Localization of Spatially Distributed Near-Field Sources with Unknown Angular Spread Shape

International audienceIn this paper, we propose to localize and characterize coherently distributed (CD) sources in near-field. Indeed, it appears that in some applications, the more the sources are close to the array of sensors, the more they can seem scattered. It thus appears of the biggest importance to take into account the angular distribution of the sources in the joint direction of arrival (DOA) and range estimation methods. The methods of the literature which consider the problem of distributed sources do not handle with the case of near field sources and require that the shape of the dispersion is known. The main contribution of the proposed method is to estimate the shape of the angular distribution using an additional shape parameter to address the case of unknown distributions. We propose to jointly estimate the DOA, the range, the spread angle and the shape of the spread distribution. Accurate estimation is then achieved even when the shape of the angular spread distribution is unknown or imperfectly known. Moreover, the proposed estimator improves angular resolution of the sources

Localisation des sources distribuées en champ proche

National audienceLa plupart des algorithmes du traitement d'antennes ont été développés avec l'hypothèse de sources ponctuelles situées en champ lointain. Certaines applications physiques n'obéissent pas à cet hypothèse, ainsi l'extension angulaire en champ proche doit être prise en compte dans le modèle. Dans ce papier, on propose un modèle généralisé pour la caractérisation des sources ayant une extension angulaire dans un champ proche. Nous proposons ensuite un algorithme d'estimation conjointe de la direction d'arrivée nominale, de la dispersion angulaire autour de cette direction et de la distance séparant la source de l'antenne. La méthode est basée sur une généralisation de l'estimateur MUSIC sur le principe de la minimisation d'un produit scalaire entre un vecteur fonction du vecteur directeur et le vecteur propre bruit de la matrice de corrélation. Nous comparons notre méthode avec un estimateur MUSIC conventionnel (source ponctuelle en champ proche). Les résultats montrent que le nouvel estimateur est plus performant en réduisant l'erreur quadratique moyenne des estimés pour les sources distribuées en champ proche. L'estimateur proposé est comparé avec la borne de Cramer-Rao (BCR)

Mycobacterium tuberculosis acquires iron by cell-surface sequestration and internalization of human holo-transferrin

Mycobacterium tuberculosis (M.tb), which requires iron for survival, acquires this element by synthesizing iron-binding molecules known as siderophores and by recruiting a host iron-transport protein, transferrin, to the phagosome. The siderophores extract iron from transferrin and transport it into the bacterium. Here we describe an additional mechanism for iron acquisition, consisting of an M.tb protein that drives transport of human holo-transferrin into M.tb cells. The pathogenic strain M.tb H37Rv expresses several proteins that can bind human holo-transferrin. One of these proteins is the glycolytic enzyme glyceraldehyde-3-phosphate dehydrogenase (GAPDH, Rv1436), which is present on the surface of M.tb and its relative Mycobacterium smegmatis. Overexpression of GAPDH results in increased transferrin binding to M.tb cells and iron uptake. Human transferrin is internalized across the mycobacterial cell wall in a GAPDH-dependent manner within infected macrophages

Mycobacterium tuberculosis H37Ra: a surrogate for the expression of conserved, multimeric proteins of M.tb H37Rv

Additional file 3. Details of primers used, experimental and theoretical molecular weights, pI values and details of post-translational modifications in GAPDH

RIS Phase Optimization via Generative Flow Networks

Abstract This letter introduces a new Machine Learning (ML) technique to learn phase shifting patterns for Reconfigurable Intelligent Surfaces (RISs). We leverage the Generative Flow Network (GFlowNet) paradigm and adapt it so as to compose a RIS phase control resulting in high communication rate. To generalize our approach for different physical layer scenarios, we use a channel chart as a latent representation of the wireless spatial environment to condition the GFlowNet. As such, the GFlowNet learns a scalable policy over RIS configurations that tailors the propagation environment in real-time. We evaluate our solution by means of simulations on a synthetic dataset, and the results corroborate its superiority compared to benchmarks, achieving more than 15% higher communication rates.Abstract This letter introduces a new Machine Learning (ML) technique to learn phase shifting patterns for Reconfigurable Intelligent Surfaces (RISs). We leverage the Generative Flow Network (GFlowNet) paradigm and adapt it so as to compose a RIS phase control resulting in high communication rate. To generalize our approach for different physical layer scenarios, we use a channel chart as a latent representation of the wireless spatial environment to condition the GFlowNet. As such, the GFlowNet learns a scalable policy over RIS configurations that tailors the propagation environment in real-time. We evaluate our solution by means of simulations on a synthetic dataset, and the results corroborate its superiority compared to benchmarks, achieving more than 15% higher communication rates

La lumière naturelle dans l’abbaye de Belmond et dans les églises médiévales de l’époque franque au Liban

La question de l’éclairement naturel dans les églises et édifices médiévaux est un vaste sujet qui touche plusieurs domaines (Reveyron 1999 : 165). Le sujet de la lumière comporte deux aspects assez différents mais complémentaires. La lumière est un facteur naturel qui est nécessaire à la vie ; elle permet le développement de l’Homme et de toutes les créatures et les espèces. Généralement, le soleil ordonne, en Asie et en Europe, les constructions religieuses (Thibaud 2000 : 248). De même, la lumière dans les lieux de culte est un élément indispensable aux fidèles pour la pratique du culte. Par ailleurs, l’apport de la religion dans la vie des fidèles est souvent considéré comme une lumière divine et une manne dans la vie quotidienne et les difficultés de la vie terrestre

