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Transfer learning for wind speed forecasting: A scalable approach for data-scarce environments
No full textInternational audienceAccurate wind speed forecasting plays a central role in the integration of wind energy into modern power systems. However, in many regions, the deployment of data-driven models is limited by the scarcity of historical wind measurements. This study investigates the potential of transfer learning (TL) to address this issue by reusing models trained on data-rich locations to forecast wind speed in underinstrumented sites. Using a dataset from 70 meteorological stations across Spain, spanning diverse climatic conditions, we compared TL with conventional direct learning (DL) using Extreme Learning Machine (ELM) and Autoregressive (AR) models. Forecasts were performed over horizons ranging from 30 minutes to 6 hours and evaluated using normalized Root Mean Squared Error (nRMSE) and normalized Mean Absolute Error (nMAE). Across 4,830 TL experiments, results show that TL achieves forecast accuracy close to that of DL, with average nRMSE ranging from 0.297 (TL) to 0.292 (DL) for 30-minute horizons, and from 0.674 to 0.640 for 360-minute forecasts. These findings confirm that TL is a robust and scalable approach for wind speed forecasting in data-scarce environments. The methodology opens promising avenues for the deployment of forecasting tools in regions where traditional data-driven models remain inapplicable due to limited local measurements
GWTC-4.0: Searches for Gravitational-Wave Lensing Signatures
No full textInternational audienceGravitational waves can be gravitationally lensed by massive objects along their path. Depending on the lens mass and the lens--source geometry, this can lead to the observation of a single distorted signal or multiple repeated events with the same frequency evolution. We present the results for gravitational-wave lensing searches on the data from the first part of the fourth LIGO--Virgo--KAGRA observing run (O4a). We search for strongly lensed events in the newly acquired data by (1) searching for an overall phase shift present in an image formed at a saddle point of the lens potential, (2) looking for pairs of detected candidates with consistent frequency evolution, and (3) identifying sub-threshold counterpart candidates to the detected signals. Beyond strong lensing, we also look for lensing-induced distortions in all detected signals using an isolated point-mass model. We do not find evidence for strongly lensed gravitational-wave signals and use this result to constrain the rate of detectable strongly lensed events and the merger rate density of binary black holes at high redshift. In the search for single distorted lensed signals, we find one outlier: GW231123_135430, for which we report more detailed investigations. While this event is interesting, the associated waveform uncertainties make its interpretation complicated, and future observations of the populations of binary black holes and of gravitational lenses will help determine the probability that this event could be lensed
Finance, paranoïa et radicalisation de la valeur
No full textInternational audienceExplore la relation entre imagination financière, extrémisme réactionnaire et pensée conspirationniste
Exploring Complementarity Problems: Applications and Specialized Algorithms
No full textInternational audienceComplementarity problems constitute a well-established class of mathematical formulations with broad applicability across various disciplines. Their intrinsic ability to model equilibrium conditions and nonlinear constraints makes them particularly effective for representing complex systems. In this chapter, we explore fundamental concepts associated with complementarity problems, with a focused examination of their application to the analysis and modeling of some classes of dynamical systems
Middle Eocene hyperthermal seasonality from Paris Basin marine mollusks
No full textInternational audienceThe Earth has experienced hyperthermal events in the past, characterized by maximum durations of hundreds thousand years, significant magnitude, global extent, and drivers associated with increases in greenhouse gas concentrations, therefore making them potential analogues for current climate change. The Middle Eocene Climatic Optimum (MECO) that occurred 40 Ma ago, is marked by a CO2-driven global warming of +4 to +6° C, affecting global temperatures. Here, we present a detailed reconstruction of seasonal fluctuations in seawater temperatures during this warming event in littoral environment, based on geochemical analyses (δ18O and Δ47) of shallow-marine mollusks from the Paris Basin. Our data show a stability in mean winter temperatures compared to pre-MECO conditions, but a marked warming of +10°C in maximum estuarine water temperatures, with a seasonal temperature range increasing from 12°C before the MECO to 22°C at the climax of the event. We demonstrate that at mid-latitudes, annual maximum shallow-water temperatures increased from 30 ± 2°C before the event to a maximum of 41 ± 4°C at the warming peak. This pattern is associated with a seasonal regime characterized by dry summers and wet winters, implying that the Paris Basin experienced a super-hot summer Mediterranean climate during the MECO
Vapor–Liquid Equilibrium Data Measurements and Modeling for the Methane + Perfluorohexane System from 293.39 to 333.38 K
