sequences of words, such as sentences, remains an open problem Recent work has explored unsupervised as well as supervised learning techniques with different training objectives to learn general purpose fixed-length sentence representations In this work, we present a simple, effective multi-task learning framework for sentence representations that combines the inductive biases of diverse training objectives in a single model We train this model on several data sources with multiple training objectives on over 100 million sentences Extensive experiments demonstrate that sharing a single recurrent sentence encoder across weakly related tasks leads to consistent improvements over previous methods We present substantial improvements in the context of transfer learning and low-resource settings using our learned general-purpose representations

Dynamic Signature Verification System Based on One Real Signature.

Abstract: The dynamic signature is a biometric trait widely used and accepted for verifying a person's identity. Current automatic signature-based biometric systems typically require five, ten, or even more specimens of a person's signature to learn intrapersonal variability sufficient to provide an accurate verification of the individual's identity. To mitigate this drawback, this paper proposes a procedure for training with only a single reference signature. Our strategy consists of duplicating the given signature a number of times and training an automatic signature verifier with each of the resulting signatures. The duplication scheme is based on a sigma lognormal decomposition of the reference signature. Two methods are presented to create human-like duplicated signatures: the first varies the strokes' lognormal parameters (stroke-wise) whereas the second modifies their virtual target points (target-wise). A challenging benchmark, assessed with multiple state-of-the-art automatic signature verifiers and multiple databases, proves the robustness of the system. Experimental results suggest that our system, with a single reference signature, is capable of achieving a similar performance to standard verifiers trained with up to five signature specimens.