Propagation of epistemic uncertainty in queueing models with unreliable server using chaos expansions

In this paper, we develop a numerical approach based on Chaos expansions to analyze the sensitivity and the propagation of epistemic uncertainty through a queueing systems with breakdowns. Here, the quantity of interest is the stationary distribution of the model, which is a function of uncertain parameters. Polynomial chaos provide an efficient alternative to more traditional Monte Carlo simulations for modelling the propagation of uncertainty arising from those parameters. Furthermore, Polynomial chaos expansion affords a natural framework for computing Sobol' indices. Such indices give reliable information on the relative importance of each uncertain entry parameters. Numerical results show the benefit of using Polynomial Chaos over standard Monte-Carlo simulations, when considering statistical moments and Sobol' indices as output quantities

Transient Electric Field Shaping With the Linear Combination of Configuration Field Method for Enhanced Spatial Control of Microwave Plasmas

The demonstration of enhanced spatial control of nanosecond microwave plasmas generated by the time reversal plasma source is presented in this paper. This new microwave plasma source relies on the spatio-temporal control of the electric field inside an all-metal plasma reactor by modifying the waveform of a high power microwave signal. More specifically, it originally used the spatio-temporal focusing capabilities of the time reversal method to focus a high electric field in a small location. However, a parasitic microwave breakdown can still occur at sharp corners or wedges inside the cavity due to the local enhancement of the residual electric field during time reversal focusing. Thus, it is proposed to use the linear combination of configuration field method to improve field control inside the reactor. Its transient electric field shaping capabilities turn out to be a good candidate for the development of a low pressure microwave ``plasma brush''

An efficient spectral method for the numerical solution to stochastic differential equations

International audienceWe consider a new approach for the numerical approximation of stochastic differential equations driven by white noise. The proposed method shares some features with the stochastic collocation techniques and, in particular, it takes advantage of the assumption of smoothness of the functional to be approximated, to achieve fast convergence. The solution to the stochastic differential equation is represented by means of Lagrange polynomials. The coefficients of the polynomial basis are functions of time and they can be computed by solving a system of deterministic ordinary differential equations. Numerical examples are presented to illustrate the accuracy and the efficiency of the proposed method

An efficient spectral method for the numerical solution to stochastic differential equations

We consider a new approach for the numerical approximation of stochastic differential equations driven by white noise. The proposed method shares some features with the stochastic collocation techniques and, in particular, it takes advantage of the assumption of smoothness of the functional to be approximated, to achieve fast convergence. The solution to the stochastic differential equation is represented by means of Lagrange polynomials. The coefficients of the polynomial basis are functions of time and they can be computed by solving a system of deterministic ordinary differential equations. Numerical examples are presented to illustrate the accuracy and the efficiency of the proposed method

