Semi-regular variables (SRVs) though closely related to Mira variables, are a less studied class of AGB stars. While asymmetry in the brightness distribution of many Mira variables is fairly well known, it is detected only in a few SRVs. Asymmetry in
the brightness distribution at the level of a few milliarcsecond (mas) can be detected by high angular resolution techniques like lunar occultations (LO), long baseline interferometry, and aperture masking interferometry. Multi-epoch LO observations have the potential to detect a departure of brightness profile from spherical symmetry. Each LO event provides a uniform disk (UD) angular diameter along the position angle of the occultation. Any significant difference in the UD angular diameter values of multi-epoch LO observations signifies a brightness asymmetry. In this paper, we report for the first time three epoch UD angular diameter values of a SRV UZ Arietis using the LO technique at 2.2 $mu m$. Optical linear polarization of the source observed by us recently is also reported. The asymmetric brightness distribution of UZ Ari suggested by a small difference in the fitted UD values for the three epochs, is discussed in the context of optical polarization exhibited by the source and the direction of polarization axis in the plane of the sky.
In most gene expression data, the number of training samples is very small compared to the large number of genes involved in the experiments. However, among the large amount of genes, only a small fraction is effective for performing a certain task.
Furthermore, a small subset of genes is desirable in developing gene expression based diagnostic tools for delivering reliable and understandable results. With the gene selection results, the cost of biological experiment and decision can be greatly reduced by analyzing only the marker genes. An important application of gene expression data in functional genomics is to classify samples according to their gene expression profiles. Feature selection (FS) is a process which attempts to select more informative features. It is one of the important steps in knowledge discovery. Conventional supervised FS methods evaluate various feature subsets using an evaluation function or metric to select only those features which are related to the decision classes of the data under consideration. This paper studies a feature selection method based on rough set theory. Further K-Means, Fuzzy C-Means (FCM) algorithm have implemented for the reduced feature set without considering class labels. Then the obtained results are compared with the original class labels. Back Propagation Network (BPN) has also been used for classification. Then the performance of K-Means, FCM, and BPN are analyzed through the confusion matrix. It is found that the BPN is performing well comparatively.
The uniform disk (UD) angular diameter measurements of two oxygen-rich Mira variables (AW Aur and BS Aur) and three semiregular (SRb) variables (GP Tau, RS Cap, RT Cap), in near Infrared K-band (2.2 micron) by lunar occultation observations are repor
ted. UD angular diameters of the two Miras and one SRV are first time measurements. In addition a method of predicting angular diameters from (V-K) colour is discussed and applied to the five sources. The effect of mass-loss enhancing measured K-band diameters is examined for Miras using (K-[12]) colour excess as an index. In our sample the measured angular diameter of one of the Miras (BS Aur) is found enhanced by nearly 40% compared to its expected value, possibly due to mass loss effects leading to formation of a circumstellar shell.
