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CV@R penalized portfolio optimization with biased stochastic mirror descent
No full textNational audienceThis article studies and solves the problem of optimal portfolio allocation with CV@R penalty when dealing with imperfectly simulated financial assets. We use a Stochastic biased Mirror Descent to find optimal resource allocation for a portfolio whose underlying assets cannot be generated exactly and may only be approximated with a numerical scheme that satisfies suitable error bounds, under a risk management constraint. We establish almost sure asymptotic properties as well as the rate of convergence for the averaged algorithm. We then focus on the optimal tuning of the overall procedure to obtain an optimized numerical cost. Our results are then illustrated numerically on simulated as well as real data set
O2 and CO2 Solubility Characterization To Understand Gas-Liquid Mass Transfer In Bioprocesses
No full textInternational audienceAerobic cultures are widely used in bioprecesses, but are often limited by the oxygen availability, the latter being poorly soluble in water. The aim of this study is to provide data for oxygen and carbon dioxide solubility in culture media and in solutions mimicking fermentation media or fermented broth, to better understand the gas transfers and limitations within aerobic cultures. Using an apparatus developped by our team, and making use of the gas-liquid equilibrium described by the Henry's law, solubilities are measured for pure oxygen and carbon dioxide in aqueous solutions of NaCl, glucose and ethanol
Laser self-injection locking to fiber Fabry-Perot resonator for frequency comb generation
No full textThis study demonstrates that self-injection locking (SIL) of a distributed feedback (DFB) laser to a high-Q fiber Fabry Perot (FFP) resonator, fabricated with highly nonlinear fiber, allows optical frequency combs (OFC) generation with a laser power as low as 100 mW. More precisely, cavity soliton (CS) regime has been observed in this configuration, along with other types of combs. The laser stabilization using SIL is described. Then the system's behavior is analyzed through modeling the laser's dynamics and comparing the model results to experimental tuning curve measurements. Our findings highlight the critical role of the initial phase of the fiber link between laser and FFP in determining the stability and effectiveness of the locking process. We explore the dynamics of the nonlinear SIL process while varying the laser current, revealing the transition from modulation instability to chaotic comb states, and eventually to soliton formation as the system moves from an effective blue-detuned to an effective red-detuned regime. Notably, the inclusion of self-phase modulation (SPM) in the SIL model predicts accessibility of the narrow soliton existence range. These results highlight the potential of SIL in FFP resonators for low-power, stable OFC generation, offering a promising path forward for practical applications
Photonic Integrated Circuit with System on Chip for Sub-Picometric Displacement Sensor
No full textNational audiencePICSONDE aims to develop an advanced embedded sensing system using optical feedback interferometry (OFI) in a laser diode for predictive maintenance applications. The system integrates a photonicintegrated circuit (PIC) to capture the frequency modulated (FM) channel of the OFI signal, and a system-on-chip (SoC) for data acquisition and processing. The project targets a quantum-limitedperformance of 0.1 pm/√Hz noise power spectrum density (PSD) over a 100 kHz bandwidth, representing the state-of-the-art in OFI systems. To achieve this, PICSONDE must overcome four main technologicalchallenges: (1) acquiring OFI signals with high dynamic range and signal-to-noise ratio, (2) extracting information from non-linear OFI signals, (3) real-time assessment of optical feedback betweenlaser and target, and (4) detecting interferometric fringes in the presence of speckle
Multilevel matrix-free method for high-performance isogeometric analysis of lattice structures
No full textInternational audienceThis paper presents a novel high-performance solver for the isogeometric analysis of lattice structures, designed to jointly exploit distributed-memory computing architectures and the specific nature of the problem. This work breaks with conventional approaches that primarily focus on multiscale homogenization or structural elements like beams and shells. Instead, it introduces a solver capable of meeting the overwhelming computational demands of full high-fidelity, fine-scale simulations of lattice structures. The solver features a two-level geometric preconditioner with a fine-level smoother based on overlapping domain decomposition, and a coarse-level correction utilizing an algebraic multigrid