Numerical analysis of pulsed local plane-wave generation in a TREC

International audienceThe feasibility of generating arbitrary wavefronts within a time-reversal electromagnetic chamber (TREC) has been demonstrated both theoretically and experimentally. Though originally motivated for EMC tests, the generation of coherent wavefronts within a reverberating cavity has a potential interest in antenna testing, too. In this paper, the generation of locally planar wavefronts is addressed by means of numerical simulations involving a 2D cavity, for a scalar electric field. The relationship between the quality of the wavefronts and its defining parameters (bandwidth, curvature, phase center, etc.) is investigated

The Landscape from Home : a GIS-based hedonic price valuation

International audienceWe estimate the hedonic price of landscape seen from houses in the urban fringe of Dijon (France). The viewshed and the land cover as seen from the ground are analyzed by geographic methods from satellite images and from a digital elevation model. The landscape attributes are then used in an econometric model based on the sales of 2667 houses, which deals with endogeneity, multicollinearity, and spatial correlations. The results show that woodland and farmland in the immediate vicinity of houses have positive prices and roads a negative price when these features can be seen by an observer located on the ground, while those prices are clearly lower (or insignificant) when such features cannot be seen: the view itself matters. The arrangement of features in fragmented landscapes commands positive hedonic prices. Landscapes and objects seen more than 100-300 m away all have insignificant hedonic prices

In situ HT-XRD study of the UO2_2-PuO2_2-Pu2_2O3_3 sub-system

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Unsupervised Individual Whales Identification: Spot the Difference in the Ocean

International audienceIdentifying organisms is a key step in accessing information related to the ecology of species. But unfortunately, this is difficult to achieve due to the level of expertise necessary to correctly identify and record living organisms. To try bridging this gap, enormous work has been done on the development of automated species identification tools such as image-based plant identification or audio recordings-based bird identification. Yet, for some groups, it is preferable to monitor the organisms at the individual level rather than at the species level. The automatizing of this problem has received much less attention than species identification. In this paper, we address the specific scenario of discovering humpack whales individuals in a large collections of pictures collected by nature observers. The process is initiated from scratch, without any knowledge on the number of individuals and without any training samples of these individuals. Thus, the problem is entirely unsupervised. To address it, we set up and experimented a scalable fine-grained matching system allowing to discover small rigid visual patterns in highly clutter background. The evaluation was conducted in blind in the context of the LifeCLEF evaluation campaign. Results show that the proposed system provides very promising results with regard to the difficulty of the task but that there is still room for improvements to reach higher recall and precision in the future

A Comparison of Methods for Calculating the Matrix Block Source Term in a Double Porosity Model

Contaminant transport in a fractured porous medium can be modeled, under appropriate conditions, with a double porosity model. Such a model consists of a parabolic equation with a coupling term describing contaminant exchange between the fractures, which have high permeability, and the matrix block, which has low permeability. A locally conservative method based on mixed finite elements is used to solve the parabolic problem, and the calculation of the coupling term, which involves the solution of diffusion equations in the matrix blocks, is based on an analytic expression. Numerical experiment- s show that this analytic method for the coupling term compares favorably to several other methods

Replenish and relax: explaining logarithmic annealing in disordered materials

Fatigue and aging of materials are, in large part, determined by the evolution of the atomic-scale structure in response to strains and perturbations. This coupling between microscopic structure and long time scales remains one of the main challenges in materials study. Focusing on a model system, ion-damaged crystalline silicon, we combine nanocalorimetric experiments with an off-lattice kinetic Monte Carlo simulation to identify the atomistic mechanisms responsible for the structural relaxation over long time scales. We relate the logarithmic relaxation, observed in a number of systems, with heat-release measurements. The microscopic mechanism associated with logarithmic relaxation can be described as a two-step replenish and relax process. As the system relaxes, it reaches deeper energy states with logarithmically growing barriers that need to be unlocked to replenish the heat-releasing events leading to lower energy configurations

Pl@ntNet-300K: a plant image dataset with high label ambiguity and a long-tailed distribution

International audienceThis paper presents a novel image dataset with high intrinsic ambiguity and a longtailed distribution built from the database of Pl@ntNet citizen observatory. It consists of 306,146 plant images covering 1,081 species. We highlight two particular features of the dataset, inherent to the way the images are acquired and to the intrinsic diversity of plants morphology: (i) the dataset has a strong class imbalance, i.e., a few species account for most of the images, and, (ii) many species are visually similar, rendering identification difficult even for the expert eye. These two characteristics make the present dataset well suited for the evaluation of set-valued classification methods and algorithms. Therefore, we recommend two set-valued evaluation metrics associated with the dataset (macro-average top-k accuracy and macro-average average-k accuracy) and we provide baseline results established by training deep neural networks using the cross-entropy loss