7,624 research outputs found

    Choreographing tragedy into the twenty-first century

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    What makes a tragedy? In the fifth century BCE this question found an answer through the conjoined forms of song and dance. Since the mid-twentieth century, and the work of the Tanztheater Wuppertal Pina Bausch, tragedy has been variously articulated as form coming apart at the seams. This thesis approaches tragedy through the work of five major choreographers and a director who each, in some way, turn back to Bausch. After exploring the Tanztheater Wuppertal’s techniques for choreographing tragedy in chapter one, I dedicate a chapter each to Dimitris Papaioannou, Akram Khan, Trajal Harrell, Ivo van Hove with Wim Vandekeybus, and Gisùle Vienne. Bringing together work in Queer and Trans* studies, Performance studies, Classics, Dance, and Classical Reception studies I work towards an understanding of the ways in which these choreographers articulate tragedy through embodiment and relation. I consider how tragedy transforms into the twenty-first century, how it shapes what it might mean to live and die with(out) one another. This includes tragic acts of mythic construction, attempts to describe a sense of the world as it collapses, colonial claims to ownership over the earth, and decolonial moves to enact new ways of being human. By developing an expanded sense of both choreography and the tragic one of my main contributions is a re-theorisation of tragedy that brings together two major pre-existing schools, to understand tragedy not as an event, but as a process. Under these conditions, and the shifting conditions of the world around us, I argue that the choreography of tragedy has and might continue to allow us to think about, name, and embody ourselves outside of the ongoing catastrophes we face

    Ethnographies of Collaborative Economies across Europe: Understanding Sharing and Caring

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    "Sharing economy" and "collaborative economy" refer to a proliferation of initiatives, business models, digital platforms and forms of work that characterise contemporary life: from community-led initiatives and activist campaigns, to the impact of global sharing platforms in contexts such as network hospitality, transportation, etc. Sharing the common lens of ethnographic methods, this book presents in-depth examinations of collaborative economy phenomena. The book combines qualitative research and ethnographic methodology with a range of different collaborative economy case studies and topics across Europe. It uniquely offers a truly interdisciplinary approach. It emerges from a unique, long-term, multinational, cross-European collaboration between researchers from various disciplines (e.g., sociology, anthropology, geography, business studies, law, computing, information systems), career stages, and epistemological backgrounds, brought together by a shared research interest in the collaborative economy. This book is a further contribution to the in-depth qualitative understanding of the complexities of the collaborative economy phenomenon. These rich accounts contribute to the painting of a complex landscape that spans several countries and regions, and diverse political, cultural, and organisational backdrops. This book also offers important reflections on the role of ethnographic researchers, and on their stance and outlook, that are of paramount interest across the disciplines involved in collaborative economy research

    Do We Fully Understand Students' Knowledge States? Identifying and Mitigating Answer Bias in Knowledge Tracing

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    Knowledge tracing (KT) aims to monitor students' evolving knowledge states through their learning interactions with concept-related questions, and can be indirectly evaluated by predicting how students will perform on future questions. In this paper, we observe that there is a common phenomenon of answer bias, i.e., a highly unbalanced distribution of correct and incorrect answers for each question. Existing models tend to memorize the answer bias as a shortcut for achieving high prediction performance in KT, thereby failing to fully understand students' knowledge states. To address this issue, we approach the KT task from a causality perspective. A causal graph of KT is first established, from which we identify that the impact of answer bias lies in the direct causal effect of questions on students' responses. A novel COunterfactual REasoning (CORE) framework for KT is further proposed, which separately captures the total causal effect and direct causal effect during training, and mitigates answer bias by subtracting the latter from the former in testing. The CORE framework is applicable to various existing KT models, and we implement it based on the prevailing DKT, DKVMN, and AKT models, respectively. Extensive experiments on three benchmark datasets demonstrate the effectiveness of CORE in making the debiased inference for KT.Comment: 13 page

    Informationsströme in digitalen Kulturen

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    Wir sind umgeben von einer Vielzahl an Informationsströmen, die uns selbstverstĂ€ndlich erscheinen. Um diese digitalen Kulturen zu beschreiben, entwickeln medienwissenschaftliche Arbeiten Theorien einer Welt im Fluss. Dabei erliegen ihre Diagnosen oftmals einem Technikfetisch und vernachlĂ€ssigen gesellschaftliche Strukturen. Mathias Denecke legt eine systematische Kritik dieser Theoriebildung vor. Dazu zeichnet er die Geschichte der Rede von strömenden Informationen in der Entwicklung digitaler Computer nach und diskutiert, wie der Begriff fĂŒr Gegenwartsbeschreibungen produktiv gemacht werden kann

    Collective Embodiment and Communal Feeling: A Critical Somatics Approach to Performance for Social Change

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    “Collective Embodiment and Communal Feeling: A Critical Somatics Approach to Performance for Social Change” argues for a novel approach to performance for social change that focuses on the sensory and somatic dimensions of collectivity as the basis for countering the atomizing politics of neoliberalism. It proposes a critical somatics approach to the deconstruction and reconfiguration of participants’ embodied subjectivities, emphasizing the cultivation of conditions that facilitate experiences of collective embodiment and affective interdependence. Whether in the kinesthetic awareness of bodies dancing together, the situational or proprioceptive awareness of a collective engaged in creative disruption, or the physical contact of activists’ clasped arms forming a human chain in protest, these conditions require multisensory engagement, improvisational coordination, and shared feeling. Based on ethnographic accounts of the phenomenological experience of collective embodiment, I argue that such experiences enact—rather than merely argue for—forms of collectivity through their operation on the level of the body. This approach to performance for social change builds on the experience of practitioners and artist-activists in an effort to preserve the core contributions of existing techniques while seeking avenues to overcome their susceptibility to the influence of increasingly ubiquitous neoliberal frameworks. Opening with a consideration of Augusto Boal’s Theatre of the Oppressed as a touchstone example, I argue that the technique’s cognitive approach to social change and its emphasis on discursive techniques contribute to the manner in which it individualizes responsibility for combating systemic oppression. Turning to Cynthia Winton-Henry and Phil Porter’s InterPlay as an example of an affective approach to performance for social change, I critique its practitioners’ culture of individualism, but identify the critical potential of its recognition of collective embodiment. Extending this analysis to protest and direct action, I explore the existential prefiguration of communities of care and the cultivation of communal feeling, an affective and collective form of embodied cognition. After offering a series of activities designed to create the conditions for experiences of collective embodiment and develop the affective bonds of communal feeling, I close with a consideration of the broader implications of positioning speculative theory at the forefront of movements’ political practice.Doctor of Philosoph

