Local cosmic string and C-field

We investigate a local cosmic string with a phenomenological energy momentum tensor as prescribed by Vilenkin, in presence of C-field . The solutions of full nonlinear Einstein's equations for exterior and interior regions of such a string are presented.Comment: 7 page

Quantifying the non-Gaussianity in the EoR 21-cm signal through bispectrum

The epoch of reionization (EoR) 21-cm signal is expected to be highly non-Gaussian in nature and this non-Gaussianity is also expected to evolve with the progressing state of reionization. Therefore the signal will be correlated between different Fourier modes ($k$). The power spectrum will not be able capture this correlation in the signal. We use a higher-order estimator -- the bispectrum -- to quantify this evolving non-Gaussianity. We study the bispectrum using an ensemble of simulated 21-cm signal and with a large variety of $k$ triangles. We observe two competing sources driving the non-Gaussianity in the signal: fluctuations in the neutral fraction ($x_{\rm HI}$) field and fluctuations in the matter density field. We find that the non-Gaussian contribution from these two sources vary, depending on the stage of reionization and on which $k$ modes are being studied. We show that the sign of the bispectrum works as a unique marker to identify which among these two components is driving the non-Gaussianity. We propose that the sign change in the bispectrum, when plotted as a function of triangle configuration $\cos{\theta}$ and at a certain stage of the EoR can be used as a confirmative test for the detection of the 21-cm signal. We also propose a new consolidated way to visualize the signal evolution (with evolving $\overline{x}_{\rm HI}$ or redshift), through the trajectories of the signal in a power spectrum and equilateral bispectrum i.e. $P(k)-B(k, k, k)$ space.Comment: 18 pages, 11 figures. Accepted for publication in MNRAS. Replaced to match the accepted versio

Quantum random walk : effect of quenching

We study the effect of quenching on a discrete quantum random walk by removing a detector placed at a position $x_D$ abruptly at time $t_R$ from its path. The results show that this may lead to an enhancement of the occurrence probability at $x_D$ provided the time of removal $t_R < t_{R}^{lim}$ where $t_{R}^{lim}$ scales as $x_D{^2}$. The ratio of the occurrence probabilities for a quenched walker ($t_R \neq 0$) and free walker ($t_R =0$) shows that it scales as $1/t_R$ at large values of $t_R$ independent of $x_D$. On the other hand if $t_R$ is fixed this ratio varies as $x_{D}^{2}$ for small $x_D$. The results are compared to the classical case. We also calculate the correlations as functions of both time and position.Comment: 5 pages, 6 figures, accepted version in PR

Global monopole, dark matter and scalar tensor theory

In this article, we discuss the space-time of a global monopole field as a candidate for galactic dark matter in the context of scalar tensor theory.Comment: 8 pages, Accepted in Mod. Phys. Lett.

Minimalist Ensemble Algorithms for Genome-Wide Protein Localization Prediction

Background Computational prediction of protein subcellular localization can greatly help to elucidate its functions. Despite the existence of dozens of protein localization prediction algorithms, the prediction accuracy and coverage are still low. Several ensemble algorithms have been proposed to improve the prediction performance, which usually include as many as 10 or more individual localization algorithms. However, their performance is still limited by the running complexity and redundancy among individual prediction algorithms. Results This paper proposed a novel method for rational design of minimalist ensemble algorithms for practical genome-wide protein subcellular localization prediction. The algorithm is based on combining a feature selection based filter and a logistic regression classifier. Using a novel concept of contribution scores, we analyzed issues of algorithm redundancy, consensus mistakes, and algorithm complementarity in designing ensemble algorithms. We applied the proposed minimalist logistic regression (LR) ensemble algorithm to two genome-wide datasets of Yeast and Human and compared its performance with current ensemble algorithms. Experimental results showed that the minimalist ensemble algorithm can achieve high prediction accuracy with only 1/3 to 1/2 of individual predictors of current ensemble algorithms, which greatly reduces computational complexity and running time. It was found that the high performance ensemble algorithms are usually composed of the predictors that together cover most of available features. Compared to the best individual predictor, our ensemble algorithm improved the prediction accuracy from AUC score of 0.558 to 0.707 for the Yeast dataset and from 0.628 to 0.646 for the Human dataset. Compared with popular weighted voting based ensemble algorithms, our classifier-based ensemble algorithms achieved much better performance without suffering from inclusion of too many individual predictors. Conclusions We proposed a method for rational design of minimalist ensemble algorithms using feature selection and classifiers. The proposed minimalist ensemble algorithm based on logistic regression can achieve equal or better prediction performance while using only half or one-third of individual predictors compared to other ensemble algorithms. The results also suggested that meta-predictors that take advantage of a variety of features by combining individual predictors tend to achieve the best performance. The LR ensemble server and related benchmark datasets are available at http://mleg.cse.sc.edu/LRensemble/cgi-bin/predict.cgi