Mining Heterogeneous Multivariate Time-Series for Learning Meaningful Patterns: Application to Home Health Telecare

For the last years, time-series mining has become a challenging issue for researchers. An important application lies in most monitoring purposes, which require analyzing large sets of time-series for learning usual patterns. Any deviation from this learned profile is then considered as an unexpected situation. Moreover, complex applications may involve the temporal study of several heterogeneous parameters. In that paper, we propose a method for mining heterogeneous multivariate time-series for learning meaningful patterns. The proposed approach allows for mixed time-series -- containing both pattern and non-pattern data -- such as for imprecise matches, outliers, stretching and global translating of patterns instances in time. We present the early results of our approach in the context of monitoring the health status of a person at home. The purpose is to build a behavioral profile of a person by analyzing the time variations of several quantitative or qualitative parameters recorded through a provision of sensors installed in the home

International and domestic technology transfers and productivity growth: Firm level evidence.

We examine the drivers of international and domestic technology transfer strategies of firms and the impact of these transfers on firms’ productivity performance in a sample of 440 Flemish innovating firms during 2003-2006. Technology transfers may occur through R&D contracting, purchase of licenses and know how, purchase of specialized machinery, hiring of specialized personnel, and various informal channels. Analysis of the drivers of technology sourcing strategies shows that combined technology sourcing strategies are more likely to be adopted by firms that 1) face resource limitations in their innovative effort 2) have a basic research orientation and conduct more R&D 3) successfully use various technology protection strategies to appropriate the benefits of innovation efforts 4) are engaged in international R&D collaboration. Estimates of a dynamic productivity model show that firms engaging in international knowledge sourcing strategies record substantially and significantly higher productivity growth. The largest impact is found for firms combining foreign transfer strategies with local technology acquisition, suggesting that a diverse external technology strategy combining local technologies as well as know how from abroad is most likely to improve firm performance.Technology transfer; Productivity; Multinational Firms;

Hemodynamically informed parcellation of cerebral FMRI data

Standard detection of evoked brain activity in functional MRI (fMRI) relies on a fixed and known shape of the impulse response of the neurovascular coupling, namely the hemodynamic response function (HRF). To cope with this issue, the joint detection-estimation (JDE) framework has been proposed. This formalism enables to estimate a HRF per region but for doing so, it assumes a prior brain partition (or parcellation) regarding hemodynamic territories. This partition has to be accurate enough to recover accurate HRF shapes but has also to overcome the detection-estimation issue: the lack of hemodynamics information in the non-active positions. An hemodynamically-based parcellation method is proposed, consisting first of a feature extraction step, followed by a Gaussian Mixture-based parcellation, which considers the injection of the activation levels in the parcellation process, in order to overcome the detection-estimation issue and find the underlying hemodynamics

Fast joint detection-estimation of evoked brain activity in event-related fMRI using a variational approach

In standard clinical within-subject analyses of event-related fMRI data, two steps are usually performed separately: detection of brain activity and estimation of the hemodynamic response. Because these two steps are inherently linked, we adopt the so-called region-based Joint Detection-Estimation (JDE) framework that addresses this joint issue using a multivariate inference for detection and estimation. JDE is built by making use of a regional bilinear generative model of the BOLD response and constraining the parameter estimation by physiological priors using temporal and spatial information in a Markovian modeling. In contrast to previous works that use Markov Chain Monte Carlo (MCMC) techniques to approximate the resulting intractable posterior distribution, we recast the JDE into a missing data framework and derive a Variational Expectation-Maximization (VEM) algorithm for its inference. A variational approximation is used to approximate the Markovian model in the unsupervised spatially adaptive JDE inference, which allows fine automatic tuning of spatial regularisation parameters. It follows a new algorithm that exhibits interesting properties compared to the previously used MCMC-based approach. Experiments on artificial and real data show that VEM-JDE is robust to model mis-specification and provides computational gain while maintaining good performance in terms of activation detection and hemodynamic shape recovery

The sol–gel route: A versatile process for up-scaling the fabrication of gas-tight thin electrolyte layers

Sol–gel routes are often investigated and adapted to prepare, by suitable chemical modifications, submicronic powders and derived materials with controlled morphology, which cannot be obtained by conventional solid state chemistry paths. Wet chemistry methods provide attractive alternative routes because mixing of species occurs at the atomic scale. In this paper, ultrafine powders were prepared by a novel synthesis method based on the sol–gel process and were dispersed into suspensions before processing. This paper presents new developments for the preparation of functional materials like yttria-stabilized-zirconia (YSZ, 8% Y2O3) used as electrolyte for solid oxide fuel cells. YSZ thick films were coated onto porous Ni-YSZ substrates using a suspension with an optimized formulation deposited by either a dip-coating or a spin-coating process. The suspension composition is based on YSZ particles encapsulated by a zirconium alkoxide which was added with an alkoxide derived colloidal sol. The in situ growth of these colloids increases significantly the layer density after an appropriated heat treatment. The derived films were continuous, homogeneous and around 20 μm thick. The possible up-scaling of this process has been also considered and the suitable processing parameters were defined in order to obtain, at an industrial scale, homogeneous, crack-free, thick and adherent films after heat treatment at 1400 °C

Global self-excited oscillations in a two-dimensional heated jet : a numerical simulation

The aim of this work was to develop a numerical methodology to gain insight in the low-density jet behaviour with a nonlinear approach. Numerical simulations are shown to differentiate convective and absolute instability regimes and to capture a self-excited global mode in an open flow : the 2D hot jet. The first part is devoted to numerical methodology and its validation on this unsteady problem which is known to be noise sensitive. The second part presents numerical results. They confirm theoretical and experimental results on the development of self-exited global oscillations of the jet column when the density ratio is lower than its critical value. The global mode and its associated Hopf bifurcation are identified