In current embedded systems, one of the major concerns is energy conservation. The dynamic voltage-scheduling (DVS) framework, which involves dynamically adjusting the voltage and frequency of the CPU, has become a well studied technique. It has been shown that if a task's computational requirement is only known probabilistically, there is no constant optimal speed for the task and the expected energy consumption is minimized by gradually increasing speed as thetaskprogresses [11].Itispossibletofindtheoptimal speed schedule if we assume continuous speed and a welldefined power function, which are assumptions that do not hold in practice. In this paper, we study the problem from a practical point of view, that is, we study the case of discrete speeds and make no restriction on the form of the power functions. Furthermore, we take into account processor idle power and speed change overhead, which were ignored in previous similar studies. We present a fully polynomial time approximation scheme (FPTAS), which has performance guarantees and usually obtains solutions very close to the optimal solution in practice. Our evaluation shows that our algorithm performs very well and generally obtains solutions within 0.1% of the optimal.

The lack of annotated data is an obstacle to the development of many natural language processing applications; the problem is especially severe when the data is non-English. Previous studies suggested the possibility of acquiring resources for non-English languages by bootstrapping from high quality English NLP tools and parallel corpora; however, the success of these approaches seems limited for dissimilar language pairs. In this paper, we propose a novel approach of combining a bootstrapped resource with a small amount of manually annotated data. We compare the proposed approach with other bootstrapping methods in the context of training a Chinese Part-of-Speech tagger. Experimental results show that our proposed approach achieves a significant improvement over EM and self-training and systems that are only trained on manual annotations. * We thank Stephen Clark, Roger Levy, Carol Nichols, and the three anonymous reviewers for their helpful comments.