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Characterization and optimization of Laser Induced Breakdown (LIB) as an ignition method to generate open field blast waves
No full textPosterInternational audienc
Giant Rydberg excitons in CuO probed by photoluminescence excitation spectroscopy
No full textInternational audienceRydberg excitons are, with their ultrastrong mutual interactions, giant optical nonlinearities, and very high sensitivity to external fields, promising for applications in quantum sensing and nonlinear optics at the single-photon level. To design quantum applications it is necessary to know how Rydberg excitons and other excited states relax to lower-lying exciton states. Here, we present photoluminescence excitation spectroscopy as a method to probe transition probabilities from various excitonic states in cuprous oxide, and we show giant Rydberg excitons at mK with principal quantum numbers up to , corresponding to a calculated diameter of 3 m
Free electron lasers driven by plasma accelerators: status and near-term prospects
No full textInternational audienceOwing to their ultra-high accelerating gradients, combined with injection inside micrometer-scale accelerating wakefield buckets, plasma-based accelerators hold great potential to drive a new generation of free-electron lasers (FELs). Indeed, the first demonstration of plasma-driven FEL gain was reported recently, representing a major milestone for the field. Several groups around the world are pursuing these novel light sources, with methodology varying in the use of wakefield driver (laser-driven or beam-driven), plasma structure, phase-space manipulation, beamline design, and undulator technology, among others. This paper presents our best attempt to provide a comprehensive overview of the global community efforts towards plasma-based FEL research and development
Objective assessment of corneal transparency in the clinical setting with standard SD-OCT devices
No full textIn-Network Data Aggregation for Ad Hoc Clustered Cognitive Radio Wireless Sensor Network
No full textInternational audienceIn cognitive radio wireless sensor networks (CRSN), the nodes act as secondary users. Therefore, they can access a channel whenever its primary user (PU) is absent. Thus, the nodes are assumed to be equipped with a spectrum sensing (SS) module to monitor the PU activity. In this manuscript, we focus on a clustered CRSN, where the cluster head (CH) performs SS, gathers the data, and sends it toward a central base station by adopting an ad hoc topology with in-network data aggregation (IDA) capability. In such networks, when the number of clusters increases, the consumed energy by the data transmission decreases, while the total consumed energy of SS increases, since more CHs need to perform SS before transmitting. The effect of IDA on CRSN performance is investigated in this manuscript. To select the best number of clusters, a study is derived aiming to extend the network lifespan, taking the SS requirements, the IDA effect, and the energy consumed by both SS and transmission into consideration. Furthermore, the collision rate between primary and secondary transmissions and the network latency are theoretically derived. Numerical results corroborate the efficiency of IDA to extend the network lifespan and minimize both the collision rate and the network latency
Dynamics of forecast‐error growth along cut‐off Sanchez and its consequence for the prediction of a high‐impact weather event over southern France
No full textInternational audienceThe representation of a high-impact weather (HIW) event over southern France is evaluated in Météo-France forecasts, and the sensitivity of the HIW forecast to the upstream upper-level flow and the Mediterranean and North Atlantic humidity structure prior to the event is quantified. The event occurred in October 2016 during the international field experiment NAWDEX. The approach of an upper-level potential vorticity (PV) cut-off, referred to as cut-off Sanchez, triggered extreme precipitation over southern France. Many 2- to 7-day ensemble forecasts predicted the maximum of the extreme precipitation and the location of the upper-level PV cut-off too far to the east. This eastward shift primarily resulted from an underestimation of the cut-off intensity two days before the HIW and the subsequent downstream propagation and amplification of these errors in the vicinity of Sanchez. Improving the representation of the cut-off two days before the event significantly improved the forecast quality. Another error source were inaccuracies in the moisture structure in the eastern North Atlantic. Specifically, an underestimation of the moisture in the warm conveyor belt inflow led to errors in the low- and upper-level circulation that eventually contributed to the eastward shift of the HIW two days later. Corrections in the eastern North Atlantic humidity structure further improved the forecast quality. On the other hand, corrections in the Mediterranean humidity structure had only a small impact on the accuracy of the forecast. The findings illustrate the importance of downstream error propagation and moist diabatic processes for the prediction of extreme weather over Europe, and demonstrate how targeted changes in the PV and humidity field a few days in advance can improve the quality of the forecasts
