Faster Algorithms for Weighted Recursive State Machines

Pushdown systems (PDSs) and recursive state machines (RSMs), which are linearly equivalent, are standard models for interprocedural analysis. Yet RSMs are more convenient as they (a) explicitly model function calls and returns, and (b) specify many natural parameters for algorithmic analysis, e.g., the number of entries and exits. We consider a general framework where RSM transitions are labeled from a semiring and path properties are algebraic with semiring operations, which can model, e.g., interprocedural reachability and dataflow analysis problems. Our main contributions are new algorithms for several fundamental problems. As compared to a direct translation of RSMs to PDSs and the best-known existing bounds of PDSs, our analysis algorithm improves the complexity for finite-height semirings (that subsumes reachability and standard dataflow properties). We further consider the problem of extracting distance values from the representation structures computed by our algorithm, and give efficient algorithms that distinguish the complexity of a one-time preprocessing from the complexity of each individual query. Another advantage of our algorithm is that our improvements carry over to the concurrent setting, where we improve the best-known complexity for the context-bounded analysis of concurrent RSMs. Finally, we provide a prototype implementation that gives a significant speed-up on several benchmarks from the SLAM/SDV project

#Bieber + #Blast = #BieberBlast: Early Prediction of Popular Hashtag Compounds

Compounding of natural language units is a very common phenomena. In this paper, we show, for the first time, that Twitter hashtags which, could be considered as correlates of such linguistic units, undergo compounding. We identify reasons for this compounding and propose a prediction model that can identify with 77.07% accuracy if a pair of hashtags compounding in the near future (i.e., 2 months after compounding) shall become popular. At longer times T = 6, 10 months the accuracies are 77.52% and 79.13% respectively. This technique has strong implications to trending hashtag recommendation since newly formed hashtag compounds can be recommended early, even before the compounding has taken place. Further, humans can predict compounds with an overall accuracy of only 48.7% (treated as baseline). Notably, while humans can discriminate the relatively easier cases, the automatic framework is successful in classifying the relatively harder cases.Comment: 14 pages, 4 figures, 9 tables, published in CSCW (Computer-Supported Cooperative Work and Social Computing) 2016. in Proceedings of 19th ACM conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2016

Target prediction and a statistical sampling algorithm for RNA-RNA interaction

It has been proven that the accessibility of the target sites has a critical influence for miRNA and siRNA. In this paper, we present a program, rip2.0, not only the energetically most favorable targets site based on the hybrid-probability, but also a statistical sampling structure to illustrate the statistical characterization and representation of the Boltzmann ensemble of RNA-RNA interaction structures. The outputs are retrieved via backtracing an improved dynamic programming solution for the partition function based on the approach of Huang et al. (Bioinformatics). The $O(N^6)$ time and $O(N^4)$ space algorithm is implemented in C (available from \url{http://www.combinatorics.cn/cbpc/rip2.html})Comment: 7 pages, 10 figure

Distribution of graph-distances in Boltzmann ensembles of RNA secondary structures

Large RNA molecules often carry multiple functional domains whose spatial arrangement is an important determinant of their function. Pre-mRNA splicing, furthermore, relies on the spatial proximity of the splice junctions that can be separated by very long introns. Similar effects appear in the processing of RNA virus genomes. Albeit a crude measure, the distribution of spatial distances in thermodynamic equilibrium therefore provides useful information on the overall shape of the molecule can provide insights into the interplay of its functional domains. Spatial distance can be approximated by the graph-distance in RNA secondary structure. We show here that the equilibrium distribution of graph-distances between arbitrary nucleotides can be computed in polynomial time by means of dynamic programming. A naive implementation would yield recursions with a very high time complexity of O(n^11). Although we were able to reduce this to O(n^6) for many practical applications a further reduction seems difficult. We conclude, therefore, that sampling approaches, which are much easier to implement, are also theoretically favorable for most real-life applications, in particular since these primarily concern long-range interactions in very large RNA molecules.Comment: Peer-reviewed and presented as part of the 13th Workshop on Algorithms in Bioinformatics (WABI2013

Admixed pellets for fast and efficient delivery of plasma enhancement gases: Investigations at AUG exploring the option for EU-DEMO

Gas and pellet injection are envisaged for particle fuelling in EU-DEMO. The gas system will provide edge and divertor fuelling and any further gas species required for operation. Pellets, mm-sized bodies formed from solid hydrogen fuel, are designed for efficient and fast core fuelling. However, they can also be employed for a more efficient delivery of plasma enhancement gases, by admixing them with the fuelling pellets. To check this option for EU-DEMO, explorative investigations have been performed at ASDEX Upgrade (AUG). The AUG system produces ice in a batch process sufficient for about 100 pellets, initially designed for operation with pure H$_2$ or D$_2$. On a trial basis, pellet formation was tested using an H$_2$/D$_2$ mixture and admixtures containing small amounts (up to 2 mol%) of N$_2$, Ar, Kr or Xe in the D$_2$ host. A homogeneous and reproducible ice composition was found for the H$_2$/D$_2$ = 1:1 case. For all the admixed gases, a depletion of the admixture in the ice with increasing atomic number is observed. Nevertheless, the fast and efficient delivery of admixed pellets was clearly demonstrated in dedicated plasma experiments at AUG. Detailed investigations showed that the Ar supplied via admixed pellets has a higher radiation efficiency and a faster radiation rise than an Ar/D$_2$ gas puff. Furthermore, Ar density measurements in a discharge with admixed pellet injection show reasonable agreement with findings of a fading admixed species’ concentration along the ice rod and assumptions on the pellet ablation location in the plasma. Investigations performed at the Oak Ridge National Laboratory with a large batch extruder using up to 2 mol% Ne in D$_2$ confirmed that production of much larger ice quantities can be achieved. These initial explorative investigations clearly reveal the great potential of admixed pellets, although they also demonstrate that further technology efforts are required before their benefits can be utilized

Convergence towards a European strategic culture? A constructivist framework for explaining changing norms.

The article contributes to the debate about the emergence of a European strategic culture to underpin a European Security and Defence Policy. Noting both conceptual and empirical weaknesses in the literature, the article disaggregates the concept of strategic culture and focuses on four types of norms concerning the means and ends for the use of force. The study argues that national strategic cultures are less resistant to change than commonly thought and that they have been subject to three types of learning pressures since 1989: changing threat perceptions, institutional socialization, and mediatized crisis learning. The combined effect of these mechanisms would be a process of convergence with regard to strategic norms prevalent in current EU countries. If the outlined hypotheses can be substantiated by further research the implications for ESDP are positive, especially if the EU acts cautiously in those cases which involve norms that are not yet sufficiently shared across countries

A parallel, distributed-memory framework for comparative motif discovery

The increasing number of sequenced organisms has opened new possibilities for the computational discovery of cis-regulatory elements ('motifs') based on phylogenetic footprinting. Word-based, exhaustive approaches are among the best performing algorithms, however, they pose significant computational challenges as the number of candidate motifs to evaluate is very high. In this contribution, we describe a parallel, distributed-memory framework for de novo comparative motif discovery. Within this framework, two approaches for phylogenetic footprinting are implemented: an alignment-based and an alignment-free method. The framework is able to statistically evaluate the conservation of motifs in a search space containing over 160 million candidate motifs using a distributed-memory cluster with 200 CPU cores in a few hours. Software available from http://bioinformatics.intec.ugent.be/blsspeller