Nonlinear set-membership estimation: a support vector machine approach

In this paper a support vector machine (SVM) approach for characterizing the feasible parameter set (FPS) in non-linear set-membership estimation problems is presented. It iteratively solves a regression problem from which an approximation of the boundary of the FPS can be determined. To guarantee convergence to the boundary the procedure includes a no-derivative line search and for an appropriate coverage of points on the FPS boundary it is suggested to start with a sequential box pavement procedure. The SVM approach is illustrated on a simple sine and exponential model with two parameters and an agro-forestry simulation model.

EU membership of the WTO: international trade disputes and judicial protection of individuals by EU Courts

The European Union (EU) is embedded in a pluralistic legal context because of the EU and its Member States’ treaty memberships and domestic laws. Where EU conduct has implications for both the EU’s international trade relations and the legal position of individual traders, it possibly affects EU and its Member States’ obligations under the law of the World Trade Organization (WTO law) as well as the Union’s own multi-layered constitutional legal order. The present paper analyses the way in which the European Court of Justice (ECJ) accommodates WTO and EU law in the context of international trade disputes triggered by the EU. Given the ECJ’s denial of direct effect of WTO law in principle, the paper focuses on the protection of rights and remedies conferred by EU law. It assesses the implications of the WTO Dispute Settlement Understanding (DSU) – which tolerates the acceptance of retaliatory measures constraining traders’ activities in sectors different from those subject to the original trade dispute (Bananas and Hormones cases) – for the protection of ‘retaliation victims’. The paper concludes that governmental discretion conferred by WTO law has not affected the applicability of EU constitutional law but possibly shapes the actual scope of EU rights and remedies where such discretion is exercised in the EU’s general interest.

Connectivity recovery in epidemic membership protocols

Epidemic protocols are a bio-inspired communication and computation paradigm for extreme-scale network system based on randomized communication. The protocols rely on a membership service to build decentralized and random overlay topologies. In a weakly connected overlay topology, a naive mechanism of membership protocols can break the connectivity, thus impairing the accuracy of the application. This work investigates the factors in membership protocols that cause the loss of global connectivity and introduces the first topology connectivity recovery mechanism. The mechanism is integrated into the Expander Membership Protocol, which is then evaluated against other membership protocols. The analysis shows that the proposed connectivity recovery mechanism is effective in preserving topology connectivity and also helps to improve the application performance in terms of convergence speed.

Voronoi, Delaunay, and Block-Structured Mesh Refinement for Solution of the Shallow-Water Equations on the Sphere

Alternative meshes of the sphere and adaptive mesh refinement could be immensely beneficial for weather and climate forecasts, but it is not clear how mesh refinement should be achieved. A finite-volume model that solves the shallow-water equations on any mesh of the surface of the sphere is presented. The accuracy and cost effectiveness of four quasi-uniform meshes of the sphere are compared: a cubed sphere, reduced latitude–longitude, hexagonal–icosahedral, and triangular–icosahedral. On some standard shallow-water tests, the hexagonal–icosahedral mesh performs best and the reduced latitude–longitude mesh performs well only when the flow is aligned with the mesh. The inclusion of a refined mesh over a disc-shaped region is achieved using either gradual Delaunay, gradual Voronoi, or abrupt 2:1 block-structured refinement. These refined regions can actually degrade global accuracy, presumably because of changes in wave dispersion where the mesh is highly nonuniform. However, using gradual refinement to resolve a mountain in an otherwise coarse mesh can improve accuracy for the same cost. The model prognostic variables are height and momentum collocated at cell centers, and (to remove grid-scale oscillations of the A grid) the mass flux between cells is advanced from the old momentum using the momentum equation. Quadratic and upwind biased cubic differencing methods are used as explicit corrections to a fast implicit solution that uses linear differencing.

Global scale water stages from SAR imagery (GlosSARi) to support global flood forecasting

Despite the success of studies attempting to integrate remotely sensed data and flood modelling and the need to provide near-real time data routinely on a global scale as well as setting up online data archives, there is to date a lack of spatially and temporally distributed hydraulic parameters to support ongoing efforts in modelling. Therefore, the objective of this project is to provide a global evaluation and benchmark data set of floodplain water stages with uncertainties and assimilation in a large scale flood model using space-borne radar imagery. An algorithm is developed for automated retrieval of water stages with uncertainties from a sequence of radar imagery and data are assimilated in a flood model using the Tewkesbury 2007 flood event as a feasibility study. The retrieval method that we employ is based on possibility theory which is an extension of fuzzy sets and that encompasses probability theory. In our case we first attempt to identify main sources of uncertainty in the retrieval of water stages from radar imagery for which we define physically meaningful ranges of parameter values. Possibilities of values are then computed for each parameter using a triangular ‘membership’ function. This procedure allows the computation of possible values of water stages at maximum flood extents along a river at many different locations. At a later stage in the project these data are then used in assimilation, calibration or validation of a flood model. The application is subsequently extended to a global scale using wide swath radar imagery and a simple global flood forecasting model thereby providing improved river discharge estimates to update the latter.