    Chatbots for Modelling, Modelling of Chatbots

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    Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informåtica. Fecha de Lectura: 28-03-202

    Seeing Ordinary Objects: The Minimal Condition, Amodal Completion, and Mental Files

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    This thesis seeks to explain the way in which we see ordinary objects like books, tables, and apples. Specifically, it is an attempt to explain the way that we are connected to the ordinary objects that populate our world despite the fact that we usually only receive sensory stimulation from small parts of them: their surfaces. I will suggest some conditions that must obtain for ordinary objects to be seen and present a conceptual schema based on the notion of ‘mental files’ that can be used to explain this phenomenon. Mental files, I argue, can not only be used to explain our perceptual connection to ordinary objects but can also dissolve some of the epistemic worries raised by amodal completion and the problem of incomplete sensory information

    Examining the representation of spatial short-term memories through the lens of resource allocation theory

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    This thesis aims to examine the nature of spatial representations in visuospatial working memory (VSWM) and the mechanism by which the oculomotor system supports VSWM maintenance. To examine these research questions, Chapter Two verifies the use of a continuous report task in measuring memory for spatial locations, showing that the representation of spatial locations is affected by the number of to-be-remembered items. In Chapter Three, a strong eccentricity effect in spatial, but not colour, working memory was observed. This result is argued to reflect that the resource involved in spatial working memory relies on topographic mapping. Chapter Four examined the distribution of resources across sequences of spatial locations. Results showed that the serial position effect, and therefore the distribution of resources, depends on whether the full sequence or a single probe is to be recalled. To examine the role of the oculomotor system, saccadic interference in spatial and colour working memory was examined in Chapter Five. Results showed that the oculomotor system is selectively involved in maintenance of spatial locations in VSWM. Performing multiple delay-period saccades resulted in an increase in guessing, but not imprecision, in spatial working memory. It is argued that spatial locations in VSWM are represented as activity peaks in a topographic cortical map. Within this map, the oculomotor system is involved in maintaining the signal to noise ratio of activity peaks for each of the to-be-remembered locations. This research makes an important and novel contribution to the literature by advancing understanding of the nature of representations within spatial working memory and interactions between VSWM and action systems

    A Survey of Zero-shot Generalisation in Deep Reinforcement Learning

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    The study of zero-shot generalisation (ZSG) in deep Reinforcement Learning (RL) aims to produce RL algorithms whose policies generalise well to novel unseen situations at deployment time, avoiding overfitting to their training environments. Tackling this is vital if we are to deploy reinforcement learning algorithms in real world scenarios, where the environment will be diverse, dynamic and unpredictable. This survey is an overview of this nascent field. We rely on a unifying formalism and terminology for discussing different ZSG problems, building upon previous works. We go on to categorise existing benchmarks for ZSG, as well as current methods for tackling these problems. Finally, we provide a critical discussion of the current state of the field, including recommendations for future work. Among other conclusions, we argue that taking a purely procedural content generation approach to benchmark design is not conducive to progress in ZSG, we suggest fast online adaptation and tackling RL-specific problems as some areas for future work on methods for ZSG, and we recommend building benchmarks in underexplored problem settings such as offline RL ZSG and reward-function variation

    Semi-automated learning strategies for large-scale segmentation of histology and other big bioimaging stacks and volumes

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    Labelled high-resolution datasets are becoming increasingly common and necessary in different areas of biomedical imaging. Examples include: serial histology and ex-vivo MRI for atlas building, OCT for studying the human brain, and micro X-ray for tissue engineering. Labelling such datasets, typically, requires manual delineation of a very detailed set of regions of interest on a large number of sections or slices. This process is tedious, time-consuming, not reproducible and rather inefficient due to the high similarity of adjacent sections. In this thesis, I explore the potential of a semi-automated slice level segmentation framework and a suggestive region level framework which aim to speed up the segmentation process of big bioimaging datasets. The thesis includes two well validated, published, and widely used novel methods and one algorithm which did not yield an improvement compared to the current state-of the-art. The slice-wise method, SmartInterpol, consists of a probabilistic model for semi-automated segmentation of stacks of 2D images, in which the user manually labels a sparse set of sections (e.g., one every n sections), and lets the algorithm complete the segmentation for other sections automatically. The proposed model integrates in a principled manner two families of segmentation techniques that have been very successful in brain imaging: multi-atlas segmentation and convolutional neural networks. Labelling every structure on a sparse set of slices is not necessarily optimal, therefore I also introduce a region level active learning framework which requires the labeller to annotate one region of interest on one slice at the time. The framework exploits partial annotations, weak supervision, and realistic estimates of class and section-specific annotation effort in order to greatly reduce the time it takes to produce accurate segmentations for large histological datasets. Although both frameworks have been created targeting histological datasets, they have been successfully applied to other big bioimaging datasets, reducing labelling effort by up to 60−70% without compromising accuracy
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