15,966 research outputs found

    Factors shaping future use and design of academic library space

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    COVID is having immediate and long-term impacts on the use of libraries. But these changes will probably not alter the importance of the academic library as a space. In the decade pre COVID libraries saw a growing number of visits, despite the increasing availability of material digitally. The first part of the paper offers an analysis of the factors driving this growth, such as changing pedagogies, diversification in the student body, new technologies plus tighter estates management. Barriers to change such as academic staff readiness, cost, and slow decision making are also presented. Then, the main body of the paper discusses emerging factors which are likely to further shape the use of library space, namely: concerns with student well-being; sustainability; equality, diversity and inclusion, and decolonisation; increasing co-design with students; and new technologies. A final model captures the inter-related factors shaping use and design of library space post COVID

    Image classification over unknown and anomalous domains

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    A longstanding goal in computer vision research is to develop methods that are simultaneously applicable to a broad range of prediction problems. In contrast to this, models often perform best when they are specialized to some task or data type. This thesis investigates the challenges of learning models that generalize well over multiple unknown or anomalous modes and domains in data, and presents new solutions for learning robustly in this setting. Initial investigations focus on normalization for distributions that contain multiple sources (e.g. images in different styles like cartoons or photos). Experiments demonstrate the extent to which existing modules, batch normalization in particular, struggle with such heterogeneous data, and a new solution is proposed that can better handle data from multiple visual modes, using differing sample statistics for each. While ideas to counter the overspecialization of models have been formulated in sub-disciplines of transfer learning, e.g. multi-domain and multi-task learning, these usually rely on the existence of meta information, such as task or domain labels. Relaxing this assumption gives rise to a new transfer learning setting, called latent domain learning in this thesis, in which training and inference are carried out over data from multiple visual domains, without domain-level annotations. Customized solutions are required for this, as the performance of standard models degrades: a new data augmentation technique that interpolates between latent domains in an unsupervised way is presented, alongside a dedicated module that sparsely accounts for hidden domains in data, without requiring domain labels to do so. In addition, the thesis studies the problem of classifying previously unseen or anomalous modes in data, a fundamental problem in one-class learning, and anomaly detection in particular. While recent ideas have been focused on developing self-supervised solutions for the one-class setting, in this thesis new methods based on transfer learning are formulated. Extensive experimental evidence demonstrates that a transfer-based perspective benefits new problems that have recently been proposed in anomaly detection literature, in particular challenging semantic detection tasks

    Data-to-text generation with neural planning

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    In this thesis, we consider the task of data-to-text generation, which takes non-linguistic structures as input and produces textual output. The inputs can take the form of database tables, spreadsheets, charts, and so on. The main application of data-to-text generation is to present information in a textual format which makes it accessible to a layperson who may otherwise find it problematic to understand numerical figures. The task can also automate routine document generation jobs, thus improving human efficiency. We focus on generating long-form text, i.e., documents with multiple paragraphs. Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or its variants. These models generate fluent (but often imprecise) text and perform quite poorly at selecting appropriate content and ordering it coherently. This thesis focuses on overcoming these issues by integrating content planning with neural models. We hypothesize data-to-text generation will benefit from explicit planning, which manifests itself in (a) micro planning, (b) latent entity planning, and (c) macro planning. Throughout this thesis, we assume the input to our generator are tables (with records) in the sports domain. And the output are summaries describing what happened in the game (e.g., who won/lost, ..., scored, etc.). We first describe our work on integrating fine-grained or micro plans with data-to-text generation. As part of this, we generate a micro plan highlighting which records should be mentioned and in which order, and then generate the document while taking the micro plan into account. We then show how data-to-text generation can benefit from higher level latent entity planning. Here, we make use of entity-specific representations which are dynam ically updated. The text is generated conditioned on entity representations and the records corresponding to the entities by using hierarchical attention at each time step. We then combine planning with the high level organization of entities, events, and their interactions. Such coarse-grained macro plans are learnt from data and given as input to the generator. Finally, we present work on making macro plans latent while incrementally generating a document paragraph by paragraph. We infer latent plans sequentially with a structured variational model while interleaving the steps of planning and generation. Text is generated by conditioning on previous variational decisions and previously generated text. Overall our results show that planning makes data-to-text generation more interpretable, improves the factuality and coherence of the generated documents and re duces redundancy in the output document

    Underwater optical wireless communications in turbulent conditions: from simulation to experimentation

