Intensity Oscillation in the Corona as Observed during the Total Solar Eclipse of 29 March 2006

We obtained the images of the eastern part of the solar corona in the Fe xiv 530.3 nm (green) and Fe x 637.4 nm (red) coronal emission lines during the total solar eclipse of 29 March 2006 at Manavgat, Antalya, Turkey. The images were obtained using a 35 cm Meade telescope equipped with a Peltier-cooled 2k x 2k CCD and 0.3 nm pass-band interference filters at the rates of 2.95 s (exposure times of 100 ms) and 2.0 s (exposure times of 300 ms) in the Fe xiv and Fe x emission lines,respectively. The analysis of the data indicates intensity variations at some locations with period of strongest power around 27 s for the green line and 20 s for the red line. These results confirm earlier findings of variations in the continuum intensity with periods in the range of 5 to 56 s by Singh et al. (Solar Phys. 170, 235, 1997). The wavelet analysis has been used to identify significant intensity oscillations at all pixels within our field of view. Significant oscillations with high probability estimates were detected for some locations only. These locations seem to follow the boundary of an active region and in the neighborhood, rather than within the loops themselves. These intensity oscillations may be caused by fast magneto-sonic waves in the solar corona and partly account for heating of the plasma in the corona.

Decode-and-forward relay beamforming for secrecy with imperfect CSI and multiple eavesdroppers

In this paper, we evaluate secrecy rates in cooperative relay beamforming in the presence of imperfect channel state information (CSI) and multiple eavesdroppers. A source-destination pair aided by.. out of.. relays, 1 <= k <= M, using decode-and-forward relay beamforming is considered. We compute the worst case secrecy rate with imperfect CSI in the presence of multiple eavesdroppers, where the number of eavesdroppers can be more than the number of relays. We solve the optimization problem for all possible relay combinations to find the secrecy rate and optimum source and relay weights subject to a total power constraint. We relax the rank-1 constraint on the complex semi-definite relay weight matrix and use S-procedure to reformulate the optimization problem that can be solved using convex semi-definite programming.

A Multi-phase Approach for Improving Information Diffusion in Social Networks

For maximizing influence spread in a social network, given a certain budget on the number of seed nodes, we investigate the effects of selecting and activating the seed nodes in multiple phases. In particular, we formulate an appropriate objective function for two-phase influence maximization under the independent cascade model, investigate its properties, and propose algorithms for determining the seed nodes in the two phases. We also study the problem of determining an optimal budget-split and delay between the two phases.

Prototype Selection on Large and Streaming Data

Since streaming data keeps coming continuously as an ordered sequence, massive amounts of data is created. A big challenge in handling data streams is the limitation of time and space. Prototype selection on streaming data requires the prototypes to be updated in an incremental manner as new data comes in. We propose an incremental algorithm for prototype selection. This algorithm can also be used to handle very large datasets. Results have been presented on a number of large datasets and our method is compared to an existing algorithm for streaming data. Our algorithm saves time and the prototypes selected gives good classification accuracy.

A Stochastic Approximation Algorithm for Quantile Estimation

In this paper, we present two new stochastic approximation algorithms for the problem of quantile estimation. The algorithms uses the characterization of the quantile provided in terms of an optimization problem in 1]. The algorithms take the shape of a stochastic gradient descent which minimizes the optimization problem. Asymptotic convergence of the algorithms to the true quantile is proven using the ODE method. The theoretical results are also supplemented through empirical evidence. The algorithms are shown to provide significant improvement in terms of memory requirement and accuracy.

Empirical Analysis of Sampling Based Estimators for Evaluating RBMs

The Restricted Boltzmann Machines (RBM) can be used either as classifiers or as generative models. The quality of the generative RBM is measured through the average log-likelihood on test data. Due to the high computational complexity of evaluating the partition function, exact calculation of test log-likelihood is very difficult. In recent years some estimation methods are suggested for approximate computation of test log-likelihood. In this paper we present an empirical comparison of the main estimation methods, namely, the AIS algorithm for estimating the partition function, the CSL method for directly estimating the log-likelihood, and the RAISE algorithm that combines these two ideas.