Remembering and imagining: the role of the self

This study investigated whether temporal clustering of autobiographical memories (AMs) around periods of self-development ( [Rathbone et al., 2008] and [Rathbone et al., 2009]) would also occur when imagining future events associated with the self. Participants completed an AM task and future thinking task. In both tasks, memories and future events were cued using participant-generated identity statements (e.g., I am a student; I will be a mother). Participants then dated their memories and future events, and finally gave an age at which each identity statement was judged to emerge. Dates of memories and future events were recoded as temporal distance from the identity statement used to cue them. AMs and future events both clustered robustly around periods of self-development, indicating the powerful organisational effect of the self. We suggest that life narrative structures are used to organise future events as well as memories.

Cooperative task support for the control room

The National Grid Company plc. owns and operates the electricity transmission network in England and Wales, the day to day running of the network being carried out by teams of engineers within the national control room. The task of monitoring and operating the transmission network involves the transfer of large amounts of data and a high degree of cooperation between these engineers. The purpose of the research detailed in this paper is to investigate the use of interfacing techniques within the control room scenario, in particular, the development of an agent based architecture for the support of cooperative tasks. The proposed architecture revolves around the use of interface and user supervisor agents. Primarily, these agents are responsible for the flow of information to and from individual users and user groups. The agents are also responsible for tackling the synchronisation and control issues arising during the completion of cooperative tasks. In this paper a novel approach to human computer interaction (HCI) for power systems incorporating an embedded agent infrastructure is presented. The agent architectures used to form the base of the cooperative task support system are discussed, as is the nature of the support system and tasks it is intended to support.

The roles of feature-specific task set and bottom-up salience in attentional capture: An ERP study

We investigated the roles of top-down task set and bottom-up stimulus salience for feature-specific attentional capture. Spatially nonpredictive cues preceded search arrays that included a color-defined target. For target-color singleton cues, behavioral spatial cueing effects were accompanied by cueinduced N2pc components, indicative of attentional capture. These effects were only minimally attenuated for nonsingleton target-color cues, underlining the dominance of top-down task set over salience in attentional capture. Nontarget-color singleton cues triggered no N2pc, but instead an anterior N2 component indicative of top-down inhibition. In Experiment 2, inverted behavioral cueing effects of these cues were accompanied by a delayed N2pc to targets at cued locations, suggesting that perceptually salient but task-irrelevant visual events trigger location-specific inhibition mechanisms that can delay subsequent target selection.

Preliminary observations on the presence of sustained tendon strain and eccentric contractions of the wrist extensors during a common manual task: implications for lateral epicondylitis

Lateral epicondylitis (LE) is hypothesized to occur as a result of repetitive, strenuous and abnormal postural activities of the elbow and wrist. There is still a lack of understanding of how wrist and forearm positions contribute to this condition during common manual tasks. In this study the wrist kinematics and the wrist extensors’ musculotendon patterns were investigated during a manual task believed to elicit LE symptoms in susceptible subjects. A 42-year-old right-handed male, with no history of LE, performed a repetitive movement involving pushing and turning a spring-loaded mechanism. Motion capture data were acquired for the upper limb and an inverse kinematic and dynamic analysis was subsequently carried out. Results illustrated the presence of eccentric contractions sustained by the extensor carpi radialis longus (ECRL), together with an almost constant level of tendon strain of both extensor carpi radialis brevis (ECRB) and extensor digitorum communis lateral (EDCL) branch. It is believed that these factors may partly contribute to the onset of LE as they are both responsible for the creation of microtears at the tendons’ origins. The methodology of this study can be used to explore muscle actions during movements that might cause or exacerbate LE.

The effects of age, enactment, and cue-action relatedness on memory for intentions in the Virtual Week task

The current study investigated the influence of encoding modality and cue-action relatedness on prospective memory (PM) performance in young and older adults using a modified version of the Virtual Week task. Participants encoded regular and irregular intentions either verbally or by physically performing the action during encoding. For half of the intentions there was a close semantic relation between the retrieval cue and the intended action, while for the remaining intentions the cue and action were semantically unrelated. For irregular tasks, both age groups showed superior PM for related intentions compared to unrelated intentions in both encoding conditions. While older adults retrieved fewer irregular intentions than young adults after verbal encoding, there was no age difference following enactment. Possible mechanisms of enactment and relatedness effects are discussed in the context of current theories of event-based PM.

