Uncertainty and sensitivity analysis of functional risk curves based on Gaussian processes

A functional risk curve gives the probability of an undesirable event as a function of the value of a critical parameter of a considered physical system. In several applicative situations, this curve is built using phenomenological numerical models which simulate complex physical phenomena. To avoid cpu-time expensive numerical models, we propose to use Gaussian process regression to build functional risk curves. An algorithm is given to provide confidence bounds due to this approximation. Two methods of global sensitivity analysis of the models' random input parameters on the functional risk curve are also studied. In particular, the PLI sensitivity indices allow to understand the effect of misjudgment on the input parameters' probability density functions

Characterization of a Multi-User Indoor Positioning System Based on Low Cost Depth Vision (Kinect) for Monitoring Human Activity in a Smart Home

An increasing number of systems use indoor positioning for many scenarios such as asset tracking, health care, games, manufacturing, logistics, shopping, and security. Many technologies are available and the use of depth cameras is becoming more and more attractive as this kind of device becomes affordable and easy to handle. This paper contributes to the effort of creating an indoor positioning system based on low cost depth cameras (Kinect). A method is proposed to optimize the calibration of the depth cameras, to describe the multi-camera data fusion and to specify a global positioning projection to maintain the compatibility with outdoor positioning systems. The monitoring of the people trajectories at home is intended for the early detection of a shift in daily activities which highlights disabilities and loss of autonomy. This system is meant to improve homecare health management at home for a better end of life at a sustainable cost for the community

A Data Fusion System to Study Synchronization in Social Activities

As the world population gets older, the healthcare system must be adapted, among others by providing continuous health monitoring at home and in the city. The social activities have a significant role in everyone health status. Hence, this paper proposes a system to perform a data fusion of signals sampled on several subjects during social activities. This study implies the time synchronization of data coming from several sensors whether these are embedded on people or integrated in the environment. The data fusion is applied to several experiments including physical, cognitive and rest activities, with social aspects. The simultaneous and continuous analysis of four subjects cardiac activity and GPS coordinates provides a new way to distinguish different collaborative activities comparing the measurements between the subjects and along time.Comment: Healthcom 201

Solving the stochastic dynamics of population growth

Population growth is a fundamental process in ecology and evolution. The population size dynamics during growth are often described by deterministic equations derived from kinetic models. Here, we simulate several population growth models and compare the size averaged over many stochastic realizations with the deterministic predictions. We show that these deterministic equations are generically bad predictors of the average stochastic population dynamics. Specifically, deterministic predictions overestimate the simulated population sizes, especially those of populations starting with a small number of individuals. Describing population growth as a stochastic birth process, we prove that the discrepancy between deterministic predictions and simulated data is due to unclosed-moment dynamics. In other words, the deterministic approach does not consider the variability of birth times, which is particularly important with small population sizes. We show that some moment-closure approximations describe the growth dynamics better than the deterministic prediction. However, they do not reduce the error satisfactorily and only apply to some population growth models. We explicitly solve the stochastic growth dynamics, and our solution applies to any population growth model. We show that our solution exactly quantifies the dynamics of a community composed of different strains and correctly predicts the fixation probability of a strain in a serial dilution experiment. Our work sets the foundations for a more faithful modeling of community and population dynamics. It will allow the development of new tools for a more accurate analysis of experimental and empirical results, including the inference of important growth parameters

Topological properties of thinning in 2-D pseudomanifolds

International audiencePreserving topological properties of objects during thinning procedures is an important issue in the field of image analysis. In the case of 2-D digital images (i.e. images defined on Z^2) such procedures are usually based on the notion of simple point. In contrast to the situation in Z^n , n>=3, it was proved in the 80s that the exclusive use of simple points in Z^2 was indeed sufficient to develop thinning procedures providing an output that is minimal with respect to the topological characteristics of the object. Based on the recently introduced notion of minimal simple set (generalising the notion of simple point), we establish new properties related to topology-preserving thinning in 2-D spaces which extend, in particular, this classical result to cubical complexes in 2-D pseudomanifolds

Plant traits co-vary with altitude in grasslands and forests in the European Alps

Biological traits that are advantageous under specific ecological conditions should be present in a large proportion of the species within an ecosystem, where those specific conditions prevail. As climatic conditions change, the frequency of certain traits in plant communities is expected to change with increasing altitude. We examined patterns of change for 13 traits in 120 exhaustive inventories of plants along five altitudinal transects (520-3,100m a.s.l.) in grasslands and in forests in western Switzerland. The traits selected for study represented the occupation of space, photosynthesis, reproduction and dispersal. For each plot, the mean trait values or the proportions of the trait states were weighted by species cover and examined in relation to the first axis of a PCA based on local climatic conditions. With increasing altitude in grasslands, we observed a decrease in anemophily and an increase in entomophily complemented by possible selfing; a decrease in diaspores with appendages adapted to ectozoochory, linked to a decrease in achenes and an increase in capsules. In lowlands, pollination and dispersal are ensured by wind and animals. However, with increasing altitude, insects are mostly responsible for pollination, and wind becomes the main natural dispersal vector. Some traits showed a particularly marked change in the alpine belt (e.g. the increase of capsules and the decrease of achenes), confirming that this belt concentrates particularly stressful conditions to plant growth and reproduction (e.g. cold, short growing season) that constrain plants to a limited number of strategies. One adaptation to this stress is to limit investment in dispersal by producing capsules with numerous, tiny seeds that have appendages limited to narrow wings. Forests displayed many of the trends observed in grasslands but with a reduced variability that is likely due to a shorter altitudinal gradien

Morphologie et reproduction des plantes le long des gradients altitudinaux

Sites valaisans: Salgesch, Mont Rogneux