30,561 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
A simple parameterisation of windbreak effects on wind speed reduction and thermal benefits of sheep
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
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
Channelling Multimodality Through a Unimodalizing Transport: Warp-U Sampler and Stochastic Bridge Sampling
Monte Carlo integration is fundamental in scientific and statistical
computation, but requires reliable samples from the target distribution, which
poses a substantial challenge in the case of multi-modal distributions.
Existing methods often involve time-consuming tuning, and typically lack
tailored estimators for efficient use of the samples. This paper adapts the
Warp-U transformation [Wang et al., 2022] to form multi-modal sampling strategy
called Warp-U sampling. It constructs a stochastic map to transport a
multi-modal density into a uni-modal one, and subsequently inverts the
transport but with new stochasticity injected. For efficient use of the samples
for normalising constant estimation, we propose (i) an unbiased estimation
scheme based coupled chains, where the Warp-U sampling is used to reduce the
coupling time; and (ii) a stochastic Warp-U bridge sampling estimator, which
improves its deterministic counterpart given in Wang et al. [2022]. Our overall
approach requires less tuning and is easier to apply than common alternatives.
Theoretically, we establish the ergodicity of our sampling algorithm and that
our stochastic Warp-U bridge sampling estimator has greater (asymptotic)
precision per CPU second compared to the Warp-U bridge estimator of Wang et al.
[2022] under practical conditions. The advantages and current limitations of
our approach are demonstrated through simulation studies and an application to
exoplanet detection
Occurrence and characteristics of microplastics in soils from greenhouse and open-field cultivation using plastic mulch film
There is a major knowledge gap concerning the extent of microplastic pollution in agronomic regions of China, which represent a plastic use hotspot. In order to clarify the amendment of agronomic region and plastic film mulching mode to microplastics distribution, the characteristics of microplastics distributed in agricultural soils from three typical regions (Beijing (BJ), Shandong (SD), and Xinjiang (XJ)) with two plastic film mulching modes (greenhouse (G) and conventional field-based film mulching (M)) in China were investigated. Microplastics weight and their response to planting regions were also evaluated in this study. The result showed that the average abundance of microplastics in soils from BJ, SD, and XJ was 1.83 × 104 items kg-1, 4.02 × 104 items kg-1, and 3.39 × 104 items kg-1, and the estimated weight of microplastics per kg of dry soils was 3.12 mg kg-1, 5.63 mg kg-1, and 7.99 mg kg-1, respectively. Microplastics in farmland were mainly of small particle size (50 to 250 μm), with their abundance decreasing with increasing particle size. Among the microplastics detected, polyethylene and polypropylene were the two dominant types present, accounting for 50.0% and 19.7%, respectively. The standard total effect of planting regions on microplastic number and weight was 31.8% and 32.3%, and plastic film mulching modes (G vs. M) could explain 34.4% of the total variation of microplastic compositions with a contribution rate of 65.6% in this study. This research provides key data for an assessment of the environmental risk of microplastics and supports the development of guidelines for the sustainable use of agricultural plastic film. Further, it is necessary to quantify and assess the contribution of other different plastic sources to microplastics in soil. Big data technologies or isotope tracer techniques may be promising approaches.</p
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