Negotiated freedom: a portfolio of original compositions

When is a work considered complete? What elements should be clearly defined in the score, and which aspects should remain adaptable to allow for personal interpretation by performers? How can I create composed music that captures the essence of the original improvised sound I perform in the moment, while also allowing for guided, organic improvisation by other performers. At the core of this research is the development of a compositional approach informed by my unique voice as a guitarist, shaped by over thirty years of performance experience across diverse musical contexts. The primary aim of this research has been to extend this guitar-based approach to composition and improvisation in chamber ensemble settings, creating a music that facilitates collaborative performance and fluid sonic creation. The compositional process is iterative. It begins with improvisations on guitar, which serve as the foundation for the musical material. This material is then notated into structured works, which are presented to performers. Following this, the works undergo workshopping, testing notation methods, and exploring the sonic possibilities of the performers and their instruments. The score is refined through repeated cycles of performance and revision, allowing the composition to evolve in response to performers' interpretations. As the research progresses, the works branch out into larger ensemble grouping, with the individual voices of the performers playing a key role in shaping the compositions. Because I perform in these works, the compositions foster a collaborative environment between composer and performer, naturally challenging traditional hierarchies. This ongoing dialogue between composer and performer creates space for spontaneity and mutual influence, with the performers' individual sounds becoming an integral part of the in-the-moment compositional process. This research has resulted in a portfolio of compositions that blend through-composed material with notation methods designed to facilitate controlled improvisation, allowing performers to manipulate specific sonic elements freely. These works embody the intricate relationship between composition and improvisatory freedom, blurring the lines between the two while preserving my distinct compositional voice. Through careful notation and iterative collaboration, I provide performers with materials that act as both a safety net and a springboard for creative freedom, enabling them to express themselves more freely and confidently. This approach fosters a sense of agency, shared ownership, and spontaneity in performance, empowering musicians to engage more fully in the creative process. Ultimately, this study contributes to contemporary composition and performance practice. The resulting works are presented in the portfolio as musical scores, recordings, and filmed performances

Social usage of Scottish Gaelic in modern Glasgow

Glasgow is a city of great cultural importance and demographic focus for speakers of Scottish Gaelic, with a long history of migration from Gaelic-speaking areas of Scotland to Glasgow characterising the relationship between language and city. This relationship continues to the present day, with a substantial portion of Census-reported Gaelic speakers living in and around Glasgow. This constitutes a challenge for language revitalisation, the theory of which has traditionally considered urban environments to be a site of language shift away from minority languages. Despite this, the reality of Gaelic’s presence in Glasgow, and the city’s demographic pull on Gaelic speakers, must be engaged with to effectively inform policy and language revitalisation efforts. This thesis develops a high-level understanding of the contemporary social usage of Gaelic in Glasgow. For reasons of scope and practicality under the conditions of the Covid-19 pandemic, research was focused on non-employment related, public usage of Gaelic, examining the spaces within which Gaelic speakers meet to determine the contexts in which Gaelic is spoken in Glasgow, and the factors which govern that usage. This research is underpinned by theoretical principles not only in reversing language shift, but also in urban sociolinguistics. Economic, structural and ecolinguistic theories allow for an understanding of Glasgow’s importance and function regarding Gaelic, while sociolinguistic theories of usage motivation and sociological theories of the social network and community of practice drive analysis of the personal interactions which constitute the social patterns of Gaelic usage in Glasgow. Through ethnographic observation, analysis of data shared by City of Glasgow Council and Glasgow University, and interviews with a range of Gaelic speakers who live and work in Glasgow, this research yields insights into the usage of Gaelic across a variety of social contexts in the city. In particular, the social networks and usage opportunities present in the arts sector and Glasgow University are highlighted as anchor points for Gaelic in a predominantly Anglophone city. The now-diminished role which Gaelic plays in religious contexts is explored, and this thesis contains potentially the last ethnographic observations of St Columba Gaelic Church and the “Highland Cathedral”. In addition, findings relating to the social experiences of Gaelic speakers in the city and the roles and social dynamics of “Gaelic” pubs, Glasgow Gaelic School and the Island associations are presented. Beyond providing a broad range of data and analysis which may inform policy and provide avenues for future, closer research, the primary findings of this thesis highlight the great importance of social connections in facilitating and motivating the usage of Gaelic in an urban environment. The social networks which make up the fabric of Glasgow’s Gaelic world and their relationship with Gaelic are influential in the prevalence of spoken Gaelic in any given setting. This phenomenon underpins the variety in the practices and experiences of Gaelic as a social language in Glasgow. It is intended that both the findings and theoretical contributions of this thesis will contribute to future developments in the study and understanding of Gaelic in Glasgow and in other urban environments

