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.

Optimal strategies: A new assignment model for transit networks

Abstract: We describe a model for the transit assignment problem with a fixed set of transit lines The traveler chooses the strategy that allows him or her to reach his or her destination at minimum expected cost First we consider the case in which no congestion effects occur For the special case in which the waiting time at a stop depends only on the combined frequency, the problem is formulated as a linear programming problem of a size that increases linearly with the network size A label-setting algorithm is developed that solves the latter problem in polynomial time Nonlinear cost extensions of the model are considered as well

Dynamo Models of the Solar Cycle

Abstract: This chapter details a series of dynamo models applicable to the sun and solar-type stars. After introducing the theoretical framework known as mean-field electrodynamics, a series of increasingly complex dynamo models are constructed, with the primary aim of reproducing the various basic observed characteristics of the solar magnetic activity cycle. Global and local magnetohydrodynamcial simulations of solar convection, and dynamo action therein, are also considered, and the resulting magnetic cycles compared and contrasted to those obtained in the simpler dynamo models. The focus throughout the chapter is on the sun, simply because the amount of available observational material on the solar magnetic field and its cycle dwarfs anything else in the astrophysical realm, in terms of spatial and temporal resolution, sensitivity, and time span.

Predicting Parameters in Deep Learning

Abstract: We demonstrate that there is significant redundancy in the parameterization of several deep learning models. Given only a few weight values for each feature it is possible to accurately predict the remaining values. Moreover, we show that not only can the parameter values be predicted, but many of them need not be learned at all. We train several different architectures by learning only a small number of weights and predicting the rest. In the best case we are able to predict more than 95% of the weights of a network without any drop in accuracy.

Albuminuria and Kidney Function Independently Predict Cardiovascular and Renal Outcomes in Diabetes

Abstract: There are limited data regarding whether albuminuria and reduced estimated GFR (eGFR) are separate and independent risk factors for cardiovascular and renal events among individuals with type 2 diabetes. The Action in Diabetes and Vascular disease: preterAx and diamicroN-MR Controlled Evaluation (ADVANCE) study examined the effects of routine BP lowering on adverse outcomes in type 2 diabetes. We investigated the effects of urinary albumin-to-creatinine ratio (UACR) and eGFR on the risk for cardiovascular and renal events in 10,640 patients with available data. During an average 4.3-yr follow-up, 938 (8.8%) patients experienced a cardiovascular event and 107 (1.0%) experienced a renal event. The multivariable-adjusted hazard ratio for cardiovascular events was 2.48 (95% confidence interval 1.74 to 3.52) for every 10-fold increase in baseline UACR and 2.20 (95% confidence interval 1.09 to 4.43) for every halving of baseline eGFR, after adjustment for regression dilution. There was no evidence of interaction between the effects of higher UACR and lower eGFR. Patients with both UACR >300 mg/g and eGFR <60 ml/min per 1.73 m(2) at baseline had a 3.2-fold higher risk for cardiovascular events and a 22.2-fold higher risk for renal events, compared with patients with neither of these risk factors. In conclusion, high albuminuria and low eGFR are independent risk factors for cardiovascular and renal events among patients with type 2 diabetes.