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CONNECTING MAX-ENTROPY WITH COMPUTATIONAL GEOMETRY, LP AND SDP
No full textInternational audienceWe consider the well-known max-(relative) entropy problem Θ(y) = infQ≪P DKL(Q P ) with Kullback-Leibler divergence on a domain Ω ⊂ R d , and with "moment" constraints h dQ = y, y ∈ R m . We show that when m ≤ d, Θ is the Cramér transform of a function v that solves a simply related computational geometry problem. Also, and remarkably, to the canonical LP: min x≥0 {c T x : A x = y}, with A ∈ R m×d , one may associate a max-entropy problem with a suitably chosen reference measure P on R d + and linear mapping h(x) = Ax, such that its associated perspective function ε Θ(y/ε) is the optimal value of the log-barrier formulation (with parameter ε) of the dual LP (and so it converges to the LP optimal value as ε → 0). An analogous result also holds for the canonical SDP: min X 0 { C, X : A(X) = y }
A lateral porous silicon electrokinetic molecular valve
No full textInternational audienceIn this study, we introduce an Electrokinetic Molecular Valve (EMV) based on Lateral Porous Silicon (LPSi) membranes. The LPSi membranes are fabricated and monolithically integrated into silicon microfluidic chips , featuring an average pore size of 25 nm. Upon proper oxidation, LPSi membranes exhibit a relative perm-selectivity of 48% in physiological solution, comparable to that of Nafion. The LPSi chip is able to extract and concentrate 1.5 fmol of fluorescein from 180 nL into 1.3 nL within 10 minutes, and to achieve a concentration factor of more than 120 at voltages less than 4.2 V. A simplified numerical model is developed to describe the electrokinetic behavior of the EMV. The model exibites good qualitative agreement with experimental results. By varying parameters within this framework (the applied voltage, membrane charge density, background ion concentration, and membrane position), the preconcentration performance of the EMV can be reliably predicted. Distinct from conventional electrokinetic concentrators, the EMV architecture mandates that the entire fluid flow through the LPSi nanochannels. This configuration enables high ion selectivity and low voltage operation, while leveraging the Donnan exclusion effect for precise molecular control, concentration, and release. With continued advancements in electrical insulation and membrane charge density, the proposed EMV holds considerable promise for integration into portable µTAS and biosensors
Self-written high-efficiency single-mode optical link using a single near-infrared photopolymerization step
No full textAccepté pour publication dans J. Lightwave Tech.International audienceWe present a method for fabricating a self-written waveguide (SWW) between two optical fibers that are single-mode (SM) at 850 nm (780HP/ core diameter: 4.4 µm). The basic principle consists in exposing an acrylic photopolymer formulation sensitive in the near-infrared range (NIR) to a laser beam transmitted simultaneously from both fibers placed face to face, to build a continuous, flexible and self-aligned optical link. The specificity of the presented process (NIR-SM-SWW) lies in the use of a writing wavelength identical to that intended for singlemode propagation in the fibers. This enables the creation in a single step of a SWW directly adapted to the fundamental mode to be transmitted. A precise pre-positioning stage is used to optimize the process. For best photochemical conditions, a coupling efficiency as high as 82 % (-0.86 dB loss) is demonstrated for a 300 µm-long link. The effect of fiber-to-fiber axial and lateral distances is also investigated to estimate the propagation loss and misalignment tolerance, respectively. In addition, measurements performed by quantitative phase optical microscopy indicate a homogeneous index profile in the guide. Using these data, optical modeling is performed and compared to experiments, confirming that a high efficiency SM link is actually fabricated, without the need for further fabrication of an external cladding. This method could therefore be easily applied to the SM connection of a SM VCSEL (vertical-cavity surface-emitting laser) to a SM fiber, which is of major interest for the development of compact optical communications and instrumentation systems
Electro-Thermo-Optical Modulation of Silicon Nitride Integrated Photonic Filters for Analog Applications
No full textInternational audienceHigh-quality spectral filters with versatile tuning mechanisms are essential for applications in photonic integrated circuits, including sensing, laser stabilization, and spectral signal processing. We report the implementation of thermo-optic (TO) and electro-optic (EO) spectral tuning in silicon nitride Mach–Zehnder interferometers (MZIs) and micro-ring resonators (MRRs) by functionalizing the devices with a PMMA:JRD1 polymer cladding and integrating titanium tracks as heaters and electrodes. The fabricated MZIs and MRRs exhibit narrow linewidths of 25–30 pm and achieved TO tuning efficiencies of 1.7 and 13 pm/mW and EO tuning efficiencies of 0.33 and 1.6 pm/V, respectively. Closed-loop regulation using TO and EO effects enables stable half-fringe locking under environmental perturbations. This simple, broadly compatible hybrid platform demonstrates a practical approach to dual-mode spectral tuning and modulation in integrated photonic filters, providing a flexible route toward compact, reconfigurable, and environmentally robust photonic circuits
Revisiting Incremental Stochastic Majorization-Minimization Algorithms with Applications to Mixture of Experts
No full textProcessing high-volume, streaming data is increasingly common in modern statistics and machine learning, where batch-mode algorithms are often impractical because they require repeated passes over the full dataset. This has motivated incremental stochastic estimation methods, including incremental stochastic Expectation-Maximization (EM) procedures formulated via stochastic approximation. In this work, we study an incremental stochastic variant of the Majorization-Minimization (MM) principle that generalizes incremental stochastic EM as a special case. Our main methodological contribution is to make this framework operational for softmax-gated mixture of experts (MoE) models: we construct tractable majorizer surrogates that yield explicit incremental updates in settings where no stochastic EM algorithm is available. We complement this development with consistency guarantees by verifying conditions under which the iterates converge to a stationary point characterized by a vanishing gradient of the objective. Empirically, for softmax-gated MoE regression, the resulting incremental stochastic MM algorithm consistently outperforms widely used stochastic optimizers, including stochastic gradient descent, root mean square propagation, adaptive moment estimation, and second-order clipped stochastic optimization. These results highlight the practical value of building a principled bridge between incremental MM theory and modern softmax-gated MoE architectures, given their central role in contemporary deep neural networks for heterogeneous data modeling and scalable conditional computation. Beyond synthetic experiments, we further validate effectiveness on two real-world datasets, including a bioinformatics study of dent maize genotypes under drought stress that integrates high-dimensional proteomics with ecophysiological traits, where incremental stochastic MM yields stable gains in predictive performance
