Greybody factor of scalar fields from black strings

The greybody factor of massless, uncharged scalar fields is studied in the background of cylindrically symmetric spacetimes, in the low-energy approximation. We discuss two cases. In the first case we derive analytical expression for the absorption probability when the spacetime is kinetically coupled with the Einstein tensor. In the second case we do the analysis in the absence of the coupling constant. For this purpose we analyze the wave equation which is obtained from Klein-Gordon equation. The radial part of the wave equation is solved in the form of the hypergeometric function in the near horizon region, whereas in the far region the solution is of the form of Bessel's function. Finally, considering continuity of the wave function we smoothly match the two solutions in the low energy approximation to get the formula for the absorption probability

Greybody factor of scalar field from Reissner-Nordstrom-de Sitter black hole

In this work we derive a general expression for the greybody factor of non-minimally coupled scalar fields in Reissner-Nordstr\"om-de Sitter spacetime in low frequency approximation. In particular case of zero momentum, greybody factor tends to zero in low frequency limit as frequency squared goes to zero for non-vanishing coupling. We also elaborate the significance of the results by giving formulae of differential energy rate and general absorption cross section. The greybody factor gives insight into the spectrum of Hawking radiations

Embed and Conquer: Scalable Embeddings for Kernel k-Means on MapReduce

The kernel $k$-means is an effective method for data clustering which extends the commonly-used $k$-means algorithm to work on a similarity matrix over complex data structures. The kernel $k$-means algorithm is however computationally very complex as it requires the complete data matrix to be calculated and stored. Further, the kernelized nature of the kernel $k$-means algorithm hinders the parallelization of its computations on modern infrastructures for distributed computing. In this paper, we are defining a family of kernel-based low-dimensional embeddings that allows for scaling kernel $k$-means on MapReduce via an efficient and unified parallelization strategy. Afterwards, we propose two methods for low-dimensional embedding that adhere to our definition of the embedding family. Exploiting the proposed parallelization strategy, we present two scalable MapReduce algorithms for kernel $k$-means. We demonstrate the effectiveness and efficiency of the proposed algorithms through an empirical evaluation on benchmark data sets.Comment: Appears in Proceedings of the SIAM International Conference on Data Mining (SDM), 201

Global-local methodologies and their application to nonlinear analysis

An assessment is made of the potential of different global-local analysis strategies for predicting the nonlinear and postbuckling responses of structures. Two postbuckling problems of composite panels are used as benchmarks and the application of different global-local methodologies to these benchmarks is outlined. The key elements of each of the global-local strategies are discussed and future research areas needed to realize the full potential of global-local methodologies are identified

An M-QAM Signal Modulation Recognition Algorithm in AWGN Channel

Computing the distinct features from input data, before the classification, is a part of complexity to the methods of Automatic Modulation Classification (AMC) which deals with modulation classification was a pattern recognition problem. Although the algorithms that focus on MultiLevel Quadrature Amplitude Modulation (M-QAM) which underneath different channel scenarios was well detailed. A search of the literature revealed indicates that few studies were done on the classification of high order M-QAM modulation schemes like128-QAM, 256-QAM, 512-QAM and1024-QAM. This work is focusing on the investigation of the powerful capability of the natural logarithmic properties and the possibility of extracting Higher-Order Cumulant's (HOC) features from input data received raw. The HOC signals were extracted under Additive White Gaussian Noise (AWGN) channel with four effective parameters which were defined to distinguished the types of modulation from the set; 4-QAM~1024-QAM. This approach makes the recognizer more intelligent and improves the success rate of classification. From simulation results, which was achieved under statistical models for noisy channels, manifest that recognized algorithm executes was recognizing in M-QAM, furthermore, most results were promising and showed that the logarithmic classifier works well over both AWGN and different fading channels, as well as it can achieve a reliable recognition rate even at a lower signal-to-noise ratio (less than zero), it can be considered as an Integrated Automatic Modulation Classification (AMC) system in order to identify high order of M-QAM signals that applied a unique logarithmic classifier, to represents higher versatility, hence it has a superior performance via all previous works in automatic modulation identification systemComment: 18 page