Architect and user interaction: The spoken representation of form and functional meaning

The paper is an investigation of the exchange of ideas and information between an architect and building users in the early stages of the building design process before the design brief or any drawings have been produced. The purpose of the research is to gain insight into the type of information users exchange with architects in early design conversations and to better understand the influence the format of design interactions and interactional behaviours have on the exchange of information. We report an empirical study of pre-briefing conversations in which the overwhelming majority of the exchanges were about the functional or structural attributes of space, discussion that touched on the phenomenological, perceptual and the symbolic meanings of space were rare. We explore the contextual features of meetings and the conversational strategies taken by the architect to prompt the users for information and the influence these had on the information provided. Recommendations are made on the format and structure of pre-briefing conversations and on designers' strategies for raising the level of information provided by the user beyond the functional or structural attributes of space.

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.

The representation of delayed intentions: a prospective subject-performed task?

To-be-enacted material is more accessible in tests of recognition and lexical decision than material not intended for action (T. Goschke J. Kuhl, 1993; R. L. Marsh, J. L. Hicks, & M. L. Bink, 1998). This finding has been attributed to the superior status of intention-related information. The current article explores an alternative (action-superiority) account that draws parallels between the intended enactment effect (IEE) and the subject-performed task effect. Using 2 paradigms, the authors observed faster recognition latencies for both enacted and to-be-enacted material. It is crucial to note that there was no evidence of an IEE for items that had already been executed during encoding. The IEE was also eliminated when motor processing was prevented after verbal encoding. These findings suggest an overlap between overt and intended enactment and indicate that motor information may be activated for verbal material in preparation for subsequent execution.

Spatial representation and attention in toddlers with Williams syndrome and Down syndrome

The nature of the spatial representations that underlie simple visually guided actions early in life was investigated in toddlers with Williams syndrome (WS), Down syndrome (DS), and healthy chronological age- and mental age-matched controls, through the use of a "double-step" saccade paradigm. The experiment tested the hypothesis that, compared to typically developing infants and toddlers, and toddlers with DS, those with WS display a deficit in using spatial representations to guide actions. Levels of sustained attention were also measured within these groups, to establish whether differences in levels of engagement influenced performance on the double-step saccade task. The results showed that toddlers with WS were unable to combine extra-retinal information with retinal information to the same extent as the other groups, and displayed evidence of other deficits in saccade planning, suggesting a greater reliance on sub-cortical mechanisms than the other populations. Results also indicated that their exploration of the visual environment is less developed. The sustained attention task revealed shorter and fewer periods of sustained attention in toddlers with DS, but not those with WS, suggesting that WS performance on the double-step saccade task is not explained by poorer engagement. The findings are also discussed in relation to a possible attention disengagement deficit in WS toddlers. Our study highlights the importance of studying genetic disorders early in development. (C) 2002 Elsevier Science Ltd. All rights reserved.

A novel spectral representation of electromyographic signals

Time/frequency and temporal analyses have been widely used in biomedical signal processing. These methods represent important characteristics of a signal in both time and frequency domain. In this way, essential features of the signal can be viewed and analysed in order to understand or model the physiological system. Historically, Fourier spectral analyses have provided a general method for examining the global energy/frequency distributions. However, an assumption inherent to these methods is the stationarity of the signal. As a result, Fourier methods are not generally an appropriate approach in the investigation of signals with transient components. This work presents the application of a new signal processing technique, empirical mode decomposition and the Hilbert spectrum, in the analysis of electromyographic signals. The results show that this method may provide not only an increase in the spectral resolution but also an insight into the underlying process of the muscle contraction.

Probability density function estimation using orthogonal forward regression

Using the classical Parzen window estimate as the target function, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density estimates. The proposed algorithm incrementally minimises a leave-one-out test error score to select a sparse kernel model, and a local regularisation method is incorporated into the density construction process to further enforce sparsity. The kernel weights are finally updated using the multiplicative nonnegative quadratic programming algorithm, which has the ability to reduce the model size further. Except for the kernel width, the proposed algorithm has no other parameters that need tuning, and the user is not required to specify any additional criterion to terminate the density construction procedure. Two examples are used to demonstrate the ability of this regression-based approach to effectively construct a sparse kernel density estimate with comparable accuracy to that of the full-sample optimised Parzen window density estimate.

Fully complex-valued radial basis function networks for orthogonal least squares regression

We consider a fully complex-valued radial basis function (RBF) network for regression application. The locally regularised orthogonal least squares (LROLS) algorithm with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF network models, is extended to the fully complex-valued RBF network. Like its real-valued counterpart, the proposed algorithm aims to achieve maximised model robustness and sparsity by combining two effective and complementary approaches. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness. By specifying an appropriate weighting for the D-optimality cost in the combined model selecting criterion, the entire model construction procedure becomes automatic. An example of identifying a complex-valued nonlinear channel is used to illustrate the regression application of the proposed fully complex-valued RBF network.

Sparse kernel density estimator using orthogonal regression based on D-optimality experimental design

A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates.

Semi-automatic knowledge extraction, representation and context-sensitive intelligent retrieval of video content using collateral context modelling with scalable ontological networks

Automatic indexing and retrieval of digital data poses major challenges. The main problem arises from the ever increasing mass of digital media and the lack of efficient methods for indexing and retrieval of such data based on the semantic content rather than keywords. To enable intelligent web interactions, or even web filtering, we need to be capable of interpreting the information base in an intelligent manner. For a number of years research has been ongoing in the field of ontological engineering with the aim of using ontologies to add such (meta) knowledge to information. In this paper, we describe the architecture of a system (Dynamic REtrieval Analysis and semantic metadata Management (DREAM)) designed to automatically and intelligently index huge repositories of special effects video clips, based on their semantic content, using a network of scalable ontologies to enable intelligent retrieval. The DREAM Demonstrator has been evaluated as deployed in the film post-production phase to support the process of storage, indexing and retrieval of large data sets of special effects video clips as an exemplar application domain. This paper provides its performance and usability results and highlights the scope for future enhancements of the DREAM architecture which has proven successful in its first and possibly most challenging proving ground, namely film production, where it is already in routine use within our test bed Partners' creative processes. (C) 2009 Published by Elsevier B.V.

Sparse multioutput radial basis function network construction using combined locally regularised orthogonal least square and D-optimality experimental design

A construction algorithm for multioutput radial basis function (RBF) network modelling is introduced by combining a locally regularised orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximised model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious RBF network model with excellent generalisation performance. The D-optimality design criterion enhances the model efficiency and robustness. A further advantage of the combined approach is that the user only needs to specify a weighting for the D-optimality cost in the combined RBF model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.