4,638 research outputs found

    Transformative learning relationships and the adult educator’s countertransference: a Jungian arts-based duoethnography

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    Transformative learning theory developed from Jack Mezirow’s seminal work on perspective transformation, is a predominant paradigm within adult education scholarship. Recent developments include Jungian perspectives in transformative learning that challenge the dominance of Mezirow’s rational conceptualisation and the exclusion of non-rational and unconscious aspects of learning. Whilst Jungian contributors elevate the role of the unconscious in transformative learning theory, scant attention is paid to the unconscious dynamics between educator and adult learner set within an intersubjective matrix. What is absent is any mention that feelings stirred up in the process of transformative learning might belong within a reciprocal relationship. Jung, who is arguably the pioneer of countertransference, offers a definite point of view about the importance of the subjective responses of the analyst and his/her ability to be influenced and impacted by the client. If the analyst is to transform others, then the analyst needs to be transformed. This relationship of mutual transformation is reconceptualised as a transformative learning relationship. A transformative learning relationship provides an intersubjective frame for exploring countertransferences and the emotional experience of the adult educator. The devised research method of collaborative imaginative engagement is an innovative post-Jungian extension of Jung’s method of active imagination, that involves two adult educators making and working with images of countertransference. The findings are presented as an arts-based duoethnographic portrayal of a co- individuation process between two adult educators. This duoethnographic process of co-individuation prototypes transformative reciprocity within the educator/learner relationship. This research addresses the imbalance or ‘one sidedness’ within transformative learning theory, that overlooks the educator’s subjective and intersubjective experience in favour of the learner’s experience. In doing so, the research contributes a more holistic and collaborative understanding of transformative learning that shows how both learner and educator can be inextricably bound together through a process of mutual transformation

    Computational Analyses of Metagenomic Data

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    Metagenomics studies the collective microbial genomes extracted from a particular environment without requiring the culturing or isolation of individual genomes, addressing questions revolving around the composition, functionality, and dynamics of microbial communities. The intrinsic complexity of metagenomic data and the diversity of applications call for efficient and accurate computational methods in data handling. In this thesis, I present three primary projects that collectively focus on the computational analysis of metagenomic data, each addressing a distinct topic. In the first project, I designed and implemented an algorithm named Mapbin for reference-free genomic binning of metagenomic assemblies. Binning aims to group a mixture of genomic fragments based on their genome origin. Mapbin enhances binning results by building a multilayer network that combines the initial binning, assembly graph, and read-pairing information from paired-end sequencing data. The network is further partitioned by the community-detection algorithm, Infomap, to yield a new binning result. Mapbin was tested on multiple simulated and real datasets. The results indicated an overall improvement in the common binning quality metrics. The second and third projects are both derived from ImMiGeNe, a collaborative and multidisciplinary study investigating the interplay between gut microbiota, host genetics, and immunity in stem-cell transplantation (SCT) patients. In the second project, I conducted microbiome analyses for the metagenomic data. The workflow included the removal of contaminant reads and multiple taxonomic and functional profiling. The results revealed that the SCT recipients' samples yielded significantly fewer reads with heavy contamination of the host DNA, and their microbiomes displayed evident signs of dysbiosis. Finally, I discussed several inherent challenges posed by extremely low levels of target DNA and high levels of contamination in the recipient samples, which cannot be rectified solely through bioinformatics approaches. The primary goal of the third project is to design a set of primers that can be used to cover bacterial flagellin genes present in the human gut microbiota. Considering the notable diversity of flagellins, I incorporated a method to select representative bacterial flagellin gene sequences, a heuristic approach based on established primer design methods to generate a degenerate primer set, and a selection method to filter genes unlikely to occur in the human gut microbiome. As a result, I successfully curated a reduced yet representative set of primers that would be practical for experimental implementation

    Capsule networks with residual pose routing

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    Capsule networks (CapsNets) have been known difficult to develop a deeper architecture, which is desirable for high performance in the deep learning era, due to the complex capsule routing algorithms. In this article, we present a simple yet effective capsule routing algorithm, which is presented by a residual pose routing. Specifically, the higher-layer capsule pose is achieved by an identity mapping on the adjacently lower-layer capsule pose. Such simple residual pose routing has two advantages: 1) reducing the routing computation complexity and 2) avoiding gradient vanishing due to its residual learning framework. On top of that, we explicitly reformulate the capsule layers by building a residual pose block. Stacking multiple such blocks results in a deep residual CapsNets (ResCaps) with a ResNet-like architecture. Results on MNIST, AffNIST, SmallNORB, and CIFAR-10/100 show the effectiveness of ResCaps for image classification. Furthermore, we successfully extend our residual pose routing to large-scale real-world applications, including 3-D object reconstruction and classification, and 2-D saliency dense prediction. The source code has been released on https://github.com/liuyi1989/ResCaps

