183 resultados para Almost Contact Manifold


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The Western European house mouse, Mus musculus domesticus, is well-known for the high frequency of Robertsonian fusions that have rapidly produced more than 50 karyotipic races, making it an ideal model for studying the mechanisms of chromosomal speciation. The mouse mandible is one of the traits studied most intensively to investigate the effect of Robertsonian fusions on phenotypic variation within and between populations. This complex bone structure has also been widely used to study the level of integration between different morphogenetic units. Here, with the aim of testing the effect of different karyotypic assets on the morphology of the mouse mandible and on its level of modularity, we performed morphometric analyses of mice from a contact area between two highly metacentric races in Central Italy. We found no difference in size, while the mandible shape was found to be different between the two Robertsonian races, even after accounting for the genetic relationships among individuals and geographic proximity. Our results support the existence of two modules that indicate a certain degree of evolutionary independence, but no difference in the strength of modularity between chromosomal races. Moreover, the ascending ramus showed more pronounced interpopulation/race phenotypic differences than the alveolar region, an effect that could be associated to their different polygenic architecture. This study suggests that chromosomal rearrangements play a role in the house mouse phenotypic divergence, and that the two modules of the mouse mandible are differentially affected by environmental factors and genetic makeup.

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State-of-the-art image-set matching techniques typically implicitly model each image-set with a Gaussian distribution. Here, we propose to go beyond these representations and model image-sets as probability distribution functions (PDFs) using kernel density estimators. To compare and match image-sets, we exploit Csiszar´ f-divergences, which bear strong connections to the geodesic distance defined on the space of PDFs, i.e., the statistical manifold. Furthermore, we introduce valid positive definite kernels on the statistical manifold, which let us make use of more powerful classification schemes to match image-sets. Finally, we introduce a supervised dimensionality reduction technique that learns a latent space where f-divergences reflect the class labels of the data. Our experiments on diverse problems, such as video-based face recognition and dynamic texture classification, evidence the benefits of our approach over the state-of-the-art image-set matching methods.

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Reciprocating ball-on-flat dry sliding friction and wear experiments have been conducted on singlephase γ-Y2Si2O7 ceramic flats in contact with AISI 52100 bearing steel and Si3N4 ceramic balls at 5-15N normal loads in an ambient environment. The kinetic friction coefficients of γ-Y2Si2O7 varied in the range over 0.53-0.63 against AISI 52100 steel and between 0.51-0.56 against Si3N4 ceramic. We found thatwear occurred predominantly during the running-in period and it almost ceased at the steady friction stage. The wear rates of γ-Y2Si2O7 were in the order of 10-4mm3/(N m). Besides, wear debris strongly influenced the friction and wear processes. The strong chemical affinity between γ-Y2Si2O7 and AISI 52100 balls led to a thick transfer layer formed on both contact surfaces of the flat and counterpart ball, which changed the direct sliding between the ball and the flat into a shearing within the transfer layer. For the γ-Y2Si2O7/Si3N4 pair, a thin silica hydrate lubricant tribofilm presented above the compressed debris entrapped in the worn track and contact ball surface. This transfer layer and the tribofilm separated the sliding couple from direct contact and contributed to the low friction coefficient and wear rate.