With the advent of information explosion age, larger capacity disk drives are used to store data and powerful devices are used to process big data. As the scale and complexity of computer systems increase, we expect these systems to provide dependable and energy-efficient services and computation. Although hard drives are reliable in general, they are the most commonly replaced hardware components. Disk failures cause data corruption and even data loss, which can significantly affect system performance and financial losses. In this dissertation research, I analyze different manifestations of disk failures in production data centers and explore data mining techniques combined with statistical analysis methods to discover categories of disk failures and their distinctive properties. I use similarity measures to quantify the degradation process of each failure type and derive the degradation signature. The derived degradation signatures are further leveraged to forecast when future disk failures may happen. Meanwhile, this dissertation also studies energy efficiency of high performance computers. Specifically, I characterize the power and energy consumption of Haswell processors which are used in multiple supercomputers, and analyze the power and energy consumption of Legion, a data-centric programming model and runtime system, and Legion applications. We find that power and energy …

Negation is a complex phenomenon present in all human languages, allowing for the uniquely human capacities of denial, contradiction, misrepresentation, lying, and irony. It is in the first place a phenomenon of semantical opposition. Sentences containing negation are generally (a) less informative than affirmative ones, (b) morphosyntactically more marked—all languages have negative markers while only a few have affirmative markers, and (c) psychologically more complex and harder to process. Negation often conveys positive meaning. This meaning ranges from implicatures to entailments. In this dissertation, I develop a system to reveal the underlying positive interpretation of negation. I first identify which words are intended to be negated (i.e, the focus of negation) and second, I rewrite those tokens to generate an actual positive interpretation. I identify the focus of negation by scoring probable foci along a continuous scale. One of the obstacles to exploring foci scoring is that no public datasets exist for this task. Thus, to study this problem I create new corpora. The corpora contain verbal, nominal and adjectival negations and their potential positive interpretations along with their scores ranging from 1 to 5. Then, I use supervised learning models for scoring the focus of negation. In order to …