Dynamic mean-field and cavity methods for diluted Ising systems

We compare dynamic mean-field and dynamic cavity as methods to describe the stationary states of dilute kinetic Ising models. We compute dynamic mean-field theory by expanding in interaction strength to third order, and compare to the exact dynamic mean-field theory for fully asymmetric networks. We show that in diluted networks the dynamic cavity method generally predicts magnetizations of individual spins better than both first order ("naive") and second order ("TAP") dynamic mean field theory

The Sensitivity of the IceCube Neutrino Detector to Dark Matter Annihilating in Dwarf Galaxies

In this paper, we compare the relative sensitivities of gamma-ray and neutrino observations to the dark matter annihilation cross section in leptophilic models such as have been designed to explain PAMELA data. We investigate whether the high energy neutrino telescope IceCube will be competitive with current and upcoming searches by gamma-ray telescopes, such as the Atmospheric Cerenkov Telescopes (ACTs) (HESS, VERITAS and MAGIC), or the Fermi Gamma Ray Space Telescope, in detecting or constraining dark matter particles annihilating in dwarf spheroidal galaxies. We find that after ten years of observation of the most promising nearby dwarfs, IceCube will have sensitivity comparable to the current sensitivity of gamma-ray telescopes only for very heavy (m_X > 7 TeV) or relatively light (m_X < 200 GeV) dark matter particles which annihilate primarily to mu+mu-. If dark matter particles annihilate primarily to tau+tau-, IceCube will have superior sensitivity only for dark matter particle masses below the 200 GeV threshold of current ACTs. If dark matter annihilations proceed directly to neutrino-antineutrino pairs a substantial fraction of the time, IceCube will be competitive with gamma-ray telescopes for a much wider range of dark matter masses.Comment: 7 pages, 3 figures. v2: references added and minor revisions. v3: as published in PRD

Unified adaptive framework for contrast enhancement of blood vessels

Information about blood vessel structures influences a lot of diseases in the medical realm. Therefore, for proper localization of blood vessels, its contrast should be enhanced properly. Since the blood vessels from all the medical angio-images have almost similar properties, a unified approach for the contrast enhancement of blood vessel structures is very useful. This paper aims to enhance the contrast of the blood vessels as well as the overall contrast of all the medical angio-images. In the proposed method, initially, the vessel probability map is extracted using hessian eigenanalysis. From the map, vessel edges and textures are derived and summed at every pixel location to frame a unique fractional differential function. The resulting fractional value from the function gives out the most optimal fractional order that can be adjusted to improve the contrast of blood vessels by convolving the image using Grunwald-Letnikov (G-L) fractional differential kernel. The vessel enhanced image is Gaussian fitted and contrast stretched to get overall contrast enhancement. This method of enhancement, when applied to medical angio-images such as the retinal fundus, Computerised Tomography (CT), Coronary Angiography (CA) and Digital Subtraction Angiography (DSA), has shown improved performance validated by the performance metrics