This paper presents new and efficient algorithms for matching stellar catalogues where the transformation between the coordinate systems of the two catalagoues is unknown and may include shearing. Finding a given object whether a star or asterism fro
m the first catalogue in the second is logarithmic in time rather than polynomial, yielding a dramatic speed up relative to a naive implementation. Both acceleration of the matching algorithm and the ability to solve for arbitrary affine transformations not only will allow the registration of stellar catalogues and images that are now impossible to use but also will find applications in machine vision and other imaging applications.
Diffraction is important when nearby substellar objects gravitationally lens distant stars. If the wavelength of the observation is comparable to the Schwarzschild radius of lensing object, diffraction leaves an observable imprint on the lensing sign
ature. The SKA may have sufficient sensitivity to detect the typical sources, giant stars in the bulge. The diffractive signatures in a lensing event break the degeneracies between the mass of the lens, its distance and proper motion.
Degree distribution of nodes, especially a power law degree distribution, has been regarded as one of the most significant structural characteristics of social and information networks. Node degree, however, only discloses the first-order structure o
f a network. Higher-order structures such as the edge embeddedness and the size of communities may play more important roles in many online social networks. In this paper, we provide empirical evidence on the existence of rich higherorder structural characteristics in online social networks, develop mathematical models to interpret and model these characteristics, and discuss their various applications in practice. In particular, 1) We show that the embeddedness distribution of social links in many social networks has interesting and rich behavior that cannot be captured by well-known network models. We also provide empirical results showing a clear correlation between the embeddedness distribution and the average number of messages communicated between pairs of social network nodes. 2) We formally prove that random k-tree, a recent model for complex networks, has a power law embeddedness distribution, and show empirically that the random k-tree model can be used to capture the rich behavior of higherorder structures we observed in real-world social networks. 3) Going beyond the embeddedness, we show that a variant of the random k-tree model can be used to capture the power law distribution of the size of communities of overlapping cliques discovered recently.
