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Characterization of joint probability distribution for large networks of random variables remains a challenging task in data science. Probabilistic graph approximation with simple topologies has practically been resorted to; typically the tree topology makes joint probability computation much simpler and can be effective for statistical inference on insufficient data. However, to characterize network components where multiple variables cooperate closely to influence others, model topologies beyond a tree are needed, which unfortunately are infeasible to acquire. In particular, our previous work has related optimal approximation of Markov networks of tree-width k >=2 closely to the graph-theoretic problem of finding maximum spanning k-tree (MSkT), which is a provably intractable task. This paper investigates optimal approximation of Markov networks with k-tree topology that retains some designated underlying subgraph. Such a subgraph may encode certain background information that arises in scientific applications, for example, about a known significant pathway in gene networks or the indispensable backbone connectivity in the residue interaction graphs for a biomolecule 3D structure. In particular, it is proved that the \beta-retaining MSkT problem, for a number of classes \beta of graphs, admit O(n^{k+1})-time algorithms for every fixed k>= 1. These \beta-retaining MSkT algorithms offer efficient solutions for approximation of Markov networks with k-tree topology in the situation where certain persistent information needs to be retained., Comment: 20 pages including references, 1 figure

RNA secondary structure is modeled with the novel arbitrary-order hidden Markov model ({\alpha}-HMM). The {\alpha}-HMM extends over the traditional HMM with capability to model stochastic events that may be in influenced by historically distant ones, making it suitable to account for long-range canonical base pairings between nucleotides, which constitute the RNA secondary structure. Unlike previous heavy-weight extensions over HMM, the {\alpha}-HMM has the flexibility to apply restrictions on how one event may influence another in stochastic processes, enabling efficient prediction of RNA secondary structure including pseudoknots., Comment: 14 pages, 5 figures, 1 table

Cyclopentadiene (CPD) is an important intermediate in the combustion of fuel and the formation of aromatics. In the present study, the kinetics and thermodynamic properties for hydrogen abstraction and addition with CPD, and the related reactions including the isomerization and decomposition on the C5H7 potential energy surface were systematically investigated by theoretical calculations. High-level ab initio calculations were adopted to obtain the stationary points on the potential energy surfaces of CPD + H. Phenomenological rate coefficients for temperature- and pressure-dependent reactions in the full potential energy surface were calculated by solving the time-dependent multiple-well RRKM/master equation, and the hydrogen abstraction reactions were based on the conventional transition state theory. In terms of the hydrogen abstraction reactions, the hydrogen abstraction from the saturated carbon atom in CPD is found to be the dominant channel. For the hydrogen addition and the associated reactions on the C5H7 PES, the allylic and vinylic cyclopentenyl radicals and C2H2 + C3H5 were found to be the most important channels and reactivity-promoting products, respectively. The previously neglected role of open-chain intermediates in the evaluation of the reaction kinetics has been suggested and the corresponding rate constants have been recommended for inclusion in the modeling of the H+ c-C5H6 reaction. Results indicate that the transformation from to straight-chain C5H7 is kinetically unfavorable due to the high strain energy of the 3-membered ring structure of the isomerization transition state. Moreover, the thermodynamic data and the calculated rate coefficients for both H atom abstraction and addition were incorporated into the kinetics model to examine the impact of the computed pressure-dependent kinetics of C5H6 + H reactions on model predictions.

Despite the high technical relevance of early flame kernel development for the reduction of cycle-to-cycle variations in spark ignition engines, there is still a need for a better fundamental understanding of the governing in-cylinder phenomena in order to enable resilient early flame growth. To isolate the effects of small- and large-scale turbulent flow motion on the young flame kernel, a three-dimensional DNS database has been designed to be representative for engine part load conditions. The analysis is focussed on flame displacement speed and flame area in order to investigate effects of flame structure and flame geometry on the global burning rate evolution. It is shown that despite a Karlovitz number of up to 13, which is at the upper range of conventional engine operation, thickening of the averaged flame structure by small-scale turbulent mixing is not observed. After ignition effects have decayed, the flame normal displacement speed recovers the behavior of a laminar unstretched premixed flame under the considered unity-Lewis-number conditions. Run-to-run variations in the global heat release rate are shown to be primarily caused by flame kernel area dynamics. The analysis of the flame area balance equation shows that turbulence causes stochastic flame kernel area growth by affecting the curvature evolution, rather than by inducing variations in total flame area production by strain. Further, it is shown that in local segments of a fully-developed planar flame with similar surface area as the investigated flame kernels, temporal variations in flame area rate-of-change occur. Contrasting to early flame kernels, these effects can be exclusively attributed to curvature variations in negatively curved flame regions.

Software systems evolve throughout their life cycles. Many revisions are produced over time. Model checking each revision of the software is impractical. Regression verification suggests reusing intermediate results from the previous verification runs. This paper proposes a fully automatic regression verification technique in the context of CEGAR. Procedure summaries, which describe the input/output behaviors of a procedure, are proposed as the intermediate results to be reused. Procedure summaries are reasonably small to store, technically easy to process, and do not require much extra computation effort to be reused. Reusing procedure summaries saves much analysis effort on the corresponding procedures. By combining regression verification and CEGAR, we propose a technique that is able to reuse procedure summaries across different abstract precisions and different program revisions. We performed extensive experiments on a large number of industrial programs (534 revisions of 89 Linux kernel device drivers). The results show that our approach can significantly improve the performance of regression verification.

The seminal work of Chow and Liu (1968) shows that approximation of a finite probabilistic system by Markov trees can achieve the minimum information loss with the topology of a maximum spanning tree. Our current paper generalizes the result to Markov networks of tree width $\leq k$, for every fixed $k\geq 2$. In particular, we prove that approximation of a finite probabilistic system with such Markov networks has the minimum information loss when the network topology is achieved with a maximum spanning $k$-tree. While constructing a maximum spanning $k$-tree is intractable for even $k=2$, we show that polynomial algorithms can be ensured by a sufficient condition accommodated by many meaningful applications. In particular, we prove an efficient algorithm for learning the optimal topology of higher order correlations among random variables that belong to an underlying linear structure., Comment: 18 pages main text, 2 pages appendix