[Review of] Daniel Friedman and Sharon Grimberg. Miss India Georgia

Miss India Georgia is an intelligent and insightful video documentary that tells the story of four Indian American teenagers, who in `the process of preparing for Atlanta\u27s annual South Asian beauty pageant reflect on the trials and tribulations of their bi-cultural lives. It is a timeless tale told over and over as each new wave of immigrants has come ashore and their children have had to resolve the incongruities of their multiple ethnicities

[Review of] Crawford Young. Ethnic Diversity and Public Policy: A Comparative Inquiry

As we come to the end of the millennium, contrary to the more democratic and progressive aspirations of earlier decades, ethnicity continues to define political and social alliances in the struggle for power and survival. Ethnic Diversity and Public Policy, edited by Crawford Young, is a timely collection of articles which address key policies growing out of the paramount need facing nations to deal with this primordial yet potent reality. The articles follow the basic premise underscored by Young -- that ethnic crises reflect profound failures of statecraft and that the state remains the ineluctable locus of policy response, Accordingly, essays in the book, drawing from experiences of many nations, deal with policy prerogatives, which are meant to foster ethnic harmony

The Global Resurgence of Ethnicity: An Inquiry into the Sociology of Ideological Discontent

This essay takes the position that global resurgence of ethnic hostilities can be seen as a manifestation of discontent with the proclaimed national ideologies. The breakdown in the conviction that adherence to an ideology and the application of a related social agenda would ameliorate the critically felt ills of a society, has resulted in the redirection of frustrations towards scapegoat minorities. Whether the ideology has been democratic secularism or socialism, the inability to deliver the cargo of economic and social well being, political stabliltiy[stability] has proven to be a direct indictment against the ideology itself. And, like opportunistic diseases, ethnic suspicion, hatred, and hostility have invaded the body politic of the national communities weakened by a crisis of ideological faith. In India, for example, the trend towards Hinduization indicates disillusionment with a forty-year experiment with secularism. This essay proposes that resurgent ethnicity has filled the vacuum created by the loss of ideology, and it takes a different trajectory to the end of ideology end of history theme of K. Marx, D. Bell, H. Marcuse, and F. Fukuyama. Its objective is to enquire into the conditions needed for ideological realization and the consequences of its loss

Analysis of simulated image sequences from sensors for restricted-visibility operations

A real time model of the visible output from a 94 GHz sensor, based on a radiometric simulation of the sensor, was developed. A sequence of images as seen from an aircraft as it approaches for landing was simulated using this model. Thirty frames from this sequence of 200 x 200 pixel images were analyzed to identify and track objects in the image using the Cantata image processing package within the visual programming environment provided by the Khoros software system. The image analysis operations are described

On Variants of k-means Clustering

\textit{Clustering problems} often arise in the fields like data mining, machine learning etc. to group a collection of objects into similar groups with respect to a similarity (or dissimilarity) measure. Among the clustering problems, specifically \textit{$k$-means} clustering has got much attention from the researchers. Despite the fact that $k$-means is a very well studied problem its status in the plane is still an open problem. In particular, it is unknown whether it admits a PTAS in the plane. The best known approximation bound in polynomial time is 9+\eps. In this paper, we consider the following variant of $k$-means. Given a set $C$ of points in $\mathcal{R}^d$ and a real $f > 0$, find a finite set $F$ of points in $\mathcal{R}^d$ that minimizes the quantity $f*|F|+\sum_{p\in C} \min_{q \in F} {||p-q||}^2$. For any fixed dimension $d$, we design a local search PTAS for this problem. We also give a "bi-criterion" local search algorithm for $k$-means which uses (1+\eps)k centers and yields a solution whose cost is at most (1+\eps) times the cost of an optimal $k$-means solution. The algorithm runs in polynomial time for any fixed dimension. The contribution of this paper is two fold. On the one hand, we are being able to handle the square of distances in an elegant manner, which yields near optimal approximation bound. This leads us towards a better understanding of the $k$-means problem. On the other hand, our analysis of local search might also be useful for other geometric problems. This is important considering that very little is known about the local search method for geometric approximation.Comment: 15 page

Approximate Clustering via Metric Partitioning

In this paper we consider two metric covering/clustering problems - \textit{Minimum Cost Covering Problem} (MCC) and $k$-clustering. In the MCC problem, we are given two point sets $X$ (clients) and $Y$ (servers), and a metric on $X \cup Y$. We would like to cover the clients by balls centered at the servers. The objective function to minimize is the sum of the $\alpha$-th power of the radii of the balls. Here $\alpha \geq 1$ is a parameter of the problem (but not of a problem instance). MCC is closely related to the $k$-clustering problem. The main difference between $k$-clustering and MCC is that in $k$-clustering one needs to select $k$ balls to cover the clients. For any \eps > 0, we describe quasi-polynomial time (1 + \eps) approximation algorithms for both of the problems. However, in case of $k$-clustering the algorithm uses (1 + \eps)k balls. Prior to our work, a $3^{\alpha}$ and a ${c}^{\alpha}$ approximation were achieved by polynomial-time algorithms for MCC and $k$-clustering, respectively, where $c > 1$ is an absolute constant. These two problems are thus interesting examples of metric covering/clustering problems that admit (1 + \eps)-approximation (using (1+\eps)k balls in case of $k$-clustering), if one is willing to settle for quasi-polynomial time. In contrast, for the variant of MCC where $\alpha$ is part of the input, we show under standard assumptions that no polynomial time algorithm can achieve an approximation factor better than $O(\log |X|)$ for $\alpha \geq \log |X|$.Comment: 19 page