Hidden-Markov Program Algebra with iteration

We use Hidden Markov Models to motivate a quantitative compositional semantics for noninterference-based security with iteration, including a refinement- or "implements" relation that compares two programs with respect to their information leakage; and we propose a program algebra for source-level reasoning about such programs, in particular as a means of establishing that an "implementation" program leaks no more than its "specification" program. This joins two themes: we extend our earlier work, having iteration but only qualitative, by making it quantitative; and we extend our earlier quantitative work by including iteration. We advocate stepwise refinement and source-level program algebra, both as conceptual reasoning tools and as targets for automated assistance. A selection of algebraic laws is given to support this view in the case of quantitative noninterference; and it is demonstrated on a simple iterated password-guessing attack

Geometric, electronic properties and the thermodynamics of pure and Al--doped Li clusters

The first--principles density functional molecular dynamics simulations have been carried out to investigate the geometric, the electronic, and the finite temperature properties of pure Li clusters (Li$_{10}$, Li$_{12}$) and Al--doped Li clusters (Li$_{10}$Al, Li$_{10}$Al$_2$). We find that addition of two Al impurities in Li$_{10}$ results in a substantial structural change, while the addition of one Al impurity causes a rearrangement of atoms. Introduction of Al--impurities in Li$_{10}$ establishes a polar bond between Li and nearby Al atom(s), leading to a multicentered bonding, which weakens the Li--Li metallic bonds in the system. These weakened Li--Li bonds lead to a premelting feature to occur at lower temperatures in Al--doped clusters. In Li$_{10}$Al$_2$, Al atoms also form a weak covalent bond, resulting into their dimer like behavior. This causes Al atoms not to `melt' till 800 K, in contrast to the Li atoms which show a complete diffusive behavior above 400 K. Thus, although one Al impurity in Li$_{10}$ cluster does not change its melting characteristics significantly, two impurities results in `surface melting' of Li atoms whose motions are confined around Al dimer.Comment: 9 pages, 7 figure

Energy performance plan analysis in a new ecological city

Conforming to urban development needs, in accordance with ecological and low-carbon requirements, is the first priority of contemporary urban construction. At the first stages of planning a new town, energy planning and analysis, and establishing sustainable energy development strategies, are methods to reinforce the ideal of an ecological city. Therefore, to meet urban planning requirements, energy planning often requires determination of the energy consumption index, and knowledge of local energy demands and natural and social environments (to build a reasonable energy structure), adjusted through the evaluation,design, and optimization of the construction of ecological cities. This paper explores energy planning through an analysis of the application of energy sources in the planning of the eco-city of Jinan City

2-((E)-{(S)-(6-Meth­oxy­quinolin-4-yl)[(2S)-8-vinyl­quinuclidin-2-yl]methyl­imino}­meth­yl)phenol

The title compound, C27H29N3O2, adopts an E configuration with respect to the C=N bond. The molecular structure is stabilized by inter­molecular O—H⋯N inter­actions between a hy­droxy H atom and the N atom on the quinoline ring

Supervised Functional PCA with Covariate Dependent Mean and Covariance Structure

Incorporating covariate information into functional data analysis methods can substantially improve modeling and prediction performance. However, many functional data analysis methods do not make use of covariate or supervision information, and those that do often have high computational cost or assume that only the scores are related to covariates, an assumption that is usually violated in practice. In this article, we propose a functional data analysis framework that relates both the mean and covariance function to covariate information. To facilitate modeling and ensure the covariance function is positive semi-definite, we represent it using splines and design a map from Euclidean space to the symmetric positive semi-definite matrix manifold. Our model is combined with a roughness penalty to encourage smoothness of the estimated functions in both the temporal and covariate domains. We also develop an efficient method for fast evaluation of the objective and gradient functions. Cross-validation is used to choose the tuning parameters. We demonstrate the advantages of our approach through a simulation study and an astronomical data analysis.Comment: 24 pages, 15 figure

Hecke Groups, Dessins d’Enfants and the Archimedean Solids

Grothendieck’s dessins d’enfants arise with ever-increasing frequency in many areas of 21st century mathematical physics. In this paper, we review the connections between dessins and the theory of Hecke groups. Focussing on the restricted class of highly symmetric dessins corresponding to the so-called Archimedean solids, we apply this theory in order to provide a means of computing representatives of the associated conjugacy classes of Hecke subgroups in each case. The aim of this paper is to demonstrate that dessins arising in mathematical physics can point to new and hitherto unexpected directions for further research. In addition, given the particular ubiquity of many of the dessins corresponding to the Archimedean solids, the hope is that the computational results of this paper will prove useful in the further study of these objects in mathematical physics contexts

Label-invariant models for the analysis of meta-epidemiological data.

Rich meta-epidemiological data sets have been collected to explore associations between intervention effect estimates and study-level characteristics. Welton et al proposed models for the analysis of meta-epidemiological data, but these models are restrictive because they force heterogeneity among studies with a particular characteristic to be at least as large as that among studies without the characteristic. In this paper we present alternative models that are invariant to the labels defining the 2 categories of studies. To exemplify the methods, we use a collection of meta-analyses in which the Cochrane Risk of Bias tool has been implemented. We first investigate the influence of small trial sample sizes (less than 100 participants), before investigating the influence of multiple methodological flaws (inadequate or unclear sequence generation, allocation concealment, and blinding). We fit both the Welton et al model and our proposed label-invariant model and compare the results. Estimates of mean bias associated with the trial characteristics and of between-trial variances are not very sensitive to the choice of model. Results from fitting a univariable model show that heterogeneity variance is, on average, 88% greater among trials with less than 100 participants. On the basis of a multivariable model, heterogeneity variance is, on average, 25% greater among trials with inadequate/unclear sequence generation, 51% greater among trials with inadequate/unclear blinding, and 23% lower among trials with inadequate/unclear allocation concealment, although the 95% intervals for these ratios are very wide. Our proposed label-invariant models for meta-epidemiological data analysis facilitate investigations of between-study heterogeneity attributable to certain study characteristics