A generalization of the Bonferroni mean based on partitions

The mean defined by Bonferroni in 1950 (known by the same name) averages all non-identical product pairs of the inputs. Its generalizations to date have been able to capture unique behavior that may be desired in some decision-making contexts such as the ability to model mandatory requirements. In this paper, we propose a composition that averages conjunctions between the respective means of a designated subset-size partition. We investigate the behavior of such a function and note the relationship within a given family as the subset size is changed. We found that the proposed function is able to more intuitively handle multiple mandatory requirements or mandatory input sets.

Knowing doesn’t mean doing—bridging the colorectal cancer screening gap

Two behavioural approaches to influence colorectal cancer screening uptake were investigated. Results suggest that the impact of messages that address (1) ease and convenience and (2) social support and endorsement may depend upon the extent to which they successfully address concerns specific to an individual’s current state of readiness to participate.

The foreign presence in the early-industrial Haut-Rhin, 1820–22 : a short history from the ‘pre-history’ of immigration to France

An attempt to limit the rights of domiciled foreigners in the Alsatian department of the Haut-Rhin in 1821 provides an opportunity to examine the impact of immigration on early-industrial society and shifting perceptions of the place of foreigners in French society in a period often omitted from histories of immigration. New conceptions of belonging become evident, which demonstrate a turn away from local and subjective bonds to community, towards bonds regulated nationally through nationality law. Imposed in an emerging urban, industrial context, the limitations of rights—on access to the biens communaux and the droit d’affouage—were traditional restrictions of rural society and modes of distinguishing the included from the excluded and were imposed on long-settled foreigners who failed to become naturalized as French citizens. The article reflects on the question why, if the concerns about immigration and industrialization turned on recently arrived foreign workers, these traditional exclusions were imposed on established resident foreigners.

Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, Part I: main content

Dynamic treatment regimes are set rules for sequential decision making based on patient covariate history. Observational studies are well suited for the investigation of the effects of dynamic treatment regimes because of the variability in treatment decisions found in them. This variability exists because different physicians make different decisions in the face of similar patient histories. In this article we describe an approach to estimate the optimal dynamic treatment regime among a set of enforceable regimes. This set is comprised by regimes defined by simple rules based on a subset of past information. The regimes in the set are indexed by a Euclidean vector. The optimal regime is the one that maximizes the expected counterfactual utility over all regimes in the set. We discuss assumptions under which it is possible to identify the optimal regime from observational longitudinal data. Murphy et al. (2001) developed efficient augmented inverse probability weighted estimators of the expected utility of one fixed regime. Our methods are based on an extension of the marginal structural mean model of Robins (1998, 1999) which incorporate the estimation ideas of Murphy et al. (2001). Our models, which we call dynamic regime marginal structural mean models, are specially suitable for estimating the optimal treatment regime in a moderately small class of enforceable regimes of interest. We consider both parametric and semiparametric dynamic regime marginal structural models. We discuss locally efficient, double-robust estimation of the model parameters and of the index of the optimal treatment regime in the set. In a companion paper in this issue of the journal we provide proofs of the main results.

Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, Part II: proofs of results

In this companion article to "Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part I: Main Content" [Orellana, Rotnitzky and Robins (2010), IJB, Vol. 6, Iss. 2, Art. 7] we present (i) proofs of the claims in that paper, (ii) a proposal for the computation of a confidence set for the optimal index when this lies in a finite set, and (iii) an example to aid the interpretation of the positivity assumption.

An optimized mean variance estimation method for uncertainty quantification of wind power forecasts

A statistical optimized technique for rapid development of reliable prediction intervals (PIs) is presented in this study. The mean-variance estimation (MVE) technique is employed here for quantification of uncertainties related with wind power predictions. In this method, two separate neural network models are used for estimation of wind power generation and its variance. A novel PI-based training algorithm is also presented to enhance the performance of the MVE method and improve the quality of PIs. For an in-depth analysis, comprehensive experiments are conducted with seasonal datasets taken from three geographically dispersed wind farms in Australia. Five confidence levels of PIs are between 50% and 90%. Obtained results show while both traditional and optimized PIs are hypothetically valid, the optimized PIs are much more informative than the traditional MVE PIs. The informativeness of these PIs paves the way for their application in trouble-free operation and smooth integration of wind farms into energy systems. © 2014 Elsevier Ltd. All rights reserved.

New variable step-sizes minimizing mean-square deviation for the lms-type algorithms

The least-mean-square-type (LMS-type) algorithms are known as simple and effective adaptation algorithms. However, the LMS-type algorithms have a trade-off between the convergence rate and steady-state performance. In this paper, we investigate a new variable step-size approach to achieve fast convergence rate and low steady-state misadjustment. By approximating the optimal step-size that minimizes the mean-square deviation, we derive variable step-sizes for both the time-domain normalized LMS (NLMS) algorithm and the transform-domain LMS (TDLMS) algorithm. The proposed variable step-sizes are simple quotient forms of the filtered versions of the quadratic error and very effective for the NLMS and TDLMS algorithms. The computer simulations are demonstrated in the framework of adaptive system modeling. Superior performance is obtained compared to the existing popular variable step-size approaches of the NLMS and TDLMS algorithms. © 2014 Springer Science+Business Media New York.

Classification of neural action potentials using mean shift clustering

 Understanding neural functions requires the observation of the activities of single neurons that are represented via electrophysiological data. Processing and understanding these data are challenging problems in biomedical engineering. A microelectrode commonly records the activity of multiple neurons. Spike sorting is a process of classifying every single action potential (spike) to a particular neuron. This paper proposes a combination between diffusion maps (DM) and mean shift clustering method for spike sorting. DM is utilized to extract spike features, which are highly capable of discriminating different spike shapes. Mean shift clustering provides an automatic unsupervised clustering, which takes extracted features from DM as inputs. Experimental results show a noticeable dominance of the features extracted by DM compared to those selected by wavelet transformation (WT). Accordingly, the proposed integrated method is significantly superior to the popular existing combination of WT and superparamagnetic clustering regarding spike sorting accuracy.

The effect of low cycle fatigue, ratcheting and mean stress relaxation on stress-strain response and microstructural development in a dual phase steel

This study examines the cyclic plastic deformation behavior and microstructural development of a dual phase steel in both symmetric and asymmetric cycling in strain and stress control modes. The low-cycle fatigue (LCF) and mean stress relaxation (MSR) tests show very similar fatigue lifetimes. However, fatigue lifetimes reduce and prominent accumulation of directional strain was observed in ratcheting. A microstructural analysis has revealed that the type of cyclic test carried out has a noticeable impact on the substructural development, and this has been correlated with differences in accumulated tensile strain. Electron backscatter diffraction investigation has shown larger in-grain misorientation for ratcheting specimen in comparison with LCF and MSR specimens. The orientation of ferrite grains was found to have very little effect on their substructural development, and strain localization commonly occurred in the ferrite at the ferrite/martensite interface.

At the discretion of management: immigration detention in Indonesia

 This chapter outlines the policy, practice, and human impact of immigration detention in Indonesia. An important part of the Indonesian immigration detention story is the role of Australia, and this chapter explains how Australian diplomacy, human resources, and funding have been central to the development of Indonesia's immigration detention network.