Weakly monotonic averaging functions

Monotonicity with respect to all arguments is fundamental to the definition of aggregation functions. It is also a limiting property that results in many important nonmonotonic averaging functions being excluded from the theoretical framework. This work proposes a definition for weakly monotonic averaging functions, studies some properties of this class of functions, and proves that several families of important nonmonotonic means are actually weakly monotonic averaging functions. Specifically, we provide sufficient conditions for weak monotonicity of the Lehmer mean and generalized mixture operators. We establish weak monotonicity of several robust estimators of location and conditions for weak monotonicity of a large class of penalty-based aggregation functions. These results permit a proof of the weak monotonicity of the class of spatial-tonal filters that include important members such as the bilateral filter and anisotropic diffusion. Our concept of weak monotonicity provides a sound theoretical and practical basis by which (monotonic) aggregation functions and nonmonotonic averaging functions can be related within the same framework, allowing us to bridge the gap between these previously disparate areas of research.

Adaptive-multi-reference least means squares filter

Adaptive filters are now becoming increasingly studied for their suitability in application to complex and non-stationary signals. Many adaptive filters utilise a reference input, that is used to form an estimate of the noise in the target signal. In this paper we discuss the application of adaptive filters for high electromyography contaminated electroencephalography data. We propose the use of multiple referential inputs instead of the traditional single input. These references are formed using multiple EMG sensors during an EEG experiment, each reference input is processed and ordered through firstly determining the Pearson’s r-squared correlation coefficient, from this a weighting metric is determined and used to scale and order the reference channels according to the paradigm shown in this paper. This paper presents the use and application of the Adaptive-Multi-Reference (AMR) Least Means Square adaptive filter in the domain of electroencephalograph signal acquisition.

Multisite analysis of the timing and outcomes of unplanned transfers from subacute to acute care

Objective The aim of the present study was to examine the timing and outcomes of patients requiring an unplanned transfer from subacute to acute care. Methods Subacute care in-patients requiring unplanned transfer to an acute care facility within four Victorian health services from 1 January to 31 December 2010 were included in the study. Data were collected using retrospective audit. The primary outcome was transfer within 24h of subacute care admission. Results In all, 431 patients (median age 81 years) had unplanned transfers; of these, 37.8% had a limitation of medical treatment (LOMT) order. The median subacute care length of stay was 43h: 29.0% of patients were transferred within 24h and 83.5% were transferred within 72h of subacute care admission. Predictors of transfer within 24h were comorbidity weighting (odds ratio (OR) 1.1, P≤0.02) and LOMT order (OR 2.1, P<0.01). Hospital admission occurred in 87.2% of patients and 15.4% died in hospital. Predictors of in-hospital mortality were comorbidity weighting (OR 1.2, P<0.01) and the number of physiological abnormalities in the 24h preceding transfer (OR 1.3, P<0.01). Conclusions There is a high rate of unplanned transfers to acute care within 24h of admission to subacute care. Unplanned transfers are associated with high hospital admission and in-hospital mortality rates. What is known about the topic? Subacute care is becoming a high acuity environment where many patients are at significant risk of clinical deterioration. Systems for recognising and responding to deteriorating patients are well developed in acute care, but still developing in subacute care. What does this paper add? This is the first Australian multisite study of clinical deterioration in patients situated in subacute care facilities. One-third of unplanned transfers occur within 24h of admission to subacute care. Patients who require unplanned transfer from subacute to acute care have unexpectedly high hospital admission rates and high in-hospital mortality rates. The frequency and completeness of physiological monitoring preceding transfer was low. What are the implications for practitioners? Patients in subacute care require regular physiological assessment and early escalation of care if there are physiological abnormalities. Risk of clinical deterioration should be a factor in the decision to admit patients to subacute care after an acute illness or injury. There is a need to improve systems for recognising and responding to deteriorating patients in subacute care settings.

On the weak monotonicity of Gini means and other mixture functions

Weak monotonicity was recently proposed as a relaxation of the monotonicity condition for averaging aggregation, and weakly monotone functions were shown to have desirable properties when averaging data corrupted with outliers or noise. We extended the study of weakly monotone averages by analyzing their ϕ-transforms, and we established weak monotonicity of several classes of averaging functions, in particular Gini means and mixture operators. Mixture operators with Gaussian weighting functions were shown to be weakly monotone for a broad range of their parameters. This study assists in identifying averaging functions suitable for data analysis and image processing tasks in the presence of outliers.

