6,490 research outputs found

    Cognitive Inhibition as a Core Component of Executive Functions:Exploring Intra- and Interindividual Differences

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    Cognitive inhibition is an essential executive function that we use in our everyday lives. Numerous factors have been claimed to influence this construct including video gaming, exercise and expertise in musical instruments. However, in this thesis, I focus on an understudied factor, the alignment of chronotype and testing time, and a heavily studied yet controversial factor, bilingualism. Throughout this thesis, with one exception, I present a series of experiments which have been conducted online. In the first empirical chapter, I examined a relatively novel Faces task which the authors have claimed to measure three cognitive processes, including two different forms of inhibition and task switching (Chapter 2). Based on this chapter's findings, I decided to use the Faces task in Chapters 3, 4 and 6. The next two chapters determined whether the alignment of time of testing and chronotype influences inhibition and task switching among the young adult (Chapter 3) and older adult (Chapter 4) population. Afterwards, I explored how conflict is resolved through a mouse tracking paradigm and by extension, whether this paradigm can be used for a variety of inhibition tasks (Chapter 5). For the final empirical chapter, I identified whether training inhibition in a verbal domain impacts inhibition in a non-verbal domain (i.e., far transfer effects). To achieve this, I investigated whether bilingualism, which can be seen as a form of cognitive training within the verbal domain, influences performance in non-verbal tasks which index inhibition (Chapter 6). The main findings of this thesis suggest that cognitive inhibition is not substantially impacted by synchrony effects nor by bilingualism. Furthermore, the findings imply that mouse tracking could be a promising tool to use to examine cognitive inhibition

    The State of the Art in Deep Learning Applications, Challenges, and Future Prospects::A Comprehensive Review of Flood Forecasting and Management

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    Floods are a devastating natural calamity that may seriously harm both infrastructure and people. Accurate flood forecasts and control are essential to lessen these effects and safeguard populations. By utilizing its capacity to handle massive amounts of data and provide accurate forecasts, deep learning has emerged as a potent tool for improving flood prediction and control. The current state of deep learning applications in flood forecasting and management is thoroughly reviewed in this work. The review discusses a variety of subjects, such as the data sources utilized, the deep learning models used, and the assessment measures adopted to judge their efficacy. It assesses current approaches critically and points out their advantages and disadvantages. The article also examines challenges with data accessibility, the interpretability of deep learning models, and ethical considerations in flood prediction. The report also describes potential directions for deep-learning research to enhance flood predictions and control. Incorporating uncertainty estimates into forecasts, integrating many data sources, developing hybrid models that mix deep learning with other methodologies, and enhancing the interpretability of deep learning models are a few of these. These research goals can help deep learning models become more precise and effective, which will result in better flood control plans and forecasts. Overall, this review is a useful resource for academics and professionals working on the topic of flood forecasting and management. By reviewing the current state of the art, emphasizing difficulties, and outlining potential areas for future study, it lays a solid basis. Communities may better prepare for and lessen the destructive effects of floods by implementing cutting-edge deep learning algorithms, thereby protecting people and infrastructure

    Component Segmentation of Engineering Drawings Using Graph Convolutional Networks

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    We present a data-driven framework to automate the vectorization and machine interpretation of 2D engineering part drawings. In industrial settings, most manufacturing engineers still rely on manual reads to identify the topological and manufacturing requirements from drawings submitted by designers. The interpretation process is laborious and time-consuming, which severely inhibits the efficiency of part quotation and manufacturing tasks. While recent advances in image-based computer vision methods have demonstrated great potential in interpreting natural images through semantic segmentation approaches, the application of such methods in parsing engineering technical drawings into semantically accurate components remains a significant challenge. The severe pixel sparsity in engineering drawings also restricts the effective featurization of image-based data-driven methods. To overcome these challenges, we propose a deep learning based framework that predicts the semantic type of each vectorized component. Taking a raster image as input, we vectorize all components through thinning, stroke tracing, and cubic bezier fitting. Then a graph of such components is generated based on the connectivity between the components. Finally, a graph convolutional neural network is trained on this graph data to identify the semantic type of each component. We test our framework in the context of semantic segmentation of text, dimension and, contour components in engineering drawings. Results show that our method yields the best performance compared to recent image, and graph-based segmentation methods.Comment: Preprint accepted to Computers in Industr

