Generalized Bayesian cloud detection for satellite imagery. part 1: technique and validation for night-time imagery over land and sea

Numerical Weather Prediction (NWP) fields are used to assist the detection of cloud in satellite imagery. Simulated observations based on NWP are used within a framework based on Bayes' theorem to calculate a physically-based probability of each pixel with an imaged scene being clear or cloudy. Different thresholds can be set on the probabilities to create application-specific cloud-masks. Here, this is done over both land and ocean using night-time (infrared) imagery. We use a validation dataset of difficult cloud detection targets for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) achieving true skill scores of 87% and 48% for ocean and land, respectively using the Bayesian technique, compared to 74% and 39%, respectively for the threshold-based techniques associated with the validation dataset.

Management compensation systems in MNCs and domestic firms. Cross-national empirical evidence

• This is a study of the relationship between institutional settings and managerial compensation systems, based on extensive cross-national survey evidence. • We compare differences in practices between Multinational Corporations (MNCs) and domestic firms across a range of capitalist archetypes. • We find that MNCs are more likely to promote compensation systems that incentivise managers in line with organisational performance compared to domestic firms. Our findings also reveal persistent diversity reflecting firm type and institutional setting. We find that the gap between MNCs and domestic firms in terms of the usage of incentive-related compensation is less pronounced in Liberal Market Economies than in other settings. This suggests that it is a combination of being an MNC and the specific home locale that moulds approaches to managerial compensation. This reflects considerable hybridisation of practices within and between settings.

An eight-parent multiparent advanced generation inter-cross population for winter-sown wheat: creation, properties, and validation

MAGIC populations represent one of a new generation of crop genetic mapping resources combining high genetic recombination and diversity. We describe the creation and validation of an eight-parent MAGIC population consisting of 1091 F7 lines of winter-sown wheat (Triticum aestivum L.). Analyses based on genotypes from a 90,000-single nucleotide polymorphism (SNP) array find the population to be well-suited as a platform for fine-mapping quantitative trait loci (QTL) and gene isolation. Patterns of linkage disequilibrium (LD) show the population to be highly recombined; genetic marker diversity among the founders was 74% of that captured in a larger set of 64 wheat varieties, and 54% of SNPs segregating among the 64 lines also segregated among the eight founder lines. In contrast, a commonly used reference bi-parental population had only 54% of the diversity of the 64 varieties with 27% of SNPs segregating. We demonstrate the potential of this MAGIC resource by identifying a highly diagnostic marker for the morphological character "awn presence/absence" and independently validate it in an association-mapping panel. These analyses show this large, diverse, and highly recombined MAGIC population to be a powerful resource for the genetic dissection of target traits in wheat, and it is well-placed to efficiently exploit ongoing advances in phenomics and genomics. Genetic marker and trait data, together with instructions for access to seed, are available at http://www.niab.com/MAGIC/.

Towards a general theory of extremes for observables of chaotic dynamical systems

In this paper we provide a connection between the geometrical properties of the attractor of a chaotic dynamical system and the distribution of extreme values. We show that the extremes of so-called physical observables are distributed according to the classical generalised Pareto distribution and derive explicit expressions for the scaling and the shape parameter. In particular, we derive that the shape parameter does not depend on the cho- sen observables, but only on the partial dimensions of the invariant measure on the stable, unstable, and neutral manifolds. The shape parameter is negative and is close to zero when high-dimensional systems are considered. This result agrees with what was derived recently using the generalized extreme value approach. Combining the results obtained using such physical observables and the properties of the extremes of distance observables, it is possible to derive estimates of the partial dimensions of the attractor along the stable and the unstable directions of the flow. Moreover, by writing the shape parameter in terms of moments of the extremes of the considered observable and by using linear response theory, we relate the sensitivity to perturbations of the shape parameter to the sensitivity of the moments, of the partial dimensions, and of the Kaplan–Yorke dimension of the attractor. Preliminary numer- ical investigations provide encouraging results on the applicability of the theory presented here. The results presented here do not apply for all combinations of Axiom A systems and observables, but the breakdown seems to be related to very special geometrical configurations.

Comparative analysis of performance and stability among composite cross populations, variety mixtures and pure lines of winter wheat in organic and conventional cropping systems

This study investigated the effects of increased genetic diversity in winter wheat (Triticum aestivum L.), either from hybridization across genotypes or from physical mixing of lines, on grain yield, grain quality, and yield stability in different cropping environments. Sets of pure lines (no diversity), chosen for high yielding ability or high quality, were compared with line mixtures (intermediate level of diversity), and lines crossed with each other in composite cross populations (CCPn, high diversity). Additional populations containing male sterility genes (CCPms) to increase outcrossing rates were also tested. Grain yield, grain protein content, and protein yield were measured at four sites (two organically-managed and two conventionally-managed) over three years, using seed harvested locally in each preceding year. CCPn and mixtures out-yielded the mean of the parents by 2.4% and 3.6%, respectively. These yield differences were consistent across genetic backgrounds but partly inconsistent across cropping environments and years. Yield stability measured by environmental variance was higher in CCPn and CCPms than the mean of the parents. An index of yield reliability tended to be higher in CCPn, CCPms and mixtures than the mean of the parents. Lin and Binns’ superiority values of yield and protein yield were consistently and significantly lower (i.e. better) in the CCPs than in the mean of the parents, but not different between CCPs and mixtures. However, CCPs showed greater early ground cover and plant height than mixtures. When compared with the (locally non-predictable) best-yielding pure line, CCPs and mixtures exhibited lower mean yield and somewhat lower yield reliability but comparable superiority values. Thus, establishing CCPs from smaller sets of high-performing parent lines might optimize their yielding ability. On the whole, the results demonstrate that using increased within-crop genetic diversity can produce wheat crops with improved yield stability and good yield reliability across variable and unpredictable cropping environments.

A new adaptive multiple modelling approach for non-linear and non-stationary systems

This paper proposes a novel adaptive multiple modelling algorithm for non-linear and non-stationary systems. This simple modelling paradigm comprises K candidate sub-models which are all linear. With data available in an online fashion, the performance of all candidate sub-models are monitored based on the most recent data window, and M best sub-models are selected from the K candidates. The weight coefficients of the selected sub-model are adapted via the recursive least square (RLS) algorithm, while the coefficients of the remaining sub-models are unchanged. These M model predictions are then optimally combined to produce the multi-model output. We propose to minimise the mean square error based on a recent data window, and apply the sum to one constraint to the combination parameters, leading to a closed-form solution, so that maximal computational efficiency can be achieved. In addition, at each time step, the model prediction is chosen from either the resultant multiple model or the best sub-model, whichever is the best. Simulation results are given in comparison with some typical alternatives, including the linear RLS algorithm and a number of online non-linear approaches, in terms of modelling performance and time consumption.