2,397 research outputs found

    Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea

    Get PDF
    ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy action’s effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (−10% per year) trend after adoption and enforcement of these laws (β2 = 0.18, p-value = 0.07; β3 = −0.10, p-value = 0.02). SHS exposure at home (β2 = 0.10, p-value = 0.09; β3 = −0.03, p-value = 0.14) and the primary cigarette smoking rate (β2 = 0.03, p-value = 0.10; β3 = 0.008, p-value = 0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK

    Simultaneous Multiparametric and Multidimensional Cardiovascular Magnetic Resonance Imaging

    Get PDF
    No abstract available

    AI: Limits and Prospects of Artificial Intelligence

    Get PDF
    The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence

    Video Summarization Using Unsupervised Deep Learning

    Get PDF
    In this thesis, we address the task of video summarization using unsupervised deep-learning architectures. Video summarization aims to generate a short summary by selecting the most informative and important frames (key-frames) or fragments (key-fragments) of the full-length video, and presenting them in temporally-ordered fashion. Our objective is to overcome observed weaknesses of existing video summarization approaches that utilize RNNs for modeling the temporal dependence of frames, related to: i) the small influence of the estimated frame-level importance scores in the created video summary, ii) the insufficiency of RNNs to model long-range frames' dependence, and iii) the small amount of parallelizable operations during the training of RNNs. To address the first weakness, we propose a new unsupervised network architecture, called AC-SUM-GAN, which formulates the selection of important video fragments as a sequence generation task and learns this task by embedding an Actor-Critic model in a Generative Adversarial Network. The feedback of a trainable Discriminator is used as a reward by the Actor-Critic model in order to explore a space of actions and learn a value function (Critic) and a policy (Actor) for video fragment selection. To tackle the remaining weaknesses, we investigate the use of attention mechanisms for video summarization and propose a new supervised network architecture, called PGL-SUM, that combines global and local multi-head attention mechanisms which take into account the temporal position of the video frames, in order to discover different modelings of the frames' dependencies at different levels of granularity. Based on the acquired experience, we then propose a new unsupervised network architecture, called CA-SUM, which estimates the frames' importance using a novel concentrated attention mechanism that focuses on non-overlapping blocks in the main diagonal of the attention matrix and takes into account the attentive uniqueness and diversity of the associated frames of the video. All the proposed architectures have been extensively evaluated on the most commonly-used benchmark datasets, demonstrating their competitiveness against other approaches and documenting the contribution of our proposals on advancing the current state-of-the-art on video summarization. Finally, we make a first attempt on producing explanations for the video summarization results. Inspired by relevant works in the Natural Language Processing domain, we propose an attention-based method for explainable video summarization and we evaluate the performance of various explanation signals using our CA-SUM architecture and two benchmark datasets for video summarization. The experimental results indicate the advanced performance of explanation signals formed using the inherent attention weights, and demonstrate the ability of the proposed method to explain the video summarization results using clues about the focus of the attention mechanism

    A new global media order? : debates and policies on media and mass communication at UNESCO, 1960 to 1980

    Get PDF
    Defence date: 24 June 2019Examining Board: Professor Federico Romero, European University Institute (Supervisor); Professor Corinna Unger, European University Institute (Second Reader); Professor Iris Schröder, Universität Erfurt (External Advisor); Professor Sandrine Kott, Université de GenèveThe 1970s, a UNESCO report claimed, would be the “communication decade”. UNESCO had started research on new means of mass communication for development purposes in the 1960s. In the 1970s, the issue evolved into a debate on the so-called “New World Information and Communication Order” (NWICO) and the democratisation of global media. It led UNESCO itself into a major crisis in the 1980s. My project traces a dual trajectory that shaped this global debate on transnational media. The first follows communications from being seen as a tool and goal of national development in the 1960s, to communications seen as catalyst for recalibrated international political, cultural and economic relations. The second relates to the recurrent attempts, and eventual failure, of various actors to engage UNESCO as a platform to promote a new global order. I take UNESCO as an observation post to study national ambitions intersecting with internationalist claims to universality, changing understandings of the role of media in development and international affairs, and competing visions of world order. Looking at the modes of this debate, the project also sheds light on the evolving practices of internationalism. Located in the field of a new international history, this study relates to the recent rediscovery of the “new order”-discourses of the 1970s as well as to the increasingly diversified literature on internationalism. With its focus on international communications and attempts at regulating them, it also contributes to an international media history in the late twentieth century. The emphasis on the role of international organisations as well as on voices from the Global South will make contributions to our understanding of the historic macro-processes of decolonisation, globalisation and the Cold War