RIS-assisted Cell-Free MIMO with Dynamic Arrivals and Departures of Users: A Novel Network Stability Approach

Reconfigurable Intelligent Surfaces (RIS) have recently emerged as a hot research topic, being widely advocated as a candidate technology for next generation wireless communications. These surfaces passively alter the behavior of propagation environments enhancing the performance of wireless communication systems. In this paper, we study the use of RIS in cell-free multiple-input multiple-output (MIMO) setting where distributed service antennas, called Access Points (APs), simultaneously serve the users in the network. While most existing works focus on the physical layer improvements RIS carry, less attention has been paid to the impact of dynamic arrivals and departures of the users. In such a case, ensuring the stability of the network is the main goal. For that, we propose an optimization framework of the phase shifts, for which we derived a low-complexity solution. We then provide a theoretical analysis of the network stability and show that our framework stabilizes the network whenever it is possible. We also prove that a low complexity solution of our framework stabilizes a guaranteed fraction (higher than 78.5%) of the stability region. We provide also numerical results that corroborate the theoretical claims

Federated Learning Games for Reconfigurable Intelligent Surfaces via Causal Representations

In this paper, we investigate the problem of robust Reconfigurable Intelligent Surface (RIS) phase-shifts configuration over heterogeneous communication environments. The problem is formulated as a distributed learning problem over different environments in a Federated Learning (FL) setting. Equivalently, this corresponds to a game played between multiple RISs, as learning agents, in heterogeneous environments. Using Invariant Risk Minimization (IRM) and its FL equivalent, dubbed FL Games, we solve the RIS configuration problem by learning invariant causal representations across multiple environments and then predicting the phases. The solution corresponds to playing according to Best Response Dynamics (BRD) which yields the Nash Equilibrium of the FL game. The representation learner and the phase predictor are modeled by two neural networks, and their performance is validated via simulations against other benchmarks from the literature. Our results show that causality-based learning yields a predictor that is 15% more accurate in unseen Out-of-Distribution (OoD) environments.Comment: 6 pages, 4 figure

Federated Learning Games for Reconfigurable Intelligent Surfaces via Causal Representations

Abstract In this paper, we investigate the problem of robust Reconfigurable Intelligent Surface (RIS) phase-shifts configuration over heterogeneous communication environments. The problem is formulated as a distributed learning problem over different environments in a Federated Learning (FL) setting. Equivalently, this corresponds to a game played between multiple RISs, as learning agents, in heterogeneous environments. Using Invariant Risk Minimization (IRM) and its FL equivalent, dubbed FL Games, we solve the RIS configuration problem by learning invariant causal representations across multiple environments and then predicting the phases. The solution corresponds to playing according to Best Response Dynamics (BRD) which yields the Nash Equilibrium of the FL game. The representation learner and the phase predictor are modeled by two neural networks, and their performance is validated via simulations against other benchmarks from the literature. Our results show that causality-based learning yields a predictor that is 15 % more accurate in unseen Out-of-Distribution (OoD) environments.Abstract In this paper, we investigate the problem of robust Reconfigurable Intelligent Surface (RIS) phase-shifts configuration over heterogeneous communication environments. The problem is formulated as a distributed learning problem over different environments in a Federated Learning (FL) setting. Equivalently, this corresponds to a game played between multiple RISs, as learning agents, in heterogeneous environments. Using Invariant Risk Minimization (IRM) and its FL equivalent, dubbed FL Games, we solve the RIS configuration problem by learning invariant causal representations across multiple environments and then predicting the phases. The solution corresponds to playing according to Best Response Dynamics (BRD) which yields the Nash Equilibrium of the FL game. The representation learner and the phase predictor are modeled by two neural networks, and their performance is validated via simulations against other benchmarks from the literature. Our results show that causality-based learning yields a predictor that is 15 % more accurate in unseen Out-of-Distribution (OoD) environments

Validation of the Arabic version of the Cohen perceived stress scale (PSS-10) among pregnant and postpartum women

<p>Abstract</p> <p>Background</p> <p>This study was conducted to evaluate the validity of the Arabic translation of the Cohen Perceived Stress Scale (PSS-10) in pregnant and postpartum women.</p> <p>Methods</p> <p>A sample of 268 women participated. These included 113 women in their third trimester of pregnancy, 97 in the postpartum period and 58 healthy female university students. GHQ-12 and EPDS were also administered to the participants. Internal consistency reliability, assessed using Cronbach's α, was 0.74.</p> <p>Results</p> <p>PSS-10 significantly correlated with both EPDS and GHQ12 (ρ = 0.58 and ρ = 0.48 respectively), and significantly increased with higher scores on stressful life events. PSS-10 scores were higher among university students who also recorded higher stressful life events scores.</p> <p>Conclusion</p> <p>The Arabic translated version of the PSS-10 showed reasonably adequate psychometric properties.</p