Sentence de mort confirmee par Arrest de la Cour de Parlement de Paris, contre Manuel Magnan compagnon cordonnier, executé, à mort, Pour avoir pris & derobé le sainct Sacrement de l'Autel, en l'Eglise Commandataire de S. Iean de Lattran le 14 Féburier, 1620. Ensemble tout le contenu du Procès, Informations, et confessions, faicte par ledict Magnã, sur ledict larrecin & Sacrilege

[Acte. 1620-02-14]

BIOMOD - a platform for ensemble forecasting of species distributions

BIOMOD is a computer platform for ensemble forecasting of species distributions, enabling the treatment of a range of methodological uncertainties in models and the examination of species-environment relationships. BIOMOD includes the ability to model species distributions with several techniques, test models with a wide range of approaches, project species distributions into different environmental conditions (e.g. climate or land use change scenarios) and dispersal functions. It allows assessing species temporal turnover, plot species response curves, and test the strength of species interactions with predictor variables. BIOMOD is implemented in R and is a freeware, open source, package

Allegresse :chrestienne: +chrétienne+ de l'heureux succes des guerres de ce royaume , et de la justice de Dieu contre les rebelles au Roy... Ensemble le tombeau de Gaspar de Colligny jadis admiral de France... Par I.T.

Comprend : De guerre, paix ; Pyramide+ renversee sur la mort de Gaspar de Coligny ; Au :Roy: +Roi+ ; Ce que Dieu touche ard

The three phases of the ensemble forecasting of niche models: geographic range and shifts in climatically suitable areas of Utetheisa ornatrix (Lepidoptera, Arctiidae)

Species' geographic ranges are usually considered as basic units in macroecology and biogeography, yet it is still difficult to measure them accurately for many reasons. About 20 years ago, researchers started using local data on species' occurrences to estimate broad scale ranges, thereby establishing the niche modeling approach. However, there are still many problems in model evaluation and application, and one of the solutions is to find a consensus solution among models derived from different mathematical and statistical models for niche modeling, climatic projections and variable combination, all of which are sources of uncertainty during niche modeling. In this paper, we discuss this approach of ensemble forecasting and propose that it can be divided into three phases with increasing levels of complexity. Phase I is the simple combination of maps to achieve a consensual and hopefully conservative solution. In Phase II, differences among the maps used are described by multivariate analyses, and Phase III consists of the quantitative evaluation of the relative magnitude of uncertainties from different sources and their mapping. To illustrate these developments, we analyzed the occurrence data of the tiger moth, Utetheisa ornatrix (Lepidoptera, Arctiidae), a Neotropical moth species, and modeled its geographic range in current and future climates.

Histoire nouvelle du nouveau monde : contenant en somme ce que les Hespagnols ont fait jusqu'à présent aux Indes occidentales, & le rude traitement qu'ils font à ces poures peuples là : ensemble, une petite histoire d'un massacre commis par les Hespagnols sur quelques François en la Floride, avec un indice des choses les plus remarquables / extraite de l'italien de M. Hierosme Benzoni... par M. Urbain Chauveton ; par M. Urbain Chauveton

Comprend : Brief discours et histoire d'un voyage de quelques François en Floride : & du massacre autant injustement que barbarement executé sur eux, par les Hespagnols, l'an mil cinq cens soixante cinq

Précis d'un cours sur l'ensemble du droit privé des Romains... (2e édition revue et corrigée sur la 4e édition allemande...) / par Théodore Marezoll,... ; traduit et annoté par C.-A. Pellat,...

Titre original : Lehrbuch der Institutionen der römischen Rechtex

Domain Adaptation in remote sensing: increasing the portability of land-cover classifiers

Among the types of remote sensing acquisitions, optical images are certainly one of the most widely relied upon data sources for Earth observation. They provide detailed measurements of the electromagnetic radiation reflected or emitted by each pixel in the scene. Through a process termed supervised land-cover classification, this allows to automatically yet accurately distinguish objects at the surface of our planet. In this respect, when producing a land-cover map of the surveyed area, the availability of training examples representative of each thematic class is crucial for the success of the classification procedure. However, in real applications, due to several constraints on the sample collection process, labeled pixels are usually scarce. When analyzing an image for which those key samples are unavailable, a viable solution consists in resorting to the ground truth data of other previously acquired images. This option is attractive but several factors such as atmospheric, ground and acquisition conditions can cause radiometric differences between the images, hindering therefore the transfer of knowledge from one image to another. The goal of this Thesis is to supply remote sensing image analysts with suitable processing techniques to ensure a robust portability of the classification models across different images. The ultimate purpose is to map the land-cover classes over large spatial and temporal extents with minimal ground information. To overcome, or simply quantify, the observed shifts in the statistical distribution of the spectra of the materials, we study four approaches issued from the field of machine learning. First, we propose a strategy to intelligently sample the image of interest to collect the labels only in correspondence of the most useful pixels. This iterative routine is based on a constant evaluation of the pertinence to the new image of the initial training data actually belonging to a different image. Second, an approach to reduce the radiometric differences among the images by projecting the respective pixels in a common new data space is presented. We analyze a kernel-based feature extraction framework suited for such problems, showing that, after this relative normalization, the cross-image generalization abilities of a classifier are highly increased. Third, we test a new data-driven measure of distance between probability distributions to assess the distortions caused by differences in the acquisition geometry affecting series of multi-angle images. Also, we gauge the portability of classification models through the sequences. In both exercises, the efficacy of classic physically- and statistically-based normalization methods is discussed. Finally, we explore a new family of approaches based on sparse representations of the samples to reciprocally convert the data space of two images. The projection function bridging the images allows a synthesis of new pixels with more similar characteristics ultimately facilitating the land-cover mapping across images.