Apparent Temperature and Air Pollution vs. Elderly Population Mortality in Metro Vancouver

Background: Meteorological conditions and air pollution in urban environments have been associated with general population and elderly mortality, showing seasonal variation. Objectives: This study is designed to evaluate the relationship between apparent temperature (AT) and air pollution (PM2.5) vs. mortality in elderly population of Metro Vancouver. Methods: Statistical analyses are performed on moving sum daily mortality rates vs. moving average AT and PM 2.5 in 1-, 2-, 3-, 5-, and 7-day models for all seasons, warm temperatures above 15uC, and cold temperatures below 10uC. Results: Approximately 37 % of the variation in all-season mortality from circulatory and respiratory causes can be explained by the variation in 7-day moving average apparent temperature (r 2 = 0.37, p,0.001). Although the analytical results from air pollution models show increasingly better prediction ability of longer time-intervals (r 2 = 0.012, p,0.001 in a 7-day model), a very weak negative association between elderly mortality and air pollution is observed. Conclusions: Apparent temperature is associated with mortality from respiratory and circulatory causes in elderly population of Metro Vancouver. In a changing climate, one may anticipate to observe potential health impacts from the projected high- and particularly from the low-temperature extremes

Active Choice of Teachers, Learning Strategies and Goals for a Socially Guided Intrinsic Motivation Learner

International audienceWe present an active learning architecture that allows a robot to actively learn which data collection strategy is most efficient for acquiring motor skills to achieve multiple outcomes, and generalise over its experience to achieve new outcomes. The robot explores its environment both via interactive learning and goal-babbling. It learns at the same time when, who and what to actively imitate from several available teachers, and learns when not to use social guidance but use active goal-oriented self-exploration. This is formalised in the framework of life-long strategic learning. The proposed architecture, called Socially Guided Intrinsic Motivation with Active Choice of Teacher and Strategy (SGIM-ACTS), relies on hierarchical active decisions of what and how to learn driven by empirical evaluation of learning progress for each learning strategy. We illustrate with an experiment where a simulated robot learns to control its arm for realising two kinds of different outcomes. It has to choose actively and hierarchically at each learning episode: 1) what to learn: which outcome is most interesting to select as a goal to focus on for goal-directed exploration; 2) how to learn: which data collection strategy to use among self-exploration, mimicry and emulation; 3) once he has decided when and what to imitate by choosing mimicry or emulation, then he has to choose who to imitate, from a set of different teachers. We show that SGIM-ACTS learns significantly more efficiently than using single learning strategies, and coherently selects the best strategy with respect to the chosen outcome, taking advantage of the available teachers (with different levels of skills)