We present a test resource partitioning (TRP) technique that simultaneously reduces test data volume, test application time, and scan power. The proposed approach is based on the use of alternating run-length codes for test data compression. We present a formal analysis of the amount of data compression obtained using alternating run-length codes. We show that a careful mapping of the don’t-car...

Although recent policy initiatives aimed at concentrating selected surgical procedures in high-volume hospitals may reduce mortality, their economic implications have not been considered fully. From the hospital perspective, the primary effect of these policies will be to redistribute surgical profits to bigger centers. From the payer perspective, prices paid for procedures will likely increase...

Previous studies have indicated a decreased risk for developing Alzheimer's disease in anti-inflammatory (AI) drug users. Yet few studies have determined whether AI drug use provides a protective effect against normal age-related changes in the brains of older adults. Regional volume changes in gray and white matter were assessed cross-sectionally using optimized voxel-based morphometry in 36 f...

Brain ischaemia (stroke) triggers an intense inflammatory response predominately mediated by the accumulation of inflammatory cells and mediators in the ischaemic brain. In this context, regulatory T (Treg) cells, a subpopulation of CD4(+) T cells with immunosuppressive and anti-inflammatory properties, are activated in the late stages of the disease. To date, the potential therapeutic usefulne...

We present supertagging-based models for Tree Adjoining Grammar parsing that use neural network architectures and dense vector representation of supertags (elementary trees) to achieve state-of-the-art performance in unlabeled and labeled attachment scores. The shift-reduce parsing model eschews lexical information entirely, and uses only the 1-best supertags to parse a sentence, providing furt...

It is argued that syntactic preference principles such as Right Association and Minimal Attachment are unsatisfactory as usually formulated. Among the difficulties are: (I) dependence on ill-specified or implausible principles of parser operation; (2) dependence on questionable assumptions about syntax; (3) lack Of provision, even in principle, for integration with semantic and pragmatic prefer...

We present the first provably optimal polynomial time dynamic programming (DP) algorithm for best-first shift-reduce parsing, which applies the DP idea of Huang and Sagae (2010) to the best-first parser of Sagae and Lavie (2006) in a non-trivial way, reducing the complexity of the latter from exponential to polynomial. We prove the correctness of our algorithm rigorously. Experiments confirm th...

We show that LR(k) parsers can be represented as abstract interpretation of Shift-Reduce parser.

We present expected F-measure training for shift-reduce parsing with RNNs, which enables the learning of a global parsing model optimized for sentence-level F1. We apply the model to CCG parsing, where it improves over a strong greedy RNN baseline, by 1.47% F1, yielding state-of-the-art results for shiftreduce CCG parsing.