S-TRIGGER: Continual State Representation Learning via Self-Triggered Generative Replay
No full textInternational audienceWe consider the problem of building a state repre-sentation model for control, in a continual learning setting. As the environment changes, the aim is to efficiently compress the sensory state information without losing past knowledge, and then use Reinforcement Learning on the resulting features for efficient policy learning. To this end, we propose S-TRIGGER, a general method for Continual State Representation Learning applicable to Variational Auto-Encoders and its many variants. The method is based on Generative Replay, i.e. the use of generated samples to maintain past knowledge. It comes along with a statistically sound method for environment change detection, which self-triggers the Generative Replay. Our experiments on VAEs show that S-TRIGGER learns state representations that allows fast and high-performing Reinforcement Learning, while avoiding catastrophic forgetting. The resulting system has a bounded size and is capable of autonomously learning new information without using past data
Dosimetric characterisation and application to radiation biology of a kHz laser-driven electron beam
No full textInternational audienceLaser-plasma accelerators can produce ultra-short electron bunches in the femtosecond to picosecond duration range, resulting in very high peak dose rates in comparison with clinical accelerators. This unique characteristic motivates their possible application to radiation biology studies to elucidate the effect of high peak dose rates and peculiar temporal structures on the biological response of living cells, which might improve the differential response between tumour and healthy tissues. Electron beams driven by kHz laser systems are an attractive option among laser-plasma accelerators since the high repetition rate can boost the mean dose rate and improve the stability of the delivered dose in comparison with J-class laser accelerators running at few Hz. In this work, we present the dosimetric characterisation of a kHz, low energy laser-driven electron source and preliminary results on in-vitro irradiation of cancer cells. A shot-to-shot dosimetry protocol enabled monitoring of the beam stability and the irradiation conditions for each cell sample. Results of survival assays on HCT116 colorectal cancer cells are in good agreement with previous findings reported in the literature and validate the robustness of the dosimetry and irradiation protocol
Stability Analysis of the RC-PLMS Adaptive Beamformer Using a Simple Transfer Function Approximation
No full textInternational audienceIn this paper, we propose a discrete time transfer function approximation for the reduced complexity parallel least mean square (RC-pLMS) adaptive beamforming algorithm. The RC-pLMS is built using a single least mean square (LMS) stage whose inputs are obtained as a linear combination of the present and past sample. Thus, in order to numerically assess the RC-pLMS stability and to determine the approximate maximum parametric value of the step size for which it remains stable, we derive its discrete time transfer function approximate. In this approximation, the input uniform linear antenna array is remodeled as a finite impulse response (FIR) fractional delay Farrow filter. Computer simulations, presented by the mean square error and beam radiation pattern, demonstrates the validity of the transfer function approximate. Additionally, the RC-pLMS stability is evaluated, with respect to the pole-zero plot, for different step sizes and the approximate upper bound value of the step size is determined
Determinants of process innovation in developing countries: Does a firm's formal versus informal status matter?
No full textProcess innovation has been documented and shown to exert positive effects on firms' economic performance, especially on productivity. In view of these effects, a stream of literature has been developed concerning its determinants. However, in the context of developing countries, the potential effect of a firm's formal versus informal status on the introduction of process innovation specifically has been overlooked. In this paper, by using firm-level data from Côte d'Ivoire, a firm's formality status is found to influence positively and significantly the introduction of process innovation. This result remains unchanged when one considers the industrial, services and trade sectors separately. Policy implications are discussed