Independent Resampling Sequential Monte Carlo Algorithms

Sequential Monte Carlo algorithms, or Particle Filters, are Bayesian filtering algorithms which propagate in time a discrete and random approximation of the a posteriori distribution of interest. Such algorithms are based on Importance Sampling with a bootstrap resampling step which aims at struggling against weights degeneracy. However, in some situations (informative measurements, high dimensional model), the resampling step can prove inefficient. In this paper, we revisit the fundamental resampling mechanism which leads us back to Rubin's static resampling mechanism. We propose an alternative rejuvenation scheme in which the resampled particles share the same marginal distribution as in the classical setup, but are now independent. This set of independent particles provides a new alternative to compute a moment of the target distribution and the resulting estimate is analyzed through a CLT. We next adapt our results to the dynamic case and propose a particle filtering algorithm based on independent resampling. This algorithm can be seen as a particular auxiliary particle filter algorithm with a relevant choice of the first-stage weights and instrumental distributions. Finally we validate our results via simulations which carefully take into account the computational budget

Semi-independent resampling for particle filtering

Among Sequential Monte Carlo (SMC) methods,Sampling Importance Resampling (SIR) algorithms are based on Importance Sampling (IS) and on some resampling-based)rejuvenation algorithm which aims at fighting against weight degeneracy. However %whichever the resampling technique used this mechanism tends to be insufficient when applied to informative or high-dimensional models. In this paper we revisit the rejuvenation mechanism and propose a class of parameterized SIR-based solutions which enable to adjust the tradeoff between computational cost and statistical performances

Topological nanophononic states by band inversion

Nanophononics is essential for the engineering of thermal transport in nanostructured electronic devices, it greatly facilitates the manipulation of mechanical resonators in the quantum regime, and could unveil a new route in quantum communications using phonons as carriers of information. Acoustic phonons also constitute a versatile platform for the study of fundamental wave dynamics, including Bloch oscillations, Wannier Stark ladders and other localization phenomena. Many of the phenomena studied in nanophononics were indeed inspired by their counterparts in optics and electronics. In these fields, the consideration of topological invariants to control wave dynamics has already had a great impact for the generation of robust confined states. Interestingly, the use of topological phases to engineer nanophononic devices remains an unexplored and promising field. Conversely, the use of acoustic phonons could constitute a rich platform to study topological states. Here, we introduce the concept of topological invariants to nanophononics and experimentally implement a nanophononic system supporting a robust topological interface state at 350 GHz. The state is constructed through band inversion, i.e. by concatenating two semiconductor superlattices with inverted spatial mode symmetries. The existence of this state is purely determined by the Zak phases of the constituent superlattices, i.e. that one-dimensional Berry phase. We experimentally evidenced the mode through Raman spectroscopy. The reported robust topological interface states could become part of nanophononic devices requiring resonant structures such as sensors or phonon lasers.Comment: 21 pages, 7 figure

Elective cancer surgery in COVID-19-free surgical pathways during the SARS-CoV-2 pandemic: An international, multicenter, comparative cohort study

PURPOSE As cancer surgery restarts after the first COVID-19 wave, health care providers urgently require data to determine where elective surgery is best performed. This study aimed to determine whether COVID-19–free surgical pathways were associated with lower postoperative pulmonary complication rates compared with hospitals with no defined pathway. PATIENTS AND METHODS This international, multicenter cohort study included patients who underwent elective surgery for 10 solid cancer types without preoperative suspicion of SARS-CoV-2. Participating hospitals included patients from local emergence of SARS-CoV-2 until April 19, 2020. At the time of surgery, hospitals were defined as having a COVID-19–free surgical pathway (complete segregation of the operating theater, critical care, and inpatient ward areas) or no defined pathway (incomplete or no segregation, areas shared with patients with COVID-19). The primary outcome was 30-day postoperative pulmonary complications (pneumonia, acute respiratory distress syndrome, unexpected ventilation). RESULTS Of 9,171 patients from 447 hospitals in 55 countries, 2,481 were operated on in COVID-19–free surgical pathways. Patients who underwent surgery within COVID-19–free surgical pathways were younger with fewer comorbidities than those in hospitals with no defined pathway but with similar proportions of major surgery. After adjustment, pulmonary complication rates were lower with COVID-19–free surgical pathways (2.2% v 4.9%; adjusted odds ratio [aOR], 0.62; 95% CI, 0.44 to 0.86). This was consistent in sensitivity analyses for low-risk patients (American Society of Anesthesiologists grade 1/2), propensity score–matched models, and patients with negative SARS-CoV-2 preoperative tests. The postoperative SARS-CoV-2 infection rate was also lower in COVID-19–free surgical pathways (2.1% v 3.6%; aOR, 0.53; 95% CI, 0.36 to 0.76). CONCLUSION Within available resources, dedicated COVID-19–free surgical pathways should be established to provide safe elective cancer surgery during current and before future SARS-CoV-2 outbreaks

Elective Cancer Surgery in COVID-19-Free Surgical Pathways During the SARS-CoV-2 Pandemic: An International, Multicenter, Comparative Cohort Study.

