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Polyandry: A Threat or An opportunity for the Sterile Insect Technique?
No full textThe sterile insect technique (SIT) is a pest control strategy based on the mass release of sterilized males to disrupt natural reproduction and suppress wild populations. However, its effectiveness can be challenged by biological factors such as female multiple mating and sperm use bias. While multiple mating is widespread among many insect species, the mechanisms governing sperm use remain poorly understood. In this study, we develop and analyze a compartmental mathematical model based on differential equations to investigate the overall impact of multiple mating on SIT efficiency. We further analyze the effect of sperm use biases with an agent-based model, calibrated on Drosophila suzukii, allowing to explore different scenarios: preferential use of first vs last sperm, of fertile vs sterile sperm, and mixed sperm use. Our results highlight how multiple mating and sperm use biases influence SIT effectiveness. In the longer term, multiple mating is disadvantageous as it requires additional releases of sterilized males to control the pest population. However, in the shorter term, it can be beneficial by disrupting further female reproductive output by "defertilizing" females mated with wild males. This study provides new information on how the way sperm is processed after mating can impact sterile insect control strategies, highlighting the limited influence of these biological processes depending on the release efforts that can be deployed
PROXQP: an Efficient and Versatile Quadratic Programming Solver for Real-Time Robotics Applications and Beyond
No full textInternational audienceConvex Quadratic programming (QP) has become a core component in the modern engineering toolkit, particularly in robotics, where QP problems are legions, ranging from real-time whole-body controllers to planning and estimation algorithms. Many of those QPs need to be solved at high frequency. Meeting timing requirements requires taking advantage of as many structural properties as possible for the problem at hand. For instance, it is generally crucial to resort to warm-starting to exploit the resemblance of consecutive control iterations. While a large range of off-the-shelf QP solvers is available, only a few are suited to exploit problem structure and warm-starting capacities adequately. In this work, we propose the PROXQP algorithm, a new and efficient QP solver that exploits QP structures by leveraging primal-dual augmented Lagrangian techniques. For convex QPs, PROXQP features a global convergence guarantee to the closest feasible QP, an essential property for safe closedloop control. We illustrate its practical performance on various standard robotic and control experiments, including a real-world closed-loop model predictive control application. While originally tailored for robotics applications, we show that PROXQP also performs at the level of state of the art on generic QP problems, making PROXQP suitable for use as an off-the-shelf solver for regular applications beyond robotics
Image quality metrics for restricted gamut images produced by laser-induced printing on plasmonic films
No full textInternational audienceLaser-induced printing is a low-cost, high-speed, non-contact method of marking large, highresolution images. Implemented on thin films containing metallic nanoparticles, the technique allows for the printing of color images with visual effects. However, these images typically have a limited color gamut compared to inkjet printing. This limitation is due to the inability to achieve high levels of saturation for all colors and to cover the sRGB hue range. While common quality metrics focus primarily on aspects such as resolution or blur, they rarely address the color aspect. This study proposes a methodology to provide image quality metrics adapted to color gamuts with unusual shapes and volumes. It aims to rank them in terms of image quality performance for any given image. In particular, this work focuses on gamuts measured in transmission and reflection that are not necessarily centered on the CIE a*b* plane and may exhibit low contrast. Psychophysical studies have been conducted to evaluate the quality of images simulated with different color gamuts. The same images were evaluated using different metrics, and an analysis based on the ANOVA model was used to determine a set of metrics that explain observers' preferences
On the surjectivity of the conditional expectation given a real random variable
No full textIn this paper, we investigate the distributions of random couples with real-valued such that any non-negative integrable random variable can be represented as a conditional expectation,, for some non-negative measurable function . Itturns out that this representation property is related to the smallness of thesupport of the conditional law of given , and in particular fails whenthis conditional law almost surely has a non-zero absolutely continuouscomponent with respect to the Lebesgue measure. We give a sufficient conditionfor the representation property and check that it is also necessary under someadditional assumptions (for instance when or are discrete). We alsoexhibit a rather involved example where the representation property holds butthe sufficient condition does not. Finally, we discuss a weakenedrepresentation property where the non-negativity of is relaxed. This studyis motivated by the calibration of time-discretized path-dependent volatilitymodels to the implied volatility surface
GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models
No full textInternational audienceState-space models (SSMs) are a powerful statistical tool for modelling time-varying systems via a latent state. In these models, the latent state is never directly observed. Instead, a sequence of observations related to the state is available. The statespace model is defined by the state dynamics and the observation model, both of which are described by parametric distributions. Estimation of parameters of these distributions is a very challenging, but essential, task for performing inference and prediction. Furthermore, it is typical that not all states of the system interact. We can therefore encode the interaction of the states via a graph, usually not fully connected. However, most parameter estimation methods do not take advantage of this feature. In this work, we propose GraphGrad, a fully automatic approach for obtaining sparse estimates of the state interactions of a non-linear state-space model via a polynomial approximation. This novel methodology unveils the latent structure of the data-generating process, allowing us to infer both the structure and value of a rich and efficient parameterisation of a general state-space model. Our method utilises a differentiable particle filter to optimise a Monte Carlo likelihood estimator. It also promotes sparsity in the estimated system through the use of suitable proximity updates, known to be more efficient and stable than subgradient methods. As shown in our paper, a number of well-known dynamical systems can be accurately represented and recovered by our method, providing basis for application to real-world scenarios
