Projected sequential Gaussian processes:a C++ tool for interpolation of large datasets with heterogeneous noise

Heterogeneous datasets arise naturally in most applications due to the use of a variety of sensors and measuring platforms. Such datasets can be heterogeneous in terms of the error characteristics and sensor models. Treating such data is most naturally accomplished using a Bayesian or model-based geostatistical approach; however, such methods generally scale rather badly with the size of dataset, and require computationally expensive Monte Carlo based inference. Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential Bayesian framework for inference in such projected processes is presented. The observations are considered one at a time which avoids the need for high dimensional integrals typically required in a Bayesian approach. A C++ library, gptk, which is part of the INTAMAP web service, is introduced which implements projected, sequential estimation and adds several novel features. In particular the library includes the ability to use a generic observation operator, or sensor model, to permit data fusion. It is also possible to cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the covariance parameters is explored, including the impact of the projected process approximation on likelihood profiles. We illustrate the projected sequential method in application to synthetic and real datasets. Limitations and extensions are discussed. © 2010 Elsevier Ltd.

Mental health pharmacists views on shared decision-making for antipsychotics in serious mental illness

Background People diagnosed with serious mental illnesses (SMIs) such as schizophrenia and bipolar affective disorder are frequently treated with antipsychotics. National guidance advises the use of shared decision-making (SDM) in antipsychotic prescribing. There is currently little data on the opinions of health professionals on the role of SDM. Objective To explore the views and experiences of UK mental health pharmacists regarding the use of SDM in antipsychotic prescribing in people diagnosed with SMI. Setting The study was conducted by interviewing secondary care mental health pharmacists in the UK to obtain qualitative data. Methods Semi-structured interviews were recorded. An inductive thematic analysis was conducted using the method of constant comparison. Main outcome measure Themes evolving from mental health pharmacists on SDM in relation to antipsychotic prescribing in people with SMI. Results Thirteen mental health pharmacists were interviewed. SDM was perceived to be linked to positive clinical outcomes including adherence, service user satisfaction and improved therapeutic relations. Despite more prescribers and service users supporting SDM, it was not seen as being practised as widely as it could be; this was attributed to a number of barriers, most predominantly issues surrounding service user’s lacking capacity to engage in SDM and time pressures on clinical staff. The need for greater effort to work around the issues, engage service users and adopt a more inter-professional approach was conveyed. Conclusion The mental health pharmacists support SDM for antipsychotic prescribing, believing that it improves outcomes. However, barriers are seen to limit implementation. More research is needed into overcoming the barriers and measuring the benefits of SDM, along with exploring a more inter-professional approach to SDM.

Automatic maintenance of category hierarchy

Category hierarchy is an abstraction mechanism for efficiently managing large-scale resources. In an open environment, a category hierarchy will inevitably become inappropriate for managing resources that constantly change with unpredictable pattern. An inappropriate category hierarchy will mislead the management of resources. The increasing dynamicity and scale of online resources increase the requirement of automatically maintaining category hierarchy. Previous studies about category hierarchy mainly focus on either the generation of category hierarchy or the classification of resources under a pre-defined category hierarchy. The automatic maintenance of category hierarchy has been neglected. Making abstraction among categories and measuring the similarity between categories are two basic behaviours to generate a category hierarchy. Humans are good at making abstraction but limited in ability to calculate the similarities between large-scale resources. Computing models are good at calculating the similarities between large-scale resources but limited in ability to make abstraction. To take both advantages of human view and computing ability, this paper proposes a two-phase approach to automatically maintaining category hierarchy within two scales by detecting the internal pattern change of categories. The global phase clusters resources to generate a reference category hierarchy and gets similarity between categories to detect inappropriate categories in the initial category hierarchy. The accuracy of the clustering approaches in generating category hierarchy determines the rationality of the global maintenance. The local phase detects topical changes and then adjusts inappropriate categories with three local operations. The global phase can quickly target inappropriate categories top-down and carry out cross-branch adjustment, which can also accelerate the local-phase adjustments. The local phase detects and adjusts the local-range inappropriate categories that are not adjusted in the global phase. By incorporating the two complementary phase adjustments, the approach can significantly improve the topical cohesion and accuracy of category hierarchy. A new measure is proposed for evaluating category hierarchy considering not only the balance of the hierarchical structure but also the accuracy of classification. Experiments show that the proposed approach is feasible and effective to adjust inappropriate category hierarchy. The proposed approach can be used to maintain the category hierarchy for managing various resources in dynamic application environment. It also provides an approach to specialize the current online category hierarchy to organize resources with more specific categories.

