Simplification, approximation and deformation of large models

The high level of realism and interaction in many computer graphic applications requires techniques for processing complex geometric models. First, we present a method that provides an accurate low-resolution approximation from a multi-chart textured model that guarantees geometric fidelity and correct preservation of the appearance attributes. Then, we introduce a mesh structure called Compact Model that approximates dense triangular meshes while preserving sharp features, allowing adaptive reconstructions and supporting textured models. Next, we design a new space deformation technique called *Cages based on a multi-level system of cages that preserves the smoothness of the mesh between neighbouring cages and is extremely versatile, allowing the use of heterogeneous sets of coordinates and different levels of deformation. Finally, we propose a hybrid method that allows to apply any deformation technique on large models obtaining high quality results with a reduced memory footprint and a high performance.

Biologically motivated model for object detection and identification in real-world scenes

The classical computer vision methods can only weakly emulate some of the multi-level parallelisms in signal processing and information sharing that takes place in different parts of the primates’ visual system thus enabling it to accomplish many diverse functions of visual perception. One of the main functions of the primates’ vision is to detect and recognise objects in natural scenes despite all the linear and non-linear variations of the objects and their environment. The superior performance of the primates’ visual system compared to what machine vision systems have been able to achieve to date, motivates scientists and researchers to further explore this area in pursuit of more efficient vision systems inspired by natural models. In this paper building blocks for a hierarchical efficient object recognition model are proposed. Incorporating the attention-based processing would lead to a system that will process the visual data in a non-linear way focusing only on the regions of interest and hence reducing the time to achieve real-time performance. Further, it is suggested to modify the visual cortex model for recognizing objects by adding non-linearities in the ventral path consistent with earlier discoveries as reported by researchers in the neuro-physiology of vision.

Extraction of tidal channel networks from aerial photographs alone and combined with laser altimetry

Tidal channel networks play an important role in the intertidal zone, exerting substantial control over the hydrodynamics and sediment transport of the region and hence over the evolution of the salt marshes and tidal flats. The study of the morphodynamics of tidal channels is currently an active area of research, and a number of theories have been proposed which require for their validation measurement of channels over extensive areas. Remotely sensed data provide a suitable means for such channel mapping. The paper describes a technique that may be adapted to extract tidal channels from either aerial photographs or LiDAR data separately, or from both types of data used together in a fusion approach. Application of the technique to channel extraction from LiDAR data has been described previously. However, aerial photographs of intertidal zones are much more commonly available than LiDAR data, and most LiDAR flights now involve acquisition of multispectral images to complement the LiDAR data. In view of this, the paper investigates the use of multispectral data for semiautomatic identification of tidal channels, firstly from only aerial photographs or linescanner data, and secondly from fused linescanner and LiDAR data sets. A multi-level, knowledge-based approach is employed. The algorithm based on aerial photography can achieve a useful channel extraction, though may fail to detect some of the smaller channels, partly because the spectral response of parts of the non-channel areas may be similar to that of the channels. The algorithm for channel extraction from fused LiDAR and spectral data gives an increased accuracy, though only slightly higher than that obtained using LiDAR data alone. The results illustrate the difficulty of developing a fully automated method, and justify the semi-automatic approach adopted.

Remote sensing of tidal networks and their relation to vegetation

The study of the morphology of tidal networks and their relation to salt marsh vegetation is currently an active area of research, and a number of theories have been developed which require validation using extensive observations. Conventional methods of measuring networks and associated vegetation can be cumbersome and subjective. Recent advances in remote sensing techniques mean that these can now often reduce measurement effort whilst at the same time increasing measurement scale. The status of remote sensing of tidal networks and their relation to vegetation is reviewed. The measurement of network planforms and their associated variables is possible to sufficient resolution using digital aerial photography and airborne scanning laser altimetry (LiDAR), with LiDAR also being able to measure channel depths. A multi-level knowledge-based technique is described to extract networks from LiDAR in a semi-automated fashion. This allows objective and detailed geomorphological information on networks to be obtained over large areas of the inter-tidal zone. It is illustrated using LIDAR data of the River Ems, Germany, the Venice lagoon, and Carnforth Marsh, Morecambe Bay, UK. Examples of geomorphological variables of networks extracted from LiDAR data are given. Associated marsh vegetation can be classified into its component species using airborne hyperspectral and satellite multispectral data. Other potential applications of remote sensing for network studies include determining spatial relationships between networks and vegetation, measuring marsh platform vegetation roughness, in-channel velocities and sediment processes, studying salt pans, and for marsh restoration schemes.

