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How do urban forms and the connectivity of urban and peri-urban habitat shape genetic diversity in European cities?
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Machine learning for experiments and theory in nonlinear fiber optics
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Roughness from Alpha Stable Images Applied to Fossil Images
No full textInternational audienceAmong the parameters used to study the texture of an object's surface is the roughness descriptor. The digital image of the object surface is used to provide roughness descriptors mainly when the surface studied is deformable or damaged by a roughness meter. This work proposes a new roughness descriptor from the image. The particularity of this descriptor is that it is adapted to surfaces containing significant fluctuation (enormous variation). This descriptor is constructed from the estimator of the spectral density of alpha stable processes. To compare our method to other methods developed in the literature, we measured the sensitivity of the descriptors to random Gaussian noise added to each image. In this work, some images of fossils representing significant variation are the subject of the images studied. This study shows that our descriptor is more sensitive than other descriptors for this kind of image (the sensitivity value is higher). Thus, the originality of this work lies in proposing a descriptor adapted to images with large fluctuations. This work falls within the framework of developing adapted methods considering different surface structures in order to provide descriptors that are more precise
Gaulois et latin en Gaule de l’Est : quelques observations à partir d’un nouvel inventaire de l’épigraphie d’Alésia (Ier s. a.C. – Ier s. p.C.)
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Hardware implementation of a novel chaos-based cryptosystem for secure image transmission
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Échanges et circulations : la quête de l’unification par les infrastructures
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Quels déterminants individuels influencent le choix des plats végétariens en restauration universitaire ?
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High-Performance Isogeometric Analysis of Lattice Structures: a Geometric hp-Multigrid Preconditioner
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Complementary models and decision-support systems for accompanying stakeholders in managing weeds with few or no herbicides
No full textCOPRAA : Knowledge and tools for preventive and operational approaches to agroecological weedy plant managementInternational audienceThis paper presents 4 tools for designing herbicide-sparse cropping systems. The "virtual field" model FLORSYS was built from experiments, and can be used to test cropping systems in the long term, with different pedoclimates and weed floras. It predicts many virtual 'measurements' on crops, weeds and soil, as well as indicators of weed impact on crop production and biodiversity. Two decision-support systems co-developed with future users (farmers, crop advisers...) also allow comparing cropping systems, one in terms of weed risk for a series of harmful weed species (OdERA), the other (DECIFLORSYS) with the same weed-impact indicators as FLORSYS. Finally, OPTIFLORSYS combines FLORSYS with optimization algorithms to propose cropping systems that meet the user's production and/or biodiversity objectives