General systems perspective on achieving continuous synergy in a project organisation

Studies of construction labour productivity have revealed that limited predictability and multi-agent social complexity make long-range planning of construction projects extremely inaccurate. Fire-fighting, a cultural feature of construction project management, social and structural diversity of involved permanent organizations, and structural temporality all contribute towards relational failures and frequent changes. The main purpose of this paper is therefore to demonstrate that appropriate construction planning may have a profound synergistic effect on structural integration of a project organization. Using the general systems theory perspective it is further a specific objective to investigate and evaluate organizational effects of changes in planning and potentials for achieving continuous project-organizational synergy. The newly developed methodology recognises that planning should also represent a continuous, improvement-leading driving force throughout a project. The synergistic effect of the process planning membership duality fostered project-wide integration, eliminated internal boundaries, and created a pool of constantly upgrading knowledge. It maintained a creative environment that resulted in a number of process-related improvements from all parts of the organization. As a result labour productivity has seen increases of more than 30%, profits have risen from an average of 12% to more than 18%, and project durations have been reduced by several days.

Livestock vaccine adoption among poor farmers in Bolivia: Remembering innovation diffusion theory

The paper explores the low uptake of livestock vaccination among poor farming communities in Bolivia utilising core elements of the original innovation diffusion theory. Contrary to the recent literature, we found that vaccination behaviour was strongly Linked to social and cultural, rather than economic, drivers. While membership in a group increased uptake, the 'hot' and 'cold' distinctions which dictate health versus illness within Andean cosmology also played a role, with vaccination viewed as a means of addressing underlying imbalances. We concluded that uptake of livestock vaccination was unlikely to improve without knowledge transfer that acknowledges local. epistemologies for Livestock disease. (C) 2008 Elsevier Ltd. All rights reserved.

Influence of socio-economic and cultural factors in rice varietal diversity management on-farm in Nepal

A questionnaire survey of 408 households explored the role of socio-economic and cultural factors in rice (Oryza sativa L.) varietal diversity management on-farm in two contrasting eco-sites in Nepal. Multiple regression outputs suggest that number of parcels of land, livestock number, number of rice ecosystems, agro-ecology (altitude), and use of chemical fertilizer have a significant positive influence on landrace diversity on-farm, while membership in farmers' groups linked to extension services has significant but negative influence on landrace diversity. Factors with significant positive influence on diversity of modern varieties on-farm were number of parcels of land and of rice ecosystems, access to irrigation, membership in farmers' groups, and use of insecticide. Within communities, resource-endowed households maintain significantly higher varietal diversity on-farm than resource-poor households and play a significant role in conserving landraces that are vulnerable to genetic erosion and those with socio-cultural and market-preferred traits. Resource-poor households also contribute to local diversity conservation but at lower richness and area coverage levels than resource-endowed households. Households where a female had assumed the role of head of household due to death or migrant work of her husband had less diversity due to lower labor availability. Landraces with socio-cultural and market-preferred traits are few in number but have potential to be conserved on-farm.

Children's representation of family mealtime in the context of maternal eating disorders

Background Recent research provides evidence for specific disturbance in feeding and growth in children of mothers with eating disorders. Aim To investigate the impact of maternal eating disorders during the post-natal year on the internal world of children, as expressed in children's representations of self and their mother in pretend mealtime play at 5 years of age. Methods Children of mothers with eating disorders (n = 33) and a comparison group (n = 24) were videotaped enacting a family mealtime in pretend play. Specific classes of children's play representations were coded blind to group membership. Univariate analyses compared the groups on representations of mother and self. Logistic regression explored factors predicting pretend play representations. Results Positive representations of the mother expressed as feeding, eating or body shape themes were more frequent in the index group. There were no other significant group differences in representations. In a logistic regression analysis, current maternal eating psychopathology was the principal predictor of these positive maternal representations. Marital criticism was associated with negative representations of the mother. Conclusions These findings suggest that maternal eating disorders may influence the development of a child's internal world, such that they are more preoccupied with maternal eating concerns. However, more extensive research on larger samples is required to replicate these preliminary findings.