Uses of airborne scanning laser altimetry for river flood modelling and coastal geomorphology

Two ongoing projects at ESSC that involve the development of new techniques for extracting information from airborne LiDAR data and combining this information with environmental models will be discussed. The first project in conjunction with Bristol University is aiming to improve 2-D river flood flow models by using remote sensing to provide distributed data for model calibration and validation. Airborne LiDAR can provide such models with a dense and accurate floodplain topography together with vegetation heights for parameterisation of model friction. The vegetation height data can be used to specify a friction factor at each node of a model’s finite element mesh. A LiDAR range image segmenter has been developed which converts a LiDAR image into separate raster maps of surface topography and vegetation height for use in the model. Satellite and airborne SAR data have been used to measure flood extent remotely in order to validate the modelled flood extent. Methods have also been developed for improving the models by decomposing the model’s finite element mesh to reflect floodplain features such as hedges and trees having different frictional properties to their surroundings. Originally developed for rural floodplains, the segmenter is currently being extended to provide DEMs and friction parameter maps for urban floods, by fusing the LiDAR data with digital map data. The second project is concerned with the extraction of tidal channel networks from LiDAR. These networks are important features of the inter-tidal zone, and play a key role in tidal propagation and in the evolution of salt-marshes and tidal flats. The study of their morphology is currently an active area of research, and a number of theories related to networks have been developed which require validation using dense and extensive observations of network forms and cross-sections. The conventional method of measuring networks is cumbersome and subjective, involving manual digitisation of aerial photographs in conjunction with field measurement of channel depths and widths for selected parts of the network. A semi-automatic technique has been developed to extract networks from LiDAR data of the inter-tidal zone. A multi-level knowledge-based approach has been implemented, whereby low level algorithms first extract channel fragments based mainly on image properties then a high level processing stage improves the network using domain knowledge. The approach adopted at low level uses multi-scale edge detection to detect channel edges, then associates adjacent anti-parallel edges together to form channels. The higher level processing includes a channel repair mechanism.

A comparison of three different models for estimating relative risk in meta-analysis of clinical trials under unobserved heterogeneity

We focus on the comparison of three statistical models used to estimate the treatment effect in metaanalysis when individually pooled data are available. The models are two conventional models, namely a multi-level and a model based upon an approximate likelihood, and a newly developed model, the profile likelihood model which might be viewed as an extension of the Mantel-Haenszel approach. To exemplify these methods, we use results from a meta-analysis of 22 trials to prevent respiratory tract infections. We show that by using the multi-level approach, in the case of baseline heterogeneity, the number of clusters or components is considerably over-estimated. The approximate and profile likelihood method showed nearly the same pattern for the treatment effect distribution. To provide more evidence two simulation studies are accomplished. The profile likelihood can be considered as a clear alternative to the approximate likelihood model. In the case of strong baseline heterogeneity, the profile likelihood method shows superior behaviour when compared with the multi-level model. Copyright (C) 2006 John Wiley & Sons, Ltd.

Hyperactive governance in the Thames Gateway

This paper examines the implementation of brownfield regeneration policies in the UK within the context of complex systems of multi-level governance. Using the regeneration of the Thames Gateway as an example, it explores how the Government's centrally driven institutional arrangements have undermined leadership in this key development project. The Government's approach to brownfield governance is characterised as one of constant intervention in the Thames Gateway in an ad hoc and incoherent fashion. Congested and fragmented governance structures are the result. These, this paper argues, have diffused the focus and undermined the leadership of policy and implementation. It is suggested that the adoption of the principles of policy mapping and weaving would bring more clarity and coherence to the governance of the Thames Gateway.

