3,221 research outputs found

    Modular lifelong machine learning

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    Deep learning has drastically improved the state-of-the-art in many important fields, including computer vision and natural language processing (LeCun et al., 2015). However, it is expensive to train a deep neural network on a machine learning problem. The overall training cost further increases when one wants to solve additional problems. Lifelong machine learning (LML) develops algorithms that aim to efficiently learn to solve a sequence of problems, which become available one at a time. New problems are solved with less resources by transferring previously learned knowledge. At the same time, an LML algorithm needs to retain good performance on all encountered problems, thus avoiding catastrophic forgetting. Current approaches do not possess all the desired properties of an LML algorithm. First, they primarily focus on preventing catastrophic forgetting (Diaz-Rodriguez et al., 2018; Delange et al., 2021). As a result, they neglect some knowledge transfer properties. Furthermore, they assume that all problems in a sequence share the same input space. Finally, scaling these methods to a large sequence of problems remains a challenge. Modular approaches to deep learning decompose a deep neural network into sub-networks, referred to as modules. Each module can then be trained to perform an atomic transformation, specialised in processing a distinct subset of inputs. This modular approach to storing knowledge makes it easy to only reuse the subset of modules which are useful for the task at hand. This thesis introduces a line of research which demonstrates the merits of a modular approach to lifelong machine learning, and its ability to address the aforementioned shortcomings of other methods. Compared to previous work, we show that a modular approach can be used to achieve more LML properties than previously demonstrated. Furthermore, we develop tools which allow modular LML algorithms to scale in order to retain said properties on longer sequences of problems. First, we introduce HOUDINI, a neurosymbolic framework for modular LML. HOUDINI represents modular deep neural networks as functional programs and accumulates a library of pre-trained modules over a sequence of problems. Given a new problem, we use program synthesis to select a suitable neural architecture, as well as a high-performing combination of pre-trained and new modules. We show that our approach has most of the properties desired from an LML algorithm. Notably, it can perform forward transfer, avoid negative transfer and prevent catastrophic forgetting, even across problems with disparate input domains and problems which require different neural architectures. Second, we produce a modular LML algorithm which retains the properties of HOUDINI but can also scale to longer sequences of problems. To this end, we fix the choice of a neural architecture and introduce a probabilistic search framework, PICLE, for searching through different module combinations. To apply PICLE, we introduce two probabilistic models over neural modules which allows us to efficiently identify promising module combinations. Third, we phrase the search over module combinations in modular LML as black-box optimisation, which allows one to make use of methods from the setting of hyperparameter optimisation (HPO). We then develop a new HPO method which marries a multi-fidelity approach with model-based optimisation. We demonstrate that this leads to improvement in anytime performance in the HPO setting and discuss how this can in turn be used to augment modular LML methods. Overall, this thesis identifies a number of important LML properties, which have not all been attained in past methods, and presents an LML algorithm which can achieve all of them, apart from backward transfer

    Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse

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    This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses. This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups. In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in users’ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018—6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena

    An exploration of adherence and persistence in overactive bladder and other long-term conditions

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    Background and aims Overactive bladder is a common, bothersome, and chronic condition associated with symptoms of urinary urgency, incontinence, increased daytime micturition frequency and nocturia. Despite exerting a significant burden on quality of life, adherence, and persistence behaviours with OAB are particularly poor in comparison with other long-term conditions. The aims of the present work were to explore themes relating to medicine-taking behaviours in OAB and other long-term conditions and to suggest ways to improve them. Methods A systematic literature review was undertaken to understand the current landscape of qualitative work exploring adherence and persistence with OAB patients. A qualitative study involving 1:1 semi-structured interviews was conducted with OAB patients to explore the context and drivers for adherence and persistence behaviours using thematic analysis. A comparative analysis was then undertaken with qualitative papers exploring medicinetaking behaviours in a chronic bowel condition, type II diabetes, and multimorbidity to explore the themes identified in the OAB study for convergence and divergence in other conditions and to contextualise the learnings from the former study. Results The systematic literature review revealed a gap in the literature of qualitative exploration of adherence and persistence behaviours in OAB patients. The OAB study found a range of drivers for non-adherent behaviours including a perceived lack of treatment efficacy, side effects, unclear instructions, and drug and condition hierarchies, as well as the rich context within which these themes sit. The comparative analysis study supported the findings of the OAB study demonstrating evidence of key themes transcending across conditions, including a perceived lack of treatment efficacy and side effects, as well as nuances associated with the OAB experience. Conclusions The present work has identified key drivers for non-adherent behaviours in OAB patients and sets out a number of recommendations categorised within the World Health Organisation’s 5 dimensions of adherence. These include addressing the poor understanding and illness perception of OAB by patients and others, by improving the provision and availability of information, as well as the work of patient support groups; scrutiny on the support within primary care to OAB patients before and after diagnosis; and the encouragement of realistic expectations of the condition and treatment with mindful use of prescriber’s language at the point of prescribing. The present work has further highlighted the utility of conceptual models of adherence such as COM-B and the NCF in understanding medicine-taking behaviours in the context of OAB

    Real Estate Investment Trusts (REITs) Corporate Governance and Investment Decision-Making in the United Kingdom, South Africa and Nigeria

