Abstract
Data-flow is a natural approach to parallelism. However, describing dependencies and control between fine-grained data-flow tasks can be complex and present unwanted overheads. TALM (TALM is an Architecture and Language for Multi-threading) introduces a user-defined coarse-grained parallel data-flow model, where programmers identify code blocks, called super-instructions, to be run in parallel and connect them in a data-flow graph. TALM has been implemented as a hybrid Von Neumann/data-flow execution system: the Trebuchet. We have observed that TALM's usefulness largely depends on how programmers specify and connect super-instructions. Thus, we present Couillard, a full compiler that creates, based on an annotated C-program, a data-flow graph and C-code corresponding to each super-instruction. We show that our toolchain allows one to benefit from data-flow execution and explore sophisticated parallel programming techniques, with small effort. To evaluate our system we have executed a set of real applications on a large multi-core machine. Comparison with popular parallel programming methods shows competitive speedups, while providing an easier parallel programing approach. More specifically, for an application that follows the wavefront method, running with big inputs, Trebuchet achieved up to 4.7% speedup over Intel (R) TBB novel flow-graph approach and up to 44% over OpenMP.

Abstract
We present PrologCheck, an automatic tool for property-based testing of programs in the logic programming language Prolog with randomised test data generation. The tool is inspired by the well known QuickCheck, originally designed for the functional programming language Haskell. It includes features that deal with specific characteristics of Prolog such as its relational nature (as opposed to Haskell) and the absence of a strong type discipline. PrologCheck expressiveness stems from describing properties as Prolog goals. It enables the definition of custom test data generators for random testing tailored for the property to be tested. Further, it allows the use of a predicate specification language that supports types, modes and constraints on the number of successful computations. We evaluate our tool on a number of examples and apply it successfully to debug a Prolog library for AVL search trees.

Abstract
Machine learning is continually being applied to a growing set of fields, including the social sciences, business, and medicine. Some fields present problems that are not easily addressed using standard machine learning approaches and, in particular, there is growing interest in differential prediction. In this type of task we are interested in producing a classifier that specifically characterizes a subgroup of interest by maximizing the difference in predictive performance for some outcome between subgroups in a population. We discuss adapting maximum margin classifiers for differential prediction. We first introduce multiple approaches that do not affect the key properties of maximum margin classifiers, but which also do not directly attempt to optimize a standard measure of differential prediction. We next propose a model that directly optimizes a standard measure in this field, the uplift measure. We evaluate our models on real data from two medical applications and show excellent results. © 2014 Springer-Verlag.

Abstract
Objective: Electronic health records (EHR) offer medical and pharmacogenomics research unprecedented opportunities to identify and classify patients at risk. EHRs are collections of highly inter-dependent records that include biological, anatomical, physiological, and behavioral observations. They comprise a patient's clinical phenome, where each patient has thousands of date-stamped records distributed across many relational tables. Development of EHR computer-based phenotyping algorithms require time and medical insight from clinical experts, who most often can only review a small patient subset representative of the total EHR records, to identify phenotype features. In this research we evaluate whether relational machine learning (ML) using inductive logic programming (ILP) can contribute to addressing these issues as a viable approach for EHR-based phenotyping. Methods: Two relational learning ILP approaches and three well-known WEKA (Waikato Environment for Knowledge Analysis) implementations of non-relational approaches (PART, J48, and JRIP) were used to develop models for nine phenotypes. International Classification of Diseases, Ninth Revision (ICD-9) coded EHR data were used to select training cohorts for the development of each phenotypic model. Accuracy, precision, recall, F-Measure, and Area Under the Receiver Operating Characteristic (AUROC) curve statistics were measured for each phenotypic model based on independent manually verified test cohorts. A two-sided binomial distribution test (sign test) compared the five ML approaches across phenotypes for statistical significance. Results: We developed an approach to automatically label training examples using ICD-9 diagnosis codes for the ML approaches being evaluated. Nine phenotypic models for each ML approach were evaluated, resulting in better overall model performance in AUROC using ILP when compared to PART (p = 0.039), J48 (p = 0.003) and JRIP (p = 0.003). Discussion: ILP has the potential to improve phenotyping by independently delivering clinically expert interpretable rules for phenotype definitions, or intuitive phenotypes to assist experts. Conclusion: Relational learning using ILP offers a viable approach to EHR-driven phenotyping.

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