29,904 research outputs found
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, Li) and Al--doped
Li clusters (LiAl, LiAl). We find that addition of two Al
impurities in Li results in a substantial structural change, while the
addition of one Al impurity causes a rearrangement of atoms. Introduction of
Al--impurities in Li 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 LiAl, 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 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
A simple parameterisation of windbreak effects on wind speed reduction and thermal benefits of sheep
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-MethÂoxyÂquinolin-4-yl)[(2S)-8-vinylÂquinuclidin-2-yl]methylÂimino}ÂmethÂyl)phenol
The title compound, C27H29N3O2, adopts an E configuration with respect to the C=N bond. The molecular structure is stabilized by interÂmolecular O—H⋯N interÂactions between a hyÂdroxy 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
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