Narrow Results

Recently, an increasing number of real-time systems are implemented on multicore systems. To fully utilize the computation power of multicore systems, the scheduling problem of the real-time parallel task model is receiving more attention. Different types of scheduling algorithms and analysis techniques have been proposed for parallel real-time tasks modeled as directed acyclic graphs (DAG). In this paper, we study the scheduling problem for DAGs under the decomposition paradigm. We propose a new schedulability test and corresponding decomposition strategy. We show that this new decomposition approach strictly dominates the latest decomposition-based approach. Simulations are conducted to evaluate the real-time performance of our proposed scheduling algorithm, against the state-of-the-art scheduling and analysis methods of different types. Experimental results show that our method consistently outperforms other global methods under different parameter settings.

This paper revisits the causal relationship between education and earnings, or the returns to education, using Thailand’s socio-economic household survey data. We show that, with the minimum modeling assumption, the causal effect of education on earnings can be nonparametrically identified only when there is no unobserved ability affecting both education and earning, and when the selection mechanisms are conditionally independent to earnings conditioning on potential experience and education. The causal effects of education on earnings are estimated using a nonparametric approach with selection bias adjustment and parametric approaches with selection bias and ability bias adjustments. The results show different estimates for returns to education across model specifications, indicating the importance of the bias adjustments and parametric assumptions.