Using Supervised Learning to Analyze the French vaccine on Twitter

International audienceAs the pandemic progressed, disinformation, fake news and conspiracy spread through many parts of society. However, the disinformation spreading through social media is, according to the literature, one of the causes of increased COVID-19 vaccine hesitancy. In this context, the analysis of social media is particularly important, but the large amount of data exchanged on social networks requires specific methods. This is why machine learning and natural language processing (NLP) models are increasingly applied to social media data.Objective: The aim of this study is to examine the capability of the CamemBERT French language model to faithfully predict elaborated categories, with the knowledge that tweets about vaccination are often ambiguous, sarcastic or irrelevant to the studied topic.Methods: A total of 901,908 unique French tweets related to vaccination published between July 12, 2021, and August 11, 2021, were extracted using the Twitter API v2. Approximately 2,000 randomly selected tweets were labeled with two types of categorization: (1) arguments for ("pros") or against ("cons") vaccination (sanitary measures included) and (2) the type of content of tweets ("scientific", "political", "social", or "vaccination status"). The CamemBERT model was fine-tuned and tested for the classification of French tweets. The model performance was assessed by computing the F1-score, and confusion matrices were obtained. Results: The accuracy of the applied machine learning reached up to 70.6% for the first classification ("pros" and "cons" tweets) and up to 90.0% for the second classification ("scientific" and "political" tweets). Furthermore, a tweet was 1.86 times more likely to be incorrectly classified by the model if it contained fewer than 170 characters (odds ratio = 1.86; 1.20 < 95% confidence interval < 2.86).Conclusions: The accuracy is affected by the classification chosen and the topic of the message examined. When the vaccine debate is jostled by contested political decisions, tweet content becomes so heterogeneous that the accuracy of the models drops for less differentiated classes. However, our tests showed that it is possible to improve the accuracy of the model by selecting tweets using a new method based on tweet size.Les réseaux sociaux participent activement à la diffusion de la désinformation sur la covid-19 et, selon de nombreuses études, auraient causés l’augmentation de la réticence vis-à-vis des vaccins anti-covid. Dans ce contexte, l’analyse des réseaux sociaux est des plus importants en matière de santé publique. Toutefois, au regard du grand volume de données échangées chaque jour par les internautes, elle nécessite des méthodes spécifiques. C’est pourquoi les chercheurs ont de plus en plus souvent recours aux modèles d’apprentissage automatisé et au traitement du langage naturel (NLP) en particulier. L’objectif de la présente étude est d’examiner la capacité du modèle CamemBERT, pré-entraîné sur la langue française, à catégoriser automatiquement les tweets traitant de la vaccination alors qu’ils sont souvent ambigus, sarcastique ou sans rapport avec le sujet.Les résultats obtenus, sur 2 000 tweets francophones, montrent que la précision de l'apprentissage automatique atteint jusqu'à 70,6 % pour la première classification (tweets « pour » et « contre ») et jusqu'à 90,0 % pour la seconde (tweets « scientifiques » et « politiques »). De plus, un tweet a 1,86 fois plus de chances d'être mal classé par le modèle s'il contient moins de 170 caractères que s'il en contient plus de 170 (odd ratio = 1,86 ; 1,20 < intervalle de confiance à 95 % < 2,86).En conclusion, la précision du modèle est affectée par la classification choisie et le sujet du message examiné. Lorsque le débat sur le vaccin est bousculé par des décisions politiques contestées, les tweets deviennent si hétérogènes que la précision des modèles chute sur les classes les moins différenciantes. Toutefois, nos tests ont également montré qu'il serait possible d'améliorer la précision du modèle en sélectionnant les tweets à l'aide d'une nouvelle méthode basée sur la taille des tweets

Climate system: A global sensitivity approach

International audienceThis article is a first attempt to develop a numerical approach to solving differential equations based on Galerkin projections and extensions of polynomial chaos to analyze the sensitivity of input parameters in the Lorenz-Stenflo climate model. The sensitivity analysis was undertaken to measure the influence of key parameters (chemical properties of the atmosphere, rotation, temperature gradient, convection motion). In addition, we do simulations of the climate model in the non-chaotic case and in the chaotic case and we calculate the Sobol indices when the parameters follow the uniform law

An Analysis of French-Language Tweets About COVID-19 Vaccines: Supervised Learning Approach

International audienceBackground:As the pandemic progressed, disinformation, fake news and conspiracy spread through many parts of society. However, the disinformation spreading through social media is, according to the literature, one of the causes of increased COVID-19 vaccine hesitancy. In this context, the analysis of social media is particularly important, but the large amount of data exchanged on social networks requires specific methods. This is why machine learning and natural language processing (NLP) models are increasingly used on social media data.Objective:The aim of this study is to examine the capability of the CamemBERT French language model to faithfully predict elaborated categories, with the knowledge that tweets about vaccination are often ambiguous, sarcastic or irrelevant to the studied topic.Methods:A total of 901,908 unique French tweets related to vaccination published between July 12, 2021, and August 11, 2021, were extracted from Twitter API v2. Approximately 2000 randomly selected tweets were labeled with two types of categorization: (1) arguments for (“pros”) or against (“cons”) vaccination (sanitary measures included) and (2) the type of content of tweets (“scientific”, “political”, “social”, or “vaccination status”). The CamemBERT model was fine-tuned and tested for the classification of French tweets. The model performance was assessed by computing the F1-score, and confusion matrices were obtained.Results:The accuracy of the applied machine learning reached up to 70.6% for the first classification (“pros” and “cons” tweets) and up to 90.0% for the second classification (“scientific” and “political” tweets). Furthermore, a tweet was 1.86 times more likely to be incorrectly classified by the model if it contained fewer than 170 characters (odds ratio = 1.86; 1.20 < 95% confidence interval < 2.86).Conclusions:The accuracy is affected by the classification chosen and the topic of the message examined. When the vaccine debate is jostled by contested political decisions, tweet content becomes so heterogeneous that the accuracy of the models drops for less differentiated classes. However, our tests also showed that it is possible to improve the accuracy of the model by selecting tweets using a new method based on tweet size