Electronic structure of the Ca3Co4O9\rm Ca_3Co_4O_9 compound from ab initio local interactions

We used fully correlated ab initio calculations to determine the effective parameters of Hubbard and t - J models for the thermoelectric misfit compound $\rm Ca_3Co_4O_9$. As for the $\rm Na_xCoO_2$ family the Fermi level orbitals are the $a_{1g}$ orbitals of the cobalt atoms ; the $e'_g$ being always lower in energy by more than 240\,meV. The electron correlation is found very large $U/t\sim 26$ as well as the parameters fluctuations as a function of the structural modulation. The main consequences are a partial $a_{1g}$ electrons localization and a fluctuation of the in-plane magnetic exchange from AFM to FM. The behavior of the Seebeck coefficient as a function of temperature is discussed in view of the ab initio results, as well as the 496\,K phase transition

Learning Descriptors for Object Recognition and 3D Pose Estimation

Detecting poorly textured objects and estimating their 3D pose reliably is still a very challenging problem. We introduce a simple but powerful approach to computing descriptors for object views that efficiently capture both the object identity and 3D pose. By contrast with previous manifold-based approaches, we can rely on the Euclidean distance to evaluate the similarity between descriptors, and therefore use scalable Nearest Neighbor search methods to efficiently handle a large number of objects under a large range of poses. To achieve this, we train a Convolutional Neural Network to compute these descriptors by enforcing simple similarity and dissimilarity constraints between the descriptors. We show that our constraints nicely untangle the images from different objects and different views into clusters that are not only well-separated but also structured as the corresponding sets of poses: The Euclidean distance between descriptors is large when the descriptors are from different objects, and directly related to the distance between the poses when the descriptors are from the same object. These important properties allow us to outperform state-of-the-art object views representations on challenging RGB and RGB-D data.Comment: CVPR 201

On the usefulness of the directional distance function in analyzing environmental policy on manure management

The purpose of this paper is to model the manure management policy implemented in the European Union, and more specifically the limit imposed on the spreading of organic nitrogen. A theoretical model is defined in such a way that a number of specificities concerning livestock production can be introduced.The theoretical framework is used to investigate how the land can be shared out optimally between the non-productive purpose of spreading manure in a manner compliant with the environmental regulation and the productive function of providing crops.Then,we define an empirical model derived from the previous theoretical model, using the directional distance function.It provides a framework for deriving shadow prices of pollutant, of productive and non productive use of land and of the constraint on organic manure involved by the European environmental regulation.Environmental Economics and Policy,

An ab-initio evaluation of the local effective interactions in the superconducting compound Na_0.35CoO_2−1.3H_2O\rm Na\_{0.35} Co O\_2-1.3H\_2O

We used ab-initio quantum chemical methods, treating explicitly the strong correlation effects within the cobalt 3d shell, as well as the screening effects on the effective integrals, for accurately determining on-site and nearest-neighbor (NN) interactions in the $\rm Na\_{0.35} Co O\_2-1.3H\_2O$ superconducting compound. The effective ligand field splitting within the $t\_{2g}$ orbitals was found to be $\delta \sim 300 \rm meV$, the $a\_{1g}$ orbital being destabilized compared to the $e\_g^\prime$ ones. The effective Hund's exchange and Coulomb repulsion were evaluated to $J\_H\sim 280 \rm meV$ and $U\sim 4.1$--$4.8 \rm eV$. The NN hopping parameters were determined within the three $t\_{2g}$ orbitals and found to be of the same order of magnitude as the $t\_{2g}$ ligand field splitting, supporting the hypothesis of a three band model for this system. Finally we evaluated the NN effective exchange integral to be antiferromagnetic and $J=-66 \rm meV$

Beyond KernelBoost

In this Technical Report we propose a set of improvements with respect to the KernelBoost classifier presented in [Becker et al., MICCAI 2013]. We start with a scheme inspired by Auto-Context, but that is suitable in situations where the lack of large training sets poses a potential problem of overfitting. The aim is to capture the interactions between neighboring image pixels to better regularize the boundaries of segmented regions. As in Auto-Context [Tu et al., PAMI 2009] the segmentation process is iterative and, at each iteration, the segmentation results for the previous iterations are taken into account in conjunction with the image itself. However, unlike in [Tu et al., PAMI 2009], we organize our recursion so that the classifiers can progressively focus on difficult-to-classify locations. This lets us exploit the power of the decision-tree paradigm while avoiding over-fitting. In the context of this architecture, KernelBoost represents a powerful building block due to its ability to learn on the score maps coming from previous iterations. We first introduce two important mechanisms to empower the KernelBoost classifier, namely pooling and the clustering of positive samples based on the appearance of the corresponding ground-truth. These operations significantly contribute to increase the effectiveness of the system on biomedical images, where texture plays a major role in the recognition of the different image components. We then present some other techniques that can be easily integrated in the KernelBoost framework to further improve the accuracy of the final segmentation. We show extensive results on different medical image datasets, including some multi-label tasks, on which our method is shown to outperform state-of-the-art approaches. The resulting segmentations display high accuracy, neat contours, and reduced noise