Generating a checking sequence with a minimum number of reset transitions

Given a finite state machine M, a checking sequence is an input sequence that is guaranteed to lead to a failure if the implementation under test is faulty and has no more states than M. There has been much interest in the automated generation of a short checking sequence from a finite state machine. However, such sequences can contain reset transitions whose use can adversely affect both the cost of applying the checking sequence and the effectiveness of the checking sequence. Thus, we sometimes want a checking sequence with a minimum number of reset transitions rather than a shortest checking sequence. This paper describes a new algorithm for generating a checking sequence, based on a distinguishing sequence, that minimises the number of reset transitions used.This work was supported in part by Leverhulme Trust grant number F/00275/D, Testing State Based Systems, Natural Sciences and Engineering Research Council (NSERC) of Canada grant number RGPIN 976, and Engineering and Physical Sciences Research Council grant number GR/R43150, Formal Methods and Testing (FORTEST)

Model-driven approach for supporting the mapping of parallel algorithms to parallel computing platforms

The trend from single processor to parallel computer architectures has increased the importance of parallel computing. To support parallel computing it is important to map parallel algorithms to a computing platform that consists of multiple parallel processing nodes. In general different alternative mappings can be defined that perform differently with respect to the quality requirements for power consumption, efficiency and memory usage. The mapping process can be carried out manually for platforms with a limited number of processing nodes. However, for exascale computing in which hundreds of thousands of processing nodes are applied, the mapping process soon becomes intractable. To assist the parallel computing engineer we provide a model-driven approach to analyze, model, and select feasible mappings. We describe the developed toolset that implements the corresponding approach together with the required metamodels and model transformations. We illustrate our approach for the well-known complete exchange algorithm in parallel computing. © 2013 Springer-Verlag

Differentiation of European cattle by AFLP fingerprinting

The Neolithic introduction of domestic cattle into Europe was followed by differential adaptation, selection, migration and genetic isolation, leading ultimately to the emergence of specialized breeds. We have studied the differentiation of European cattle by amplified fragment length polymorphism (AFLP) fingerprinting. Combining AFLP data sets from two laboratories yielded 81 biallelic polymorphic markers scored in 19-22 individual animals from 51 breeds. Model-based clustering differentiated Podolian cattle as well as French and Alpine breeds from other European cattle. AFLP genetic distances correlated well with microsatellite-based genetic distances calculated for the same breeds. However, the AFLP data emphasized the divergence of taurine and indicine cattle relative to the variation among European breeds and indicated an Eastern influence on Italian and Hungarian Podolian breeds. This probably reflects import from the East after the original introduction of domestic cattle into Europe. Our data suggest that Italian cattle breeds are relatively diverse at the DNA sequence level

A network flow model for biclustering via optimal re-ordering of data matrices

Biclustering, Mixed-integer linear optimization (MILP),

Approximation in preemptive stochastic online scheduling

We present a first constant performance guarantee for preemptive stochastic scheduling to minimize the sum of weighted completion times. For scheduling jobs with release dates on identical parallel machines we derive a policy with a guaranteed performance ratio of 2 which matches the currently best known result for the corresponding deterministic online problem. Our policy applies to the recently introduced stochastic online scheduling model in which jobs arrive online over time. In contrast to the previously considered nonpreemptive setting, our preemptive policy extensively utilizes information on processing time distributions other than the first (and second) moments. In order to derive our result we introduce a new nontrivial lower bound on the expected value of an unknown optimal policy that we derive from an optimal policy for the basic problem on a single machine without release dates. This problem is known to be solved optimally by a Gittins index priority rule. This priority index also inspires the design of our policy