2,889 research outputs found
Electronic structure of the 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
. As for the family the Fermi level orbitals
are the orbitals of the cobalt atoms ; the being always lower
in energy by more than 240\,meV. The electron correlation is found very large
as well as the parameters fluctuations as a function of the
structural modulation. The main consequences are a partial 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
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
superconducting compound. The effective ligand field splitting within the
orbitals was found to be , the
orbital being destabilized compared to the ones. The effective
Hund's exchange and Coulomb repulsion were evaluated to
and --. The NN hopping parameters were determined within
the three orbitals and found to be of the same order of magnitude as
the 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
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
- …