Moving Forward as a Family: Crafting a 2-Generation Strategy for Central Texas, PRP 192

United Way for Greater Austin commissioned this policy research project to guide their focus on helping low socioeconomic families achieve greater financial stability through the development of a Two-Generation (2-Gen) strategy for the Central Texas region. Two-Gen programs emphasize the importance of education as a means for better economic outcomes. High-quality early childhood education programs allow children to make critical neural connections during a period of substantial growth and development, ultimately better preparing them for pre-kindergarten programs and academic success in subsequent years. Adults working low-paying jobs encounter barriers to career advancement due to lacking credentials or relevant education. It is not uncommon for parents working long hours for low wages to have at least one child in need of high-quality early childhood education, yet they are unable to enroll their child in such programs due to issues such as cost, transportation, and time away from work. Two-Gen programs seek to resolve the issues complicating this problem of financial instability by providing high-quality educational and training programs for both parents and children, which are even more effective when intentionally coordinated so that the family develops as a single unit in a positive direction. The research consisted of a literature review; a program scan at the local, state, and federal levels; and site visits within Austin, Dallas, and San Antonio, as well as Boston and Miami. Data collected specific to the Central Texas region include a labor market analysis, a needs assessment, and a mapping of current organizational assets. Obtaining and analyzing this data allowed the team to better understand 2-Gen program development, outcomes, impact measurements, and areas for improvement. The research team developed practical applications for the information collected, ultimately contributing to the proposed anti-poverty strategy through the intentional coordination of 2-Gen services by leveraging existing organizational assets to best address the area’s most salient needs. In addition, the team proposed an evaluation strategy involving cost-benefit equations, program evaluation metrics, and a screening tool to predict the likelihood of a program achieving successful outcomes. The report concludes with policy recommendations at the local, state, and federal levels, as well as a summary of the populations affected by financial instability and future directions for this field.United Way for Greater AustinPublic Affair

Color Image Clustering using Block Truncation Algorithm

With the advancement in image capturing device, the image data been generated at high volume. If images are analyzed properly, they can reveal useful information to the human users. Content based image retrieval address the problem of retrieving images relevant to the user needs from image databases on the basis of low-level visual features that can be derived from the images. Grouping images into meaningful categories to reveal useful information is a challenging and important problem. Clustering is a data mining technique to group a set of unsupervised data based on the conceptual clustering principal: maximizing the intraclass similarity and minimizing the interclass similarity. Proposed framework focuses on color as feature. Color Moment and Block Truncation Coding (BTC) are used to extract features for image dataset. Experimental study using K-Means clustering algorithm is conducted to group the image dataset into various clusters

Approximate Graph Coloring by Semidefinite Programming

We consider the problem of coloring k-colorable graphs with the fewest possible colors. We present a randomized polynomial time algorithm that colors a 3-colorable graph on $n$ vertices with min O(Delta^{1/3} log^{1/2} Delta log n), O(n^{1/4} log^{1/2} n) colors where Delta is the maximum degree of any vertex. Besides giving the best known approximation ratio in terms of n, this marks the first non-trivial approximation result as a function of the maximum degree Delta. This result can be generalized to k-colorable graphs to obtain a coloring using min O(Delta^{1-2/k} log^{1/2} Delta log n), O(n^{1-3/(k+1)} log^{1/2} n) colors. Our results are inspired by the recent work of Goemans and Williamson who used an algorithm for semidefinite optimization problems, which generalize linear programs, to obtain improved approximations for the MAX CUT and MAX 2-SAT problems. An intriguing outcome of our work is a duality relationship established between the value of the optimum solution to our semidefinite program and the Lovasz theta-function. We show lower bounds on the gap between the optimum solution of our semidefinite program and the actual chromatic number; by duality this also demonstrates interesting new facts about the theta-function

Intrinsic Linking and Knotting in Virtual Spatial Graphs

We introduce a notion of intrinsic linking and knotting for virtual spatial graphs. Our theory gives two filtrations of the set of all graphs, allowing us to measure, in a sense, how intrinsically linked or knotted a graph is; we show that these filtrations are descending and non-terminating. We also provide several examples of intrinsically virtually linked and knotted graphs. As a byproduct, we introduce the {\it virtual unknotting number} of a knot, and show that any knot with non-trivial Jones polynomial has virtual unknotting number at least 2.Comment: 13 pages, 13 figure