Commerce équitable : de quelle équité parle-t-on?

Le commerce équitable s’est imposé sur la scène de la consommation responsable. Mais en quoi est-il plus équitable que le commerce conventionnel? Et dans quel sens doit-on entendre dans son cas le concept d’équité? Un détour par la philosophie morale et les théories de la justice d’Aristote, Hume et Rawls et une analyse de ses pratiques telles qu’elles ressortent de nombreuses études de terrain invitent à prendre ses prétentions à l’équité “cum grano salis”

La ville, matériau artistique

Au début des années cinquante, des peintres sont sortis de leur atelier pour aller arracher des affiches. Quelques années plus tard, des compositeurs sont partis à la recherche de sons urbains qu'ils ont intégrés tels quels dans leurs œuvres. En faisant de la ville une réserve de matériaux, ces artistes, affichistes et compositeurs de musique concrète, ont profondément modifié notre regard sur celle-ci. Ils nous ont amenés à porter un jugement esthétique sur ce qui apparaissait n'être que des nuisances. Ils ont contribué à l'esthétisation de l'espace urbain et à la découverte d'une nouvelle beauté : celle de la ville " canaille ".In the early fifties, painters went out of their workshop to tear posters, a few years later, composers started integrating in their works urban sounds they had recorded. In making the city a store of materials, these " affichistes " and electroacoustic composers have profoundly changed our view of it. They made us carry an aesthetic judgment on what appeared to be only a nuisance. They have thus contributed to the aestheticization of the urban space and to the discovery of the beauties of the urban " canaille "

Inverting hyperspectral images with Gaussian Regularized Sliced Inverse Regression

International audienceIn the context of hyperspectral image analysis in planetology, we show how to estimate the physical parameters that generate the spectral infrared signal reflected by Mars. The training approach we develop is based on the estimation of the functional relationship between parameters and spectra, using a database of synthetic spectra generated by a physical model. The high dimension of spectra is reduced by using Gaussian regularized inverse regression to overcome the curse of dimensionality. Compared with a basic k-nearest neighbors approach or a Partial Least Square (PLS) regression, estimates are more accurate and are thus promising

Estimation of Mars surface physical properties from hyperspectral images using Sliced Inverse Regression

Visible and near infrared imaging spectroscopy is a key remote sensing technique to study and monitor planet Mars. Indeed it allows the detection, mapping and characterization of minerals as well as volatile species that often constitute the first step toward the resolution of key climatic and geological issues. These tasks are carried out by the spectral analysis of the solar light reflected in different directions by the materials forming the top few millimeters or centimeters of the ground. The chemical composition, granularity, texture, physical state, etc. of the materials determine the morphology of the hundred thousands spectra that typically constitute an image. Radiative transfer models simulating the propagation of solar light through the Martian atmosphere and surface and then to the sensor aim at evaluating numerically the direct and quantitative link between parameters and spectra. Then techniques must be applied in order to reverse the link and evaluate the properties of atmospheric and surface materials from the spectra. Processing all the pixels of an image finally provides physical and structural maps. We use a regularized version of SIR method (K.C. Li, Sliced Inverse Regression for dimension reduction, Journal of the American Statistical Association, 86:316-327, 1991) combined to a linear interpolation to reverse the previous numerical link. For that purpose we first generate numerous cor- responding pairs of parameters - synthetic spectra by direct radiative transfer modeling in order to constitute a learning database. The SIR step allows to reduce the dimension of the spectra (usually 184 wavelengths) in order to overcome the curse of dimensionality. Then, a linear interpolation is used to relate the reduced components of a spectrum to a given physical parameter value. Such inverted link is applied to a real dataset of hyperspectral images collected by the OMEGA instrument (Mars Express mission)

Nonlinear Data Representation for Visual Learning

We are given a set of points in a high dimensional space. For instance, this set can represent many visual appearances of an object, a face or a hand. We address the problem of approximating this set by a manifold in order to have a compact representation of the object appearance. When the scattering of this set is approximately an ellipsoid, then the problem has a well-known solution given by Principal Components Analysis (PCA). Yet, in some situations like object deplacement learning or face learning this linear technique can be ill-adapted and nonlinear approximation must be introduced. The method we propose can be seen as a Non Linear PCA (NLPCA), the main difficulty being that the data points are not ordered. We propose an index to find projection axes encouraging the choice of axes which preserve as well as possible the structure of the closest point neighborhood. These axes determine an order for visiting all the points when smoothing. Finally, a new criterion, called "generalization error" is introduced to determine the smoothing rate, that is the spline number of knots in this case. Experimental results conclude this paper: the method is tested on artificial data and on two data sets coming from databases used in visual learning

Chrétiens au Proche-Orient

[video:interview-de-bernard-heyberger-chretiens-au-proche-orient] Comme cela a déjà été souligné, aujourd’hui se multiplient les travaux consacrés aux minorités chrétiennes du Proche-Orient, un dynamisme qui contraste fortement avec ce que l’on pouvait observer il y a une vingtaine d’années (B. Heyberger, 2010b ; F. McCallum, 2010a ; P. Rowe, 2010 ; N. Van Doorn-Harder, 2010 ; L. Robson, 2011). Sur l’histoire longue comme sur l’analyse des situations actuelles, la recherche récente a contribu..

Imagerie multi-fréquentielle d'un réservoir géothermal au Lamentin (Martinique, France) par méthode de Longue Electrode Mise-à-la-Masse

National audienceWithin the frame of geothermal exploration, a CSEM survey was performed at the Le Lamentin area (Martinique, French Indies) using 400m long energized metallic casings of two deep exploration boreholes as long electrodes for current injection (the so-called double Longue Electrode Mise-à-la-Masse setup, hereafter 2xLEMAM). Apparent resistivity maps were derived from the in-phase electric fields. Frequency dependent apparent resistivity maps and profiles reveal a very conductive area north of the Fort de France Bay connected to a known poly-phased geothermal system and shallow salt water intrusion. The most conductive body is proposed to be the geo-electrical signature of an active hydrothermal system, superimposed on the signature of a conductive fossilized geothermal system. It is spatially well correlated with high temperature borehole logs

Non linear modelling of scattered multivariate data and its application to shape change

International audienceWe are given a set of points in a space of high dimension. For instance, this set may represent many visual appearances of an object, a face or a hand. We address the problem of approximating this set by a manifold in order to have a compact representation of the object appearance. When the scattering of this set is approximately an ellipsoid, then the problem has a well-known solution given by Principal Components Analysis (PCA). However, in some situations like object displacement learning or face learning this linear technique may be ill-adapted and nonlinear approximation has to be introduced. The method we propose can be seen as a Non Linear PCA (NLPCA), the main difficulty being that the data are not ordered. We propose an index which favours the choice of axes preserving the neighborhood of the nearest neighbours. These axes determine an order for visiting all the points when smoothing. Finally a new criterion, called "generalization error", is introduced to determine the smoothing rate, that is the knot number of the spline fitting. Experimental results conclude this paper: the method is tested on artificial data and on two data bases used in visual learning