method. By leveraging the multiscale nature of the lattice structures, a matrix-free approach is employed at the fine level to perform matrix-vector products and apply transfer operators based on spline -refinement. The structural similarities of the cells are also exploited through a reduced-order modeling procedure applied within each subdomain, which is used to efficiently compute the corresponding local solves within the fine-level smoother. A series of numerical experiments in both 2D and 3D, spanning various micro- and macro-geometries, are conducted to evaluate the efficiency of the solver in terms of memory usage, computational time, and robustness with respect to mesh refinement, spline degree, and problem size. Notably, an industrially representative spiral channel regenerative cooling thrust chamber lattice structure, consisting of over 66,000 cells, is simulated in minutes using thousands of processes
A method for site-specifically tethering the enzyme urease to DNA origami with sustained activity
No full textInternational audienceAttaching enzymes to nanostructures has proven useful to the study of enzyme functionality under controlled conditions and has led to new technologies. Often, the utility and interest of enzyme-tethered nanostructures lie in how the enzymatic activity is affected by how the enzymes are arranged in space. Therefore, being able to conjugate enzymes to nanostructures while preserving the enzymatic activity is essential. In this paper, we present a method to conjugate single-stranded DNA to the enzyme urease while maintaining enzymatic activity. We show evidence of successful conjugation and quantify the variables that affect the conjugation yield. We also show that the enzymatic activity is unchanged after conjugation compared to the enzyme in its native state. Finally, we demonstrate the tethering of urease to nanostructures made using DNA origami with high site-specificity. Decorating nanostructures with enzymatically-active urease may prove to be useful in studying, or even utilizing, the functionality of urease in disciplines ranging from biotechnology to soft-matter physics. The techniques we present in this paper will enable researchers across these fields to modify enzymes without disrupting their functionality, thus allowing for more insightful studies into their behavior and utility
Meeting of EuronanoLab at ENRIS (Bolognia) : Experts group Thin Film session: Kick off for Thin Film european group
No full textInternational audienceThe steering committee of EuronanoLab requested the implementation of the kick-off of Thin Films. Pascal Dubreuil, along with Susana de Freitas and Matias Trujillo, co-organized the session of the expert group in person with oral presentations from national networks about ALD, CVD, ALD technics and all thin film materials . A program (meeting online, workshops) for the year 2025 was then presented and validated in the session
Policy Optimization via Adv2: Adversarial Learning on Advantage Functions
No full textInternational audienceWe revisit the reduction of learning in adversarial Markov decision processes [MDPs] to adversarial learning based on --values; this reduction has been considered in a number of recent articles as one building block to perform policy optimization. Namely, we first consider and extend this reduction in an ideal setting where an oracle provides value functions: it may involve any adversarial learning strategy (not just exponential weights) and it may be based indifferently on --values or on advantage functions. We then present two extensions: on the one hand, convergence of the last iterate for a vast class of adversarial learning strategies (again, not just exponential weights), satisfying a property called monotonicity of weights; on the other hand, stronger regret criteria for learning in MDPs, inherited from the stronger regret criteria of adversarial learning called strongly adaptive regret and tracking regret. Third, we demonstrate how adversarial learning, also referred to as aggregation of experts, relates to aggregation (orchestration) of expert policies: we obtain stronger forms of performance guarantees in this setting than existing ones, via yet another, simple reduction. Finally, we discuss the impact of the reduction of learning in adversarial MDPs to adversarial learning in the practical scenarios where transition kernels are unknown and value functions must be learned. In particular, we review the literature and note that many strategies for policy optimization feature a policy-improvement step based on exponential weights with estimated --values. Our main message is that this step may be replaced by the application of any adversarial learning strategy on estimated --values or on estimated advantage functions. We leave the empirical evaluation of these twists for future research