Interplay of dust alignment, grain growth and magnetic fields in polarization: lessons from the emission-to-extinction ratio

Polarized extinction and emission from dust in the interstellar medium (ISM) are hard to interpret, as they have a complex dependence on dust optical properties, grain alignment and magnetic field orientation. This is particularly true in molecular clouds. The data available today are not yet used to their full potential. The combination of emission and extinction, in particular, provides information not available from either of them alone. We combine data from the scientific literature on polarized dust extinction with Planck data on polarized emission and we use them to constrain the possible variations in dust and environmental conditions inside molecular clouds, and especially translucent lines of sight, taking into account magnetic field orientation. We focus on the dependence between \lambda_max -- the wavelength of maximum polarization in extinction -- and other observables such as the extinction polarization, the emission polarization and the ratio of the two. We set out to reproduce these correlations using Monte-Carlo simulations where the relevant quantities in a dust model -- grain alignment, size distribution and magnetic field orientation -- vary to mimic the diverse conditions expected inside molecular clouds. None of the quantities chosen can explain the observational data on its own: the best results are obtained when all quantities vary significantly across and within clouds. However, some of the data -- most notably the stars with low emission-to-extinction polarization ratio -- are not reproduced by our simulation. Our results suggest not only that dust evolution is necessary to explain polarization in molecular clouds, but that a simple change in size distribution is not sufficient to explain the data, and point the way for future and more sophisticated models

Deterministic construction of nodal surfaces within quantum Monte Carlo: the case of FeS

In diffusion Monte Carlo (DMC) methods, the nodes (or zeroes) of the trial wave function dictate the magnitude of the fixed-node (FN) error. Within standard DMC implementations, they emanate from short multideterminant expansions, \textit{stochastically} optimized in the presence of a Jastrow factor. Here, following a recent proposal, we follow an alternative route by considering the nodes of selected configuration interaction (sCI) expansions built with the CIPSI (Configuration Interaction using a Perturbative Selection made Iteratively) algorithm. In contrast to standard implementations, these nodes can be \textit{systematically} and \textit{deterministically} improved by increasing the size of the sCI expansion. The present methodology is used to investigate the properties of the transition metal sulfide molecule FeS. This apparently simple molecule has been shown to be particularly challenging for electronic structure theory methods due to the proximity of two low-energy quintet electronic states of different spatial symmetry. In particular, we show that, at the triple-zeta basis set level, all sCI results --- including those extrapolated at the full CI (FCI) limit --- disagree with experiment, yielding an electronic ground state of $^{5}\Sigma^+$ symmetry. Performing FN-DMC simulation with sCI nodes, we show that the correct $^{5}\Delta$ ground state is obtained if sufficiently large expansions are used. Moreover, we show that one can systematically get accurate potential energy surfaces and reproduce the experimental dissociation energy as well as other spectroscopic constants.Comment: 8 pages, 2 figure and 4 table

Hybrid stochastic-deterministic calculation of the second-order perturbative contribution of multireference perturbation theory

A hybrid stochastic-deterministic approach for computing the second-order perturbative contribution $E^{(2)}$ within multireference perturbation theory (MRPT) is presented. The idea at the heart of our hybrid scheme --- based on a reformulation of $E^{(2)}$ as a sum of elementary contributions associated with each determinant of the MR wave function --- is to split $E^{(2)}$ into a stochastic and a deterministic part. During the simulation, the stochastic part is gradually reduced by dynamically increasing the deterministic part until one reaches the desired accuracy. In sharp contrast with a purely stochastic MC scheme where the error decreases indefinitely as $t^{-1/2}$ (where $t$ is the computational time), the statistical error in our hybrid algorithm displays a polynomial decay $\sim t^{-n}$ with $n=3-4$ in the examples considered here. If desired, the calculation can be carried on until the stochastic part entirely vanishes. In that case, the exact result is obtained with no error bar and no noticeable computational overhead compared to the fully-deterministic calculation. The method is illustrated on the F$_2$ and Cr$_2$ molecules. Even for the largest case corresponding to the Cr$_2$ molecule treated with the cc-pVQZ basis set, very accurate results are obtained for $E^{(2)}$ for an active space of (28e,176o) and a MR wave function including up to $2 \times 10^7$ determinants.Comment: 8 pages, 5 figure

Biochemical diversity in the genus Coffea L. : chlorogenic acids, caffeine and mozambioside contents

L'étendue et la nature de la diversité biochimique de la cerise verte de café ont été établies grâce à un grand échantillonnage du genre #coffea$, représenté par 9 espèces de la région Guinéo-Congolaise, 9 espèces de l'Afrique de l'Est, et 7 espèces de Madagascar. Les analyses HPLC ont été utilisées pour déterminer la teneur en caféine, en acide chlorogénique et en mozambioside. L'analyse des données des principaux composés montre l'existence de 2 voies métaboliques. L'une entraîne la synthèse de petites quantités d'acide chlorogénique (4,5$ est étudiée en relation avec l'origine biogéographique des plants. (Résumé d'auteur

Core collections of plant genetic resources

L'organisation génétique du pool génétique des caféiers est examiné à 3 niveaux : biogéographie, ressources génétiques et données disponibles. Cette analyse montre qu'une collection noyau de caféiers devrait consister en 88 groupes de diversité de 3 types en fonction du niveau de connaissance : un groupe pour #Coffea arabica, un groupe contenant des espèces biens connues telle que #C. liberia et #C. canephora et une catégorie avec un grand nombre d'espèces négligées. Différentes stratégies sont appliquées en fonction des catégories. Après avoir défini les groupes de diversité, des tests sont réalisés, en utilisant les données de #C. liberica, par une nouvelle méthode (la stratégie des composantes principales). Les résultats montrent que 50 % de l'inertie peut être obtenue avec 10 % des 338 génotypes, 90 % est obtenue avec 50 % des génotypes. (Résumé d'auteur

On-line Human Activity Recognition from Audio and Home Automation Sensors: comparison of sequential and non-sequential models in realistic Smart Homes

International audienceAutomatic human Activity Recognition (AR) is an important process for the provision of context-aware services in smart spaces such as voice-controlled smart homes. In this paper, we present an on-line Activities of Daily Living (ADL) recognition method for automatic identification within homes in which multiple sensors, actuators and automation equipment coexist, including audio sensors. Three sequence-based models are presented and compared: a Hidden Markov Model (HMM), Conditional Random Fields (CRF) and a sequential Markov Logic Network (MLN). These methods have been tested in two real Smart Homes thanks to experiments involving more than 30 participants. Their results were compared to those of three non-sequential models: a Support Vector Machine (SVM), a Random Forest (RF) and a non-sequential MLN. This comparative study shows that CRF gave the best results for on-line activity recognition from non-visual, audio and home automation sensors