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Inductance and capacitance parasitic prediction thanks to data analysis applied to SiC MOSFET wide frequency band characterization
No full textInternational audienceThe paper presents a wide frequency band characterization for modeling discrete SiC MOSFET -TO247 package. The data analysis is conducted for several components and for three configurations driving to several impedances to characterize. An accurate RLC model of the transmission behavior between two ports is given over 1 MHz to 1 GHz. Moreover, the performance of the differential impedance, which is a generic indicator that eases the identification process, is discussed. Some conclusions are provided on the best way to run the characterization
ANNUAL FRENCH WORKSHOP ON WELL-BEING IN ASTROPHYSICS : CREATING A POSITIVE AND INCLUSIVE WORK ENVIRONMENT
No full textInternational audienceDiversity is very important within an organisation because it fosters creativity and innovation, but it can occasionally lead to difficulties in understanding others, bias towards colleagues or even discrimination. Here we provide a summary of the ideas discussed during the 2025 French Well-being in Astrophysics workshop held at the French National astronomy meeting, which can help promote a positive and inclusive working environment, regardless of age, experience, origin, religion, gender, sexual orientation or disability. These included better understanding colleagues and current problems through a national survey, methods and feedback from mentoring programmes and ideas for preventing conflict
Un algorithme d'échange pour l'optimisation simultanée des erreur d'approximation et d'évaluation en précision finie de polynômes d'approximation
No full textInternational audienceThe finite precision implementation of mathematical functions frequently depends on polynomial approximations. A key characteristic of this approach is that rounding errors occur both when representing the coefficients of the polynomial on a finite number of bits, and when evaluating it in finite precision arithmetic. Hence, to find a best polynomial, for a given fixed degree, norm and interval, it is necessary to account for both the approximation error and the floating-point evaluation error. While efficient algorithms were already developed for taking into account the approximation error, the evaluation part is usually a posteriori handled, in an ad-hoc manner. Here, we formulate a semi-infinite linear optimization problem whose solution is a best polynomial with respect to the supremum norm of the sum of both errors. This problem is then solved with an iterative exchange algorithm, which can be seen as an extension of the well-known Remez exchange algorithm. An open-source C implementation using the Sollya library is presented and tested on several examples, which are then analyzed and compared against state-of-the-art Sollya routines
One-Shot Learning in Hybrid System Identification: A New Modular Paradigm
No full textInternational audienceIdentification of hybrid systems requires learning models that capture both discrete transitions and continuous dynamics from observational data. Traditional approaches follow a stepwise process, separating trace segmentation and mode-specific regression, which often leads to inconsistencies due to unmodeled interdependencies. In this paper, we propose a new iterative learning paradigm that jointly optimizes segmentation and flow function identification. The method incrementally constructs a hybrid model by evaluating and expanding candidate flow functions over observed traces, introducing new modes only when existing ones fail to explain the data. The approach is modular and agnostic to the choice of the regression technique, allowing the identification of hybrid systems with varying levels of complexity. Empirical results on benchmark examples demonstrate that the proposed method produces more compact models compared to traditional techniques, while supporting flexible integration of different regression methods. By favoring fewer, more generalizable modes, the resulting models are not only likely to reduce complexity but also simplify diagnostic reasoning, improve fault isolation, and enhance robustness by avoiding overfitting to spurious mode changes
Mutants of p53 sustain tumor growth under mechanical compression
