Large-scale inference in the focally damaged human brain

Clinical outcomes in focal brain injury reflect the interactions between two distinct anatomically distributed patterns: the functional organisation of the brain and the structural distribution of injury. The challenge of understanding the functional architecture of the brain is familiar; that of understanding the lesion architecture is barely acknowledged. Yet, models of the functional consequences of focal injury are critically dependent on our knowledge of both. The studies described in this thesis seek to show how machine learning-enabled high-dimensional multivariate analysis powered by large-scale data can enhance our ability to model the relation between focal brain injury and clinical outcomes across an array of modelling applications. All studies are conducted on internationally the largest available set of MR imaging data of focal brain injury in the context of acute stroke (N=1333) and employ kernel machines at the principal modelling architecture. First, I examine lesion-deficit prediction, quantifying the ceiling on achievable predictive fidelity for high-dimensional and low-dimensional models, demonstrating the former to be substantially higher than the latter. Second, I determine the marginal value of adding unlabelled imaging data to predictive models within a semi-supervised framework, quantifying the benefit of assembling unlabelled collections of clinical imaging. Third, I compare high- and low-dimensional approaches to modelling response to therapy in two contexts: quantifying the effect of treatment at the population level (therapeutic inference) and predicting the optimal treatment in an individual patient (prescriptive inference). I demonstrate the superiority of the high-dimensional approach in both settings

Heterogeneous recognition of bioacoustic signals for human-machine interfaces

Human-machine interfaces (HMI) provide a communication pathway between man and machine. Not only do they augment existing pathways, they can substitute or even bypass these pathways where functional motor loss prevents the use of standard interfaces. This is especially important for individuals who rely on assistive technology in their everyday life. By utilising bioacoustic activity, it can lead to an assistive HMI concept which is unobtrusive, minimally disruptive and cosmetically appealing to the user. However, due to the complexity of the signals it remains relatively underexplored in the HMI field. This thesis investigates extracting and decoding volition from bioacoustic activity with the aim of generating real-time commands. The developed framework is a systemisation of various processing blocks enabling the mapping of continuous signals into M discrete classes. Class independent extraction efficiently detects and segments the continuous signals while class-specific extraction exemplifies each pattern set using a novel template creation process stable to permutations of the data set. These templates are utilised by a generalised single channel discrimination model, whereby each signal is template aligned prior to classification. The real-time decoding subsystem uses a multichannel heterogeneous ensemble architecture which fuses the output from a diverse set of these individual discrimination models. This enhances the classification performance by elevating both the sensitivity and specificity, with the increased specificity due to a natural rejection capacity based on a non-parametric majority vote. Such a strategy is useful when analysing signals which have diverse characteristics, false positives are prevalent and have strong consequences, and when there is limited training data available. The framework has been developed with generality in mind with wide applicability to a broad spectrum of biosignals. The processing system has been demonstrated on real-time decoding of tongue-movement ear pressure signals using both single and dual channel setups. This has included in-depth evaluation of these methods in both offline and online scenarios. During online evaluation, a stimulus based test methodology was devised, while representative interference was used to contaminate the decoding process in a relevant and real fashion. The results of this research provide a strong case for the utility of such techniques in real world applications of human-machine communication using impulsive bioacoustic signals and biosignals in general

Changes in the British and Irish flora - the role of genome size

Unprecedented anthropogenic changes are causing drastic shifts in biodiversity, species ranges and the survival of plants. Understanding which attributes put plants at risk is of vital importance for safeguarding the natural world. Genome size is a fundamental plant attribute with strong links to a variety of plant traits and its study opens novel areas of ecological research, leading to a new understanding of plant responses to environmental changes. The aim of this thesis is to consider the role that genome size plays at landscape scales. To achieve this aim, I assembled an inventory of the flora of Britain and Ireland and analysed species distribution patterns within the flora over time, together with information on land use, climate and nutrient deposition changes across the past three decades. Distinctive spatial patterns of mean genome size per hectad of Britain and Ireland were found across time, with a steady increase in mean genome size since the 1980s. A particular driver of the patterns appears to be land use, with areas especially impacted by humans containing plant communities characterised by larger mean genome sizes. Genome size, along with a set of functional traits and niche descriptors, were all informative characters in a random forest algorithm predicting species trends, achieving 70% prediction accuracy. The effect of genome size was found to be indirect, mediated via its influence on functional traits, which in turn lead to differing niche requirements and temporal trends. The results suggest that the effects of genome size on plant growth, fitness and response to the abiotic environment impacts landscape scale species compositions. Genome size emerges as an important meta-trait to consider when monitoring and anticipating biodiversity changes in response to environmental change and could be used in models that guide conservation efforts