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    Adopting Real Estate Investment Trusts (REITs) has been relatively slow due to corporate governance issues and a limited understanding of investment decision-making processes. This study aims to enhance the performance of REITs by developing a Corporate Governance Scoring Framework and improving the investment decision-making process. A mixed-method research strategy was employed to gather data on investment decisionmaking processes and corporate governance in the UK, SA, and Nigeria from 2014-2019. Qualitative data was collected through semi-structured telephone interviews with key decision-makers in the three regimes and analysed using content and discourse analysis techniques. Quantitative data was obtained from the annual financial reports of listed REITs during the study period and analysed using OLS, fixed effects, and random effect models. The Integrated Corporate Governance Index (ICGI), a self-scoring framework, was used to measure the quality of corporate governance strength. The qualitative analysis identified four stages in the investment decision-making process: strategy, search, analysis and adjustment, and consultation or decision and review. The interviews revealed that the board, remuneration, and fee proxies were relevant factors across all three regimes, with audit and ownership also significant in the developing regimes of SA and Nigeria. The board's reputation, experience, and management role were highlighted as crucial during the decision-making process. Performance factors such as 'Operational Stability,' 'Tenant Quality,' 'Experience,' and metrics including 'Rental Income,' 'Dividend Payment,' and 'Yield' were identified. The quantitative analysis demonstrated that adherence to corporate governance codes was highest in the UK, followed by SA and Nigeria. Regression analysis results showed that a higher ICGI score improved return on assets (ROA) and return on equity (ROE) in the UK but not in SA and Nigeria. The index did not significantly impact firm value in the UK and pooled country analysis, but it led to better firm valuation in SA. In the Nigeria REIT regime, the ICGI harmed firm valuation. The study concluded that adherence to country-level corporate governance was more predictive of operational performance than firm valuation. In summary, this study contributes to the existing knowledge by providing insights into the investment decision-making processes of REITs and the importance of corporate governance in improving their performance. The developed Corporate Governance Scoring Framework offers a valuable tool for evaluating the quality of corporate governance in REITs, but further refinement is necessary to keep up with evolving policies

    Development of linguistic linked open data resources for collaborative data-intensive research in the language sciences

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    Making diverse data in linguistics and the language sciences open, distributed, and accessible: perspectives from language/language acquistiion researchers and technical LOD (linked open data) researchers. This volume examines the challenges inherent in making diverse data in linguistics and the language sciences open, distributed, integrated, and accessible, thus fostering wide data sharing and collaboration. It is unique in integrating the perspectives of language researchers and technical LOD (linked open data) researchers. Reporting on both active research needs in the field of language acquisition and technical advances in the development of data interoperability, the book demonstrates the advantages of an international infrastructure for scholarship in the field of language sciences. With contributions by researchers who produce complex data content and scholars involved in both the technology and the conceptual foundations of LLOD (linguistics linked open data), the book focuses on the area of language acquisition because it involves complex and diverse data sets, cross-linguistic analyses, and urgent collaborative research. The contributors discuss a variety of research methods, resources, and infrastructures. Contributors Isabelle Barrière, Nan Bernstein Ratner, Steven Bird, Maria Blume, Ted Caldwell, Christian Chiarcos, Cristina Dye, Suzanne Flynn, Claire Foley, Nancy Ide, Carissa Kang, D. Terence Langendoen, Barbara Lust, Brian MacWhinney, Jonathan Masci, Steven Moran, Antonio Pareja-Lora, Jim Reidy, Oya Y. Rieger, Gary F. Simons, Thorsten Trippel, Kara Warburton, Sue Ellen Wright, Claus Zin

    A Narratological Inquiry into U.S. African Refugee Youths’ Educational Experiences

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    Title from PDF of title page, viewed May 24, 2023Dissertation advisor: Omiunota N. UkpokoduVitaIncludes bibliographical references (pages 282-351)Dissertation (Ph.D.)--Curriculum and Instruction, Social Science Consortium. University of Missouri--Kansas City, 2023The arrival of refugees over the past two decades changed the face of classrooms in Kansas City, Missouri, resulting in refugee youth being unprepared for post-secondary opportunities. This critical narrative study aimed to explore the lived experiences of African refugee youth (aged 18 and above) attending public high school in the MidwestUnited States. Based on current and recently arriving African refugee populations, 10 participants originally hailed from Somalia, Congo, Liberia, Sudan, and Burundi. A crystalized theoretical framework of socio-cultural, migratory, and critical race theory guided qualitative narratological data analysis collected via interviews focusing on the participants' educational experiences. Data analysis followed descriptive and interpretive coding to analyze and identify themes, trends, and patterns providing insight into participants’ experiences and how they affected their academic and social endeavors. Findings revealed that participants’ escape, cultural experience, U.S. resettlement, academic shock, intolerance, toil/exertion, challenges, recurrence, defensive mechanism, beneficial encounter, academic effect, and social illumination all influenced their educational experiences. In addition, in-depth theoretical analysis exposed systemic societal racism among every theme, thereby illuminating deep-rootedracism as the primary factor negatively affecting African refugee youths’ U.S. iii educational experiences. These findings help identify strategies and interventions supporting African refugee youth preparing for post-secondary opportunities.Introduction -- Literature review -- Methodology -- Study results and findings -- Interpretation, recommendations, implication
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