5 resultados para MULTISCALE FRACTAL DIMENSION

em Université de Lausanne, Switzerland


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This paper presents a semisupervised support vector machine (SVM) that integrates the information of both labeled and unlabeled pixels efficiently. Method's performance is illustrated in the relevant problem of very high resolution image classification of urban areas. The SVM is trained with the linear combination of two kernels: a base kernel working only with labeled examples is deformed by a likelihood kernel encoding similarities between labeled and unlabeled examples. Results obtained on very high resolution (VHR) multispectral and hyperspectral images show the relevance of the method in the context of urban image classification. Also, its simplicity and the few parameters involved make the method versatile and workable by unexperienced users.

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Fractal geometry is a fundamental approach for describing the complex irregularities of the spatial structure of point patterns. The present research characterizes the spatial structure of the Swiss population distribution in the three Swiss geographical regions (Alps, Plateau and Jura) and at the entire country level. These analyses were carried out using fractal and multifractal measures for point patterns, which enabled the estimation of the spatial degree of clustering of a distribution at different scales. The Swiss population dataset is presented on a grid of points and thus it can be modelled as a "point process" where each point is characterized by its spatial location (geometrical support) and a number of inhabitants (measured variable). The fractal characterization was performed by means of the box-counting dimension and the multifractal analysis was conducted through the Renyi's generalized dimensions and the multifractal spectrum. Results showed that the four population patterns are all multifractals and present different clustering behaviours. Applying multifractal and fractal methods at different geographical regions and at different scales allowed us to quantify and describe the dissimilarities between the four structures and their underlying processes. This paper is the first Swiss geodemographic study applying multifractal methods using high resolution data.

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Geographical isolation and polyploidization are central concepts in plant evolution. The hierarchical organization of archipelagos in this study provides a framework for testing the evolutionary consequences for polyploid taxa and populations occurring in isolation. Using amplified fragment length polymorphism and simple sequence repeat markers, we determined the genetic diversity and differentiation patterns at three levels of geographical isolation in Olea europaea: mainland-archipelagos, islands within an archipelago, and populations within an island. At the subspecies scale, the hexaploid ssp. maroccana (southwest Morocco) exhibited higher genetic diversity than the insular counterparts. In contrast, the tetraploid ssp. cerasiformis (Madeira) displayed values similar to those obtained for the diploid ssp. guanchica (Canary Islands). Geographical isolation was associated with a high genetic differentiation at this scale. In the Canarian archipelago, the stepping-stone model of differentiation suggested in a previous study was partially supported. Within the western lineage, an east-to-west differentiation pattern was confirmed. Conversely, the easternmost populations were more related to the mainland ssp. europaea than to the western guanchica lineage. Genetic diversity across the Canarian archipelago was significantly correlated with the date of the last volcanic activity in the area/island where each population occurs. At the island scale, this pattern was not confirmed in older islands (Tenerife and Madeira), where populations were genetically homogeneous. In contrast, founder effects resulted in low genetic diversity and marked genetic differentiation among populations of the youngest island, La Palma.

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The European Mouse Mutagenesis Consortium is the European initiative contributing to the international effort on functional annotation of the mouse genome. Its objectives are to establish and integrate mutagenesis platforms, gene expression resources, phenotyping units, storage and distribution centers and bioinformatics resources. The combined efforts will accelerate our understanding of gene function and of human health and disease.