A new source detection algorithm using FDR

The False Discovery Rate (FDR) method has recently been described by Miller et al (2001), along with several examples of astrophysical applications. FDR is a new statistical procedure due to Benjamini and Hochberg (1995) for controlling the fraction of false positives when performing multiple hypothesis testing. The importance of this method to source detection algorithms is immediately clear. To explore the possibilities offered we have developed a new task for performing source detection in radio-telescope images, Sfind 2.0, which implements FDR. We compare Sfind 2.0 with two other source detection and measurement tasks, Imsad and SExtractor, and comment on several issues arising from the nature of the correlation between nearby pixels and the necessary assumption of the null hypothesis. The strong suggestion is made that implementing FDR as a threshold defining method in other existing source-detection tasks is easy and worthwhile. We show that the constraint on the fraction of false detections as specified by FDR holds true even for highly correlated and realistic images. For the detection of true sources, which are complex combinations of source-pixels, this constraint appears to be somewhat less strict. It is still reliable enough, however, for a priori estimates of the fraction of false source detections to be robust and realistic.Comment: 17 pages, 7 figures, accepted for publication by A

Image Coaddition with Temporally Varying Kernels

Large, multi-frequency imaging surveys, such as the Large Synaptic Survey Telescope (LSST), need to do near-real time analysis of very large datasets. This raises a host of statistical and computational problems where standard methods do not work. In this paper, we study a proposed method for combining stacks of images into a single summary image, sometimes referred to as a template. This task is commonly referred to as image coaddition. In part, we focus on a method proposed in previous work, which outlines a procedure for combining stacks of images in an online fashion in the Fourier domain. We evaluate this method by comparing it to two straightforward methods through the use of various criteria and simulations. Note that the goal is not to propose these comparison methods for use in their own right, but to ensure that additional complexity also provides substantially improved performance

Anomalous Spin Polarization of GaAs Two-Dimensional Hole Systems

We report measurements and calculations of the spin-subband depopulation, induced by a parallel magnetic field, of dilute GaAs two-dimensional (2D) hole systems. The results reveal that the shape of the confining potential dramatically affects the values of in-plane magnetic field at which the upper spin subband is depopulated. Most surprisingly, unlike 2D electron systems, the carrier-carrier interaction in 2D hole systems does not significantly enhance the spin susceptibility. We interpret our findings using a multipole expansion of the spin density matrix, and suggest that the suppression of the enhancement is related to the holes' band structure and effective spin j=3/2.Comment: 6 pages, 4 figures, substantially extended discussion of result

Interpretation of the angular dependence of the de Haas-van Alphen effect in MgB_2

We present detailed results for the amplitude and field dependence of the de Haas-van Alphen (dHvA) signal arising from the electron-like $\pi$ sheet of Fermi surface in MgB_2. Our data and analysis show that the dip in dHvA amplitude when the field is close to the basal plane is caused by a beat between two very similar dHvA frequencies and not a spin-zero effect as previously assumed. Our results imply that the Stoner enhancement factors in MgB_2 are small on both the Sigma and Pi sheets.Comment: 4 pages with figures. Submitted to PR

Spectral Measures of Bipartivity in Complex Networks

We introduce a quantitative measure of network bipartivity as a proportion of even to total number of closed walks in the network. Spectral graph theory is used to quantify how close to bipartite a network is and the extent to which individual nodes and edges contribute to the global network bipartivity. It is shown that the bipartivity characterizes the network structure and can be related to the efficiency of semantic or communication networks, trophic interactions in food webs, construction principles in metabolic networks, or communities in social networks.Comment: 16 pages, 1 figure, 1 tabl

The Association between Pathological Internet Use and Comorbid Psychopathology: A Systematic Review

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Fingerprint for Network Topologies

A network's topology information can be given as an adjacency matrix. The bitmap of sorted adjacency matrix(BOSAM) is a network visualisation tool which can emphasise different network structures by just looking at reordered adjacent matrixes. A BOSAM picture resembles the shape of a flower and is characterised by a series of 'leaves'. Here we show and mathematically prove that for most networks, there is a self-similar relation between the envelope of the BOSAM leaves. This self-similar property allows us to use a single envelope to predict all other envelopes and therefore reconstruct the outline of a network's BOSAM picture. We analogise the BOSAM envelope to human's fingerprint as they share a number of common features, e.g. both are simple, easy to obtain, and strongly characteristic encoding essential information for identification.Comment: 12papes, 3 figures, in pres