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Metrological analysis to extract the qualified observables, corrected from temperature effects
No full textInternational audienceThe study focuses on the metrological analysis of data obtained in the ANR SCaNING project as part of the monitoring of concrete by sensors embedded in the structure. We are particularly interested in measurements of ultrasonic velocity by longitudinal wave transmission, electrical resistivity and permittivity by electromagnetic method with capacitive sensors. A procedure for evaluating measurement uncertainties meeting the recommendations of international standards is established then applied to each method. The factors influencing the measurements are established and studied in order to be taken into account in the calculated uncertainties. Concrete blocks measuring 30x30x30cm are instrumented and subjected, in a drying oven, to temperature variations. The temperature calibration curves are established. Monitoring the drying of a slab made in the same concrete, measuring 1x1x0.3m, instrumented by the three techniques is then carried out on the basis of measurement records qualified by uncertainties and corrected for temperature effects. Only one side (1x1m) of the slab is left to the ambient air, the other surfaces are protected by waterproof films. The water content gradients of the specimen are studied and monitored on the thickness of the slab
Federated Majorize-Minimization: Beyond Parameter Aggregation
No full textThis paper proposes a unified approach for designing stochastic optimization algorithms that robustly scale to the federated learning setting. Our work studies a class of Majorize-Minimization (MM) problems, which possesses a linearly parameterized family of majorizing surrogate functions. This framework encompasses (proximal) gradient-based algorithms for (regularized) smooth objectives, the Expectation Maximization algorithm, and many problems seen as variational surrogate MM. We show that our framework motivates a unifying algorithm called Stochastic Approximation Stochastic Surrogate MM (SA-SSMM), which includes previous stochastic MM procedures as special instances. We then extend SA-SSMM to the federated setting, while taking into consideration common bottlenecks such as data heterogeneity, partial participation, and communication constraints; this yields FedMM. The originality of FedMM is to learn locally and then aggregate information characterizing the surrogate majorizing function, contrary to classical algorithms which learn and aggregate the original parameter. Finally, to showcase the flexibility of this methodology beyond our theoretical setting, we use it to design an algorithm for computing optimal transport maps in the federated setting
Integration of the evaporable spin-crossover complex [Fe(HB(1,2,4-triazol-1-yl) 3 ) 2 ] into organic field-effect transistors: towards multifunctional OFET devices
No full textInternational audienceIntegrating stimuli-responsive molecular switches into organic electronic devices opens interesting perspectives to achieve unprecedented functionalities. However, significant challenges arise in maintaining device functionalities and ensuring synergy with the molecular properties. Here, we described three different ways of incorporating thin films of the molecular spin crossover (SCO) complex [Fe(HB(1,2,4-triazol-1-yl)3)2] into an organic field-effect transistor (OFET) device. The fabrication of high-quality films was enabled by the use of vacuum thermal evaporation, which permitted the deposition of the SCO compound either on the surface of the organic semiconductor or at the semiconductor/dielectric interface. In device configurations where the SCO layer was not in contact with the conduction channel, changes in the drain-source current were observed near the spin crossover temperature, suggesting a potential synergistic effect. These results provide valuable guidance for the design and integration of bistable-material-based functional devices
Verrouillage de soliton par auto-injection sur Fabry-Pérot fibré pour la synthèse microonde faible bruit de phase
No full textRemerciements : CNES et Agence Innovation DéfenceNational audienceLe résonateur Fabry-Perot fibré (FFP) constitue une plateforme innovante, comme en témoigne l'intérêt croissant pour son utilisation, notamment dans la génération de peignes de fréquence [1] et la stabilisation par auto-injection [2]. Il offre une alternative compacte et robuste aux microrésonateurs et aux anneaux fibrés, comblant l'écart entre ces deux technologies en termes de taux de répétition, avec des fréquences de l'ordre du GHz. Grâce aux faibles pertes intrinsèques des fibres, le FFP atteint de très hauts facteurs de qualité (jusqu'à 5.10 9 ) [3], et permet la génération de peignes très étalés (jusqu'à 28 THz [1]). Le FFP est constitué d'un segment de fibre optique équipé de connecteurs FC-PC, à la surface desquels des miroirs à haute réflectivité sont déposés, présentant une bande passante de 80 nm centrés à 1.55 µm. Alors que la génération de solitons de cavité dans la littérature repose généralement sur des pompages à des niveaux de puissance proches du watt, accompagnés de balayages en fréquence jusqu'à l'atteinte du « step » soliton, le tout au prix de systèmes de stabilisation électronique complexes [1], nous rapportons ici l'observation de solitons obtenus par verrouillage par auto-injection (self-injection locking, SIL) dans un FFP hautement non-linéaire, avec seulement 100 mW de puissance en pompage continu et un bruit de phase ultra-faible