Epidemic K-Means clustering

The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered useless by a single communication failure or high latency in communication paths. This work proposes a fully decentralised algorithm (Epidemic K-Means) which does not require global communication and is intrinsically fault tolerant. The proposed distributed K-Means algorithm provides a clustering solution which can approximate the solution of an ideal centralised algorithm over the aggregated data as closely as desired. A comparative performance analysis is carried out against the state of the art distributed K-Means algorithms based on sampling methods. The experimental analysis confirms that the proposed algorithm is a practical and accurate distributed K-Means implementation for networked systems of very large and extreme scale.

Advances in sequential data assimilation and numerical weather forecasting: an Ensemble Transform Kalman-Bucy Filter, a study on clustering in deterministic Ensemble Square Root Filters, and a test of a new time stepping scheme in an atmospheric model

This dissertation deals with aspects of sequential data assimilation (in particular ensemble Kalman filtering) and numerical weather forecasting. In the first part, the recently formulated Ensemble Kalman-Bucy (EnKBF) filter is revisited. It is shown that the previously used numerical integration scheme fails when the magnitude of the background error covariance grows beyond that of the observational error covariance in the forecast window. Therefore, we present a suitable integration scheme that handles the stiffening of the differential equations involved and doesn’t represent further computational expense. Moreover, a transform-based alternative to the EnKBF is developed: under this scheme, the operations are performed in the ensemble space instead of in the state space. Advantages of this formulation are explained. For the first time, the EnKBF is implemented in an atmospheric model. The second part of this work deals with ensemble clustering, a phenomenon that arises when performing data assimilation using of deterministic ensemble square root filters in highly nonlinear forecast models. Namely, an M-member ensemble detaches into an outlier and a cluster of M-1 members. Previous works may suggest that this issue represents a failure of EnSRFs; this work dispels that notion. It is shown that ensemble clustering can be reverted also due to nonlinear processes, in particular the alternation between nonlinear expansion and compression of the ensemble for different regions of the attractor. Some EnSRFs that use random rotations have been developed to overcome this issue; these formulations are analyzed and their advantages and disadvantages with respect to common EnSRFs are discussed. The third and last part contains the implementation of the Robert-Asselin-Williams (RAW) filter in an atmospheric model. The RAW filter is an improvement to the widely popular Robert-Asselin filter that successfully suppresses spurious computational waves while avoiding any distortion in the mean value of the function. Using statistical significance tests both at the local and field level, it is shown that the climatology of the SPEEDY model is not modified by the changed time stepping scheme; hence, no retuning of the parameterizations is required. It is found the accuracy of the medium-term forecasts is increased by using the RAW filter.

Ensemble clustering in deterministic ensemble Kalman filters

Ensemble clustering (EC) can arise in data assimilation with ensemble square root filters (EnSRFs) using non-linear models: an M-member ensemble splits into a single outlier and a cluster of M−1 members. The stochastic Ensemble Kalman Filter does not present this problem. Modifications to the EnSRFs by a periodic resampling of the ensemble through random rotations have been proposed to address it. We introduce a metric to quantify the presence of EC and present evidence to dispel the notion that EC leads to filter failure. Starting from a univariate model, we show that EC is not a permanent but transient phenomenon; it occurs intermittently in non-linear models. We perform a series of data assimilation experiments using a standard EnSRF and a modified EnSRF by a resampling though random rotations. The modified EnSRF thus alleviates issues associated with EC at the cost of traceability of individual ensemble trajectories and cannot use some of algorithms that enhance performance of standard EnSRF. In the non-linear regimes of low-dimensional models, the analysis root mean square error of the standard EnSRF slowly grows with ensemble size if the size is larger than the dimension of the model state. However, we do not observe this problem in a more complex model that uses an ensemble size much smaller than the dimension of the model state, along with inflation and localisation. Overall, we find that transient EC does not handicap the performance of the standard EnSRF.

Fault tolerant decentralised K-Means clustering for asynchronous large-scale networks

The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks, such as massively parallel processors and clusters of workstations. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered useless by a single communication failure or high latency in communication paths. The lack of scalable and fault tolerant global communication and synchronisation methods in large-scale systems has hindered the adoption of the K-Means algorithm for applications in large networked systems such as wireless sensor networks, peer-to-peer systems and mobile ad hoc networks. This work proposes a fully distributed K-Means algorithm (EpidemicK-Means) which does not require global communication and is intrinsically fault tolerant. The proposed distributed K-Means algorithm provides a clustering solution which can approximate the solution of an ideal centralised algorithm over the aggregated data as closely as desired. A comparative performance analysis is carried out against the state of the art sampling methods and shows that the proposed method overcomes the limitations of the sampling-based approaches for skewed clusters distributions. The experimental analysis confirms that the proposed algorithm is very accurate and fault tolerant under unreliable network conditions (message loss and node failures) and is suitable for asynchronous networks of very large and extreme scale.