It Is NL-complete to Decide Whether a Hairpin Completion of Regular Languages Is Regular

The hairpin completion is an operation on formal languages which is inspired by the hairpin formation in biochemistry. Hairpin formations occur naturally within DNA-computing. It has been known that the hairpin completion of a regular language is linear context-free, but not regular, in general. However, for some time it is was open whether the regularity of the hairpin completion of a regular language is is decidable. In 2009 this decidability problem has been solved positively by providing a polynomial time algorithm. In this paper we improve the complexity bound by showing that the decision problem is actually NL-complete. This complexity bound holds for both, the one-sided and the two-sided hairpin completions

Towards Static Analysis of Functional Programs using Tree Automata Completion

This paper presents the first step of a wider research effort to apply tree automata completion to the static analysis of functional programs. Tree Automata Completion is a family of techniques for computing or approximating the set of terms reachable by a rewriting relation. The completion algorithm we focus on is parameterized by a set E of equations controlling the precision of the approximation and influencing its termination. For completion to be used as a static analysis, the first step is to guarantee its termination. In this work, we thus give a sufficient condition on E and T(F) for completion algorithm to always terminate. In the particular setting of functional programs, this condition can be relaxed into a condition on E and T(C) (terms built on the set of constructors) that is closer to what is done in the field of static analysis, where abstractions are performed on data.Comment: Proceedings of WRLA'14. 201

Implementation Methods for Estimating Haplotypes with Grid Computing Technology

Introduction: PHASE is a software for haplotype reconstruction and recombination rate estimation from population data and implements the methods for estimating haplotypes from population genotype data. This software require enormous computing power due to the complexity algorithms used. Recent advancement in grid computing and multi-core processing technology has resulted in the creating of virtual supercomputers (that are capable of massive parallel computations).

Method: PHASE version 2.0 was downloaded from PHASE’s homepage (http://stephenslab.uchicago.edu/software.html), and installed on 10 Apple iMac computers (20 CPUs) running Mac OS 10.5 using Apple™ Xserve as a controller. A PHP web-based application was developed to enable easy job submission and result retrieval. Haplotype estimation jobs for PHASE were submitted using a web browser running either from Windows®, Linux® or Macintosh™ OS 10.5 operating systems. Jobs consisting of 20 input files of genomic loci (SNPs) were submitted to the Xgrid via the controller and the job completion time for each run were recorded and compared.

Results: The job completion time for estimating 20 input files and returning the results to the controller using XGrid (20 CPUs) took 4,980 seconds compared with 58,800 seconds using a single computer (2 CPUs), representing 91.5% reduction in computing time

A new lower bound approach for single-machine multicriteria scheduling

The concept of maximum potential improvement has played an important role in computing lower bounds for single-machine scheduling problems with composite objective functions that are linear in the job completion times. We introduce a new method for lower bound computation; objective splitting. We show that it dominates the maximum potential improvement method in terms of speed and quality