9 research outputs found

    Query-Dependent Banding (QDB) for Faster RNA Similarity Searches

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    When searching sequence databases for RNAs, it is desirable to score both primary sequence and RNA secondary structure similarity. Covariance models (CMs) are probabilistic models well-suited for RNA similarity search applications. However, the computational complexity of CM dynamic programming alignment algorithms has limited their practical application. Here we describe an acceleration method called query-dependent banding (QDB), which uses the probabilistic query CM to precalculate regions of the dynamic programming lattice that have negligible probability, independently of the target database. We have implemented QDB in the freely available Infernal software package. QDB reduces the average case time complexity of CM alignment from LN (2.4) to LN (1.3) for a query RNA of N residues and a target database of L residues, resulting in a 4-fold speedup for typical RNA queries. Combined with other improvements to Infernal, including informative mixture Dirichlet priors on model parameters, benchmarks also show increased sensitivity and specificity resulting from improved parameterization

    Better synchronous binarization for machine translation

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    Binarization of Synchronous Context Free Grammars (SCFG) is essential for achieving polynomial time complexity of decoding for SCFG parsing based machine translation sys-tems. In this paper, we first investigate the excess edge competition issue caused by a left-heavy binary SCFG derived with the method of Zhang et al. (2006). Then we propose a new binarization method to mitigate the problem by exploring other alternative equivalent bi-nary SCFGs. We present an algorithm that ite-ratively improves the resulting binary SCFG, and empirically show that our method can im-prove a string-to-tree statistical machine trans-lations system based on the synchronous bina-rization method in Zhang et al. (2006) on the NIST machine translation evaluation tasks.

    Radical Artificial Intelligence: A Postmodern Approach

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    Radical Artificial Intelligence: A Postmodern Approach

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    Radical Artificial Intelligence: A Postmodern Approach

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    The dynamic response of end-clamped monolithic beams and sandwich beams has been measured by loading the beams at mid-span using metal foam projectiles. The AISI 304 stainless-steel sandwich beams comprise two identical face sheets and either prismatic Y-frame or corrugated cores. The resistance to shock loading is quantified by the permanent transverse deflection at mid-span of the beams as a function of projectile momentum. The prismatic cores are aligned either longitudinally along the beam length or transversely. It is found that the sandwich beams with a longitudinal core orientation have a higher shock resistance than the monolithic beams of equal mass. In contrast, the performance of the sandwich beams with a transverse core orientation is very similar to that of the monolithic beams. Three-dimensional finite element (FE) simulations are in good agreement with the measured responses. The FE calculations indicate that strain concentrations in the sandwich beams occur at joints within the cores and between the core and face sheets; the level of maximum strain is similar for the Y-frame and corrugated core beams for a given value of projectile momentum. The experimental and FE results taken together reveal that Y-frame and corrugated core sandwich beams of equal mass have similar dynamic performances in terms of rear-face deflection, degree of core compression and level of strain within the beam
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