Intelligent facial emotion recognition using a layered encoding cascade optimization model

In this research, we propose a facial expression recognition system with a layered encoding cascade optimization model. Since generating an effective facial representation is a vital step to the success of facial emotion recognition, a modified Local Gabor Binary Pattern operator is first employed to derive a refined initial face representation and we then propose two evolutionary algorithms for feature optimization including (i) direct similarity and (ii) Pareto-based feature selection, under the layered cascade model. The direct similarity feature selection considers characteristics within the same emotion category that give the minimum within-class variation while the Pareto-based feature optimization focuses on features that best represent each expression category and at the same time provide the most distinctions to other expressions. Both a neural network and an ensemble classifier with weighted majority vote are implemented for the recognition of seven expressions based on the selected optimized features. The ensemble model also automatically updates itself with the most recent concepts in the data. Evaluated with the Cohn-Kanade database, our system achieves the best accuracies when the ensemble classifier is applied, and outperforms other research reported in the literature with 96.8% for direct similarity based optimization and 97.4% for the Pareto-based feature selection. Cross-database evaluation with frontal images from the MMI database has also been conducted to further prove system efficiency where it achieves 97.5% for Pareto-based approach and 90.7% for direct similarity-based feature selection and outperforms related research for MMI. When evaluated with 90° side-view images extracted from the videos of the MMI database, the system achieves superior performances with >80% accuracies for both optimization algorithms. Experiments with other weighting and meta-learning combination methods for the construction of ensembles are also explored with our proposed ensemble showing great adpativity to new test data stream for cross-database evaluation. In future work, we aim to incorporate other filtering techniques and evolutionary algorithms into the optimization models to further enhance the recognition performance.

Perceived fairness of pay among people with and without disabilities: a propensity score matched analysis of working Australians

OBJECTIVES: Equity and fairness at work are associated with a range of organizational and health outcomes. Past research suggests that workers with disabilities experience inequity in the workplace. It is difficult to conclude whether the presence of disability is the reason for perceived unfair treatment due to the possible confounding of effect estimates by other demographic or socioeconomic factors. METHODS: The data source was the Household, Income, and Labor Dynamics in Australia (HILDA) survey (2001-2012). Propensity for disability was calculated from logistic models including gender, age, education, country of birth, and father's occupational skill level as predictors. We then used nearest neighbor (on propensity score) matched analysis to match workers with disabilities to workers without disability. RESULTS: Results suggest that disability is independently associated with lower fairness of pay after controlling for confounding factors in the propensity score matched analysis; although results do suggest less than half a standard deviation difference, indicating small effects. Similar results were apparent in standard multivariable regression models and alternative propensity score analyses (stratification, covariate adjustment using the propensity score, and inverse probability of treatment weighting). CONCLUSIONS: Whilst neither multivariable regression nor propensity scores adjust for unmeasured confounding, and there remains the potential for other biases, similar results for the two methodological approaches to confounder adjustment provide some confidence of an independent association of disability with perceived unfairness of pay. Based on this, we suggest that the disparity in the perceived fairness of pay between people with and without disabilities may be explained by worse treatment of people with disabilities in the workplace.

Mining allocating patterns in one-sum weighted items

An Association Rule (AR) is a common knowledge model in data mining that describes an implicative cooccurring relationship between two disjoint sets of binary-valued transaction database attributes (items), expressed in the form of an "antecedent⇒ consequent" rule. A variant of the AR is the Weighted Association Rule (WAR). With regard to a marketing context, this paper introduces a new knowledge model in data mining -ALlocating Pattern (ALP). An ALP is a special form of WAR, where each rule item is associated with a weighting score between 0 and 1, and the sum of all rule item scores is 1. It can not only indicate the implicative co-occurring relationship between two (disjoint) sets of items in a weighted setting, but also inform the "allocating" relationship among rule items. ALPs can be demonstrated to be applicable in marketing and possibly a surprising variety of other areas. We further propose an Apriori based algorithm to extract hidden and interesting ALPs from a "one-sum" weighted transaction database. The experimental results show the effectiveness of the proposed algorithm. © 2008 IEEE.