Emotion and anticipation in an enactive framework for cognition (response to Andy Clark)

Undeniably, anticipation plays a crucial role in cognition. By what means, to what extent, and what it achieves remain open questions. In a recent BBS target article, Clark (in press) depicts an integrative model of the brain that builds on hierarchical Bayesian models of neural processing (Rao and Ballard, 1999; Friston, 2005; Brown et al., 2011), and their most recent formulation using the free-energy principle borrowed from thermodynamics (Feldman and Friston, 2010; Friston, 2010; Friston et al., 2010). Hierarchical generative models of cognition, such as those described by Clark, presuppose the manipulation of representations and internal models of the world, in as much detail as is perceptually available. Perhaps surprisingly, Clark acknowledges the existence of a “virtual version of the sensory data” (p. 4), but with no reference to some of the historical debates that shaped cognitive science, related to the storage, manipulation, and retrieval of representations in a cognitive system (Shanahan, 1997), or accounting for the emergence of intentionality within such a system (Searle, 1980; Preston and Bishop, 2002). Instead of demonstrating how this Bayesian framework responds to these foundational questions, Clark describes the structure and the functional properties of an action-oriented, multi-level system that is meant to combine perception, learning, and experience (Niedenthal, 2007).

A conceptual framework for aligning business and information systems from organisational learning perspective

The three decades of on-going executives’ concerns of how to achieve successful alignment between business and information technology shows the complexity of such a vital process. Most of the challenges of alignment are related to knowledge and organisational change and several researchers have introduced a number of mechanisms to address some of these challenges. However, these mechanisms pay less attention to multi-level effects, which results in a limited un-derstanding of alignment across levels. Therefore, we reviewed these challenges from a multi-level learning perspective and found that business and IT alignment is related to the balance of exploitation and exploration strategies with the intellec-tual content of individual, group and organisational levels.

Mapping the trickle out benefits of social and environmental enterprises: a pilot study of an East African eco business

• Objectives The objective of this paper is to propose a framework for mapping the sustainable development and poverty alleviation impacts of social and environmental enterprises in Africa. This framework is then piloted with reference to an East African Ecobusiness. • Prior Work This paper is based on data collected as part of a wider research project examining social and environmental enterprises across the 19 countries of Southern and Eastern Africa. In total, the sustainable development and poverty alleviation impacts of 20 in-depth case studies in 4 countries are being examined. • Approach Data was collected using in-depth interviews with multiple stakeholders associated with the case study business. Secondary materials were also analysed and a quantitative survey of customers undertaken. • Results In addition to their impacts on the environment, African eco businesses can also have substantial social, economic and wider poverty alleviation impacts. This paper maps the impacts of a case study East African ecobusiness, as part of developing a social and environmental enterprise impact framework for Africa and the wider developing world. In our case study, positive and negative impacts are identified, while questions are raised in relation to tradeoffs between social and environmental objectives and temporal dimensions of impact. The usefulness of existing frameworks for understanding the social, environmental and development impacts of these kinds of organisations are also considered. • Implications This paper outlines the necessity of building an African-centric impact map to capture the multi-level poverty alleviation and sustainable development impacts of social and environmental enterprise activity in developing world environments. The framework proposed also offers guidance to businesses operating in Africa about the factors that might be considered as part of their wider social and environmental responsibilities. • Value Assessing the impact of social and environmental enterprises, especially as a route to development within low income countries, is receiving increasing attention in academia and beyond. This paper presents a useful contribution to the scarce literature on social and environmental enterprises in Africa.

CSR: the co-evolution of grocery multiples in the UK (2005-2010)

Purpose – The purpose of this paper is to investigate if CSR is balanced between firm and wider society interests. Design/methodology/approach – A qualitative interpretive hermeneutic approach is used to analyse a variety of publically published secondary sources on the CSR of Tesco, Sainsburys, Morrisons and Co-operative in the UK grocery multiple sector (2005-2010). Findings – CSR strategic outcomes currently favour the firm more than society interests. A multilayered framework in the form of Social Responsibility of the Corporation (SRC) is designed and offered in support of balancing the business-society relationship more evenly. Research limitations/implications – This study is limited to firms originating from within the UK grocery multiple sector. Asda could not be included in the study as it does not publish CSR reports annually in the UK after becoming part of Walmart group. Practical implications – A framework for multi-level standardised definition of CSR in the form of SRC is offered. The inclusion of employees and members of the public on CSR/SRC boards is recommended to foster wider collaboration. The SRC framework promotes standardisation at global level while respecting diversity and firm heterogeneity at firm level. The findings may further contribute to GRI; UN Global Compact; WEF dialogues. Social implications – Recommendations are made to extend CSR board diversity for improved dialogue with communities. The SRC framework may be applied at global; national; industry and firm level. The framework can be applied internationally or locally. Future studies may offer quantitative attributes for balancing CSR/SRC. Originality/value – A globally unique and universally applicable framework for evaluating CSR activities is proposed. Future studies may extend the authors' framework to other industries, national environments or globally in the pursuit of balance between firm and society. Furthermore, firms may also adopt the framework to support CSR activities.

