Simulation of a method to directly image exoplanets around multiple stars systems

Direct imaging of extra-solar planets has now become a reality, especially with the deployment and commissioning of the first generation of specialized ground-based instruments such as the GPI, SPHERE, P1640 and SCExAO. These systems will allow detection of planets 1e7 times fainter than their host star. For space-based missions, such as EXCEDE, EXO-C, EXO-S, WFIRST-AFTA, different teams have shown in laboratories contrasts reaching 1e-10 within a few diffraction limits from the star using a combination of a coronagraph to suppress light coming from the host star and a wavefront control system. These demonstrations use a deformable mirror (DM) to remove residual starlight (speckles) created by the imperfections of telescope. However, all these current and future systems focus on detecting faint planets around a single host star or unresolved binaries/multiples, while several targets or planet candidates are located around nearby binary stars such as our neighbor star Alpha Centauri. Until now, it has been thought that removing the light of a companion star is impossible with the current technology, excluding binary star systems from target lists of direct imaging missions. Direct imaging around binaries or multiples systems at a level of contrast allowing Earth-like planets detection is challenging because the region of interest, where a dark zone is essential, is contaminated by the light coming from the host star's companion. We propose a method to simultaneously correct aberration sand diffraction of light coming from the target star. This method works even if the companion star is outside the control region of the DM (beyond its half-Nyquist frequency), by taking advantage of aliasing effects.Comment: 8 pages, 13 figures, SPIE Astronomical Telescope and Instrumentation conferenc

Do remittances affect poverty and inequality ? Evidence from Mali.

Using a 2006 household survey in Mali, we compare current poverty rates and inequality levels with counterfactual ones in the absence of migration and remittances. With proper hypotheses on migrants and a selection model, we are able to impute a counterfactual income for households currently receiving remittances. We show that remittances reduce poverty rates by 5% to 11% and the Gini coefficient by about 5%. Households in the bottom quintiles are more dependent on remittances, which are less substitutable by additional workforce.Indicateurs de pauvreté; Mali; Envois de fonds; Travailleurs migrants;

Stochastic Low-Rank Kernel Learning for Regression

We present a novel approach to learn a kernel-based regression function. It is based on the useof conical combinations of data-based parameterized kernels and on a new stochastic convex optimization procedure of which we establish convergence guarantees. The overall learning procedure has the nice properties that a) the learned conical combination is automatically designed to perform the regression task at hand and b) the updates implicated by the optimization procedure are quite inexpensive. In order to shed light on the appositeness of our learning strategy, we present empirical results from experiments conducted on various benchmark datasets.Comment: International Conference on Machine Learning (ICML'11), Bellevue (Washington) : United States (2011