PURPOSE: As cancer surgery restarts after the first COVID-19 wave, health care providers urgently require data to determine where elective surgery is best performed. This study aimed to determine whether COVID-19-free surgical pathways were associated with lower postoperative pulmonary complication rates compared with hospitals with no defined pathway. PATIENTS AND METHODS: This international, multicenter cohort study included patients who underwent elective surgery for 10 solid cancer types without preoperative suspicion of SARS-CoV-2. Participating hospitals included patients from local emergence of SARS-CoV-2 until April 19, 2020. At the time of surgery, hospitals were defined as having a COVID-19-free surgical pathway (complete segregation of the operating theater, critical care, and inpatient ward areas) or no defined pathway (incomplete or no segregation, areas shared with patients with COVID-19). The primary outcome was 30-day postoperative pulmonary complications (pneumonia, acute respiratory distress syndrome, unexpected ventilation). RESULTS: Of 9,171 patients from 447 hospitals in 55 countries, 2,481 were operated on in COVID-19-free surgical pathways. Patients who underwent surgery within COVID-19-free surgical pathways were younger with fewer comorbidities than those in hospitals with no defined pathway but with similar proportions of major surgery. After adjustment, pulmonary complication rates were lower with COVID-19-free surgical pathways (2.2% v 4.9%; adjusted odds ratio [aOR], 0.62; 95% CI, 0.44 to 0.86). This was consistent in sensitivity analyses for low-risk patients (American Society of Anesthesiologists grade 1/2), propensity score-matched models, and patients with negative SARS-CoV-2 preoperative tests. The postoperative SARS-CoV-2 infection rate was also lower in COVID-19-free surgical pathways (2.1% v 3.6%; aOR, 0.53; 95% CI, 0.36 to 0.76). CONCLUSION: Within available resources, dedicated COVID-19-free surgical pathways should be established to provide safe elective cancer surgery during current and before future SARS-CoV-2 outbreaks

Contributions to Monte Carlo methods and their application to statistical filtering

Cette thèse s’intéresse au problème de l’inférence bayésienne dans les modèles probabilistes dynamiques. Plus précisément nous nous focalisons sur les méthodes de Monte Carlo pour l’intégration. Nous revisitons tout d’abord le mécanisme d’échantillonnage d’importance avec rééchantillonnage, puis son extension au cadre dynamique connue sous le nom de filtrage particulaire, pour enfin conclure nos travaux par une application à la poursuite multi-cibles.En premier lieu nous partons du problème de l’estimation d’un moment suivant une loi de probabilité, connue à une constante près, par une méthode de Monte Carlo. Tout d’abord,nous proposons un nouvel estimateur apparenté à l’estimateur d’échantillonnage d’importance normalisé mais utilisant deux lois de proposition différentes au lieu d’une seule. Ensuite,nous revisitons le mécanisme d’échantillonnage d’importance avec rééchantillonnage dans son ensemble afin de produire des tirages Monte Carlo indépendants, contrairement au mécanisme usuel, et nous construisons ainsi deux nouveaux estimateurs.Dans un second temps nous nous intéressons à l’aspect dynamique lié au problème d’inférence bayésienne séquentielle. Nous adaptons alors dans ce contexte notre nouvelle technique de rééchantillonnage indépendant développée précédemment dans un cadre statique.Ceci produit le mécanisme de filtrage particulaire avec rééchantillonnage indépendant, que nous interprétons comme cas particulier de filtrage particulaire auxiliaire. En raison du coût supplémentaire en tirages requis par cette technique, nous proposons ensuite une procédure de rééchantillonnage semi-indépendant permettant de le contrôler.En dernier lieu, nous considérons une application de poursuite multi-cibles dans un réseau de capteurs utilisant un nouveau modèle bayésien, et analysons empiriquement les résultats donnés dans cette application par notre nouvel algorithme de filtrage particulaire ainsi qu’un algorithme de Monte Carlo par Chaînes de Markov séquentielThis thesis deals with integration calculus in the context of Bayesian inference and Bayesian statistical filtering. More precisely, we focus on Monte Carlo integration methods. We first revisit the importance sampling with resampling mechanism, then its extension to the dynamic setting known as particle filtering, and finally conclude our work with a multi-target tracking application. Firstly, we consider the problem of estimating some moment of a probability density, known up to a constant, via Monte Carlo methodology. We start by proposing a new estimator affiliated with the normalized importance sampling estimator but using two proposition densities rather than a single one. We then revisit the importance sampling with resampling mechanism as a whole in order to produce Monte Carlo samples that are independent, contrary to the classical mechanism, which enables us to develop two new estimators. Secondly, we consider the dynamic aspect in the framework of sequential Bayesian inference. We thus adapt to this framework our new independent resampling technique, previously developed in a static setting. This yields the particle filtering with independent resampling mechanism, which we reinterpret as a special case of auxiliary particle filtering. Because of the increased cost required by this technique, we next propose a semi independent resampling procedure which enables to control this additional cost. Lastly, we consider an application of multi-target tracking within a sensor network using a new Bayesian model, and empirically analyze the results from our new particle filtering algorithm as well as a sequential Markov Chain Monte Carlo algorith

