The n-tuple network: coping with sub-optimality

The subject of this thesis is the n-tuple net.work (RAMnet). The major advantage of RAMnets is their speed and the simplicity with which they can be implemented in parallel hardware. On the other hand, this method is not a universal approximator and the training procedure does not involve the minimisation of a cost function. Hence RAMnets are potentially sub-optimal. It is important to understand the source of this sub-optimality and to develop the analytical tools that allow us to quantify the generalisation cost of using this model for any given data. We view RAMnets as classifiers and function approximators and try to determine how critical their lack of' universality and optimality is. In order to understand better the inherent. restrictions of the model, we review RAMnets showing their relationship to a number of well established general models such as: Associative Memories, Kamerva's Sparse Distributed Memory, Radial Basis Functions, General Regression Networks and Bayesian Classifiers. We then benchmark binary RAMnet. model against 23 other algorithms using real-world data from the StatLog Project. This large scale experimental study indicates that RAMnets are often capable of delivering results which are competitive with those obtained by more sophisticated, computationally expensive rnodels. The Frequency Weighted version is also benchmarked and shown to perform worse than the binary RAMnet for large values of the tuple size n. We demonstrate that the main issues in the Frequency Weighted RAMnets is adequate probability estimation and propose Good-Turing estimates in place of the more commonly used :Maximum Likelihood estimates. Having established the viability of the method numerically, we focus on providillg an analytical framework that allows us to quantify the generalisation cost of RAMnets for a given datasetL. For the classification network we provide a semi-quantitative argument which is based on the notion of Tuple distance. It gives a good indication of whether the network will fail for the given data. A rigorous Bayesian framework with Gaussian process prior assumptions is given for the regression n-tuple net. We show how to calculate the generalisation cost of this net and verify the results numerically for one dimensional noisy interpolation problems. We conclude that the n-tuple method of classification based on memorisation of random features can be a powerful alternative to slower cost driven models. The speed of the method is at the expense of its optimality. RAMnets will fail for certain datasets but the cases when they do so are relatively easy to determine with the analytical tools we provide.

Attributions, cognitions, and coping styles:teleworkers' reactions to work-related problems

Based on the attributional reformulation of learned helplessness theory (Abramson, Seligman, & Teasdale, 1978) and Lazarus and Launier's (1978) primary-secondary appraisal theory of stress, the present study sought to examine teleworkers' reactions to their work-related problems. The role of attributions about the sources, and cognitions about the consesquences, of these problems in promoting positive adaptation was addressed. In particular, it was predicted that teleworkers who made optimistic attributions and cognitions would be more likely to employ problem-focused coping strategies and, as a result, report more positive psychological and job-related outcomes. Based on a survey sample of 192 teleworkers, the results indicated that a tendency to engage in self-blame was related to the use of emotion-focused coping strategies. In turn, there was evidence linking emotion-focused coping strategies to negative outcomes and problem-focused coping strategies to positive outcomes. The results are discussed in relation to attributional approaches to stress which highlight the importance of cognitions about the consequences of negative events. Finally, implications for the training of teleworkers are presented.

Relaxing claims:coping with uncertainty while evaluating assumptions at run time

Self-adaptation enables software systems to respond to changing environmental contexts that may not be fully understood at design time. Designing a dynamically adaptive system (DAS) to cope with this uncertainty is challenging, as it is impractical during requirements analysis and design time to anticipate every environmental condition that the DAS may encounter. Previously, the RELAX language was proposed to make requirements more tolerant to environmental uncertainty, and Claims were applied as markers of uncertainty that document how design assumptions affect goals. This paper integrates these two techniques in order to assess the validity of Claims at run time while tolerating minor and unanticipated environmental conditions that can trigger adaptations. We apply the proposed approach to the dynamic reconfiguration of a remote data mirroring network that must diffuse data while minimizing costs and exposure to data loss. Results show RELAXing Claims enables a DAS to reduce adaptation costs. © 2012 Springer-Verlag.

Can illness perceptions and coping predict psychological distress amongst allergy sufferers?

Objective. The aim of the present study was to measure the extent to which illness perceptions and coping strategies are associated with the levels of psychological distress amongst allergy sufferers. Design and method. One hundred and fifty-six allergy sufferers (all members of Allergy U.K.) completed a postal survey consisting of the Revised Illness Perception Questionnaire (IPQ-R) and the COPE. Psychological distress was measured using the General Health Questionnaire (GHQ-28) and the Perceived Stress Scale (PSS). Results. Multiple regression analyses indicated that illness perceptions explained between 6 and 26% of variance on measures of psychological distress; coping strategies explained between 12 and 25%. A strong illness identity and emotional representations of the allergy were associated with higher levels of psychological distress; as were less adaptive coping strategies such as focusing on and venting of emotions. Strong personal control beliefs were associated with the lower levels of distress, as were adaptive coping strategies such as positive reinterpretation and growth. Coping partially mediated the link between the illness perceptions and the outcome; however, illness identity, emotional representations and personal control retained an independent significant association with psychological distress. Conclusion. The findings support a role for illness perceptions and coping in explaining levels of psychological distress amongst allergy sufferers. This has implications for targeted health interventions aimed at reducing the strength of illness identity and emotional representations and increasing a sense of control and the use of more adaptive coping strategies.

The paradox of social resilience:how cognitive strategies and coping mechanisms attenuate and accentuate resilience

This paper examines two concepts, social vulnerability and social resilience, often used to describe people and their relationship to a disaster. Social vulnerability is the exposure to harm resulting from demographic and socioeconomic factors that heighten the exposure to disaster. Social resilience is the ability to avoid disaster, cope with change and recover from disaster. Vulnerability to a space and social resilience through society is explored through a focus on the elderly, a group sometimes regarded as having low resilience while being particularly vulnerable. Our findings explore the degree to which an elderly group exposed to coastal flood risk exhibits social resilience through both cognitive strategies, such as risk perception and self-perception, as well as through coping mechanisms, such as accepting change and self-organisation. These attenuate and accentuate the resilience of individuals through their own preparations as well as their communities' preparations and also contribute to the delusion of resilience which leads individuals to act as if they are more resilient than they are in reality, which we call negative resilience. Thus, we draw attention to three main areas: the degree to which social vulnerability can disguise its social resilience; the role played by cognitive strategies and coping mechanisms on an individual's social resilience; and the high risk aspects of social resilience. © 2014 Elsevier Ltd. All rights reserved.

Coping strategies against extreme weather events:a survey of SMEs in the UK

Small and Medium-scale Enterprises (SMEs); which generate more than one half of the employment and turnover, form an important sector of the UK economy. In fact, SMEs are considered as the backbone of the UK economy due to their significant economic and societal importance. Despite SMEs being the main drivers of the UK economy, they are also said to be the most vulnerable to the impacts from various disruptions such as Extreme Weather Events (EWEs). Consequently, increased intensity and frequency of weather extremes in the UK during the recent past has created a significant impact on the SME community. As the threat of EWEs is expected to further increase in future, the need for SMEs to implement effective coping mechanisms to manage the effects of EWEs is also increasing. This paper aims to identify and evaluate the current coping mechanisms implemented by SMEs to ensure their business continuity in the event of a weather extreme. The paper presents the findings of a questionnaire survey, conducted as part of "Community Resilience to Extreme Weather - CREW" research project, addressing this issue. It is identified that SMEs mostly rely on generic business continuity strategies as opposed to property level protection measures. The paper highlights the importance of raising the uptake of coping strategies by SMEs, as many were found without adequate coping strategies to deal with the risk of EWEs.