    The regulation of digital platforms: the case of pagoPA

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    How can EU regulation affect innovation. Digital revolution: How big data have changed the world and the legal landscape. The regulation of digital platforms in Europe. Digital revolution: How distributed ledger technologies are changing the world and the legal landscape. Regulation of digital payments: the case of pagopa

    A Survey on Event-based News Narrative Extraction

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    Narratives are fundamental to our understanding of the world, providing us with a natural structure for knowledge representation over time. Computational narrative extraction is a subfield of artificial intelligence that makes heavy use of information retrieval and natural language processing techniques. Despite the importance of computational narrative extraction, relatively little scholarly work exists on synthesizing previous research and strategizing future research in the area. In particular, this article focuses on extracting news narratives from an event-centric perspective. Extracting narratives from news data has multiple applications in understanding the evolving information landscape. This survey presents an extensive study of research in the area of event-based news narrative extraction. In particular, we screened over 900 articles that yielded 54 relevant articles. These articles are synthesized and organized by representation model, extraction criteria, and evaluation approaches. Based on the reviewed studies, we identify recent trends, open challenges, and potential research lines.Comment: 37 pages, 3 figures, to be published in the journal ACM CSU

    Sensing Collectives: Aesthetic and Political Practices Intertwined

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    Are aesthetics and politics really two different things? The book takes a new look at how they intertwine, by turning from theory to practice. Case studies trace how sensory experiences are created and how collective interests are shaped. They investigate how aesthetics and politics are entangled, both in building and disrupting collective orders, in governance and innovation. This ranges from populist rallies and artistic activism over alternative lifestyles and consumer culture to corporate PR and governmental policies. Authors are academics and artists. The result is a new mapping of the intermingling and co-constitution of aesthetics and politics in engagements with collective orders

    A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

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    Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure

    “Not the story you want, I’m sure”: Mental health recovery and the narratives of people from marginalised communities

