Nonuniversal Effects in the Homogeneous Bose Gas

Effective field theory predicts that the leading nonuniversal effects in the homogeneous Bose gas arise from the effective range for S-wave scattering and from an effective three-body contact interaction. We calculate the leading nonuniversal contributions to the energy density and condensate fraction and compare the predictions with results from diffusion Monte Carlo calculations by Giorgini, Boronat, and Casulleras. We give a crude determination of the strength of the three-body contact interaction for various model potentials. Accurate determinations could be obtained from diffusion Monte Carlo calculations of the energy density with higher statistics.Comment: 24 pages, RevTex, 5 ps figures, included with epsf.te

Spectrophotovoltaic orbital power generation

A system with 1000 : 1 concentration ratio is defined, using a cassegrain telescope as the first stage concentration (270 x) and compound parabolic concentrators (CPC) for the second stage concentration of 4.7 x for each spectral band. Using reported state of the art (S.O.A.) solar cells device parameters and considering structural losses due to optics and beamsplitters, the efficiencies of one to four cell systems were calculated with efficiencies varying from approximately 22% to 30%. Taking into account cost of the optics, beamsplitter, radiator, and the cost of developing new cells the most cost effective system is the GaAs/Si system

Modeling radiation belt radial diffusion in ULF wave fields: 2. Estimating rates of radial diffusion using combined MHD and particle codes

[1] Quantifying radial transport of radiation belt electrons in ULF wave fields is essential for understanding the variability of the trapped relativistic electrons. To estimate the radial diffusion coefficients (DLL), we follow MeV electrons in realistic magnetospheric configurations and wave fields calculated from a global MHD code. We create idealized pressure-driven MHD simulations for controlled solar wind velocities (hereafter referred to as pressure-driven Vx simulations) with ULF waves that are comparable to GOES data under similar conditions, by driving the MHD code with synthetic pressure profiles that mimic the pressure variations of a particular solar wind velocity. The ULF wave amplitude, in both magnetic and electric fields, increases at larger radial distance and during intervals with higher solar wind velocity and pressure fluctuations. To calculate DLL as a function of solar wind velocity (Vx = 400 and 600 km/s), we follow 90 degree pitch angle electrons in magnetic and electric fields of the pressure-driven Vx simulations. DLL is higher at larger radial distance and for the case with higher solar wind velocity and pressure variations. Our simulated DLL values are relatively small compared to previous studies which used larger wave fields in their estimations. For comparison, we scale our DLL values to match the wave amplitudes of the previous studies with those of the idealized MHD simulations. After the scaling, our DLL values for Vx = 600 km/s are comparable to theDLL values derived from Polar measurements during nonstorm intervals. This demonstrates the use of MHD models to quantify the effect of pressure-driven ULF waves on radiation belt electrons and thus to differentiate the radial diffusive process from other mechanisms

Provenance analysis for instagram photos

As a feasible device fingerprint, sensor pattern noise (SPN) has been proven to be effective in the provenance analysis of digital images. However, with the rise of social media, millions of images are being uploaded to and shared through social media sites every day. An image downloaded from social networks may have gone through a series of unknown image manipulations. Consequently, the trustworthiness of SPN has been challenged in the provenance analysis of the images downloaded from social media platforms. In this paper, we intend to investigate the effects of the pre-defined Instagram images filters on the SPN-based image provenance analysis. We identify two groups of filters that affect the SPN in quite different ways, with Group I consisting of the filters that severely attenuate the SPN and Group II consisting of the filters that well preserve the SPN in the images. We further propose a CNN-based classifier to perform filter-oriented image categorization, aiming to exclude the images manipulated by the filters in Group I and thus improve the reliability of the SPN-based provenance analysis. The results on about 20, 000 images and 18 filters are very promising, with an accuracy higher than 96% in differentiating the filters in Group I and Group II