Measuring employee innovation:a review of existing scales and the development of the innovative behavior and innovation support inventories across cultures

Purpose - The paper develops a model of employee innovative behavior conceptualizing it as distinct from innovation outputs and as a multi-faceted behavior rather than a simple count of ‘innovative acts’ by employees. It understands individual employee innovative behaviors as a micro-foundation of firm intrapreneurship that is embedded in and influenced by contextual factors such as managerial, organizational and cultural support for innovation. Building from a review of existing employee innovative behavior scales and theoretical considerations we develop and validate the Innovative Behavior Inventory (IBI) and the Innovation Support Inventory (ISI). Design/methodology/approach – Two pilot studies, a third validation study in the Czech Republic and a fourth cross-cultural validation study using population representative samples from Switzerland, Germany, Italy and the Czech Republic (N=2812 employees and 450 entrepreneurs) were conducted. Findings - Both inventories were reliable and showed factorial, criterion, convergent and discriminant validity as well as cross-cultural equivalence. Employee innovative behavior was supported as comprising of idea generation, idea search, idea communication, implementation starting activities, involving others and overcoming obstacles. Managerial support was the most proximal contextual influence on innovative behavior and mediated the effect of organizational support and national culture. Originality/value - The paper advances our understanding of employee innovative behavior as a multi-faceted phenomenon and the contextual factors influencing it. Where past research typically focuses on convenience samples within a particular country, we offer first robust evidence that our model of employee innovative behavior generalizes across cultures and types of samples. Our model and the IBI and ISI inventories enable researchers to build a deeper understanding of the important micro-foundation underpinning intrapreneurial behavior in organizations and allow practitioners to identify their organizations’ strengths and weaknesses related to intrapreneurship.

Metaphors in Hugo Chávez's political discourse:conceptualizing nation, revolution and opposition

This book provides the first comprehensive analysis of metaphors used by Hugo Chávez in his efforts to construct and legitimize his Bolivarian Revolution. It focuses on metaphors drawn from three of his most frequent target domains: the nation, his revolution, and the opposition. The author argues that behind an official discourse of inclusion, Chávez's choice of metaphors contributes to the construction of a polarizing discourse of exclusion in which his political opponents are represented as enemies of the nation. Chávez constructs this polarizing discourse of exclusion by combining metaphors that conceptualize: (a) the nation as a person who has been resurrected by his government, as a person ready to fight for his revolution, or as Chávez himself; (b) the revolution as war; and (c) members of the opposition as war combatants or criminals. At the same time, by making explicit references in his discourse about the revolution as the continuation of Simón Bolívar's wars of independence, Chávez contributes to represent opponents as enemies of the nation, given that in the Venezuelan collective imaginary Bolívar is the symbol of the nation's emancipation.

Measuring and managing performance in extended enterprises

Purpose - The purpose of this research paper is to demonstrate how existing performance measurement may be adopted to measure and manage performance in extended enterprises. Design/methodology/approach - The paper reviews the literature in performance measurement and extended enterprises. It explains the collaborative architecture of an extended enterprise and demonstrates this architecture through a case study. A model for measuring and managing performance in extended enterprises is developed using the case study. Findings - The research found that due to structural differences between traditional and extended enterprises, the systems required to measure and manage the performance of extended enterprises, whilst being based upon existing performance measurement frameworks, would be structurally and operationally different. Based on this, a model for measuring and managing performance in extended enterprises is proposed which includes intrinsic and extrinsic inter-enterprise coordinating measures. Research limitations/implications - There are two limitations this research. First, the evidence is based on a single case, thus further cases should be studied to establish the generalisibility of the presented results. Second, the practical limitations of the EE performance measurement model should be established through longitudinal action research. Practical implications - In practice the model proposed requires collaborating organisations to be more open and share critical performance information with one another. This will require change in practices and attitudes. Originality/value - The main contribution this paper makes is that it highlights the structural differences between traditional and collaborative enterprises and specifies performance measurement and management requirements of these collaborative organisations. © Emerald Group Publishing Limited.