No full textInternational audienceThis study presents vapor–liquid equilibrium (VLE) data for the methane + perfluorohexane system across five isotherms (293.39 to 333.38 K) with pressures up to 6.669 MPa. The experimental data were obtained via a “static-analytical” based setup incorporating a Rapid Online Sampler Injector (ROLSI) for sampling of equilibrium phases and a gas chromatograph (GC) for analyzing phase composition. Expanded uncertainties for temperature (T), pressure (P) and phase compositions (x, y) are estimated within 0.07 K, 0.011 MPa, and 0.020, 0.030, respectively. The experimental VLE data were regressed via the direct method using both the Peng–Robinson and the Soave–Redlich–Kwong equations of state. In each case, the Mathias–Copeman α function was employed, and the calculations were performed using either the Wong–Sandler or the predictive Soave-Redlich-Kwong mixing rule in combination with the nonrandom two-liquid activity coefficient model. The model parameters were refined using a simplex algorithm optimized through the flash calculation objective function. The calculated average absolute deviation (AADxy) and bias (Biasxy) values between the measured data and the models were both below 4%, indicating satisfactory data regression. Comparative analysis of the CO2 + C6F14, C2H6 + C6F14 and CH4 + C6F14 systems highlights C6F14’s strong selectivity as a physical solvent for C2H6 and CO2 over CH4
Polyhedral Unmixing: Bridging Semantic Segmentation with Hyperspectral Unmixing via Polyhedral-Cone Partitioning
No full textSemantic segmentation and hyperspectral unmixing are two central problems in spectral image analysis. The former assigns each pixel a discrete label corresponding to its material class, whereas the latter estimates pure material spectra, called endmembers, and, for each pixel, a vector representing material abundances in the observed scene. Despite their complementarity, these two problems are usually addressed independently. This paper aims to bridge these two lines of work by formally showing that, under the linear mixing model, pixel classification by dominant materials induces polyhedral-cone regions in the spectral space. We leverage this fundamental property to propose a direct segmentation-to-unmixing pipeline that performs blind hyperspectral unmixing from any semantic segmentation by constructing a polyhedral-cone partition of the space that best fits the labeled pixels. Signed distances from pixels to the estimated regions are then computed, linearly transformed via a change of basis in the distance space, and projected onto the probability simplex, yielding an initial abundance estimate. This estimate is used to extract endmembers and recover final abundances via matrix pseudo-inversion. Because the segmentation method can be freely chosen, the user gains explicit control over the unmixing process, while the rest of the pipeline remains essentially deterministic and lightweight. Beyond improving interpretability, experiments on three real datasets demonstrate the effectiveness of the proposed approach when associated with appropriate clustering algorithms, and show consistent improvements over recent deep and non-deep state-of-the-art methods. The code is available at: https://github.com/antoine-bottenmuller/polyhedral-unmixin
Functional maps regularization for high quality mesh morphing applied to shape registration in computed tomography
No full textInternational audienceAbstract We propose an efficient surface mesh morphing method using the functional maps framework combined with Tikhonov regularization. This approach allows us to obtain high-quality meshes suitable for physical simulations. Our target application is solving partial differential equations (PDEs) on industrial components observed via computed tomography, with an automatic boundary condition assignment. We show how the morphing of a reference mesh can be formulated directly in the functional (spectral) domain, without requiring predefined correspondences or symmetry assumptions. First, the functional maps framework provides a robust way to establish continuous and orientation-preserving correspondences between non-rigid shapes, ensuring an accurate alignment between the ideal CAD model, form Computer Aided Design (CAD), and the real-world scanned part. The morphing process is further refined using Tikhonov regularization, ensuring accurate mesh correspondence while preserving mesh quality, which is essential for solving partial differential equations. Afterward we present numerical results on turbine blade cores in aeronautics, which feature complex geometries with cooling channels that may vary due to manufacturing variations and operational constraints. Finally, we demonstrate the effectiveness of our method through a numerical computation by calculating the Laplace-Beltrami spectrum on the morphed mesh using a linear finite element method. This approach significantly improves simulation accuracy by incorporating real geometric variations, making it highly suitable for industrial applications
Accepter (ou non) de vivre à proximité d’une centrale nucléaire, une question loin d’être réglée
No full textInternational audienc
LA TRANSMISSION DES SAVOIRS EN ENTREPRISE FACE AUX TRANSITIONS
No full textCette étude s'inscrit dans le cadre de l'un des axes de recherche de la Chaire FIT 2 , consacré au pilotage des savoirs et savoir-faire face aux transitions 1 . L'ambition de ce working paper est de proposer un cadrage théorique destiné à aider les entreprises à organiser les différents formats de transmission des savoirs, rendus nécessaires par les transitions démographique, digitale et environnementale, et leurs impacts présents et à venir sur l'organisation du travail. Ce document vise à clarifier les concepts et les défis actuels des transmissions des savoirs dans les organisations, en s'appuyant sur une revue approfondie de la littérature académique en gestion de la connaissance stratégique et en management des savoirs, complétée par des interviews auprès de neuf experts de la transmission en entreprise 2 . L'étude permet, d'une part, d'interroger les limites des approches classiques de la transmission des savoirs face aux enjeux spécifiques des transitions contemporaines et, d'autre part, de construire un cadre conceptuel qui servira de base à une future étude empirique destinée à identifier et classifier les pratiques de transmission sur le terrain