Scalable Sparse Cox's Regression for Large-Scale Survival Data via Broken Adaptive Ridge

This paper develops a new scalable sparse Cox regression tool for sparse high-dimensional massive sample size (sHDMSS) survival data. The method is a local $L_0$-penalized Cox regression via repeatedly performing reweighted $L_2$-penalized Cox regression. We show that the resulting estimator enjoys the best of $L_0$- and $L_2$-penalized Cox regressions while overcoming their limitations. Specifically, the estimator is selection consistent, oracle for parameter estimation, and possesses a grouping property for highly correlated covariates. Simulation results suggest that when the sample size is large, the proposed method with pre-specified tuning parameters has a comparable or better performance than some popular penalized regression methods. More importantly, because the method naturally enables adaptation of efficient algorithms for massive $L_2$-penalized optimization and does not require costly data driven tuning parameter selection, it has a significant computational advantage for sHDMSS data, offering an average of 5-fold speedup over its closest competitor in empirical studies

A Review of High School Level Astronomy Student Research Projects over the last two decades

Since the early 1990s with the arrival of a variety of new technologies, the capacity for authentic astronomical research at the high school level has skyrocketed. This potential, however, has not realized the bright-eyed hopes and dreams of the early pioneers who expected to revolutionise science education through the use of telescopes and other astronomical instrumentation in the classroom. In this paper, a general history and analysis of these attempts is presented. We define what we classify as an Astronomy Research in the Classroom (ARiC) project and note the major dimensions on which these projects differ before describing the 22 major student research projects active since the early 1990s. This is followed by a discussion of the major issues identified that affected the success of these projects and provide suggestions for similar attempts in the future.Comment: Accepted for Publication in PASA. 26 page

Towards an Expressive and Scalable Framework for expressing Join Point Models

Join point models are one of the key features in aspectoriented programming languages and tools. They provide\ud software engineers means to pinpoint the exact locations in programs (join points) to weave in advices. Our experience in modularizing concerns in a large embedded system showed that existing join point models and their underlying program representations are not expressive enough. This prevents the selection of some join points of our interest. In this paper, we motivate the need for more fine-grained join point models within more expressive source code representations. We propose a new program representation called a program graph, over which more fine-grained join point models can be defined. In addition, we present a simple language to manipulate program graphs to perform source code transformations. This language thus can be used for specifying complex weaving algorithms over program graphs