​​Estimating costs and impacts of decarbonising heat in the public sector​

In this report we provide evidence on the costs, opportunities and barriers of decarbonising heat in Scotland’s public sector buildings. We use modelling to estimate the capital cost of moving to clean heating systems in all public sector buildings in. We gather evidence on the opportunities offered by decarbonising Scotland’s public sector buildings as well as the barriers to doing so by assessing wider themes including practicalities and operational considerations

Leveller social networks and their sectarian dynamic: 1646-1658

This thesis re-examines the so-called ‘Levellers’ of the English Revolutionary period alongside concurrent sectarian activity. The period under examination is the years 1646 to 1658, commencing with the gradual emergence of Leveller rhetoric from within London’s tolerationist discourse, and ending with the death of Oliver Cromwell in 1658, which commenced a period of recalibration and unrest across the Protectorate. The thesis argues that there was very little of what could be considered a distinct political Leveller movement, as previous scholarship has suggested, rather that Leveller networks and discourse existed within sectarian circles across the nation. It is argued that the nature of Leveller activity was far more dependent on a godly belief system and discourse within sectarian congregations than has previously been acknowledged. Rather than the traditional Leveller leadership, this work foregrounds sectarian preachers and polemicists, and it is argued that the Leveller platform was dependent on preachers and congregational circles as its primary means of development. Individuals such as Samual Oates, Samuel Chidley, Samuel Hyland and Jeremiah Ives are presented along with others as lay-preachers who were crucial nodes for driving Leveller activity. The thesis suggests that a Christian underpinning of Leveller beliefs, such as Right Reason, or an emphasis on godly righteousness, have been overlooked in previous scholarship and that these are further signals of a fundamentally Christian foundation to Leveller arguments. While a Reformed two-kingdoms theory, the separation of the civil and spiritual state, was at the heart of Leveller campaigns, issues of a democratic legislature rarely featured. This thesis demonstrates that Leveller arguments which were found in both civilian and military circles often employed a millenarian position, relating to the anticipated Second Coming, and much of their Christian belief informed their desires for an egalitarian and tolerationist civil government. This thesis will utilise the language of social networks in their informal exploration, suggesting that Levellersim is best understood as moving through a series of interwoven networks, rather than a coherent and organised movement. A social network vocabulary is utilised to assist in the understanding of Leveller social connections and the ways in which Leveller arguments were transmitted among godly congregations and social affiliations. After re-examining the period of Leveller activity most discussed in previous scholarship, the period 1646 to 1649, the thesis continues its analysis of ongoing sectarian Leveller activity into the Commonwealth and Protectorate years. This thesis concludes by examining the presence of Leveller arguments and social networks within Fifth Monarchist congregations, suggesting that the two persuasions need not have always been mutually exclusive. Some ministers and polemicists within the Fifth Monarchy community, including John Rogers, Samuel Hyland and James Frese did promote Leveller ideas as a godly position in preparation for the rule of Christ’s directly appointed Saints

