Les registres d’assises de l’Anjou et du Maine à la fin du Moyen Âge : de la coutume à la pratique… In: Normes juridiques et pratiques judiciaires du Moyen Âge à l’époque contemporaine

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Parlementaires contre ducs et pairs : les fondements théoriques d'un conflit au sein des élites (fin XVIIe siècle-début XVIIIe siècle)

L'article compare la pensée d'un duc et pair, Saint-Simon, et celle d'un magistrat, Durand, avocat général au parlement de Bourgogne, à propos de la place respective que doivent occuper dans la société de l'époque la haute magistrature et la haute noblesse. Cette présentation permet une analyse des fondements théoriques d'un conflit au sein des élites à l'apogée de la monarchie absolue française.The paper compares the thoughts of a duke and peer, Saint-Simon, and of a magistrate, Durand, general advocate in Burgundy's parliament, about respective positions which should be occupied by high magistrature and high nobility. This presentation allows an analysis of the theorical foundations of a conflict in the lap of social elites at french absolute monarchy's zenith

Parlementaires contre ducs et pairs : les fondements théoriques d'un conflit au sein des élites (fin XVIIe siècle-début XVIIIe siècle)

L'article compare la pensée d'un duc et pair, Saint-Simon, et celle d'un magistrat, Durand, avocat général au parlement de Bourgogne, à propos de la place respective que doivent occuper dans la société de l'époque la haute magistrature et la haute noblesse. Cette présentation permet une analyse des fondements théoriques d'un conflit au sein des élites à l'apogée de la monarchie absolue française.The paper compares the thoughts of a duke and peer, Saint-Simon, and of a magistrate, Durand, general advocate in Burgundy's parliament, about respective positions which should be occupied by high magistrature and high nobility. This presentation allows an analysis of the theorical foundations of a conflict in the lap of social elites at french absolute monarchy's zenith

Justice, infrajustice, parajustice et extra justice dans la France d'Ancien Régime

Dans la France d'Ancien Régime, une proportion importante du traitement de la criminalité n'est pas assurée par la justice, mais par l'infrajustice. Mais si l'attention portée à l'infrajustice constitue un progrès essentiel de la recherche, on peut craindre qu'elle ne débouche sur une nouvelle « illusion historio graphique » : on a trop tendance à exagérer sa fréquence et à en faire une sorte de panacée, oubliant ainsi qu'une part importante de la criminalité échappe au traitement non seulement judiciaire, mais aussi infrajudiciaire. Cette erreur découle d'une définition à la fois imprécise et excessive de l'infrajustice, trop souvent confondue avec le traitement social de tous les écarts aux normes. Cet article cherche donc à préciser les limites de l'influence de la justice dans le traitement des conflits, à définir précisément l'infrajustice et ses modalités, et à insister sur les comportements qui n'appartiennent ni à la justice ni à l'infrajustice, que je propose de regrouper pour les uns sous le terme de « parajustice », pour les autres sous celui d'« extrajustice ».In France, under the Old Régime, a high proportion of crimes did not come before the courts but were dealt on an infrajudicial level. But, although interest in infrajustice constitutes a crucial step for research, the fear that it may turn out to be yet another « historiographical illusion » is altogether legitimate : there is a tendency to exaggerate the frequency of the phenomenon and to make it a sort of panacea, thus forgetting that a great amount of criminal behavior not only is not dealt with by the judicial system, but is missed by the infrajudicial system as well. This error stems from a not only vague but excessive definition of infrajustice, which is too often confused with the social treatment of all abnormal behavior. The present article therefore seeks to specify the limits of the justice system in dealing with conflicts, to clearly define what constitutes infrajustice and its modalities, and to underscore the behaviors that do not belong to either the justice or the infrajustice system, which I would suggest putting under the heading, for the first, of « parajustice » and, for the second, « extrajustice »

Time-Space Tradeoff in Deep Learning Models for Crop Classification on Satellite Multi-Spectral Image Time Series

International audienceIn this article, we investigate several structured deep learning models for crop type classification on multi-spectral time series. In particular, our aim is to assess the respective importance of spatial and temporal structures in such data. With this objective, we consider several designs of convolutional, recurrent, and hybrid neural networks, and assess their performance on a large dataset of freely available Sentinel-2 imagery. We find that the best-performing approaches are hybrid configurations for which most of the parameters (up to 90%) are allocated to modeling the temporal structure of the data. Our results thus constitute a set of guidelines for the design of bespoke deep learning models for crop type classification

Mixture of Experts with Uncertainty Voting for Imbalanced Deep Regression Problems

Data imbalance is ubiquitous when applying machine learning to real-world problems, particularly regression problems. If training data are imbalanced, the learning is dominated by the densely covered regions of the target distribution, consequently, the learned regressor tends to exhibit poor performance in sparsely covered regions. Beyond standard measures like over-sampling or re-weighting, there are two main directions to handle learning from imbalanced data. For regression, recent work relies on the continuity of the distribution; whereas for classification there has been a trend to employ mixture-of-expert models and let some ensemble members specialize in predictions for the sparser regions. Here, we adapt the mixture-of-experts approach to the regression setting. A main question when using this approach is how to fuse the predictions from multiple experts into one output. Drawing inspiration from recent work on probabilistic deep learning, we propose to base the fusion on the aleatoric uncertainties of individual experts, thus obviating the need for a separate aggregation module. In our method, dubbed MOUV, each expert predicts not only an output value but also its uncertainty, which in turn serves as a statistically motivated criterion to rely on the right experts. We compare our method with existing alternatives on multiple public benchmarks and show that MOUV consistently outperforms the prior art, while at the same time producing better calibrated uncertainty estimates. Our code is available at link-upon-publication