    Tinto: Multisensor Benchmark for 3-D Hyperspectral Point Cloud Segmentation in the Geosciences

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    The increasing use of deep learning techniques has reduced interpretation time and, ideally, reduced interpreter bias by automatically deriving geological maps from digital outcrop models. However, accurate validation of these automated mapping approaches is a significant challenge due to the subjective nature of geological mapping and the difficulty in collecting quantitative validation data. Additionally, many state-of-the-art deep learning methods are limited to 2-D image data, which is insufficient for 3-D digital outcrops, such as hyperclouds. To address these challenges, we present Tinto, a multisensor benchmark digital outcrop dataset designed to facilitate the development and validation of deep learning approaches for geological mapping, especially for nonstructured 3-D data like point clouds. Tinto comprises two complementary sets: 1) a real digital outcrop model from Corta Atalaya (Spain), with spectral attributes and ground-truth data and 2) a synthetic twin that uses latent features in the original datasets to reconstruct realistic spectral data (including sensor noise and processing artifacts) from the ground truth. The point cloud is dense and contains 3242964 labeled points. We used these datasets to explore the abilities of different deep learning approaches for automated geological mapping. By making Tinto publicly available, we hope to foster the development and adaptation of new deep learning tools for 3-D applications in Earth sciences. The dataset can be accessed through this link: https://doi.org/10.14278/rodare.2256

    Non-Market Food Practices Do Things Markets Cannot: Why Vermonters Produce and Distribute Food That\u27s Not For Sale

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    Researchers tend to portray food self-provisioning in high-income societies as a coping mechanism for the poor or a hobby for the well-off. They describe food charity as a regrettable band-aid. Vegetable gardens and neighborly sharing are considered remnants of precapitalist tradition. These are non-market food practices: producing food that is not for sale and distributing food in ways other than selling it. Recent scholarship challenges those standard understandings by showing (i) that non-market food practices remain prevalent in high-income countries, (ii) that people in diverse social groups engage in these practices, and (iii) that they articulate diverse reasons for doing so. In this dissertation, I investigate the persistent pervasiveness of non-market food practices in Vermont. To go beyond explanations that rely on individual motivation, I examine the roles these practices play in society. First, I investigate the prevalence of non-market food practices. Several surveys with large, representative samples reveal that more than half of Vermont households grow, hunt, fish, or gather some of their own food. Respondents estimate that they acquire 14% of the food they consume through non-market means, on average. For reference, commercial local food makes up about the same portion of total consumption. Then, drawing on the words of 94 non-market food practitioners I interviewed, I demonstrate that these practices serve functions that markets cannot. Interviewees attested that non-market distribution is special because it feeds the hungry, strengthens relationships, builds resilience, puts edible-but-unsellable food to use, and aligns with a desired future in which food is not for sale. Hunters, fishers, foragers, scavengers, and homesteaders said that these activities contribute to their long-run food security as a skills-based safety net. Self-provisioning allows them to eat from the landscape despite disruptions to their ability to access market food such as job loss, supply chain problems, or a global pandemic. Additional evidence from vegetable growers suggests that non-market settings liberate production from financial discipline, making space for work that is meaningful, playful, educational, and therapeutic. Non-market food practices mend holes in the social fabric torn by the commodification of everyday life. Finally, I synthesize scholarly critiques of markets as institutions for organizing the production and distribution of food. Markets send food toward money rather than hunger. Producing for market compels farmers to prioritize financial viability over other values such as stewardship. Historically, people rarely if ever sell each other food until external authorities coerce them to do so through taxation, indebtedness, cutting off access to the means of subsistence, or extinguishing non-market institutions. Today, more humans than ever suffer from chronic undernourishment even as the scale of commercial agriculture pushes environmental pressures past critical thresholds of planetary sustainability. This research substantiates that alternatives to markets exist and have the potential to address their shortcomings

    Dynamic Circular Network-Based Federated Dual-View Learning for Multivariate Time Series Anomaly Detection

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    Multivariate time-series data exhibit intricate correlations in both temporal and spatial dimensions. However, existing network architectures often overlook dependencies in the spatial dimension and struggle to strike a balance between long-term and short-term patterns when extracting features from the data. Furthermore, industries within the business community are hesitant to share their raw data, which hinders anomaly prediction accuracy and detection performance. To address these challenges, the authors propose a dynamic circular network-based federated dual-view learning approach. Experimental results from four open-source datasets demonstrate that the method outperforms existing methods in terms of accuracy, recall, and F1_score for anomaly detection

