Modelling Einstein cluster using Einasto profile

We demonstrate a general relativistic approach to model dark matter halos using the Einstein cluster, with the matter stress-energy generated by collisionless particles moving on circular geodesics in all possible angular directions and orbital radii. Such matter, as is known, allows an anisotropic pressure profile with non-zero tangential but zero radial pressure. We use the Einasto density profile for the Einstein cluster. Analytical studies on its properties (metric functions) and stability issues are investigated. Further, to establish this model (with the Einasto profile) as one for a dark matter halo, we use the SPARC galactic rotation curve data and estimate the best-fit values for the model parameters. General relativistic features (beyond the Keplerian velocities) such as the tangential pressure profile, are quantitatively explored. Thus, Einstein clusters with the Einasto profile, which tally well with observations, may be considered as a viable model for dark matter halos.Comment: 18 pages, 6 figure

Student and Teacher Attendance: The Role of Shared Goods in Reducing Absenteeism

A theoretical model is advanced that demonstrates that, if teacher and student attendance generate a shared good, then teacher and student attendance will be mutually reinforcing.� Using data from the Northwest Frontier Province of Pakistan, empirical evidence supporting that proposition is advanced.� Controlling for the endogeneity of teacher and student attendance, the most powerful factor raising teacher attendance is the attendance of the children in the school, and the most important factor influencing child attendance is the presence of the teacher.� The results suggest that one important avenue to be explored in developing policies to reduce teacher absenteeism is to focus on raising the attendance of children.Absenteeism; teacher attendance; student attendance; shared good; Northwest Frontier Province; Pakistan

The financial crisis: impact on BRIC and policy response

The paper looks at the transmission channels by which the financial crisis affected the four emerging economies- Brazil, Russia, India and China, the degree and extent of the impact of the crisis, the subsequent policy interventions which enabled recovery and an assessment of how successful recovery has been in these economies. We conclude by noting that in the long term global recovery will necessitate a rebalancing of the world economy which in turn means that the hub of global consumption has to shift from the west to the global south, particularly to BRICs

The financial crisis: impact on BRIC and policy response

The paper looks at the transmission channels by which the financial crisis affected the four emerging economies- Brazil, Russia, India and China, the degree and extent of the impact of the crisis, the subsequent policy interventions which enabled recovery and an assessment of how successful recovery has been in these economies. We conclude by noting that in the long term global recovery will necessitate a rebalancing of the world economy which in turn means that the hub of global consumption has to shift from the west to the global south, particularly to BRICs

Sexual variation in the inter-triradial distance of the palm among Bengali Hindu population of Kolkata, India

Palm prints are one of the most important forensic tools for human identification in medico-legal investigation. Palm prints are often used for forensic sex estimation to narrow down the pool of suspects through a process of elimination. The aim of this study was to test whether a novel approach of sex estimation from palmar inter-triradial distances previously posited by Badiye and colleagues [Journal of Forensic and Legal Medicine, 2019; 65(March):22–26] can be used as a primary tool for forensic sexing. For this study the bilateral palm prints from 200 Bengali Hindu adults (100 male, 100 female) were collected using traditional ink printing method and were analysed. Descriptive statistics were presented in tables and linear discriminant analysis was conducted to estimate the extent of sexual dimorphism in the inter-triradial distances and to find out variables with the strongest sex discriminating potential. Binary logistic regression analysis (BLR) was performed to derive sex estimation equations. Sexual dimorphism has been found to be statistically significant (p< 0.001) using linear discriminant analysis with a sexing accuracy of 79.0 percent for the left and 79.5 percent for the right palm. Distance between a and t triradius has been found to be the most influential on this model followed by the combined abcd-t distance. For the BLR analysis, the correct classification percentage was found to be the highest on the a-t distance of the right palm with a success rate of 80.5 percent which is closely followed by the combined abcd-t distance which has a classification success rate of 80.0 percent for the right palm. The present study has concluded that, inter-triradial distance of the palm is fairly dimorphic sexually but can only be used as a supplementary tool in inference of sex for medico-legal investigation. Due to a higher accuracy, the distance between a and t triradius has been proposed to be used instead of combined abcd-t distance which was suggested in the original study conducted by Badiye and colleagues (2019)

Maximizing all margins: Pushing face recognition with Kernel Plurality

We present two theses in this paper: First, performance of most existing face recognition algorithms improves if instead of the whole image, smaller patches are individually classified followed by label aggregation using voting. Second, weighted plurality voting outperforms other popular voting methods if the weights are set such that they maximize the victory margin for the winner with respect to each of the losers. Moreover, this can be done while taking higher order relationships among patches into account using kernels. We call this scheme Kernel Plurality. We verify our proposals with detailed experimental results and show that our framework with Kernel Plurality improves the performance of various face recognition algorithms beyond what has been previously reported in the literature. Furthermore, on five different benchmark datasets - Yale A, CMU PIE, MERL Dome, Extended Yale B and Multi-PIE, we show that Kernel Plurality in conjunction with recent face recognition algorithms can provide state-of-the-art results in terms of face recognition rates.Engineering and Applied Science