Homogenization of variational problems in manifold valued Sobolev spaces

Homogenization of integral functionals is studied under the constraint that admissible maps have to take their values into a given smooth manifold. The notion of tangential homogenization is defined by analogy with the tangential quasiconvexity introduced by Dacorogna, Fonseca, Maly and Trivisa \cite{DFMT}. For energies with superlinear or linear growth, a $\Gamma$-convergence result is established in Sobolev spaces, the homogenization problem in the space of functions of bounded variation being the object of \cite{BM}.Comment: 22 page

Homogenization of variational problems in manifold valued BV-spaces

This paper extends the result of \cite{BM} on the homogenization of integral functionals with linear growth defined for Sobolev maps taking values in a given manifold. Through a $\Gamma$-convergence analysis, we identify the homogenized energy in the space of functions of bounded variation. It turns out to be finite for $BV$-maps with values in the manifold. The bulk and Cantor parts of the energy involve the tangential homogenized density introduced in \cite{BM}, while the jump part involves an homogenized surface density given by a geodesic type problem on the manifold.Comment: 32 page

Few Graphene layer/Carbon-Nanotube composite Grown at CMOS-compatible Temperature

We investigate the growth of the recently demonstrated composite material composed of vertically aligned carbon nanotubes capped by few graphene layers. We show that the carbon nanotubes grow epitaxially under the few graphene layers. By using a catalyst and gaseous carbon precursor different from those used originally we establish that such unconventional growth mode is not specific to a precise choice of catalyst-precursor couple. Furthermore, the composite can be grown using catalyst and temperatures compatible with CMOS processing (T < 450\degree C).Comment: 4 pages, 4 figure

On overfitting and asymptotic bias in batch reinforcement learning with partial observability

This paper provides an analysis of the tradeoff between asymptotic bias (suboptimality with unlimited data) and overfitting (additional suboptimality due to limited data) in the context of reinforcement learning with partial observability. Our theoretical analysis formally characterizes that while potentially increasing the asymptotic bias, a smaller state representation decreases the risk of overfitting. This analysis relies on expressing the quality of a state representation by bounding L1 error terms of the associated belief states. Theoretical results are empirically illustrated when the state representation is a truncated history of observations, both on synthetic POMDPs and on a large-scale POMDP in the context of smartgrids, with real-world data. Finally, similarly to known results in the fully observable setting, we also briefly discuss and empirically illustrate how using function approximators and adapting the discount factor may enhance the tradeoff between asymptotic bias and overfitting in the partially observable context.Comment: Accepted at the Journal of Artificial Intelligence Research (JAIR) - 31 page

Natural occupation numbers in two-electron quantum rings

Natural orbitals (NOs) are central constituents for evaluating correlation energies through efficient approximations. Here, we report the closed-form expression of the NOs of two-electron quantum rings, which are prototypical finite-extension systems and new starting points for the development of exchange-correlation functionals in density functional theory. We also show that the natural occupation numbers for these two-electron paradigms are in general non-vanishing and follow the same power law decay as atomic and molecular two-electron systems. C 2016 AIP Publishing LLC