Language adapts: exploring the cultural dynamics of iterated learning

Human languages are not just tools for transmitting cultural ideas, they are themselves culturally transmitted. This single observation has major implications for our understanding of how and why languages around the world are structured the way they are, and also for how scientists should be studying them. Accounting for the origins of what turns out to be such a uniquely human ability is, and should be, a priority for anyone interested in what makes us different from every other lifeform on Earth. The way the scientific community thinks about language has seen considerable changes over the years. In particular, we have witnessed movements away from a purely descriptive science of language, towards a more explanatory framework that is willing to embrace the difficult questions of not just how individual languages are currently structured and used, but also how and why they got to be that way in the first place. Seeing languages as historical entities is, of course, nothing new in linguistics. Seeing languages as complex adaptive systems, undergoing processes of evolution at multiple levels of interaction however, is. Broadly speaking, this thesis explores some of the implications that this perspective on language has, and argues that in addition to furthering our understanding of the processes of biological evolution and the mechanisms of individual learning required specifically for language, we also need to be mindful of the less well-understood cultural processes that mediate between the two. Human communication systems are not just direct expressions of our genes. Neither are they independently acquired by learners anew at every generation. Instead, languages are transmitted culturally from one generation to another, creating an opportunity for a different kind of evolutionary channel to exist. It is a central aim of this thesis to explore some of the adaptive dynamics that such a cultural channel has, and investigate the extent to which certain structural and statistical properties of language can be directly explained as adaptations to the transmission process and the learning biases of speakers. In order to address this aim, this thesis takes an experimental approach. Building on a rich set of empirical results from various computational simulations and mathematical models, it presents a novel methodological framework for exploring one type of cultural transmission mechanism, iterated learning, in the laboratory using human participants. In these experiments, we observe the evolution of artificial languages as they are acquired and then transmitted to new learners. Although there is no communication involved in these studies, and participants are unaware that their learning efforts are being propagated to future learners, we find that many functional features of language emerge naturally from the different constraints imposed upon them during transmission. These constraints can take a variety of forms, both internal and external to the learner. Taken collectively, the data presented here suggest several points: (i) that iterated language learning experiments can provide us with new insights about the emergence and evolution of language; (ii) that language-like structure can emerge as a result of cultural transmission alone; and (iii) that whilst structure in these systems has the appearance of design, and is in some sense ‘created’ by intentional beings, its emergence is in fact wholly the result of non-intentional processes. Put simply, cultural evolution plays a vital role in language. This work extends our framework for understanding it, and offers a new method for investigating it

Exploring classifier attribute interactions and time series using constrained randomisations

