University of Marne-la-Vallée

About: University of Marne-la-Vallée is a based out in . It is known for research contribution in the topics: Estimator & Context (language use). The organization has 831 authors who have published 1855 publications receiving 55316 citations.

Spectroscopic control of iron oxide dissolution in two ferralitic soils

Abstract: Summary Second derivative diffuse reflectance spectroscopy (DRS) in the visible range has been used to characterize changes in colour and identify the nature of Fe oxides which withstand reduction during experimental yellowing of reddish materials. It is accepted that haematite dissolves preferentially and faster than goethite, and Al-substitution controls the dissolution kinetics of Fe oxides. However, DRS has shown that haematite is more resistant than predicted and that some Fe-oxides, probably trapped within kaolinite particles, are inaccessible to solvents. DRS allows the nature of dissolved phases at each deferration step to be determined and changes in Al-content of residual phases throughout deferration to be followed. It also demonstrated that Helmholtz coordinates correlate very well with changes in Fe-oxide mineralogy and are preferable to redness ratings when monitoring differential dissolution of Fe oxides through colour measurements. DRS is a powerful and sensitive technique for monitoring the dissolution of Fe oxides in soils.

Sharp Oracle Inequalities for High-Dimensional Matrix Prediction

Abstract: We observe (Xi,Yi)i=1n where the Yi's are real valued outputs and the Xi's are m × T matrices We observe a new entry X and we want to predict the output Y associated with it We focus on the high-dimensional setting, where mT ≫ n This includes the matrix completion problem with noise, as well as other problems We consider linear prediction procedures based on different penalizations, involving a mixture of several norms: the nuclear norm, the Frobenius norm and the l1-norm For these procedures, we prove sharp oracle inequalities, using a statistical learning theory point of view A surprising fact in our results is that the rates of convergence do not depend on m and T directly The analysis is conducted without the usually considered incoherency condition on the unknown matrix or restricted isometry condition on the sampling operator Moreover, our results are the first to give for this problem an analysis of penalization (such as nuclear norm penalization) as a regularization algorithm: our oracle inequalities prove that these procedures have a prediction accuracy close to the deterministic oracle one, given that the reguralization parameters are well-chosen

Metric Learning for Image Registration.

Abstract: Image registration is a key technique in medical image analysis to estimate deformations between image pairs. A good deformation model is important for high-quality estimates. However, most existing approaches use ad-hoc deformation models chosen for mathematical convenience rather than to capture observed data variation. Recent deep learning approaches learn deformation models directly from data. However, they provide limited control over the spatial regularity of transformations. Instead of learning the entire registration approach, we learn a spatially-adaptive regularizer within a registration model. This allows controlling the desired level of regularity and preserving structural properties of a registration model. For example, diffeomorphic transformations can be attained. Our approach is a radical departure from existing deep learning approaches to image registration by embedding a deep learning model in an optimization-based registration algorithm to parameterize and data-adapt the registration model itself.