A Set of Robotic Inductive Tasks to Monitor Human Cognitive Effort
No full textInternational audienceMental state monitoring methods are particularly promising for Human-Robot Interaction (HRI). Indeed, evaluating users' mental states in real-time using portable acquisition devices would help to build a better model of the ongoing interaction. Yet, the study of human mental state requires standardized inductive tasks in order to produce a robust ground truth for baseline measurements. Hence cognitive effort is often induced using dual-task paradigms, where the robot has a limited impact in the inductive process and that do not allow to modulate human mental state through robot behavior. To address such an issue, this study proposes to validate three inductive tasks adapted from neuropsychology to HRI: the N-Back Task, the Sternberg Task, and the Cognitive Shifting Task. Each task was designed to induce cognitive effort through robot behavior only, avoiding the need for dual-task paradigms. The validation involved 24 participants per task, performing both the original version and the robot one with robot video clips. Expected outcomes included a decreased accuracy, as well as increased response times and subjective effort at higher difficulty levels. Results confirmed that the robotic tasks effectively induce cognitive effort, though they also introduce stronger cognitive demands than traditional letter-based tasks. The validated tasks provide novel robust tools for HRI research, with all resources and data openly accessible for community use, therefore paving the way for promoting reproducibility and replicability of HRI research
Diffusive gradient in thin film for ultra-trace methylmercury measurements in the coastal and open sea
No full textInternational audienceMonomethylmercury (MMHg) is a potent neurotoxin causing neurodevelopmental delays and cardiovascular and immunological issues. Human exposure primarily occurs through seafood consumption due to MMHg bioaccumulation and biomagnification from seawater into marine organisms. Determining MMHg in seawater at ultratrace concentrations poses logistical and analytical challenges. Diffusive Gradient in Thin-film (DGT) samplers represent a promising solution, which captures time-averaged concentrations by preconcentrating in situ MMHg over a defined exposure time. DGT manufactured with 3-mercaptopropyl-functionalized silica (3MFS) in agarose and polyacrylamide gels were tested and compared for the determination of MMHg present in open ocean and coastal waters. Different elution methods using acidic thiourea were tested to reach precise, accurate and quantitative elution of MMHg from the binding gel. We found that polyacrylamide-3MFS binding gels display a higher elution efficiency (94 ± 3 %), precision and better handling compared to agarose-3MFS gels (41 ± 6 %). A unique mooring line installed in the South Western Tropical Pacific Ocean, provided monthly DGT-MMHg concentrations over a year showing potential seasonal differences in MMHg concentrations ranging between 18 and 106 fM. DGT were also deployed in shallow Peruvian coastal waters, exhibiting higher MMHg concentrations (170 ± 97, n = 26) with typical benthopelagic gradients. DGT-MMHg concentrations were in good agreement with discrete water samples analyzed by reference methods using isotope dilution. DGTs offer complementary advantages over oceanographic cruises, notably in situ preconcentration, low blanks, minimal logistical requirements and cost-effectiveness. DGTs represent a valuable tool for studying the marine MMHg cycle for evaluating the implementation of the Minamata Convention
POLYNOMIAL ARGMIN FOR RECOVERY AND APPROXIMATION OF MULTIVARIATE DISCONTINUOUS FUNCTIONS
No full textInternational audienceWe propose to approximate a (possibly discontinuous) multivariate function f (x) on a compact set by the partial minimizer arg miny p(x, y) of an appropriate polynomial p whose construction can be cast in a univariate sum of squares (SOS) framework, resulting in a highly structured convex semidefinite program. In a number of non-trivial cases (e.g. when f is a piecewise polynomial) we prove that the approximation is exact with a low-degree polynomial p. Our approach has three distinguishing features: (i) It is mesh-free and does not require the knowledge of the discontinuity locations. (ii) It is model-free in the sense that we only assume that the function to be approximated is available through samples (point evaluations). (iii) The size of the semidefinite program is independent of the ambient dimension and depends linearly on the number of samples. We also analyze the sample complexity of the approach, proving a generalization error bound in a probabilistic setting. This allows for a comparison with machine learning approaches
New nonlinear model for the prediction of components breakdown subject to transient disturbances
No full textDynamic models of electrostatic discharge (ESD)protection devices have many advantages in simulating the response of these components when they are subjected to very high transient pulses, especially in the cases of electromagnetic pulse residues (EMPs). However, a major problem with these models is that they do not consider the dynamics of variation of the component as a function of the energy flowing through it. In this paper, we will explore a way to improve dynamic simulation by building electrical models from frequency measurements, which can consider very fast current variations. Through a concrete case of a filter including a transient voltage suppressor (TVS) in parallel with a capacitor, we will demonstrate that the proposed modeling approach can predict the destruction of the latter