77 research outputs found

    Understanding Probabilistic Programs

    Get PDF
    We present two views of probabilistic programs and their relationship. An operational interpretation as well as a weakest pre-condition semantics are provided for an elementary probabilistic guarded command language. Our study treats important features such as sampling, conditioning, loop divergence, and non-determinism

    Lost in folding space? Comparing four variants of the thermodynamic model for RNA secondary structure prediction

    Get PDF
    Janssen S, Schudoma C, Steger G, Giegerich R. Lost in folding space? Comparing four variants of the thermodynamic model for RNA secondary structure prediction. BMC Bioinformatics. 2011;12(1): 429.BACKGROUND:Many bioinformatics tools for RNA secondary structure analysis are based on a thermodynamic model of RNA folding. They predict a single, "optimal" structure by free energy minimization, they enumerate near-optimal structures, they compute base pair probabilities and dot plots, representative structures of different abstract shapes, or Boltzmann probabilities of structures and shapes. Although all programs refer to the same physical model, they implement it with considerable variation for different tasks, and little is known about the effects of heuristic assumptions and model simplifications used by the programs on the outcome of the analysis.RESULTS:We extract four different models of the thermodynamic folding space which underlie the programs RNAfold, RNAshapes, and RNAsubopt. Their differences lie within the details of the energy model and the granularity of the folding space. We implement probabilistic shape analysis for all models, and introduce the shape probability shift as a robust measure of model similarity. Using four data sets derived from experimentally solved structures, we provide a quantitative evaluation of the model differences.CONCLUSIONS:We find that search space granularity affects the computed shape probabilities less than the over- or underapproximation of free energy by a simplified energy model. Still, the approximations perform similar enough to implementations of the full model to justify their continued use in settings where computational constraints call for simpler algorithms. On the side, we observe that the rarely used level 2 shapes, which predict the complete arrangement of helices, multiloops, internal loops and bulges, include the "true" shape in a rather small number of predicted high probability shapes. This calls for an investigation of new strategies to extract high probability members from the (very large) level 2 shape space of an RNA sequence. We provide implementations of all four models, written in a declarative style that makes them easy to be modified. Based on our study, future work on thermodynamic RNA folding may make a choice of model based on our empirical data. It can take our implementations as a starting point for further program development

    Query Complexity of Inversion Minimization on Trees

    Full text link
    We consider the following computational problem: Given a rooted tree and a ranking of its leaves, what is the minimum number of inversions of the leaves that can be attained by ordering the tree? This variation of the problem of counting inversions in arrays originated in mathematical psychology, with the evaluation of the Mann--Whitney statistic for detecting differences between distributions as a special case. We study the complexity of the problem in the comparison-query model, used for problems like sorting and selection. For many types of trees with nn leaves, we establish lower bounds close to the strongest known in the model, namely the lower bound of log2(n!)\log_2(n!) for sorting nn items. We show: (a) log2((α(1α)n)!)O(logn)\log_2((\alpha(1-\alpha)n)!) - O(\log n) queries are needed whenever the tree has a subtree that contains a fraction α\alpha of the leaves. This implies a lower bound of log2((k(k+1)2n)!)O(logn)\log_2((\frac{k}{(k+1)^2}n)!) - O(\log n) for trees of degree kk. (b) log2(n!)O(logn)\log_2(n!) - O(\log n) queries are needed in case the tree is binary. (c) log2(n!)O(klogk)\log_2(n!) - O(k \log k) queries are needed for certain classes of trees of degree kk, including perfect trees with even kk. The lower bounds are obtained by developing two novel techniques for a generic problem Π\Pi in the comparison-query model and applying them to inversion minimization on trees. Both techniques can be described in terms of the Cayley graph of the symmetric group with adjacent-rank transpositions as the generating set. Consider the subgraph consisting of the edges between vertices with the same value under Π\Pi. We show that the size of any decision tree for Π\Pi must be at least: (i) the number of connected components of this subgraph, and (ii) the factorial of the average degree of the complementary subgraph, divided by nn. Lower bounds on query complexity then follow by taking the base-2 logarithm.Comment: 54 pages, 18 figures, full version of paper appearing in the Proceedings of the 2023 ACM-SIAM Symposium on Discrete Algorithm

    Torsion volume forms

    Full text link
    We introduce volume forms on mapping stacks in derived algebraic geometry using a parametrized version of the Reidemeister-Turaev torsion. In the case of derived loop stacks we describe this volume form in terms of the Todd class. In the case of mapping stacks from surfaces, we compare it to the symplectic volume form. As an application of these ideas, we construct canonical orientation data for cohomological DT invariants of closed oriented 3-manifolds.Comment: 58 page

    Category Theoretic Models of Data Refinement

    Get PDF
    We give an account of the use of category theory in modelling data refinement over the past twenty years. We start with Tony Hoare's formulation of data refinement in category theoretic terms, explain how the category theory may be made precise in generality and with elegance, using the notion of structure respecting lax transformation, for a first order imperative language, then study two main alternatives for extending that category theoretic analysis in order to account for higher order languages. The first is given by adjoint simulations; the second is given by the notion of lax logical relation. These provide techniques that can be used for a combined language, such as an imperative language with procedure passing.18 page(s
    corecore