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    Underwater optical wireless communication (UOWC) is a technology that aims to apply high speed optical wireless communication (OWC) techniques to the underwater channel. UOWC has the potential to provide high speed links over relatively short distances as part of a hybrid underwater network, along with radio frequency (RF) and underwater acoustic communications (UAC) technologies. However, there are some difficulties involved in developing a reliable UOWC link, namely, the complexity of the channel. The main focus throughout this thesis is to develop a greater understanding of the effects of the UOWC channel, especially underwater turbulence. This understanding is developed from basic theory through to simulation and experimental studies in order to gain a holistic understanding of turbulence in the UOWC channel. This thesis first presents a method of modelling optical underwater turbulence through simulation that allows it to be examined in conjunction with absorption and scattering. In a stationary channel, this turbulence induced scattering is shown to cause and increase both spatial and temporal spreading at the receiver plane. It is also demonstrated using the technique presented that the relative impact of turbulence on a received signal is lower in a highly scattering channel, showing an in-built resilience of these channels. Received intensity distributions are presented confirming that fluctuations in received power from this method follow the commonly used Log-Normal fading model. The impact of turbulence - as measured using this new modelling framework - on link performance, in terms of maximum achievable data rate and bit error rate is equally investigated. Following that, experimental studies comparing both the relative impact of turbulence induced scattering on coherent and non-coherent light propagating through water and the relative impact of turbulence in different water conditions are presented. It is shown that the scintillation index increases with increasing temperature inhomogeneity in the underwater channel. These results indicate that a light beam from a non-coherent source has a greater resilience to temperature inhomogeneity induced turbulence effect in an underwater channel. These results will help researchers in simulating realistic channel conditions when modelling a light emitting diode (LED) based intensity modulation with direct detection (IM/DD) UOWC link. Finally, a comparison of different modulation schemes in still and turbulent water conditions is presented. Using an underwater channel emulator, it is shown that pulse position modulation (PPM) and subcarrier intensity modulation (SIM) have an inherent resilience to turbulence induced fading with SIM achieving higher data rates under all conditions. The signal processing technique termed pair-wise coding (PWC) is applied to SIM in underwater optical wireless communications for the first time. The performance of PWC is compared with the, state-of-the-art, bit and power loading optimisation algorithm. Using PWC, a maximum data rate of 5.2 Gbps is achieved in still water conditions

    The Importance of Soft Skills for Strengthening Agency in Female Entrepreneurship Programmes

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    This paper is part of the MUVA Paper Series on female entrepreneurship. It focuses on how soft skills in female entrepreneurship programmes strengthen agency and impact economic empowerment of women entrepreneurs in low- and middle-income countries (LMICs). It draws on both the literature and lessons learned from Mozambique-based social incubator MUVA. By exploring MUVA’s entrepreneurship experience, this paper contributes to debates in the literature about the importance of soft skills in female entrepreneurship programmes for enhanced self-esteem, self-confidence and self-efficacy to strengthen agency

    Innovation systems’ response to changes in the institutional impulse: Analysis of the evolution of the European energy innovation system from FP7 to H2020

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    This study addresses how the institutional impulse developed by the European Union influenced the evolution of the European energy innovation system. Considering the contributing role of innovation systems in the development of new knowledge and technology, it can be stated that the institutional impulse achieved by the European Union through the research framework programmes creates a network of relations between entities and projects. This enables the exchange of information and expertise, which is considered a key element for innovation development. Previous studies have attempted to determine whether institutional impulse is an essential element in understanding the efficiency of innovation systems and their related research policies. However, their investigations have yielded inconclusive results. Using the CORDIS database of the European Commission, this study aims to fill this gap by assessing the European energy innovation system for two periods (2007–2013 and 2014–2020) through two of its research funding programmes—FP7 and H2020—thereby contributing to the literature in the innovation systems field. Social network analysis has been conducted to examine how changes in the institutional impulse, reflected in the new objectives in the research funding programmes, are associated with changes in the structural and topological properties of the innovation systems’ underlying networks. The first contribution indicates that the innovation system responds to changes in the goals of funding programmes, as the taxonomy, topology, and structural properties of their underlying networks underwent modifications due to the newly proposed objectives. The second contribution shows that network properties (cohesion and centrality metrics) can explain the efficiency and effectiveness of innovation systems, drawing useful conclusions for policymakers and individual entities. This last contribution also has important policymaking implications, as it provides the basis for understanding how innovation policy goals can be achieved by changing the institutional impulse to direct the innovation system towards these objectives
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