The 'veil' of control: exploring the attitudes and perceptions of UK design controllers

In the UK, architectural design is regulated through a system of design control for the public interest, which aims to secure and promote ‘quality’ in the built environment. Design control is primarily implemented by locally employed planning professionals with political oversight, and independent design review panels, staffed predominantly by design professionals. Design control has a lengthy and complex history, with the concept of ‘design’ offering a range of challenges for a regulatory system of governance. A simultaneously creative and emotive discipline, architectural design is a difficult issue to regulate objectively or consistently, often leading to policy that is regarded highly discretionary and flexible. This makes regulatory outcomes difficult to predict, as approaches undertaken by the ‘agents of control’ can vary according to the individual. The role of the design controller is therefore central, tasked with the responsibility of interpreting design policy and guidance, appraising design quality and passing professional judgment. However, little is really known about what influences the way design controllers approach their task, providing a ‘veil’ over design control, shrouding the basis of their decisions. This research engaged directly with the attitudes and perceptions of design controllers in the UK, lifting this ‘veil’. Using in-depth interviews and Q-Methodology, the thesis explores this hidden element of control, revealing a number of key differences in how controllers approach and implement policy and guidance, conceptualise design quality, and rationalise their evaluations and judgments. The research develops a conceptual framework for agency in design control – this consists of six variables (Regulation; Discretion; Skills; Design Quality; Aesthetics; and Evaluation) and it is suggested that this could act as a ‘heuristic’ instrument for UK controllers, prompting more reflexivity in relation to evaluating their own position, approaches, and attitudes, leading to better practice and increased transparency of control decisions.

Complex extreme learning machine applications in terahertz pulsed signals feature sets

This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed algorithm should also be very useful in other applications requiring the classification of very large datasets.

The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration

Background: In many experimental pipelines, clustering of multidimensional biological datasets is used to detect hidden structures in unlabelled input data. Taverna is a popular workflow management system that is used to design and execute scientific workflows and aid in silico experimentation. The availability of fast unsupervised methods for clustering and visualization in the Taverna platform is important to support a data-driven scientific discovery in complex and explorative bioinformatics applications. Results: This work presents a Taverna plugin, the Biological Data Interactive Clustering Explorer (BioDICE), that performs clustering of high-dimensional biological data and provides a nonlinear, topology preserving projection for the visualization of the input data and their similarities. The core algorithm in the BioDICE plugin is Fast Learning Self Organizing Map (FLSOM), which is an improved variant of the Self Organizing Map (SOM) algorithm. The plugin generates an interactive 2D map that allows the visual exploration of multidimensional data and the identification of groups of similar objects. The effectiveness of the plugin is demonstrated on a case study related to chemical compounds. Conclusions: The number and variety of available tools and its extensibility have made Taverna a popular choice for the development of scientific data workflows. This work presents a novel plugin, BioDICE, which adds a data-driven knowledge discovery component to Taverna. BioDICE provides an effective and powerful clustering tool, which can be adopted for the explorative analysis of biological datasets.

Memory and burstiness in dynamic networks

A discrete-time random process is described, which can generate bursty sequences of events. A Bernoulli process, where the probability of an event occurring at time t is given by a fixed probability x, is modified to include a memory effect where the event probability is increased proportionally to the number of events that occurred within a given amount of time preceding t. For small values of x the interevent time distribution follows a power law with exponent −2−x. We consider a dynamic network where each node forms, and breaks connections according to this process. The value of x for each node depends on the fitness distribution, \rho(x), from which it is drawn; we find exact solutions for the expectation of the degree distribution for a variety of possible fitness distributions, and for both cases where the memory effect either is, or is not present. This work can potentially lead to methods to uncover hidden fitness distributions from fast changing, temporal network data, such as online social communications and fMRI scans.