The Principle of Strong Diminishing Transfer

We reconsider the principles of diminishing transfer (introduced by Kolm [1976]) and dual diminishing transfer (introduced by Mehran [1976]). It appears that if a Rank Dependent Expected Utility (RDEU) maximizer respects the principle of diminishing (resp. dual diminishing) transfer, then he behaves in accordance with the Expected Utility model (resp. Yaari's dual model).This leads us to define the principle of strong diminishing transfer, which is a combination of the principles of diminishing and dual diminishing transfer. We give necessary conditions for a RDEU maximizer to respect this principle.These results are applied to the problem of inequality measurement.Inequality Measurement, Principle of Diminishing Transfers

The Principle of Strong Diminishing Transfer

International audienceWe reconsider the principles of diminishing transfer (introduced by Kolm [1976]) and dual diminishing transfer (introduced by Mehran [1976]). It appears that if a Rank Dependent Expected Utility (RDEU) maximizer respects the principle of diminishing (resp. dual diminishing) transfer, then he behaves in accordance with the Expected Utility model (resp. Yaari's dual model).This leads us to define the principle of strong diminishing transfer, which is a combination of the principles of diminishing and dual diminishing transfer. We give necessary conditions for a RDEU maximizer to respect this principle.These results are applied to the problem of inequality measurement

Robust output feedback sampling control based on second order sliding mode

International audienceThis paper proposes a new second order sliding mode output feedback controller. This latter is developped in the case of finite sampling frequency and is using only output information in order to ensure desired trajectory tracking with high accuracy in a finite time in spite of uncertainties and perturbations. This new strategy is evaluated in simulations on an academic example

Osteopontin: an early innate immune marker of Escherichia coli mastitis harbors genetic polymorphisms with possible links with resistance to mastitis

<p>Abstract</p> <p>Background</p> <p>Mastitis is the most important disease in dairy cows and it causes significant lost of profit to producers. Identification of the genes, and their variants, involved in innate immune responses is essential for the understanding of this inflammatory disease and to identify potential genetic markers for resistance to mastitis. The progeny of dairy cows would benefit from receiving favourable alleles that support greater resistance to infection, thus reducing antibiotic use. This study aims to identify a key gene in the innate immune response to mastitis, led us to evaluate its genetic association with somatic cell score (SCS), which is an indicator of clinical mastitis, and to evaluate its impact on other traits related to milk production.</p> <p>Results</p> <p>The osteopontin transcript (<it>SPP1</it>) was identified in the somatic cells from cows experimentally infected with <it>Escherichia coli</it>. By selecting bulls with extreme estimated breeding values (EBVs) for SCS, which is an indicator of mammary gland health, four DNA polymorphisms in the <it>SPP1 </it>genomic sequence were found. Statistical analysis revealed that the SNP <it>SPP1c.-1301G>A </it>has an impact on EBV for SCS (<it>P </it>< 0.001) Using an allele substitution model, <it>SPP1c.-1251C>T</it>, <it>SPP1c.-430G>A</it>, and <it>SPP1c.*40A>C </it>have an impact on SCS whereas <it>SPP1c.-1301G>A </it>has an effect on the EBVs for milk yield (second and third lactations), fat and protein percentages (all three lactations). Analysis revealed statistically significant differences between haplotype groups at a comparison-wise level with sire EBVS for SCS for the first (<it>P </it>= 0.012), second (<it>P </it>< 0.001), and third (<it>P </it>< 0.001) lactations.</p> <p>Conclusion</p> <p>This study reports the link between DNA polymorphisms of <it>SPP1</it>, the number of milk immune cells and, potentially, the susceptibility to mastitis. These SNPs were identified by <it>in silico </it>search to be located in transcription factor recognition sites which factors are presumably involved in the Th1 immune response and in the Th2 regulation pathway. Indeed, one SNP abolished the SP1 recognition site, whereas another SNP affected the transcription binding factor IKAROS. All together, these findings support the genetic potential of these variants in terms of selection for the improvement of mastitis resistance in dairy cows.</p

Myocardial Infarction Quantification From Late Gadolinium Enhancement MRI Using Top-hat Transforms and Neural Networks

Significance: Late gadolinium enhanced magnetic resonance imaging (LGE-MRI) is the gold standard technique for myocardial viability assessment. Although the technique accurately reflects the damaged tissue, there is no clinical standard for quantifying myocardial infarction (MI), demanding most algorithms to be expert dependent. Objectives and Methods: In this work a new automatic method for MI quantification from LGE-MRI is proposed. Our novel segmentation approach is devised for accurately detecting not only hyper-enhanced lesions, but also microvascular-obstructed areas. Moreover, it includes a myocardial disease detection step which extends the algorithm for working under healthy scans. The method is based on a cascade approach where firstly, diseased slices are identified by a convolutional neural network (CNN). Secondly, by means of morphological operations a fast coarse scar segmentation is obtained. Thirdly, the segmentation is refined by a boundary-voxel reclassification strategy using an ensemble of CNNs. For its validation, reproducibility and further comparison against other methods, we tested the method on a big multi-field expert annotated LGE-MRI database including healthy and diseased cases. Results and Conclusion: In an exhaustive comparison against nine reference algorithms, the proposal achieved state-of-the-art segmentation performances and showed to be the only method agreeing in volumetric scar quantification with the expert delineations. Moreover, the method was able to reproduce the intra- and inter-observer variability ranges. It is concluded that the method could suitably be transferred to clinical scenarios.Comment: Submitted to IEE