Contributions aux méthodes de Monte Carlo et leur application au filtrage statistique

This thesis deals with integration calculus in the context of Bayesian inference and Bayesian statistical filtering. More precisely, we focus on Monte Carlo integration methods. We first revisit the importance sampling with resampling mechanism, then its extension to the dynamic setting known as particle filtering, and finally conclude our work with a multi-target tracking application. Firstly, we consider the problem of estimating some moment of a probability density, known up to a constant, via Monte Carlo methodology. We start by proposing a new estimator affiliated with the normalized importance sampling estimator but using two proposition densities rather than a single one. We then revisit the importance sampling with resampling mechanism as a whole in order to produce Monte Carlo samples that are independent, contrary to the classical mechanism, which enables us to develop two new estimators. Secondly, we consider the dynamic aspect in the framework of sequential Bayesian inference. We thus adapt to this framework our new independent resampling technique, previously developed in a static setting. This yields the particle filtering with independent resampling mechanism, which we reinterpret as a special case of auxiliary particle filtering. Because of the increased cost required by this technique, we next propose a semi independent resampling procedure which enables to control this additional cost. Lastly, we consider an application of multi-target tracking within a sensor network using a new Bayesian model, and empirically analyze the results from our new particle filtering algorithm as well as a sequential Markov Chain Monte Carlo algorithmCette thèse s’intéresse au problème de l’inférence bayésienne dans les modèles probabilistes dynamiques. Plus précisément nous nous focalisons sur les méthodes de Monte Carlo pour l’intégration. Nous revisitons tout d’abord le mécanisme d’échantillonnage d’importance avec rééchantillonnage, puis son extension au cadre dynamique connue sous le nom de filtrage particulaire, pour enfin conclure nos travaux par une application à la poursuite multi-cibles.En premier lieu nous partons du problème de l’estimation d’un moment suivant une loi de probabilité, connue à une constante près, par une méthode de Monte Carlo. Tout d’abord,nous proposons un nouvel estimateur apparenté à l’estimateur d’échantillonnage d’importance normalisé mais utilisant deux lois de proposition différentes au lieu d’une seule. Ensuite,nous revisitons le mécanisme d’échantillonnage d’importance avec rééchantillonnage dans son ensemble afin de produire des tirages Monte Carlo indépendants, contrairement au mécanisme usuel, et nous construisons ainsi deux nouveaux estimateurs.Dans un second temps nous nous intéressons à l’aspect dynamique lié au problème d’inférence bayésienne séquentielle. Nous adaptons alors dans ce contexte notre nouvelle technique de rééchantillonnage indépendant développée précédemment dans un cadre statique.Ceci produit le mécanisme de filtrage particulaire avec rééchantillonnage indépendant, que nous interprétons comme cas particulier de filtrage particulaire auxiliaire. En raison du coût supplémentaire en tirages requis par cette technique, nous proposons ensuite une procédure de rééchantillonnage semi-indépendant permettant de le contrôler.En dernier lieu, nous considérons une application de poursuite multi-cibles dans un réseau de capteurs utilisant un nouveau modèle bayésien, et analysons empiriquement les résultats donnés dans cette application par notre nouvel algorithme de filtrage particulaire ainsi qu’un algorithme de Monte Carlo par Chaînes de Markov séquentie

