Abstract
In order for parallel logic programming systems to become popular; they should serve the broadest range of applications. To achieve this goal, designers of parallel logic programming systems would like to exploit maximum parallelism for existing and novel applications. ideally by supporting both and-parallelism and or-parallelism. Unfortunately; the combination of both forms of parallelism is a hard problem, and available proposals cannot match the efficiency of; say, or-parallel only systems. We propose a novel approach to And/Or Parallelism in logic programs. Our initial observation is that stack copying, the most popular technique in or-parallel systems, does not work well with And/Or systems because network management is much more complex. Copying is also a significant problem in operating system where the copy-on-write (COW) has been dcl eloped to address the problem We demonstrate that this technique can also be applied to And/Or systems, and present both shared memory and distributed shared memory designs.

Abstract

Abstract
Precision-recall (PR) curves and the areas under them are widely used to summarize machine learning results, especially for data sets exhibiting class skew. They are often used analogously to ROC curves and the area under ROC curves. It is known that PR curves vary as class skew changes. What was not recognized before this paper is that there is a region of PR space that is completely unachievable, and the size of this region depends only on the skew. This paper precisely characterizes the size of that region and discusses its implications for empirical evaluation methodology in machine learning. Copyright 2012 by the author(s)/owner(s).

Abstract

Abstract
We study how probabilistic reasoning and inductive querying can be combined within ProbLog, a recent probabilistic extension of Prolog. ProbLog can be regarded as a database system that supports both probabilistic and inductive reasoning through a variety of querying mechanisms. After a short introduction to ProbLog, we provide a survey of the different types of inductive queries that ProbLog supports, and show how it can be applied to the mining of large biological networks. © 2010 Springer Science+Business Media, LLC.

Abstract
Transcriptional regulation play an important role in every cellular decision. Gaining an understanding of the dynamics that govern how a cell will respond to diverse environmental cues is difficult using intuition alone. We introduce logic-based regulation models based on state-of-the-art work on statistical relational learning, to show that network hypotheses can be generated from existing gene expression data for use by experimental biologists.