No full textInternational audienceContext : Solid tumors are subjected to mechanical stimuli arising from their growth in confined environments. Growth-induced pressure builds up in tumors such as pancreatic cancer and rises alongside the occurrence of genetic alterations during tumorigenesis. This study aims to understand the so far unknown relationship between genetic alterations and cancer cell behavior under compressive stress. Results : Using isogenic cell lines with engineered p53 mutations, we showed that the p53 background influences cell response to compression. Tumor growth under compression increased in cells harboring a mutated-truncated p53 form. This mutation blocked caspase 3 cleavage and promoted survival and growth through PI3K-AKT activation and dysregulation of c-FOS and FOSB transcription factors network. Mutated-truncated p53 cells displayed a unique behavior and heightened an activation state under compression. Conclusion : Mechanical compression and p53 mutations together drive tumor growth. p53 status could be a biomarker for predicting tumor adaptation to mechanical stress and efficiency of therapies targeting mechanosensitive pathways. Teaser : Mechanical compression and p53 mutations together enhance cancer cell survival and growth, driving solid tumor progression
D7.1 Architecture V1
No full textThe document provides a comprehensive overview of the architecture for the E2CC (EcoEdge to Cloud Continuum) project, which aims to establish a robust platform for cloud-edgeorchestration. The architecture is designed to support continuous data processing and storage, addressing the demands of modern IoT applications while ensuring competitiveness in European industries. This deliverable describes the key architectural components along athree-layers structure: 1) Orchestration Layer: This layer serves as an API to facilitate seam-less integration of cloud and Edge infrastructure products. 2) Orchestration and servicesLayer: It enables the development and deployment of applications, ensuring that services can be efficiently managed and scaled. It includes services focused on AI/MLOps, security, and decarbonization, which can be utilized by applications or the central platform itself. 3) Verti-cal Applications: These consist of specific use cases that operate on top of the platform, leveraging the services provided by the underlying layers.After providing the general overview of this architecture, each software component within thearchitecture is described in terms of its functionalities, technical specifications, software dependencies, and expected timelines for development and integration. The resulting development timeline of this architecture is also provided.Overall, the architecture of the E2CC project is structured to provide a flexible, secure, andefficient framework for integrating cloud and Edge services. It aims to facilitate innovative applications and ensure that European industries can effectively leverage data in a rapidlyevolving digital landscape. The architecture is expected to evolve further as the project progresses, with subsequent deliverables providing more detailed insights into its implementation and capabilities
A hierarchy of convex relaxations for the total variation distance
No full textGiven two measures µ, ν on Rd that satisfy Carleman's condition, we provide a numerical scheme to approximate as closely as desired the total variation distance between µ and ν. It consists of solving a sequence (hierarchy) of convex relaxations whose associated sequence of optimal values converges to the total variation distance, an additional illustration of the versatility of the Moment-SOS hierarchy. Indeed each relaxation in the hierarchy is a semidefinite program whose size increases with the number of involved moments. It has an optimal solution which is a couple of degree-2n pseudo-moments which converge, as n grows, to moments of the Hahn-Jordan decomposition of µ-ν
Investigation of intrinsic properties of high-quality fiber Fabry–Perot resonators
No full textInternational audienceFiber Fabry–Perot (FFP) resonators of a few centimeters are optimized as a function of the reflectivity of the mirrors and the dimensions of the intra-cavity waveguide. Loaded quality factor in excess of 10^9, with an optimum of 4 × 10^9, together with an intrinsic quality factor larger than 10^10 and intrinsic finesse in the range of 10^5 have been measured. An application to the stabilization of laser frequency fluctuations is presented
Détection des Potentiels d'Erreurs pour Améliorer la Collaboration Humain-Robot : Une Revue Préliminaire
No full textInternational audienceError-related potentials (ErrPs) have been studied to evaluate wrong decisions or actions in several contexts. An ErrP is an electrical potential on the scalp generated by the perception of errors and occurs unwittingly. In human-robot collaboration (HRC), ErrP detection can be shown as feedback or trigger an action to adapt the system to the user. This contributes to the improvement of HRC, taking into account user performance. However, to our knowledge, the detection of ErrPs in HRC has not been widely explored, resulting in only a few studies. This preliminary systematic review will present work on ErrP-based interfaces related to adaptation, control, and neuroergonomics for HRC. 11 articles were included after the exclusion criteria of the review stages. The average accuracy of ErrP detection was between 54% and 87.19%. In most cases, the authors simulated the occurrence of unexpected behavior of the robot. The