    Sound Event Detection by Exploring Audio Sequence Modelling

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    Everyday sounds in real-world environments are a powerful source of information by which humans can interact with their environments. Humans can infer what is happening around them by listening to everyday sounds. At the same time, it is a challenging task for a computer algorithm in a smart device to automatically recognise, understand, and interpret everyday sounds. Sound event detection (SED) is the process of transcribing an audio recording into sound event tags with onset and offset time values. This involves classification and segmentation of sound events in the given audio recording. SED has numerous applications in everyday life which include security and surveillance, automation, healthcare monitoring, multimedia information retrieval, and assisted living technologies. SED is to everyday sounds what automatic speech recognition (ASR) is to speech and automatic music transcription (AMT) is to music. The fundamental questions in designing a sound recognition system are, which portion of a sound event should the system analyse, and what proportion of a sound event should the system process in order to claim a confident detection of that particular sound event. While the classification of sound events has improved a lot in recent years, it is considered that the temporal-segmentation of sound events has not improved in the same extent. The aim of this thesis is to propose and develop methods to improve the segmentation and classification of everyday sound events in SED models. In particular, this thesis explores the segmentation of sound events by investigating audio sequence encoding-based and audio sequence modelling-based methods, in an effort to improve the overall sound event detection performance. In the first phase of this thesis, efforts are put towards improving sound event detection by explicitly conditioning the audio sequence representations of an SED model using sound activity detection (SAD) and onset detection. To achieve this, we propose multi-task learning-based SED models in which SAD and onset detection are used as auxiliary tasks for the SED task. The next part of this thesis explores self-attention-based audio sequence modelling, which aggregates audio representations based on temporal relations within and between sound events, scored on the basis of the similarity of sound event portions in audio event sequences. We propose SED models that include memory-controlled, adaptive, dynamic, and source separation-induced self-attention variants, with the aim to improve overall sound recognition

    Cycling Through the Pandemic : Tactical Urbanism and the Implementation of Pop-Up Bike Lanes in the Time of COVID-19

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    Provides an international overview on how tactical urbanism was implemented to give more space to cycling Demonstrates the conceptual framework surrounding tactical urbanism and how it plays out theoretically Proposes new methodological insights to understand the effects of tactical urbanism intervention

    Faithful Low-Resource Data-to-Text Generation through Cycle Training

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    Methods to generate text from structured data have advanced significantly in recent years, primarily due to fine-tuning of pre-trained language models on large datasets. However, such models can fail to produce output faithful to the input data, particularly on out-of-domain data. Sufficient annotated data is often not available for specific domains, leading us to seek an unsupervised approach to improve the faithfulness of output text. Since the problem is fundamentally one of consistency between the representations of the structured data and text, we evaluate the effectiveness of cycle training in this work. Cycle training uses two models which are inverses of each other: one that generates text from structured data, and one which generates the structured data from natural language text. We show that cycle training, when initialized with a small amount of supervised data (100 samples in our case), achieves nearly the same performance as fully supervised approaches for the data-to-text generation task on the WebNLG, E2E, WTQ, and WSQL datasets. We perform extensive empirical analysis with automated evaluation metrics and a newly designed human evaluation schema to reveal different cycle training strategies' effectiveness of reducing various types of generation errors. Our code is publicly available at https://github.com/Edillower/CycleNLG.Comment: 19 pages, 4 figures, ACL 202

    Posthuman Creative Styling can a creative writer’s style of writing be described as procedural?

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    This thesis is about creative styling — the styling a creative writer might use to make their writing unique. It addresses the question as to whether such styling can be described as procedural. Creative styling is part of the technique a creative writer uses when writing. It is how they make the text more ‘lively’ by use of tips and tricks they have either learned or discovered. In essence these are rules, ones the writer accrues over time by their practice. The thesis argues that the use and invention of these rules can be set as procedures. and so describe creative styling as procedural. The thesis follows from questioning why it is that machines or algorithms have, so far, been incapable of producing creative writing which has value. Machine-written novels do not abound on the bookshelves and writing styled by computers is, on the whole, dull in comparison to human-crafted literature. It came about by thinking how it would be possible to reach a point where writing by people and procedural writing are considered to have equal value. For this reason the thesis is set in a posthuman context, where the differences between machines and people are erased. The thesis uses practice to inform an original conceptual space model, based on quality dimensions and dynamic-inter operation of spaces. This model gives an example of the procedures which a posthuman creative writer uses when engaged in creative styling. It suggests an original formulation for the conceptual blending of conceptual spaces, based on the casting of qualities from one space to another. In support of and informing its arguments are ninety-nine examples of creative writing practice which show the procedures by which style has been applied, created and assessed. It provides a route forward for further joint research into both computational and human-coded creative writing
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