The discriminative functional mixture model for a comparative analysis of bike sharing systems

Bike sharing systems (BSSs) have become a means of sustainable intermodal transport and are now proposed in many cities worldwide. Most BSSs also provide open access to their data, particularly to real-time status reports on their bike stations. The analysis of the mass of data generated by such systems is of particular interest to BSS providers to update system structures and policies. This work was motivated by interest in analyzing and comparing several European BSSs to identify common operating patterns in BSSs and to propose practical solutions to avoid potential issues. Our approach relies on the identification of common patterns between and within systems. To this end, a model-based clustering method, called FunFEM, for time series (or more generally functional data) is developed. It is based on a functional mixture model that allows the clustering of the data in a discriminative functional subspace. This model presents the advantage in this context to be parsimonious and to allow the visualization of the clustered systems. Numerical experiments confirm the good behavior of FunFEM, particularly compared to state-of-the-art methods. The application of FunFEM to BSS data from JCDecaux and the Transport for London Initiative allows us to identify 10 general patterns, including pathological ones, and to propose practical improvement strategies based on the system comparison. The visualization of the clustered data within the discriminative subspace turns out to be particularly informative regarding the system efficiency. The proposed methodology is implemented in a package for the R software, named funFEM, which is available on the CRAN. The package also provides a subset of the data analyzed in this work.Comment: Published at http://dx.doi.org/10.1214/15-AOAS861 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Les Entrailles de la voix

Édith Piaf et Diane Dufresne font partie d'une longue série de performeurs à la voix vibrante. Ces deux chanteuses ont en commun l'émoi qui affleure comme l'aspect le plus sensible de leur voix, l'énergie première, primitive qui module le souffle à l'endroit où se soude la jointure entre le dit et le nondit. Une passion palpite dans leur voix et fait corps avec l'air. Elle se donne à reconnaître dans le froid de la plaque gravée. Plus qu'un tympan de l'auditeur, c'est à son ventre, à ses viscères que s'agrippe le chant arraché à la matière du corps. La geste vocale comprend des paroles, une narration où la chanteuse apparaîÎt souvent aussi comme actrice du drame. Sur ces scénarios, l'intégration musicale s'organise vers la construction d'un support émotif reposant sur un crescendo mélodique, intensif et rythmique. Toutes ces structures du texte, paroles et musique, ont bâti une architecture acoustique dans laquelle la voix pourra se déployer. Ce dont l'auditeur jouit comme d'une immédiateté vibre en fait depuis des profondeurs d'autant plus utiles qu'elles sont imperceptibles.Édith Piaf and Diane Dufresne belong to a larger group of highly emotional performers. They share the same voice where a vibrating emotion is the primal energy, at the juncture of utterance and silence. The passion is such that it makes its way even through the cold black vinyl plate. Such a voice appeals to the listener's flesh more than to his hearing. The song is carried through the words and the music. The lyrics appear very often to be repetitive scripts where the singer is at the same time narrator and actor. Upon these fabulae, the music will build an emotional crescendo. The voice sounds and resonates through this complex literary and musical architecture. The listener will enjoy a spontaneous warm vibration that is the more useful for not being perceptible

Adaptive Linear Models for Regression: improving prediction when population has changed

International audienceThe general setting of regression analysis is to identify a relationship between a response variable Y and one or several explanatory variables X by using a learning sample. In a prediction framework, the main assumption for predicting Y on a new sample of observations is that the regression model Y=f(X)+e is still valid. Unfortunately, this assumption is not always true in practice and the model could have changed. We therefore propose to adapt the original regression model to the new sample by estimating a transformation between the original regression function f(X) and the new one f*(X). The main interest of the proposed adaptive models is to allow the build of a regression model for the new population with only a small number of observations using the knowledge on the reference population. The efficiency of this strategy is illustrated by applications on artificial and real datasets, including the modeling of the housing market in different U.S. cities. A package for the R software dedicated to the adaptive linear models is available on the author's web page

Relation between respiratory variations in pulse oximetry plethysmographic waveform amplitude and arterial pulse pressure in ventilated patients.

IntroductionRespiratory variation in arterial pulse pressure is a reliable predictor of fluid responsiveness in mechanically ventilated patients with circulatory failure. The main limitation of this method is that it requires an invasive arterial catheter. Both arterial and pulse oximetry plethysmographic waveforms depend on stroke volume. We conducted a prospective study to evaluate the relationship between respiratory variation in arterial pulse pressure and respiratory variation in pulse oximetry plethysmographic (POP) waveform amplitude.MethodThis prospective clinical investigation was conducted in 22 mechanically ventilated patients. Respiratory variation in arterial pulse pressure and respiratory variation in POP waveform amplitude were recorded simultaneously in a beat-to-beat evaluation, and were compared using a Spearman correlation test and a Bland-Altman analysis.ResultsThere was a strong correlation (r2 = 0.83; P < 0.001) and a good agreement (bias = 0.8 +/- 3.5%) between respiratory variation in arterial pulse pressure and respiratory variation in POP waveform amplitude. A respiratory variation in POP waveform amplitude value above 15% allowed discrimination between patients with respiratory variation in arterial pulse pressure above 13% and those with variation of 13% or less (positive predictive value 100%).ConclusionRespiratory variation in arterial pulse pressure above 13% can be accurately predicted by a respiratory variation in POP waveform amplitude above 15%. This index has potential applications in patients who are not instrumented with an intra-arterial catheter