We introduce xLIN, a novel linear abstract machine that introduces no penalty in computing with non-linear resources, while ensuring that computing with linear resources takes no space and constant time. Garbage collection is not required for linear objects, while still present for non-linear objects. We propose a weak calculus of explicit substitutions as its foundations, and show how it may b...

The problem of finding dominators in a flowgraph arises in many kinds of global code optimization and other settings. In 1979 Lengauer and Tarjan gave an almost-linear-time algorithm to find dominators. In 1985 Harel claimed a lineartime algorithm, but this algorithm was incomplete; Alstrup et al. [1999] gave a complete and “simpler” linear-time algorithm on a random-access machine. In 1998, Bu...

We design a method for weighting linear support vector machine classifiers or random hyperplanes, to obtain classifiers whose accuracy is comparable to the accuracy of a non-linear support vector machine classifier, and whose results can be readily visualized. We conduct a simulation study to examine how our weighted linear classifiers behave in the presence of known structure. The results show...

This paper studies the parallel machine scheduling problem subject to machine and job deterioration in a batched delivery system. By the machine deterioration effect, we mean that each machine deteriorates over time, at a different rate. Moreover, job processing times are increasing functions of their starting times and follow a simple linear deterioration. The objective functions are minimizin...

Modern statistical machine translation (SMT) systems usually use a linear combination of features to model the quality of each translation hypothesis. The linear combination assumes that all the features are in a linear relationship and constrains that each feature interacts with the rest features in an linear manner, which might limit the expressive power of the model and lead to a under-fit m...

As larger and more diverse parallel texts become available, how can we leverage heterogeneous data to train robust machine translation systems that achieve good translation quality on various test domains? This challenge has been addressed so far by repurposing techniques developed for domain adaptation, such as linear mixture models which combine estimates learned on homogeneous subdomains. Ho...

Persistence diagrams have been widely recognized as a compact descriptor for characterizing multiscale topological features in data. When many datasets are available, statistical features embedded in those persistence diagrams can be extracted by applying machine learnings. In particular, the ability for explicitly analyzing the inverse in the original data space from those statistical features...

We present xLIN, a novel linear abstract machine that introduces no penalty in computing with non-linear resources, while computing with linear resources might, in principle, be as fast as a series of destructive updates. Garbage collection would not be required for linear objects, while still present for non-linear objects. We propose a weak calculus of explicit substitutions as its foundation...

In this paper, a neural network model reference adaptive system speed observer is designed, which can be used in speed control of linear induction motors (LIMs). Dynamical equations of LIM have been considered accurate. In other words, the end effect and the electrical losses of the motor have been included in the motor equivalent circuit. Then equations of the reference model and adaptive mode...