Conservativity and Weak Consistency of a Class of Staggered Finite Volume Methods for the Euler Equations

We address a class of schemes for the Euler equations with the following features: the space discretization is staggered, possible upwinding is performed with respect to the material velocity only and the internal energy balance is solved, with a correction term designed on consistency arguments. These schemes have been shown in previous works to preserve the convex of admissible states and have been extensively tested numerically. The aim of the present paper is twofold: we derive a local total energy equation satisfied by the solutions, so that the schemes are in fact conservative, and we prove that they are consistent in the Lax-Wendroff sense

Prédiction sélective des traitements pour le suivi d'objet

International audienceL'un des problèmes majeurs du suivi d'objet en vidéo est la dérive, c'est-à-dire la mauvaise localisation à un instant donné d'une entité désignée dans une image initiale. Les nombreux algorithmes de suivi visuel ou trackers sont tous susceptibles de dériver lorsqu'ils sont confrontés à diverses sources de nuisance (occultations, changements d'apparence ou d'illumination, mouvements irréguliers de la cible et de la caméra...) mais sont également caracté-risés par des niveaux de robustesse variés à ces phéno-mènes. L'approche proposée par notre travail est de s'ins-pirer d'une caractéristique de la vision naturelle, l'atten-tion visuelle, considérée ici comme un ensemble de mé-canismes prédictifs de sélection de l'information ou des ressources. Dans le problème étudié, son rôle est alors de combiner dynamiquement les trackers selon leur capacité à assurer globalement le suivi à partir de mesures d'auto-évaluation en ligne de leur comportement et de leur adé-quation au contexte. L'effort est porté sur la combinaison de trackers hétérogènes en coût de calcul et en structure fonctionnelle, et sur la capacité de les munir générique-ment de telles fonctions d'auto-évaluation. Mots Clef attention visuelle, vision par ordinateur, suivi d'objet, pré-diction de dérives, fusion Abstract One of the key problems in video object tracking is drift, this is the bad localization of an entity initially appointed in an image at a given moment. The numerous visual tracking algorithms or trackers are all likely to drift when they are confronted with diverse sources of disturbances (occlusions, appearance changes or illuminations, irregular movements of the target and the camera) but are also characterized by different levels of robustness in response to these phenomena. The approach proposed by our work is inspired by a characteristic of natural vision, visual attention , considered here as a set of predictive mechanisms of selection of the information or the resources. In the studied problem, its role is then to dynamically combine trackers according to their capacity to globally insure tracking from measures of on-line self-assessment of their behaviour and their adequacy to the context. In our combination of heterogeneous trackers, the efforts concern the cost of calculation , the functional structure, and the capacity to equip them generically with such self-assessment functions

Canine Population Structure: Assessment and Impact of Intra-Breed Stratification on SNP-Based Association Studies

In canine genetics, the impact of population structure on whole genome association studies is typically addressed by sampling approximately equal numbers of cases and controls from dogs of a single breed, usually from the same country or geographic area. However one way to increase the power of genetic studies is to sample individuals of the same breed but from different geographic areas, with the expectation that independent meiotic events will have shortened the presumed ancestral haplotype around the mutation differently. Little is known, however, about genetic variation among dogs of the same breed collected from different geographic regions.In this report, we address the magnitude and impact of genetic diversity among common breeds sampled in the U.S. and Europe. The breeds selected, including the Rottweiler, Bernese mountain dog, flat-coated retriever, and golden retriever, share susceptibility to a class of soft tissue cancers typified by malignant histiocytosis in the Bernese mountain dog. We genotyped 722 SNPs at four unlinked loci (between 95 and 271 per locus) on canine chromosome 1 (CFA1). We showed that each population is characterized by distinct genetic diversity that can be correlated with breed history. When the breed studied has a reduced intra-breed diversity, the combination of dogs from international locations does not increase the rate of false positives and potentially increases the power of association studies. However, over-sampling cases from one geographic location is more likely to lead to false positive results in breeds with significant genetic diversity.These data provide new guidelines for association studies using purebred dogs that take into account population structure

Exemplar based metric learning for robust visual localization

This paper presents an exemplar based metric learning framework dedicated to robust visual localization in com-plex scenes, e.g. street images. The proposed framework learns off-line a specific (local) metric for each image of the database, so that the distance between a database image and a query image representing the same scene is smaller than the distance between the current image and other images of the database. To achieve this goal, we generate geometric and photometric transformations as proxies for query im-ages. From the generated constraints, the learning problem is cast as a convex optimization problem over the cone of positive semi-definite matrices, which is efficiently solved using a projected gradient descent scheme. Successful ex-periments, conducted using a freely available geo-referenced image database, reveal that the proposed method significantly improves results over the metric in the input space, while being as efficient at test time. In addition, we show that the model learns discriminating features for the localization task, and is able to gain invariance to meaningful transformations. Index Terms — content-based image retrieval, supervised metric learning, visual localization, place recognition 1. CONTEXT The problem tackled in this paper is visual localization at a street level [1] [2] [3]. Visual localization methods differ from each other by the type of features extracted online (2D only [2] and/or 3D [4]), the matching method (dedicated or not to large scale [5] [6]), and the a priori information used (geo-referenced database image [2], 3D model [3], 2D road-map [7], mobile cell phone information [6],...). Our typical scenario is the precise localization of a vehicle whose ap-proximate localization is known. Our problem is cast to an image retrieval (IR) problem using only 2D features extracted from acquired images and 2D geo-referenced database im-age (Fig. 1). Exploiting only image content is challenging because, even if query and database images depict the same scene, camera-view points, illumination and colorimetry are different, the scene itself may have changed over time an