Confinement opto-phononique au sein de résonateurs GaAs/AlAs

The work carried out in this thesis addresses the conception and the experimental characterization of opto-phononic resonators. These structures enable the confinement of optical modes and mechanical vibrations at very high frequencies (from few tens up to few hundreds of GHz). This study has been carried out on multilayered nanometric systems, fabricated from III-V semiconductor materials. These nanophononic platforms have been characterized through high resolution Raman scattering measurements. The experimental methods and the numerical tools that we have developed in this thesis have allowed us to explore novel confinement strategies for acoustic phonons in acoustic superlattices, with resonance frequencies around 350 GHz. In particular, we have studied the acoustic properties of two nanophononic resonators. The first acoustic cavity proposed in this manuscript enables the confinement of mechanical vibrations by adiabatically changing the acoustic band-diagram of a one-dimensional phononic crystal. In the second system, we take advantage of the topological invariants characterizing one dimensional periodic structures, in order to create an interface state between two phononic distributed Bragg reflectors. We have then focused on the study of opto-phononic cavities allowing the simultaneous confinement of light and of high frequency mechanical vibrations. We have measured, by Raman scattering spectroscopy, the acoustic properties of planar nanophononic structures embedded in three-dimensional micropillar optical resonators. Finally, in the last sections of this manuscript, we investigate the optomechanical properties of GaAs/AlAs micropillar cavities. We have performed numerical simulations through the finite element method that allowed us to explain the three-dimensional confinement mechanisms of optical and mechanical modes in these systems, and to calculate the main optomechanical parameters. This work shows that GaAs/AlAs micropillars present very interesting properties for future optomechanical experiments, such as very high mechanical resonance frequencies, large optical and mechanical quality factors at room temperature, and high values for the vacuum optomechanical coupling factors and for the Q • f productsCes travaux de thèse portent sur la conception et sur la caractérisation expérimentale de résonateurs opto-phononiques. Ces structures permettent le confinement simultané de modes optiques et de vibrations mécaniques de très haute fréquence (plusieurs dizaines jusqu’à plusieurs centaines de GHz). Cette étude a été effectuée sur des systèmes multicouches à l’échelle nanométrique, fabriqués à partir de matériaux semiconducteurs de type III-V. Ces derniers ont été caractérisés par des mesures de spectroscopie Raman de haute résolution. Grâce aux méthodes expérimentales et aux outils numériques développés, nous avons pu explorer de nouvelles stratégies de confinement pour des phonons acoustiques au sein de super-réseaux nanophononiques, à des fréquences de résonance de l’ordre de 350 GHz. En particulier, nous avons étudié les propriétés acoustiques de deux types de résonateurs planaires. Le premier est basé sur la modification adiabatique du diagramme de bande d’un cristal phononique unidimensionnel. Dans le deuxième système, nous utilisons les invariants topologiques caractérisant ces structures périodiques, afin de créer un état d’interface entre deux miroirs de Bragg phononiques. Nous nous sommes ensuite intéressés à l’étude de cavités opto-phononiques permettant le confinement tridimensionnel de la lumière et de vibrations mécaniques de haute fréquence. Nous avons mesuré par spectroscopie Raman les propriétés acoustiques de résonateurs phononiques planaires placés à l’intérieur de cavités optiques tridimensionnelles, de type micropiliers. Enfin, la dernière partie de cette thèse porte sur l’étude théorique des propriétés optomécaniques de micropiliers GaAs/AlAs. Nous avons effectué des simulations numériques par éléments finis, nous permettant d’expliquer les mécanismes de confinement tridimensionnel de modes acoustiques et optiques dans ces systèmes, et de calculer les principaux paramètres optomécaniques. Les résultats de cette étude démontrent que les micropilier GaAs/AlAs possèdent des caractéristiques prometteuses pour de futures expériences en optomécanique, telles que des fréquences de résonance acoustiques très élevées, de hauts facteurs de qualités mécaniques et optiques à température ambiante, ou encore de fortes valeurs pour les facteurs de couplage optomécaniques et pour le produit Q •

Particle filters with independent resampling

International audienceIn many signal processing applications we aim to track a state of interest given available observations. Among existing techniques, sequential Monte Carlo filters are importance sampling-based algorithms meant to propagate in time a set of weighted particles which represent the a posteriori density of interest. As is well known weights tend to degenerate over time, and resampling is a commonly used rescue for discarding particles with low weight. Unfortunately conditionally independent resampling produces a set of dependent samples and the technique suffers from sample impoverishment. In this paper we modify the resampling step of particle filtering techniques in order to produce independent samples per iteration. We validate our technique via simulation

An improved SIR-based sequential Monte Carlo algorithm

International audienceSequential Monte Carlo (SMC) algorithms are based on importance sampling (IS) techniques. Resampling has been introduced as a tool for fighting the weight degeneracy problem. However, for a fixed sample size N, the resampled particles are dependent, are not drawn exactly from the target distribution, nor are weighted properly. In this paper, we revisit the resampling mechanism and propose a scheme where the resampled particles are (conditionally) independent and weighted properly. We validate our results via simulations