Reconstruction d'images en tomographie photoacoustique avec régularisation combinée variation totale -Cauchy
No full textInternational audiencePhotoacoustic tomography (PAT) is a biomedical imaging technique for which image reconstruction is numerically demanding. We propose a reconstruction method based on the minimization of a cost function involving a Cauchy-type regularization applied to the norm of the gradient, offering an alternative to the total variation. It is minimized using a modified BFGS algorithm that takes into account its non-convexity and offers fast convergence. On a simple numerical experiment, in a PAT context, we show that this regularization offers a better reconstruction than the original total variation in an order of magnitude faster calculation time.La tomographie photoacoustique (PAT) est une technique d'imagerie biomédicale pour laquelle la reconstruction des images est exigeante numériquement. Nous proposons une méthode de reconstruction reposant sur la minimisation d'une fonction coût utilisant une régularisation de type Cauchy appliquée sur la norme du gradient comme alternative à la variation totale. Elle est minimisée à l'aide d'un algorithme de BFGS modifié tenant compte de sa non-convexité et offrant une convergence rapide. Sur une expérience numérique simple, dans un contexte PAT, nous montrons que cette nouvelle régularisation mène à une reconstruction de meilleure qualité que celle obtenue par variation totale et ce, en un temps de calcul d'un ordre de grandeur plus rapide
Avancement du nanosatellite NIMPH et de sa charge utile optique-RF pour le test de fibres optiques dopées Erbium
No full textInternational audienc
Sums of squares certificates for polynomial moment inequalities
No full text26 pagesInternational audienceThis paper introduces and develops the algebraic framework of moment polynomials, which are polynomial expressions in commuting variables and their formal mixed moments. Their positivity and optimization over probability measures supported on semialgebraic sets and subject to moment polynomial constraints is investigated. A positive solution to Hilbert's 17th problem for pseudo-moments is given. On the other hand, moment polynomials positive on actual measures are shown to be sums of squares and formal moments of squares up to arbitrarily small perturbation of their coefficients. When only measures supported on a bounded semialgebraic set are considered, a stronger algebraic certificate for moment polynomial positivity is derived. This result gives rise to a converging hierarchy of semidefinite programs for moment polynomial optimization. Finally, as an application, two nonlinear Bell inequalities from quantum physics are settled
Processus de Galton-Watson renforcés II: Comportements asymptotiques
No full textThe hypothesis in Theorem 1.1(ii) is now optimal, and the proof of this result was substantially simplifiedInternational audienceReinforced Galton-Watson processes have been introduced in arxiv:2306.02476 as population models with non-overlapping generations, such that reproduction events along genealogical lines can be repeated at random. We investigate here some of their sample path properties such as asymptotic growth rates and survival, for which the effects of reinforcement on the evolution appear quite strikingly
Data-driven identification-free approach for nonlinear structural dynamics
No full textInternational audienceIn the field of structural dynamics, accurately identifying and simulating nonlinear structures represents a significant challenge due to the inherent complexity of the phenomena involved. The traditional model-based approach, whether physics-oriented or data-driven, is inherently susceptible to epistemic uncertainties associated with the modeling and identification process. This work presents an alternative data-driven identification-free approach, inspired by the recently proposed data-driven computational mechanics (DDCM) paradigm. Unlike traditional model-based approaches that require explicit models of restoring forces for numerical integration, the proposed approach enables the simulation of nonlinear dynamical systems directly from measured restoring force datasets by formulating it as a double distance minimization between the dataset and discrete dynamic equilibrium constraints. In this study, we present the formulation and proof-of-concept of a novel data-driven solver based on a restoring force dataset to address nonlinearities in structural dynamics problems. A comprehensive numerical study of a Duffing oscillator with symmetric nonlinearity demonstrates the high prediction accuracy of the data-driven solver. The experimental application of the data-driven solver is also demonstrated for a Hybrid Nonlinear Energy Sink (HNES)