Synergistic use of Lagrangian dispersion and radiative transfer modelling with satellite and surface remote sensing measurements for the investigation of volcanic plumes: the Mount Etna eruption of 25–27 October 2013

Abstract. In this paper we combine SO2 and ash plume dispersion modelling with satellite and surface remote sensing observations to study the regional influence of a relatively weak volcanic eruption from Mount Etna on the optical and micro-physical properties of Mediterranean aerosols. We analyse the Mount Etna eruption episode of 25–27 October 2013. The evolution of the plume along the trajectory is investigated by means of the FLEXible PARTicle Lagrangian dispersion (FLEXPART) model. The satellite data set includes true colour images, retrieved values of volcanic SO2 and ash, estimates of SO2 and ash emission rates derived from MODIS (MODerate resolution Imaging Spectroradiometer) observations and estimates of cloud top pressure from SEVIRI (Spinning Enhanced Visible and InfraRed Imager). Surface remote sensing measurements of aerosol and SO2 made at the ENEA Station for Climate Observations (35.52° N, 12.63° E; 50 m a.s.l.) on the island of Lampedusa are used in the analysis. The combination of these different data sets suggests that SO2 and ash, despite the initial injection at about 7.0 km altitude, reached altitudes around 10–12 km and influenced the column average aerosol particle size distribution at a distance of more than 350 km downwind. This study indicates that even a relatively weak volcanic eruption may produce an observable effect on the aerosol properties at the regional scale. The impact of secondary sulfate particles on the aerosol size distribution at Lampedusa is discussed and estimates of the clear-sky direct aerosol radiative forcing are derived. Daily shortwave radiative forcing efficiencies, i.e. radiative forcing per unit AOD (aerosol optical depth), are calculated with the LibRadtran model. They are estimated between −39 and −48 W m−2 AOD−1 at the top of the atmosphere and between −66 and −49 W m−2 AOD−1 at the surface, with the variability in the estimates mainly depending on the aerosol single scattering albedo. These results suggest that sulfate particles played a large role in the transported plume composition and radiative forcing, while the contribution by ash particles was small in the volcanic plume arriving at Lampedusa during this event

Seawater redox variations during the deposition of the Kimmeridge Clay Formation, United Kingdom (Upper Jurassic): evidence from molybdenum isotopes and trace metal ratios

The Kimmeridge Clay Formation (KCF) and its equivalents worldwide represent one of the most prolonged periods of organic carbon accumulation of the Mesozoic. In this study, we use the molybdenum (Mo) stable isotope system in conjunction with a range of trace metal paleoredox proxies to assess how seawater redox varied both locally and globally during the deposition of the KCF. Facies with lower organic carbon contents (TOC 1–7 wt %) were deposited under mildly reducing (suboxic) conditions, while organic-rich facies (TOC >7 wt %) accumulated under more strongly reducing (anoxic or euxinic) local conditions. Trace metal abundances are closely linked to TOC content, suggesting that the intensity of reducing conditions varied repeatedly during the deposition of the KCF and may have been related to orbitally controlled climate changes. Long-term variations in ?98/95Mo are associated with the formation of organic-rich intervals and are related to third-order fluctuations in relative sea level. Differences in the mean ?98/95Mo composition of the organic-rich intervals suggest that the global distribution of reducing conditions was more extensive during the deposition of the Pectinatites wheatleyensis and lower Pectinatites hudlestoni zones than during the deposition of the upper Pectinatites hudlestoni and Pectinatites pectinatus zones. The global extent of reducing conditions during the Kimmerigidan was greater than today but was less widespread than during the Toarcian (Early Jurassic) oceanic anoxic event. This study also demonstrates that the Mo isotope system in Jurassic seawater responded to changes in redox conditions in a manner consistent with its behavior in present-day sedimentary environment

Characterization of digital medical images utilizing support vector machines

BACKGROUND: In this paper we discuss an efficient methodology for the image analysis and characterization of digital images containing skin lesions using Support Vector Machines and present the results of a preliminary study. METHODS: The methodology is based on the support vector machines algorithm for data classification and it has been applied to the problem of the recognition of malignant melanoma versus dysplastic naevus. Border and colour based features were extracted from digital images of skin lesions acquired under reproducible conditions, using basic image processing techniques. Two alternative classification methods, the statistical discriminant analysis and the application of neural networks were also applied to the same problem and the results are compared. RESULTS: The SVM (Support Vector Machines) algorithm performed quite well achieving 94.1% correct classification, which is better than the performance of the other two classification methodologies. The method of discriminant analysis classified correctly 88% of cases (71% of Malignant Melanoma and 100% of Dysplastic Naevi), while the neural networks performed approximately the same. CONCLUSION: The use of a computer-based system, like the one described in this paper, is intended to avoid human subjectivity and to perform specific tasks according to a number of criteria. However the presence of an expert dermatologist is considered necessary for the overall visual assessment of the skin lesion and the final diagnosis

South Atlantic paleobathymetry since early Cretaceous

We present early Cretaceous to present paleobathymetric reconstructions and quantitative uncertainty estimates for the South Atlantic, offering a strong basis for studies of paleocirculation, paleoclimate and paleobiogeography. Circulation in an initially salty and anoxic ocean, restricted by the topography of the Falkland Plateau, Rio Grande Ridge and Walvis Rise, favoured deposition of thick evaporites in shallow water of the Brazilian-Angolan margins. This ceased as sea oor spreading propagated northwards, opening an equatorial gateway to shallow and intermediate circulation. This gateway, together with subsiding volcano-tectonic barriers would have played a key role in Late Cretaceous climate changes. Later deepening and widening of the South Atlantic, together with gateway opening at Drake Passage would lead, by mid-Miocene (∼15 Ma) to the establishment of modern-style thermohaline circulation