A neurofuzzy network knowledge extraction and extended gram-Schmidt algorithm for model subspace decomposition

This paper introduces a new neurofuzzy model construction and parameter estimation algorithm from observed finite data sets, based on a Takagi and Sugeno (T-S) inference mechanism and a new extended Gram-Schmidt orthogonal decomposition algorithm, for the modeling of a priori unknown dynamical systems in the form of a set of fuzzy rules. The first contribution of the paper is the introduction of a one to one mapping between a fuzzy rule-base and a model matrix feature subspace using the T-S inference mechanism. This link enables the numerical properties associated with a rule-based matrix subspace, the relationships amongst these matrix subspaces, and the correlation between the output vector and a rule-base matrix subspace, to be investigated and extracted as rule-based knowledge to enhance model transparency. The matrix subspace spanned by a fuzzy rule is initially derived as the input regression matrix multiplied by a weighting matrix that consists of the corresponding fuzzy membership functions over the training data set. Model transparency is explored by the derivation of an equivalence between an A-optimality experimental design criterion of the weighting matrix and the average model output sensitivity to the fuzzy rule, so that rule-bases can be effectively measured by their identifiability via the A-optimality experimental design criterion. The A-optimality experimental design criterion of the weighting matrices of fuzzy rules is used to construct an initial model rule-base. An extended Gram-Schmidt algorithm is then developed to estimate the parameter vector for each rule. This new algorithm decomposes the model rule-bases via an orthogonal subspace decomposition approach, so as to enhance model transparency with the capability of interpreting the derived rule-base energy level. This new approach is computationally simpler than the conventional Gram-Schmidt algorithm for resolving high dimensional regression problems, whereby it is computationally desirable to decompose complex models into a few submodels rather than a single model with large number of input variables and the associated curse of dimensionality problem. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

Knowing who likes who: The early developmental basis of coalition understanding

Group biases based on broad category membership appear early in human development. However, like many other primates humans inhabit social worlds also characterised by small groups of social coalitions which are not demarcated by visible signs or social markers. A critical cognitive challenge for a young child is thus how to extract information concerning coalition structure when coalitions are dynamic and may lack stable and outwardly visible cues to membership. Therefore, the ability to decode behavioural cues of affiliations present in everyday social interactions between individuals would have conferred powerful selective advantages during our evolution. This would suggest that such an ability may emerge early in life, however, little research has investigated the developmental origins of such processing. The present paper will review recent empirical research which indicates that in the first 2 years of life infants achieve a host of social-cognitive abilities that make them well adapted to processing coalition-affiliations of others. We suggest that such an approach can be applied to better understand the origins of intergroup attitudes and biases. Copyright © 2010 John Wiley & Sons, Ltd.

Neurofuzzy network model construction using Bézier-Bernstein polynomial functions

Neurofuzzy modelling systems combine fuzzy logic with quantitative artificial neural networks via a concept of fuzzification by using a fuzzy membership function usually based on B-splines and algebraic operators for inference, etc. The paper introduces a neurofuzzy model construction algorithm using Bezier-Bernstein polynomial functions as basis functions. The new network maintains most of the properties of the B-spline expansion based neurofuzzy system, such as the non-negativity of the basis functions, and unity of support but with the additional advantages of structural parsimony and Delaunay input space partitioning, avoiding the inherent computational problems of lattice networks. This new modelling network is based on the idea that an input vector can be mapped into barycentric co-ordinates with respect to a set of predetermined knots as vertices of a polygon (a set of tiled Delaunay triangles) over the input space. The network is expressed as the Bezier-Bernstein polynomial function of barycentric co-ordinates of the input vector. An inverse de Casteljau procedure using backpropagation is developed to obtain the input vector's barycentric co-ordinates that form the basis functions. Extension of the Bezier-Bernstein neurofuzzy algorithm to n-dimensional inputs is discussed followed by numerical examples to demonstrate the effectiveness of this new data based modelling approach.

Generalized neurofuzzy network modeling algorithms using Bezier-Bernstein polynomial functions and additive decomposition

This paper introduces a new neurofuzzy model construction algorithm for nonlinear dynamic systems based upon basis functions that are Bezier-Bernstein polynomial functions. This paper is generalized in that it copes with n-dimensional inputs by utilising an additive decomposition construction to overcome the curse of dimensionality associated with high n. This new construction algorithm also introduces univariate Bezier-Bernstein polynomial functions for the completeness of the generalized procedure. Like the B-spline expansion based neurofuzzy systems, Bezier-Bernstein polynomial function based neurofuzzy networks hold desirable properties such as nonnegativity of the basis functions, unity of support, and interpretability of basis function as fuzzy membership functions, moreover with the additional advantages of structural parsimony and Delaunay input space partition, essentially overcoming the curse of dimensionality associated with conventional fuzzy and RBF networks. This new modeling network is based on additive decomposition approach together with two separate basis function formation approaches for both univariate and bivariate Bezier-Bernstein polynomial functions used in model construction. The overall network weights are then learnt using conventional least squares methods. Numerical examples are included to demonstrate the effectiveness of this new data based modeling approach.