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    Background: The dominant narrative in mental health policy and practice has shifted in the 21st century from one of chronic ill health or incurability to an orientation towards recovery. A recovery-based approach is now the most frequently used in services in the Global North, and its relevance has also been explored in Global South settings. Despite the ubiquity of the recovery approach, people experiencing poverty, homelessness, intersecting oppressions (based for example on race, ethnicity, gender, sexuality or ability), and other forms of social marginalisation remain under-represented within recovery-oriented research. More inclusive research has been called for to ensure that knowledge of recovery processes is not based solely on the experiences of the relatively well-resourced. Personal narratives of recovery from mental distress have played a central role in the establishment of the recovery approach within mental health policy and practice. Originating in survivor/service-user movements, the use of ‘recovery narratives’ has now become widespread for diverse purposes, including staff training to improve service delivery and increase empathy, public health campaigns to challenge stigma, online interventions to increase access to self-care resources, and as a distinctive feature of peer support. Research suggests that recovery-focused narratives can have benefits and also risks for narrators and recipients. At the same time, the elicitation of such narratives by healthcare researchers, educators and practitioners has been problematised by survivor-researchers and other critical theorists, as a co-option of lived experience for neoliberal purposes. Following a systematic review of empirical research studies undertaken on characteristics of recovery narratives (presented in Chapter 4), a need for empirical research on the narratives of people from socially marginalised groups was identified. What kinds of stories might we/they be telling, and what are their experiences of telling their stories? What do their experiences tell us about the use of stories within a recovery approach? Aim: Drawing on a body of critical scholarship, my aim is to conduct an empirical inquiry into (i) characteristics of recovery stories told by people from socially marginalised groups, and (ii) their experiences of telling their stories in formal and everyday settings. Method: I undertook a critical narrative inquiry based on the stories of 77 people from marginalised groups, collected in the context of a wider study. This comprised narratives from people with lived experience of mental distress who additionally met one or more of the following criteria: (i) had experiences of psychosis; (ii) were from Black, Asian and other minoritised ethnic communities; (iii) are under-served by services (operationalised as lesbian, gay, bi, trans, queer + communities (LGBTQ+) or people identified as having multiple and complex needs); or (iv) had peer support roles. Two-part interviews were conducted (18 conducted by me). Part A consisted of an open-ended question designed to elicit a narrative, and part B was a semi-structured interview inviting participants to reflect on their experiences of telling their recovery stories in different contexts. Following Riessman’s analytical approach, I undertook three forms of analysis: a structural narrative analysis of Part A across the dataset (informed by a preliminary conceptual framework developed in Chapter 4); a thematic analysis of Part B where participants additionally reflected on telling their stories; and an in-depth performative narrative analysis of two accounts (parts A and B) from people with multiple and complex needs. Findings: In a structural analysis of Part A, the recovery narratives told by people from marginalised groups were found to be diverse and multidimensional. Most (97%) could be characterised by the nine dimensions described in the preliminary conceptual framework (Genre; Positioning; Emotional Tone; Relationship with Recovery; Trajectory; Turning Points; Narrative Sequence; Protagonists; and Use of Metaphors). Each dimension of the framework contained a number of different types. These were expanded as a result of the structural analysis to contain more types: for example, a ‘cyclical’ type of trajectory was added), and a more comprehensive typology of recovery narratives was produced. Two narratives were found to be ‘outliers’, in that their structure, form and content could not adequately be described by the majority of existing dimensions and types. These served as exemplars of the framework’s limitations. In a thematic analysis of Part B, my overarching finding was that power differentials between narrators and recipients could be seen as the key factor affecting participants’ experiences of telling their recovery stories in formal and everyday settings. Four themes describing the possibilities and problems raised by telling their stories were identified: (i) ‘Challenging the status quo’; (ii) ‘Risky consequences’; (iii) ‘Producing acceptable stories’ and (iv) ‘Untellable stories’. In a performative analysis of two narratives of people with multiple and complex needs (Parts A and B), I found two contrasting ways of responding to the invitation to tell a recovery story: a ‘narrative of personal lack’ and a ‘narrative of resistance’. I demonstrate how the genre of ‘recovery narrative’, with its focus on transformation at the level of personal identity, may function to occlude social and structural causes of distress, and reinforce ideas of personal responsibility for ongoing distress in the face of unchanging living conditions. Conclusion: The recovery narratives of people from socially marginalised groups are diverse and multidimensional. Told in some contexts, they may hold power to challenge the status quo. However, telling stories of lived experience and recovery is risky, and there may be pressure on narrators to produce ‘acceptable’ stories, or to omit or de-emphasise experiences which challenge dominant cultural narratives. A recovery-based approach to the use of lived experience narratives in research and practice may be contributing towards an over-emphasis on individualist approaches to the reduction of distress. This over-emphasis can be seen to reflect what has been identified as a global trend towards the ‘instrumental’ use of personal narratives for utilitarian purposes based on market values. Attention to power differentials and structural as well as agentic factors is vital to ensure that the use of narratives in research and practice does not contribute towards a decontextualised, reductionist form of recovery which pays insufficient attention to the economic, institutional and political injustices that people experiencing mental distress may systematically endure. A sensitive and socially just use of lived experience narratives will remain alert to a variety of power dimensions present within the contexts in which they are shared and hear

    A Comprehensive Review of Data-Driven Co-Speech Gesture Generation

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    Gestures that accompany speech are an essential part of natural and efficient embodied human communication. The automatic generation of such co-speech gestures is a long-standing problem in computer animation and is considered an enabling technology in film, games, virtual social spaces, and for interaction with social robots. The problem is made challenging by the idiosyncratic and non-periodic nature of human co-speech gesture motion, and by the great diversity of communicative functions that gestures encompass. Gesture generation has seen surging interest recently, owing to the emergence of more and larger datasets of human gesture motion, combined with strides in deep-learning-based generative models, that benefit from the growing availability of data. This review article summarizes co-speech gesture generation research, with a particular focus on deep generative models. First, we articulate the theory describing human gesticulation and how it complements speech. Next, we briefly discuss rule-based and classical statistical gesture synthesis, before delving into deep learning approaches. We employ the choice of input modalities as an organizing principle, examining systems that generate gestures from audio, text, and non-linguistic input. We also chronicle the evolution of the related training data sets in terms of size, diversity, motion quality, and collection method. Finally, we identify key research challenges in gesture generation, including data availability and quality; producing human-like motion; grounding the gesture in the co-occurring speech in interaction with other speakers, and in the environment; performing gesture evaluation; and integration of gesture synthesis into applications. We highlight recent approaches to tackling the various key challenges, as well as the limitations of these approaches, and point toward areas of future development.Comment: Accepted for EUROGRAPHICS 202
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