Application of analytic hierarchy process for measuring and comparing the global performance of intensive care units

Purpose: To develop a model for the global performance measurement of intensive care units (ICUs) and to apply that model to compare the services for quality improvement. Materials and Methods: Analytic hierarchy process, a multiple-attribute decision-making technique, is used in this study to evolve such a model. The steps consisted of identifying the critical success factors for the best performance of an ICU, identifying subfactors that influence the critical factors, comparing them pairwise, deriving their relative importance and ratings, and calculating the cumulative performance according to the attributes of a given ICU. Every step in the model was derived by group discussions, brainstorming, and consensus among intensivists. Results: The model was applied to 3 ICUs, 1 each in Barbados, Trinidad, and India in tertiary care teaching hospitals of similar setting. The cumulative performance rating of the Barbados ICU was 1.17 when compared with that of Trinidad and Indian ICU, which were 0.82 and 0.75, respectively, showing that the Trinidad and Indian ICUs performed 70% and 64% with respect to Barbados ICU. The model also enabled identifying specific areas where the ICUs did not perform well, which helped to improvise those areas. Conclusions: Analytic hierarchy process is a very useful model to measure the global performance of an ICU. © 2005 Elsevier Inc. All rights reserved.

A combined neural network and DEA for measuring efficiency of large scale datasets

Data Envelopment Analysis (DEA) is one of the most widely used methods in the measurement of the efficiency and productivity of Decision Making Units (DMUs). DEA for a large dataset with many inputs/outputs would require huge computer resources in terms of memory and CPU time. This paper proposes a neural network back-propagation Data Envelopment Analysis to address this problem for the very large scale datasets now emerging in practice. Neural network requirements for computer memory and CPU time are far less than that needed by conventional DEA methods and can therefore be a useful tool in measuring the efficiency of large datasets. Finally, the back-propagation DEA algorithm is applied to five large datasets and compared with the results obtained by conventional DEA.

The development of a short scale measuring aggressiveness and anger in competitive athletes

Objectives: The study of aggression and anger in competitive sport relies on accurate and economical measurement via observation, interview and questionnaire. Unfortunately, extant questionnaires have been criticised for having poor validity, are not sport specific, or reflect mood states rather than trait qualities. Therefore, a measure of trait anger and aggressiveness in competitive athletes was developed. Method: A list of statements representing aggressiveness and anger was generated and distributed to competitive athletes from diverse sports. Exploratory and confirmatory analyses were used to verify the theoretically predicted factor structure. Correlations with an extant measure of aggression and anger were used to ascertain concurrent validity. Discriminant validity was tested by comparing males with females, and aggressive with non-aggressive footballers. Results: A 12-item scale (Competitive Aggressiveness and Anger Scale, CAAS) consisting of two subscales was derived using principal component factor analysis with oblimin rotation. Confirmatory factor analysis using structural equation modelling confirmed the overall structure. Test-retest correlation, construct and discriminant validities were good, supporting the utility of the scale as a measure of athlete trait aggressiveness and anger. Conclusions: The CAAS appears to be a useful measure of athletic anger and aggressiveness. Its brevity and ability to discriminate aggressive from non-aggressive athletes should prove useful for future research concerning aggressive behaviour in competitive athletes. © 2006 Elsevier Ltd. All rights reserved.

Measuring aggressiveness and anger, but not aggression? A response to the CAAS critique

Objectives: The Competitive Aggressiveness and Anger Scale (CAAS) was developed to measure antecedents of aggression in sport. The critique attacks the CAAS on three points: (1) the definition of aggression in sport adopted, (2) the‘‘one size fits all’’ element in the thinking behind the scale’s development, (3) the nature of the CAAS Anger and Aggressiveness items. The objectives of this response is to address misunderstandings in the critique. Methods: We identified a number of false assumptions that undermine the validity of the critique and attempt to clarify our position with respect to the criticisms made. Results: (1) The CAAS is being criticised for a definition that it did not use. (2) We accepted that the CAAS may not be suitable for everyone in our limitations section and fully accept the limitations of any scale. We have since undertaken a large research project to establish whether the scale is valid across and within specific sports. (3) The fundamental misunderstanding inherent throughout the critique is that the CAAS was designed as a measure of aggression, rather than anger and aggressiveness, rendering the critique of its items redundant. Conclusions: The critique misrepresents the authors of the CAAS and fails to present a coherent argument against its use. We hope to clarify our position here. The evidence to date suggests that the CAAS is a valid measure of anger and aggressiveness in many sports and that these concepts reliably differentiate players who admit unsanctioned aggression from those who do not.

Measuring and assessing the effects and extent of international bond market integration

We review briefly the literature on international financial integration, especially as it pertains to bond market integration. This contextualizes the review we than provide of a number of papers contained in a special issue of this Journal. © 2005 Elsevier B.V. All rights reserved.

Measuring and modelling the influence of investor behaviour on investment decision making

Guest editorial