Abstract Boys and girls establish relatively stable gender stereotyped behavior patterns by middle childhood. Parentreport questionnaires measuring children’s gender-related behavior enable researchers to conduct large-scale screenings of community samples of children. For school-aged children, two

and then shrinking them toward the center of the cluster by a specified fraction. Having more than one representative point per cluster allows CURE to adjust well to the geometry of non-spherical shapes and the shrinking helps to dampen the effects of outliers. To handle large databases, CURE employs a combination

estimates of effects of class size on test scores. The resulting identification strategy can be viewed as an application of Donald Campbell’s regression-discontinuity design to the class-size question. The estimates show that reducing class size induces a significant and substantial increase in test scores

Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first

-ior of an arbitrary section of execution in a program. To this end we examine the use of Basic Block Vectors. We quantify the effectiveness of Basic Block Vectors in capturing program behavior across several different architectural met-rics, explore the large scale behavior of several programs, and develop a set

that the capital asset pricing model is misspecttied. The size elfect is not linear in the market value; the main effect occurs for very small tirms while there is little difference m return between average sized and large firms. It IS not known whether size per se is responsible for the effect or whether size

This paper presents Scribe, a scalable application-level multicast infrastructure. Scribe supports large numbers of groups, with a potentially large number of members per group. Scribe is built on top of Pastry, a generic peer-to-peer object location and routing substrate overlayed on the Internet

We show how to learn a Mahanalobis distance metric for k-nearest neighbor (kNN) classification by semidefinite programming. The metric is trained with the goal that the k-nearest neighbors always belong to the same class while examples from different classes are separated by a large margin

estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.

Advances in 3D scanning technologies have enabled the practical creation of meshes with hundreds of millions of polygons. Traditional algorithms for display, simplification, and progressive transmission of meshes are impractical for data sets of this size. We describe a system for representing