A stochastic use of the Kurdyka-Lojasiewicz property: Investigation of optimization algorithms behaviours in a non-convex differentiable framework
No full textInternational audienceAsymptotic analysis of generic stochastic algorithms often relies on descent conditions. In a convex setting, some technical shortcuts can be considered to establish asymptotic convergence guarantees of the associated scheme. However, in a non-convex setting, obtaining similar guarantees is usually more complicated, and relies on the use of the Kurdyka-Łojasiewicz (KŁ) property. While this tool has become popular in the field of deterministic optimization, it is much less widespread in the stochastic context and the few works making use of it are essentially based on trajectory-by-trajectory approaches. In this paper, we propose a new framework for using the KŁ property in a non-convex stochastic setting based on conditioning theory. We show that this framework allows for deeper asymptotic investigations on stochastic schemes verifying some generic descent conditions. We further show that our methodology can be used to prove convergence of generic stochastic gradient descent (SGD) schemes, and unifies conditions investigated in multiple articles of the literature
Quantifying Inter-and Intra-Subject Variability of Sensorimotor Desynchronization Induced by Median Nerve Stimulation and Motor Imagery for BCI
No full textInternational audienceMotor Imagery-based Brain-Computer Interfaces (MI-BCIs) enable users to control external devices by interpreting sensorimotor activity recorded via ElectroEncephaloGraphy (EEG). Median Nerve Stimulation (MNS) has recently emerged as a promising alternative motor task for BCI applications. However, intra-and inter-subject EEG variability remains a major challenge, affecting BCI system reliability. While variability is a well-known issue, its precise sources and impact on different EEG patterns remain unclear, with a lack of formal and quantitative studies of BCI variability. Thus, this study quantifies intra-and inter-subject variability in MNSinduced sensorimotor desynchronization (ERD) and compares it with that of MI. Results show that MI elicits stronger ERD with lower intra-subject variability, suggesting more consistent activation patterns, while inter-subject variability is similar between tasks. Additionally, the variability of classification accuracies based on Riemannian geometry exhibits a similar trend. These findings provide insights into EEG variability and its implications for BCI design. Identifying stable neural patterns could improve MI-and MNS-based BCIs, particularly for applications such as intraoperative awareness monitoring
Analyse et identification de Plans de Gestion de Données disciplinaires pouvant être utilisés comme exemple: Produit du Groupe de Travail PGD Disciplinaires de RDA France
No full textRDA France, le chapitre français de la Research Data Alliance (RDA), a mis en place en mars 2023 le groupe de travail RDA France – Plans de Gestion de Données disciplinaires (GT PGD-DISC), avec l’objectif de constituer un corpus de plans de gestion des données (PGD) disciplinaires pouvant être utilisés comme exemple. Ce document décrit en introduction les raisons pour lesquelles RDA France a créé ce groupe de travail et la manière dont le groupe a été constitué (Section 1), puis la méthodologie employée pour élaborer le corpus (Section 2) et le contenu de celui-ci, en termes de données et de métadonnées (Section 3). La maintenance envisagée pour le corpus est décrite en conclusion (Section 4).La Version 1 de la liste de plans de gestion de données disciplinaires résultant du travail du groupe est publiée dans l’entrepôt de l’infrastructure Recherche Data Gouv (https://doi.org/10.57745/ZSWLYJ)
Trafficking of luteinizing hormone receptor directs the differential signal activation between luteinizing hormone and chorionic gonadotropin
No full textInternational audienceLuteinizing hormone (LH) and human choriogonadotropin (hCG) support distinct reproductive events via differential activation of the luteinizing hormone receptor (LHCGR). LH-mediated LHCGR trafficking is known to be key in activating and regulating its signal responses, yet whether LH and hCG differentially direct LHCGR trafficking is unknown. Bioluminescence resonance energy transfer (BRET) trafficking biosensors and highresolution TIRF imaging demonstrated that LH induces rapid internalization and recycling via an APPL1linked very early endosomal pathway, while hCG-mediated receptor trafficking and recycling is slower, and preferentially involves beta-arrestins and accumulation in endocytic compartments positive for early and late endosomal markers. Receptor internalization was differentially required for Gq, Gi and Gs protein-mediated signals, revealing distinct LH- vs hCG-trafficking signatures that may be fundamental to preserving unique hormone signalling patterns and their impact at the genomic level. This study supports different LH vs hCG modes of action on the LHCGR through differential post-endocytic sorting of the receptor, providing a potential 'location bias' mechanism underlying the distinct physiological roles of these two gonadotropins. Short title: LH vs hCG receptor internalization
Specx: a C++ task-based runtime system for heterogeneous distributed architectures
No full textInternational audienceParallelization is needed everywhere, from laptops and mobile phones to supercomputers. Among parallel programming models, task-based programming has demonstrated a powerful potential and is widely used in high-performance scientific computing. Not only does it allow for efficient parallelization across distributed heterogeneous computing nodes, but it also allows for elegant source code structuring by describing hardwareindependent algorithms. In this paper, we present Specx, a task-based runtime system written in modern C++. Specx supports distributed heterogeneous computing by simultaneously exploiting CPUs and GPUs (CUDA/HIP) and incorporating communication into the task graph. We describe the specificities of Specx and demonstrate its potential by running parallel applications. This document is a preliminary version of the publication on Specx, which does not include the benchmarks. We invite the readers to regularly check if a new version is available online