Testing deep neural networks across different computational configurations

Deep Neural Networks (DNNs) typically consist of complex architectures and require enormous processing power. Consequently, developers and researchers use Deep Learning (DL) frameworks to build them (e.g., Keras and PyTorch), apply compiler optimizations to improve their inference time performance (e.g., constant folding and operator fusion), and deploy them on hardware accelerators to parallelize their computations (e.g., GPUs and TPUs). We concisely refer to these aspects as the computational environment of Deep Neural Networks. However, the extent to which the behavior of a DNN model (i.e., output label inference correctness and computation times) is affected when different configurations are selected across the computational environment, is overlooked in the literature. For example, if a DNN model is deployed on two different GPU devices, will it give the same predictions, and how will its computation times deviate across the devices? Given that DNNs are deployed on safety-critical domains (e.g., autonomous driving), it is important to understand the extent to which DNNs are affected by these aspects. For that purpose, we present DeltaNN, a tool that allows DNN model compilation and deployment under different configurations, as well as comparison of model behavior across them. Using DeltaNN, we conducted a set of experiments on widely used Convolutional Neural Network (CNN) models performing image classification. We built these models using different DL frameworks, converted them across different DL framework configurations, compiled on a set of optimizations and deployed on GPU devices of varying capabilities. Our experiments with different configurations led to two main observations: (1) while DNNs typically generate the same predictions across different GPU devices and compiler optimization settings, this is not true when utilizing different DL frameworks, and especially when converting from one DL framework to another (e.g., converting from Keras to PyTorch), a common practice across developers to enable model portability and extensibility; and (2) optimizations are not a panacea of inference time improvement across different devices, as the same optimization strategies that improve execution times on high-end GPUs were found to degrade them when applied on models deployed on low-end GPUs. To mitigate the faults related to the conversion process, we implemented a framework called FetaFix. FetaFix performs automatic fault detection by comparing a number of aspects across the source and the converted target DNN model, such as model parameters, hyperparameters and structure. It then applies a number of fault repair strategies related to these aspects and checks how the converted model performs in comparison to its source counterpart. FetaFix was able to repair 93% of the problematic cases identified by DeltaNN. Finally, we explored the effects of faults present in the target hardware acceleration device code towards DNN model correctness. Inspired by traditional mutation testing, we built MutateNN, a tool that generates DNN model mutants containing target device code faults. We then generated a number of faults in the target device code of numerous CNN models performing classification and evaluated how these models behaved across different hardware acceleration devices. We observed that faults related to conditional operations, as well as drastic changes in arithmetic types, considerably affected model correctness. We conclude that different configurations of computational environment aspects can affect DNN model behavior. Our contributions summarize to (1) an empirical study on how the computational environment affects DNN model behavior, performed by a tool (DeltaNN) implemented specifically for that purpose, (2) a framework (FetaFix) that automatically detects faults related to model input, structure and parameters in converted DNN models across DL frameworks and repairs them, and (3) a utility (MutateNN) that introduces faults in the target code of DNN models associated with deployment on different hardware acceleration devices, and evaluates the effects of these faults on model correctness

Early warning, early action in protracted crises

‘Lightning talk’ presented by Enock Nyakundi (Save the Children) at the Jameel Observatory Community of Practice meeting, Addis Ababa, 13-14 May 202

Reuse of excavated peat on wind farm development sites

Built development on peatlands results in the excavation and disturbance of peat. This project investigates the available evidence on the impacts and opportunities regarding the reuse of excavated peat and examines the opportunities, impacts, and challenges associated with the reuse of excavated peat from windfarm construction sites. We propose a hierarchy of peat reuse options based on environmental impact and offer recommendations for data collection and monitoring to enhance the evidence base

Generating environments and pre-training agents for efficient reinforcement learning