A preliminary phylogenetic study of late Palaeozoic spiriferoid brachiopods using cladistic and Bayesian approaches

Abstract The brachiopod Superfamily Spiriferoidea diversified greatly and was widely distributed in the late Palaeozoic (Carboniferous–Permian), and yet its phylogeny has been seldom investigated with analytical methods. This is reflected in the current flux of very different classification schemes for this superfamily. This paper provides the first attempt to investigate the phylogenetic relationships of spiriferoid brachiopods through both cladistic and Bayesian analyses involving 24 discrete and continuous characters. The continuous characters, from morphometric data, have been separately discretized using the gap weighting method, and the ‘as such’ option in TNT. Our results highlight the potential significance of continuous characters in reconstructing and elucidating phylogenies, as much as qualitative characters. Building on the outcomes of the analyses, we also briefly evaluate existing classification schemes of Spiriferoidea. We found that none of the existing classifications fully reflect the phylogeny properly; major families within the superfamily, such as Spiriferidae, Choristitidae, and Trigonotretidae, turned out to be polyphyletic. Although this study is considered preliminary, due to the selection of and restriction to certain taxa, combined with the use of a relatively small number of characters, it nevertheless demonstrates that potentially the true phylogenetic relationships of spiriferoid taxa sharply contrast with any of the existing classification schemes. This highlights the need to develop an alternative scheme that takes into account a more comprehensive range of phylogenetic variables.

The passion plays of celebrity culture

If there is a cultural arena today where passion plays a central and heightened role, then it is in the affecting and textural operations of celebrity. Celebrity representations are crafted out of passionate aesthetic signifiers and impassioned pleas to the senses, to the emotions and to the exaggerations of feeling that the consumer or fan is asked to register and then fully embody. Celebrity culture attempts to turn one into a passionate creature, ruled by the heart, lost in a sea of desires and desiring wants and needs, as the adoring figure that moves us, moves intimately before us. Such passions can and do go unrequited, of course; some are resisted and rejected, and some celebrity passions register as fully carnal and liberating encounters. That is to say, the plays of celebrity passion serve (hetero) normative and policed accounts of feeling and belonging in the world, fuel a desire for commodity objects and material possessions, and yet also open up the possibility for engagements that are violent, liberal and unregulated. In this article, I will explore the ways in which celebrity culture engages with passion and through the idea of it involving a modern form of the passion play. Following Lauren Berlant, I will argue that the passion ignited by the celebrity works to contain and regulate desire, and yet also offers up the opportunity for sensorial engagements that violate and resist the normative terms of desiring. I will suggest celebrity figures are themselves caught up in this passion play, suffering and feeling deeply at the same time, while channelling this violent crisis to their fans as they do so. Finally, I will write the article passionately, from an impassioned perspective, measuring and weighting my own desires in the contradictions and tensions of passion as they emerge in the body of the writer before you. This article is part of a themed issue entitled ‘Passion’.

Synchronization of singular Markovian jumping complex networks with additive time-varying delays via pinning control

Abstract
This study examines the problem of synchronization for singular complex dynamical networks with Markovian jumping parameters and two additive time-varying delay components. The complex networks consist of m modes which switch from one mode to another according to a Markovian chain with known transition probability. Pinning control strategies are designed to make the singular complex networks synchronized. Based on the appropriate Lyapunov-Krasovskii functional, introducing some free weighting matrices and using convexity of matrix functions, a novel synchronization criterion is derived. The proposed sufficient conditions are established in the form of linear matrix inequalities. Finally, a numerical example is presented to illustrate the effectiveness of the obtained results.

Resolving future fire management conflicts using multicriteria decision making

Management strategies to reduce the risks to human life and property from wildfire commonly involve burning native vegetation. However, planned burning can conflict with other societal objectives such as human health and biodiversity conservation. These conflicts are likely to intensify as fire regimes change under future climates and as growing human populations encroach farther into fire-prone ecosystems. Decisions about managing fire risks are therefore complex and warrant more sophisticated approaches than are typically used. We applied a multicriteria decision making approach (MCDA) with the potential to improve fire management outcomes to the case of a highly populated, biodiverse, and flammable wildland-urban interface. We considered the effects of 22 planned burning options on 8 objectives: house protection, maximizing water quality, minimizing carbon emissions and impacts on human health, and minimizing declines of 5 distinct species types. The MCDA identified a small number of management options (burning forest adjacent to houses) that performed well for most objectives, but not for one species type (arboreal mammal) or for water quality. Although MCDA made the conflict between objectives explicit, resolution of the problem depended on the weighting assigned to each objective. Additive weighting of criteria traded off the arboreal mammal and water quality objectives for other objectives. Multiplicative weighting identified scenarios that avoided poor outcomes for any objective, which is important for avoiding potentially irreversible biodiversity losses. To distinguish reliably among management options, future work should focus on reducing uncertainty in outcomes across a range of objectives. Considering management actions that have more predictable outcomes than landscape fuel management will be important. We found that, where data were adequate, an MCDA can support decision making in the complex and often conflicted area of fire management.