Gaining insight into structures and properties in data is a central problem in data mining and knowledge discovery. This is essential when the data is to be used, e.g., in decision-making. In this thesis we consider investigating the structure of data in two cases: temporal structures in time series, and attribute interactions utilised by classifiers.  Time series are ubiquitous and represent an important type of data. We investigate temporal structures in time series, focusing on interval sequences. We seek explanations for observed properties by constructing and evaluating null hypotheses describing the internal properties of the time series. We approach this as a hypothesis testing problem where observed time series are compared to randomly generated instances. The properties being investigated are modelled in terms of constraints on the randomisations, allowing complex relationships to be examined and explained. Furthermore, we apply computational methods in the analysis of a sleep study to explain the relationship between time series representing heart rate variability and performance on a psychomotor vigilance test.  Classification has wide applicability in multiple domains, however, many high-performing classifiers are essentially opaque, black-box algorithms, making it difficult to gain insight into the basis for predictions. In classifier analysis we consider attribute interactions utilised by classifiers. An interaction means that two or more attributes jointly carry information with respect to, e.g., a class label. We study two different types of interactions. Firstly, we investigate relationships between attributes in a dataset and show how this is related to factorising the class-conditional joint data distribution, such that attributes in the same factor are interacting while attributes in different factors are independent, given the class. We devise a method for testing the hypothesis that a dataset originates from a generating distribution with a particular factorised form. Secondly, we investigate how classifiers exploit attribute interactions in making predictions and develop a novel framework based on constrained randomisations for partitioning the attributes of a dataset into groups based on how they are jointly exploited by the classifier. The methods developed here are useful in several data analysis applications, e.g., in enhancing the interpretability of opaque classifiers, detecting adverse drug interactions in pharmacovigilance, anonymising data and gaining insight into the structure of datasets.Ett centralt problem inom data- och kunskapsutvinning är hur man skall få insikt i datans egenskaper och struktur. Detta är viktigt då datan används t.ex. för beslutsfattande. I denna avhandling granskas strukturer i data i två fall: temporala strukturer i tidsserier, samt hur klassificerare använder sig av samverkan mellan attribut i datan. Tidsserier är typiskt förekommande och utgör en viktig typ av data. Vi undersöker temporala strukturer i tidsserier, med fokus på intervallsekvenser. Vi skapar nollhypoteser som beskriver tidsseriens interna egenskaper och söker med hjälp av dessa förklaringar för observerade egenskaper. Vi närmar oss detta som ett hypotestestningsproblem, där den observerade tidsserien jämförs mot slumpmässigt skapade instanser. Egenskaperna vi undersöker beskrivs genom begränsade randomiseringar, vilket gör det möjligt att undersöka och hitta förklaringar till komplexa förhållanden. Vidare använder vi beräkningsmetoder i analysen av data från en sömnstudie, för att förklara förhållandet mellan tidsserier beskrivande hjärtfrekvensvariabilitet och psykomotorisk vaksamhet. Klassificering har ett brett tillämpningsområde inom olika områden, men ett problem är att många av de mest effektiva klassificerarna är opaka svarta lådor, vilket gör det är svårt att få inblick i hur förutsägelserna görs. Vi studerar här hur klassificerare utnyttjar attributinteraktioner i datan. En interaktion innebär att två eller fler attribut samverkar med avseende på t.ex. en klassvariabel. Vi betraktar två typer av interaktioner. Det första fallet utgörs av förhållandet mellan attribut i datamängden. Vi visar hur detta problem är relaterat till en faktorisering av attributens klassberoende simultanfördelning, så att attribut i samma faktor samverkar, medan attribut i olika faktorer är oberoende, med hänsyn till klassvariabeln. Vi presenterar en ny metod för att undersöka hypotesen att datan härstammar från en distribution med en särskild faktoriserad form. Det andra fallet gäller hur klassificerare utnyttjar attributinteraktioner för förutsägelser och vi presenterar en ny metod för att dela in attributen i grupper på basen av hur algoritmen utnyttjar deras samverkan. Metoden bygger på begränsad randomisering av datan. Metoderna vi utvecklat är generella och användbara inom analys av data och de möjliggör t.ex. förståelse av svårtolkade klassificerare, undersökning av samverkan mellan mediciner inom farmakovigilans, anonymisering av data samt bättre insikt i datans struktur

ISIPTA'07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications

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Error characterisation and reduction in trapped ion quantum computers - One Woman’s Guide to the Ion-ing

For the field of quantum computing and technology to continue its growth and begin to solve truly useful problems, it must overcome the limitations imposed by errors affecting quantum devices. Leading modern devices can be classed as “noisy intermediate-scale quantum” or NISQ devices. These are devices that have not realised fault-tolerance nor utilise error correction, but nonetheless could solve genuinely useful, classically intractable problems. However, the scalability and reliability of current NISQ devices is limited by the impact of errors. This thesis uses the principles of quantum control to work towards improving quantum technology reliability. By characterising the errors affecting a quantum device and tailoring robust, dynamic control solutions, I am able to achieve superior performance in a trapped-ion quantum device compared to its baseline or primitive operation. I begin by characterising our experimental system, a trapped 171Yb+ ion. Following on, I demonstrate three quantum control techniques. First, I work to improve the measurement fidelity of a trapped-ion hyperfine qubit using electron shelving to a metastable level. Second, this work develops and demonstrates an error characterisation tool to diagnose the correlation properties of errors affecting a quantum device. Building from this, I demonstrate the use of dynamically corrected gates to both improve single-qubit gate fidelities and reduce error correlations temporally between gates and spatially between qubits. Finally, I discuss and implement a more robust and flexible two-qubit entangling Mølmer-Sørensen gate using phase modulation of the interaction laser. All of this work adds to the “toolkit” of quantum control and can be used to improve both the reliability of modern device performance -- in particular by reducing susceptibility to noise -- and assist with building up to larger numbers of qubits and gates by tailoring more robust, scalable entangling gates between qubits

Timely and reliable evaluation of the effects of interventions: a framework for adaptive meta-analysis (FAME)

Most systematic reviews are retrospective and use aggregate data AD) from publications, meaning they can be unreliable, lag behind therapeutic developments and fail to influence ongoing or new trials. Commonly, the potential influence of unpublished or ongoing trials is overlooked when interpreting results, or determining the value of updating the meta-analysis or need to collect individual participant data (IPD). Therefore, we developed a Framework for Adaptive Metaanalysis (FAME) to determine prospectively the earliest opportunity for reliable AD meta-analysis. We illustrate FAME using two systematic reviews in men with metastatic (M1) and non-metastatic (M0)hormone-sensitive prostate cancer (HSPC)