robot mistakes occurred randomly between 25% and 35% of the total trials. Some works focused on the robot learning process and adaptation between humans and robots. The mental model and the robot behavior policy were updated based on the decoded ErrPs during collaborative interactions. ErrPs played a critical role by accelerating the adaptation process. Control-related works have included ErrP detection/features as input inside the control loop or algorithm. The proposed framework enabled the learning algorithm to converge faster than conventional methods. Other studies assessed the influence of mental workload variability in the adaptation process, given that a high mental workload affects the cognitive processes needed to perceive errors. Thus, Er-rPs present advantages for enhancing HRC, and this review opens the way to further developments in the robotic domain. We will continue this work by elaborating strategies to take advantage of the ErrP detection to design pipelines and protocols that would be used in future works.Les potentiels d'erreur (ErrPs) ont été étudiés pour évaluer les mauvaises décisions ou actions dans divers contextes. L'ErrP est un potentiel électrique au niveau du scalp généré par la perception d'erreurs et survient involontairement. Dans la collaboration humain-robot (HRC), la détection d'ErrPs peut être présentée comme un retour d'information ou déclencher une action afin d'adapter le système à l'utilisateur. Cela contribue à l'amélioration de l'HRC, en tenant compte des performances de l'utilisateur. Cependant, à notre connaissance, la détection d'ErrPs dans l'HRC n'a pas été largement explorée, ce qui n'a donné lieu qu'à quelques études. Cette revue systématique préliminaire présentera des travaux sur les interfaces basées sur les ErrPs concernant l'adaptation, le contrôle et la neuroergonomie dans l'HRC. 11 articles ont été inclus après avoir satisfait aux critères d'exclusion des étapes de revue. La précision moyenne de détection d'ErrPs se situait entre 54\% et 87,19\%. Dans la plupart des cas, les auteurs ont simulé l'apparition d'un comportement inattendu du robot. Les erreurs du robot se sont produites de manière aléatoire entre 25\% et 35\% du total des essais. Certains travaux se sont concentrés sur le processus d'apprentissage et d'adaptation du robot entre humains et robots. Le modèle mental et la politique comportementale du robot ont été mis à jour en fonction des ErrPs décodés lors des interactions collaboratives. Les ErrPs ont joué un rôle essentiel en accélérant le processus d'adaptation. Les travaux liés au contrôle ont inclus la détection et les caractéristiques des ErrPs comme entrées dans la boucle de contrôle ou l'algorithme. Le cadre proposé a permis à l'algorithme d'apprentissage de converger plus rapidement que les méthodes conventionnelles. D'autres études ont évalué l'influence de la variabilité de la charge mentale sur le processus d'adaptation, sachant qu'une charge mentale élevée affecte les processus cognitifs nécessaires à la perception des erreurs. Ainsi, les ErrPs constituent des avantages pour améliorer l'HRC et cette revue ouvre la voie à de nouveaux développements dans le domaine robotique. Nous poursuivrons ce travail en élaborant des stratégies permettant de tirer parti de la détection des ErrPs pour concevoir des pipelines et des protocoles qui seraient utilisés dans les travaux futurs
Approximating the Shapley value with sampling : survey and new stratification techniques
No full textIn game theory, a cooperative game can be used to model a system able to provide a service to a set of users for a certain cost. An important topic related to cooperative games is the cost sharing which distributes the total cost of the system among the players. Shapley proposed in 1953 a cost-sharing mechanism nowadays known as the Shapley value. It is usually interpreted as assigning to each player the cost this player induces on the system. This cost assignment has many desirable properties but it is very time consuming to compute exactly when the number of users is large. Thus, a large effort has been directed towards its approximation. Many general purpose algorithms for computing the Shapley value use sampling, and in particular stratified sampling, in order to obtain a good approximation. In this paper, we provide a comprehensive overview for approximating the Shapley value with sampling with a focus on how and why stratified sampling helps enhancing the precision of the approximation. We also propose a new paradigm to apply stratification which is more flexible than the currently used schemes and allows one to adjust the stratification decisions to the result of the sampling process. We suggest a methodology to create datasets containing a large number of random games to test Shapley approximation algorithms. Finally, we report an extensive experimental study of all the algorithms described in the paper on the datasets created using the previous methodology