Activities between activities of focus-Relevant when assessing DNA transfer probabilities

Consideration of the indirect transfer of 'touch DNA' is increasingly becoming part of criminal investigations. Focus is often concentrated on the actions relating to the pick-up of the relevant DNA and key actions associated with transfer to the exhibit from which the sample in question was collected. There is often a time lapse between such actions. As any contact can influence the gain and/or loss of DNA, it is relevant to have an awareness of what hands touch during everyday activities in order to assist consideration of what may be occurring during potential time lapses within contemplated scenarios. To gain an appreciation of the manner and frequency of hands contacting various surfaces during everyday activities, we analysed several videos of individuals performing a variety of general activities. The findings indicate that several items are touched over a relatively short period of time. Appreciation and consideration of general activities that may have occurred between key focus activities are necessary to assess any impact these may have on what is deposited at the final collection site. The information this provides is imperative when weighting alternative transfer scenario propositions.

An integrated workplace mental health intervention in a policing context: protocol for a cluster randomised control trial

BACKGROUND: In this paper, we present the protocol for a cluster-randomised trial to evaluate the implementation and effectiveness of a workplace mental health intervention in the state-wide police department of the south-eastern Australian state of Victoria. n. The primary aims of the intervention are to improve psychosocial working conditions and mental health literacy, and secondarily to improve mental health and organisational outcomes.

METHODS/DESIGN: The intervention was designed collaboratively with Victoria Police based on a mixed methods pilot study, and combines multi-session leadership coaching for the senior officers within stations (e.g., Sergeants, Senior Sergeants) with tailored mental health literacy training for lower and upper ranks. Intervention effectiveness will be evaluated using a two-arm cluster-randomised trial design, with 12 police stations randomly assigned to the intervention and 12 to the non-intervention/usual care control condition. Data will be collected from all police members in each station (estimated at >20 per station). Psychosocial working conditions (e.g., supervisory support, job control, job demands), mental health literacy (e.g., knowledge, confidence in assisting someone who may have a mental health problem), and mental health will be assessed using validated measures. Organisational outcomes will include organisational depression disclosure norms, organisational cynicism, and station-level sickness absence rates. The trial will be conducted following CONSORT guidelines. Identifying data will not be collected in order to protect participant privacy and to optimise participation, hence changes in primary and secondary outcomes will be assessed using a two-sample t-test comparing summary measures by arm, with weighting by cluster size.

DISCUSSION: This intervention is novel in its integration of stressor-reduction and mental health literacy-enhancing strategies. Effectiveness will be rigorously evaluated, and if positive results are observed, the intervention will be adapted across Victoria Police (total employees ~16,500) as well as possibly in other policing contexts, both nationally and internationally.

Biased experts and similarity based weights in preferences aggregation

In a group decision making setting, we consider the potential impact an expert can have on the overall ranking by providing a biased assessment of the alternatives that differs substantially from the majority opinion. In the framework of similarity based averaging functions, we show that some alternative approaches to weighting the experts' inputs during the aggregation process can minimize the influence the biased expert is able to exert.

Linear functional observers with guaranteed ε-convergence for discrete time-delay systems with input/output disturbances

The problem of designing linear functional observers for discrete time-delay systems with unknown-but-bounded disturbances in both the plant and the output is considered for the first time in this paper. A novel approach to design a minimum-order observer is proposed to guarantee that the observer error is ϵ-convergent, which means that the estimate converges robustly within an ϵ-bound of the true state. Conditions for the existence of this observer are first derived. Then, by utilising an extended Lyapunov-Krasovskii functional and the free-weighting matrix technique, a sufficient condition for ϵ-convergence of the observer error system is given. This condition is presented in terms of linear matrix inequalities with two parameters needed to be tuned, so that it can be efficiently solved by incorporating a two-dimensional search method into convex optimisation algorithms to obtain the smallest possible value for ϵ. Three numerical examples, including the well-known single-link flexible joint robotic system, are given to illustrate the feasibility and effectiveness of our results.