Educational Implications of School Systems at Different Stages of Schooling

In educating students national public school systems use different methods of grouping students by ability across schools. We consider four different school systems of student allocation at different stages of schooling and their educational implications. Our two-period model suggests that both the frequency and sequence of ability grouping play an important role in producing educational implications. As different households prefer different combinations of school systems, the overall performance of a school system is determined by how households are distributed over income and a child's ability and the voting of households.Education, Comprehensive and Selective School Systems

School Systems and Efficiency and Equity of Education

How students should be allocated to schools to achieve educational goals is one of important debates on the construction of school systems. Promoters of comprehensive and selective school systems fail to reach a consensus on implications of each system for efficiency and equity of education. This paper examines impacts of different systems of student allocation on educational goals, using a simple economic model. It argues that how a selective system is designed matters a great deal in a comparison between comprehensive and selective systems: different designs of a selective system can yield widely different educational implications compared with those from a comprehensive system. A judicious use of a selective system can at times achieve educational goals better than a comprehensive system. Given our finding that different households prefer different school systems, we suggest that by offering multiple subsystems, the educational planner can enhance educational attainments of households beyond those achieved by a single national system.Education, Comprehensive and Selective School Systems

CP violating supersymmetric contributions to the electroweak ρ\rho parameter

Effects of CP violation on the supersymmetric electroweak correction to the $\rho$ parameter are investigated. To avoid the EDM constraints, we require that arg$(\mu)<10^{-2}$ and the non-universal trilinear couplings $A_f=(0,0,A_0)$ and also assume that gluinos are heavier than 400 GeV. The CP phase $\phi_t=$ arg($A_0$) leads to large enhancement of the relative mass splittings between $\tilde{t}_2$ and $\tilde{b}_L(\tilde{t}_1)$, which in turn reduces the one-loop contribution of the stop and sbottom to $\Delta \rho$. For small $\tan \beta$, such a CP violating effect is prominent. We also study how much the two-loop gluon and gluino contributions are affected by the CP phase. Possible contributions to the $\rho$ parameter arising from the Higgs sector with CP violation are discussed.Comment: 14 pages, Revtex, 4 eps figures, to appear in Phys. Rev. D (Rapid Comm.

TPA: Fast, Scalable, and Accurate Method for Approximate Random Walk with Restart on Billion Scale Graphs

Given a large graph, how can we determine similarity between nodes in a fast and accurate way? Random walk with restart (RWR) is a popular measure for this purpose and has been exploited in numerous data mining applications including ranking, anomaly detection, link prediction, and community detection. However, previous methods for computing exact RWR require prohibitive storage sizes and computational costs, and alternative methods which avoid such costs by computing approximate RWR have limited accuracy. In this paper, we propose TPA, a fast, scalable, and highly accurate method for computing approximate RWR on large graphs. TPA exploits two important properties in RWR: 1) nodes close to a seed node are likely to be revisited in following steps due to block-wise structure of many real-world graphs, and 2) RWR scores of nodes which reside far from the seed node are proportional to their PageRank scores. Based on these two properties, TPA divides approximate RWR problem into two subproblems called neighbor approximation and stranger approximation. In the neighbor approximation, TPA estimates RWR scores of nodes close to the seed based on scores of few early steps from the seed. In the stranger approximation, TPA estimates RWR scores for nodes far from the seed using their PageRank. The stranger and neighbor approximations are conducted in the preprocessing phase and the online phase, respectively. Through extensive experiments, we show that TPA requires up to 3.5x less time with up to 40x less memory space than other state-of-the-art methods for the preprocessing phase. In the online phase, TPA computes approximate RWR up to 30x faster than existing methods while maintaining high accuracy.Comment: 12pages, 10 figure