Modeling attitudes toward uncertainty through the use of the Sugeno integral

The aim of the paper is to present under uncertainty, and in an ordinal framework, an axiomatic treatment of the Sugeno integral in terms of preferences which parallels some earlier derivations devoted to the Choquet integral. Some emphasis is given to the characterization of uncertainty aversion.Sugeno integral; uncertainty aversion; preference relations; ordinal information

Example of object based image analysis applied to coarse resolution images. Application to landscape classification in France

Object based image analysis (OBIA) is a powerful technique for classification of remote sensing images. Although it is generally used with very high resolution images, its application is not linked to a particular spatial resolution, and OBIA can be applied to classify coarse resolution images, such as MODIS, Meris, and the future Sentinel-3 sensor, OLCI. In this study, a segmentation of the French landscapes is made from MODIS images, including vegetation and texture indices, by applying OBIA. Different segmentations have been generated using different segmentation parameters and input variables. Since no ground data is available for training and validating the classification, unsupervised evaluation methods are used to select the best input variables and the best segmentation parameters. The best segmentation is shown to be the one including texture indices, and leads to 84 radiometrically homogeneous regions. From the results of the segmentation, a non supervised classification is performed and 36 different classes are identified. (Résumé d'auteur

A survey of exemplar-based texture synthesis

Exemplar-based texture synthesis is the process of generating, from an input sample, new texture images of arbitrary size and which are perceptually equivalent to the sample. The two main approaches are statistics-based methods and patch re-arrangement methods. In the first class, a texture is characterized by a statistical signature; then, a random sampling conditioned to this signature produces genuinely different texture images. The second class boils down to a clever "copy-paste" procedure, which stitches together large regions of the sample. Hybrid methods try to combine ideas from both approaches to avoid their hurdles. The recent approaches using convolutional neural networks fit to this classification, some being statistical and others performing patch re-arrangement in the feature space. They produce impressive synthesis on various kinds of textures. Nevertheless, we found that most real textures are organized at multiple scales, with global structures revealed at coarse scales and highly varying details at finer ones. Thus, when confronted with large natural images of textures the results of state-of-the-art methods degrade rapidly, and the problem of modeling them remains wide open.Comment: v2: Added comments and typos fixes. New section added to describe FRAME. New method presented: CNNMR

Access to Physician Services: Does Supplemental Insurance Matter? Evidence from France

In France, public health insurance is universal but incomplete, with private payments accounting for roughly 25 percent of all spending. As a result, most people have supplemental private health insurance. We investigate the effects of such insurance on the utilization of physician services using data from the 1998 Enquˆte Sant‚ Protection Sociale, a nationally representative survey of the French population. Our results indicate that insurance has a strong and significant effect on the utilization of physician services. Individuals with supplemental coverage have substantially more physician visits than those without. In a context where patients are free to choose their provider, we find no evidence that adults with supplemental insurance are more likely to visit a specialist than a general practitioner.

Recalibrating a sugarcane crop model using thermal infrared data

Coupling remotely sensed data with crop model is known to improve the estimation of crop variables by the model. The recalibration coupling approach tends to reduce the differences between observation and simulation by optimizing the value of one of the model's parameter. In this study, we used this approach with a sugarcane model and Crop Water Stress Index calculated using remotely sensed thermal infrared data in order to optimize the value of the root depth parameter thanks to measured and simulated AET/MET ratio. The effect of the root depth recalibration has also been assessed on the yield estimation, which showed good trends with a significant enhancement of the estimated yield. (Résumé d'auteur

Reducing Ammonia Losses By Adding FeCI3 During Composting Of Sewage Sludge

The release of ammonia nitrogen during composting of sewage sludge mixed with a lignocellulosic bulking agent leads to a reduction in the agronomic value of the final compost and to harmful effects on the environment. We propose adding a cheap salt FeCl3 which can be used without special precaution to reduce ammonia losses by decreasing pH conditions. An in-vessel co-composting experiment was conducted in a large reactor (100 L) in which FeCl3 was added to sludge mixed with a bulking agent (pine shavings and sawdust) and compared with a control mixture without FeCl3. Temperature, oxygen consumption and pH were monitored throughout the composting of both mixtures. The final balance of organic matter, organic and inorganic nitrogen permitted to conclude that the addition of FeCl3 reduced nitrogen loss (by a factor of 2.4 in relation to the control) and increased mineralisation of the organic nitrogen by 1.6

A comparison of two coupling methods for improving a sugarcane model yield estimation with a NDVI-derived variable.

Coupling remote sensing data with crop model has been shown to improve accuracy of the model yield estimation. MOSICAS model simulates sugarcane yield in controlled conditions plot, based on different variables, including the interception efficiency index (?i). In this paper, we assessed the use of remote sensing data to sugarcane growth modeling by 1) comparing the sugarcane yield simulated with and without satellite data integration in the model, and 2) comparing two approaches of satellite data forcing. The forcing variable is the interception efficiency index (?i). The yield simulations are evaluated on a data set of cane biomass measured on four on-farm fields, over three years, in Reunion Island. Satellite data are derived from a SPOT 10 m resolution time series acquired during the same period. Three types of simulations have been made: a raw simulation (where the only input data are daily precipitations, daily temperatures and daily global radiations), a partial forcing coupling method (where MOSICAS computed values of ?i have been replaced by NDVI computed ?i for each available satellite image), and complete forcing method (where all MOSICAS simulated ?i have been replaced by NDVI computed ?i). Results showed significant improvements of the yield's estimation with complete forcing approach (with an estimation of the yield 8.3 % superior to the observed yield), but minimal differences between the yields computed with raw simulations and those computed with partial forcing approach (with a mean overestimation of respectively 34.7 and 35.4 %). Several enhancements can be made, especially by optimizing MOSICAS parameters, or by using other remote sensing index, like NDWI. (Résumé d'auteur