What drives ICT clustering in European Cities?

ICT clusters have attracted much attention because of their rapid growth and their value for other economic activities. Using a nested multi-level model, we examine how conditions at the country level and at the city level affect ICT clustering activity in 227 cities across 22 European countries. We test for the influence of three country regulations (starting a business, registering property, enforcing contracts) and two city conditions (proximity to university, network density) on ICT clustering. We consider heterogeneity within the sector and study two types of ICT activities: ICT product firms and ICT content firms. Our results indicate that country conditions and city conditions each have idiosyncratic implications for ICT clustering, and further, that these can vary by activities in ICT products or ICT content manufacturing.

Resting tremor classification and detection in Parkinson's disease patients

Parkinson is a neurodegenerative disease, in which tremor is the main symptom. This paper investigates the use of different classification methods to identify tremors experienced by Parkinsonian patients.Some previous research has focussed tremor analysis on external body signals (e.g., electromyography, accelerometer signals, etc.). Our advantage is that we have access to sub-cortical data, which facilitates the applicability of the obtained results into real medical devices since we are dealing with brain signals directly. Local field potentials (LFP) were recorded in the subthalamic nucleus of 7 Parkinsonian patients through the implanted electrodes of a deep brain stimulation (DBS) device prior to its internalization. Measured LFP signals were preprocessed by means of splinting, down sampling, filtering, normalization and rec-tification. Then, feature extraction was conducted through a multi-level decomposition via a wavelettrans form. Finally, artificial intelligence techniques were applied to feature selection, clustering of tremor types, and tremor detection.The key contribution of this paper is to present initial results which indicate, to a high degree of certainty, that there appear to be two distinct subgroups of patients within the group-1 of patients according to the Consensus Statement of the Movement Disorder Society on Tremor. Such results may well lead to different resultant treatments for the patients involved, depending on how their tremor has been classified. Moreover, we propose a new approach for demand driven stimulation, in which tremor detection is also based on the subtype of tremor the patient has. Applying this knowledge to the tremor detection problem, it can be concluded that the results improve when patient clustering is applied prior to detection.

Fashionline: CSR case of a U.K. fashion retailer

This paper presents findings from a corporate social responsibility (CSR) case-study of a UK fashion retailer informed through 22 multi-level internal and 5 external semi-structured interviews. Our research explores the relationship between various stakeholders and their perceptions of CSR issues. Sixteen themes emerge. We find disparity in CSR perceptions at different levels of organization and in views of consumers. Our recommendations are that the UK fashion retailer needs to – better understand normative purpose of CSR and in doing so balance social and environmental factors more equally; engage more widely at an early stage with broader stakeholders (global supply chain; consumers, employees, local populations, regulators, industry players); and better communicate (at different levels internally) along with integrate (external policy makers and with industry) design and implementation of CSR strategies. The authors conclude a gap remains for better CSR frameworks for the UK fashion retailer.

Intraindividual relations between achievement goals and discrete achievement emotions: an experience sampling approach

Theories on the link between achievement goals and achievement emotions focus on their within-person functional relationship (i.e., intraindividual relations). However, empirical studies have failed to analyze these intraindividual relations and have instead examined between-person covariation of the two constructs (i.e., interindividual relations). Aiming to better connect theory and empirical research, the present study (N = 120 10th grade students) analyzed intraindividual relations by assessing students’ state goals and emotions using experience sampling (N = 1,409 assessments within persons). In order to replicate previous findings on interindividual relations, students’ trait goals and emotions were assessed using self-report questionnaires. Despite being statistically independent, both types of relations were consistent with theoretical expectations, as shown by multi-level modeling: Mastery goals were positive predictors of enjoyment and negative predictors of boredom and anger; performance-approach goals were positive predictors of pride; and performance-avoidance goals were positive predictors of anxiety and shame. Reasons for the convergence of intra- and interindividual findings, directions for future research, and implications for educational practice are discussed.