Reinforcement learning provides a compellingly universal approach for learning to achieve an objective, specified by a reward function, by trial-and-error interaction with an environment. While this approach has the versatility to be applied to almost any objective in any environment, it can be inhibitively inefficient. Humans are able to learn to achieve new objectives and improve their capabilities via reinforcement learning over human timescales by building on prior knowledge and capabilities. However, a large proportion of the reinforcement literature considers the traditional problem of learning to perform a task tabula-rasa. In this thesis, we aim to improve the efficiency of reinforcement learning, including both the sample efficiency and computational efficiency, by incorporating environment understanding and knowledge of prior behaviours via more information-dense supervised learning objectives. In the first half of the thesis, we aim to acquire knowledge about the environment that can be leveraged for reinforcement learning. We begin by considering how to optimally combine an agent’s partial observations into a unified representation of an environment. We introduce a novel approach that can more effectively integrate partial information into a single representation than other self-supervised approaches. We next develop this general idea of learning environment representations into a diffusion-based approach for learning a full generative model of an environment. An agent can then perform model-based reinforcement learning by interacting with its environment model rather than the true environment, thereby reducing the environment dependency, and improving the sample efficiency of reinforcement learning. We demonstrate that our diffusion-based approach can more effectively capture visual details compared to related world modelling approaches, leading to greater performance and sample efficiency. However, this doesn’t reduce the computational cost of reinforcement learning, and in fact increases it, due to the additional cost of environment modelling. In the second half of the thesis, we therefore aim to reduce the computational cost of tabula-rasa reinforcement learning by incorporating imitation learning on prior behaviours to provide an initial behaviour that can be efficiently improved with model-free reinforcement learning. We begin by considering the offline-only case from proprioceptive states with a clear objective, and demonstrate our proposed value-based approach leads to improved performance and computational efficiency over imitation and reinforcement learning approaches in isolation. We then extend this general idea to the more general offline-to-online case from visual observations without a well-defined reward function. The training procedure proposed is analogous to that used for modern large language models, providing many exciting directions for future research. We conclude by considering the future directions of generative world models and generalist agents

The non-canonical role of the outer kinetochore protein KNL-1 in axonal development

The nervous system is composed of specialized cells; glia and neurons, that form an interconnected network to relay information. Proper transmission of information relies on the two main compartments within neurons, dendrites, that receive information, and axons, that relay the information through specialized domains called synapses. Axon development is a multi-step process that involves, axon outgrowth, guidance, termination and synaptogenesis. In every step of axonal development rearrangements in the microtubule and actin cytoskeleton are essential to mediate the morphological changes that the axon undergoes. The molecular mechanism governing cytoskeletal regulation during axon development is still not fully characterized. Recent findings have highlighted a novel, non-canonical role for the outer kinetochore protein network, KMN (Knl-1, Mis12, Ndc80), in neuronal development. KMN, primarily recognized for its role in tethering chromosomes to spindle microtubules during chromosome segregation in cell division, has emerged as a potential key cytoskeletal regulator in neurons. This work investigates the noncanonical neuronal role of the outer kinetochore signalling and scaffolding protein KNL-1, in brain organization and axon development. In my thesis I show that KNL-1 is essential for axon organization and termination in the nervous system of C. elegans. In the first part I show that KNL-1 is required for the organization of the C. elegans nerve ring axons and ganglia organization in the brain. Specifically, loss of KNL-1 affects the correct placement and fasciculation of the axons within the nerve. Structure-function analysis of KNL-1 showed that this function requires both the signalling and microtubule binding domains of KNL-1. The second part of my work reveals an essential role for KNL-1 in axon termination, a process whereby the axonal growth cone is destabilized and stops its growth upon reaching its target. The effect of KNL-1 in axon termination, requires reorganization of F-actin at the axonal tip and is regulated by microtubule dynamics. In the final part, I have used Correlative Light-Electron Microscopy and a GFPTrap of KNL-1 in C. elegans embryonic neurons to identify the neuronal structures and proteins that KNL-1 associates within the axon. KNL-1 associates with endo-lysosomal structures in the cell body and synaptic vesicles in the axon. Mass spectrometry analysis revealed a synaptic protein as a potential interactor-candidate of KNL-1. This work showed a new potential link of KNL-1’s neuronal activity with synaptic organization and function. Overall, this study provides insights into the mechanism by which the outer kinetochore component KNL-1 functions in brain development, identifies a novel role for this protein in axon termination and reveals neuronal interactors of KNL-1 highlighting a potential role of the protein in synapses

From Local Governance to Constitution Building: Yemen after 10 Years of Conflict

Ten years into the devastating conflict between Ansar Allah and Yemen’s internationally recognized government, governance has undergone significant transformations. In 2025, local and substate governance structures across Yemen present a diverse and evolving landscape, indicative of the complex realities on the ground. This report delves into the myriad forms of governance that exist in Yemen in 2025 and examines some of the future constitutional challenges these forms may present. As fragmentation and contestation affect an increasing number of countries globally, this study provides essential insights into the analytical challenges faced by various decision makers and analysts. It also offers a critical insight into the fluid nature of governance in a rapidly changing world