    The politics of content prioritisation online governing prominence and discoverability on digital media platforms

    Get PDF
    This thesis examines the governing systems and industry practices shaping online content prioritisation processes on digital media platforms. Content prioritisation, and the relative prominence and discoverability of content, are investigated through a critical institutional lens as digital decision guidance processes that shape online choice architecture and influence users’ access to content online. This thesis thus shows how prioritisation is never neutral or static and cannot be explained solely by political economic or neoclassical economics approaches. Rather, prioritisation is dynamically shaped by the institutional environment and by the clash between existing media governance systems and those emerging for platform governance. As prioritisation processes influence how audiovisual media services are accessed online, posing questions about the public interest in such forms of intermediation is key. In that context, this research asks how content prioritisation is governed on digital media platforms, and what the elements of a public interest framework for these practices might be. To address these questions, I use a within case study comparative research design focused on the United Kingdom, collecting data by means of semi-structured interviews and document analysis. Through a thematic analysis, I then investigate how institutional arrangements influence both organisational strategies and interests, as well as the relationships among industry and policy actors involved, namely, platform organisations, pay-TV operators, technology manufacturers, content providers including public service media, and regulators. The results provide insights into the ‘black box’ of content prioritisation across three interconnected dimensions: technical, market, and regulatory. In each dimension, a battle between industry and policy actors emerges to influence prioritisation online. As the UK Government and regulator intend to develop new prominence rules, the dispute takes on a normative dimension and gives rise to contested visions of what audiovisual services should be prioritised to the final users, and which private- and public-interest-driven criteria are (or should) be used to determine that. Finally, the analysis shows why it is crucial to reflect on how the public interest is interpreted and operationalised as new prominence regulatory regimes emerge with a variety of sometimes contradictory implications for media pluralism, diversity and audience freedom of choice. The thesis therefore indicates the need for new institutional arrangements and a public interest-driven framework for prioritisation on digital media platforms. Such a framework conceives of public interest content standards as an institutional imperative for media and platform organisations and prompts regulators to develop new online content regulation that is appropriate to changing forms of digital intermediation and emerging audiovisual market conditions. While the empirical focus is on the UK, the implications of the research findings are also considered in the light of developments in the European Union and Council of Europe initiatives that bear on the future discoverability of public interest media services and related prominence regimes

    The Slightest Attachment: When Psychiatric Spaces Enact Affinities

    Get PDF
    While the disciplinary architecture of hospitals has long prevailed in psychiatry, many care teams now work in smaller structures, within communities. The author explores one of these places: Drawing on fieldwork in a psychiatric day center for teenagers, she traces how spatial arrangements matter in the care practice. From a corner in which one can withdraw, to a kitchen inviting to hang around, or displayed artworks that pique one's curiosity, caregivers use the material environment to stir up the slightest affinity from teenagers. This study thus expands our idea of what attachment is, and makes us more able to recognize the subtle dynamics between care, things, and spaces. With a preface by Jeannette Pols

    Investigating compositional visual knowledge through challenging visual tasks

    Get PDF
    Human vision manifests remarkable robustness to recognize objects from the visual world filled with a chaotic, dynamic assortment of information. Computationally, our visual system is challenged by the enormous variability in two-dimensional projected images as a function of viewpoint, lighting, material, articulation as well as occlusion. Many past research investigated the underlying representations and computational principles that support human vision robustness with controlled and simplified visual stimuli. Nevertheless, the generality of these findings was unclear until tested on more challenging and more naturalistic stimuli. In this thesis, I study human vision robustness with several challenging visual tasks and more naturalistic stimuli, including the recognition of occluded objects and the recognition of non-rigid human bodies from natural images of scenes. I use psychophysics, functional magnetic resonance imaging as well as computational modeling approaches to measure human vision robustness and examine the hierarchical, compositional framework as the underlying principle where the representation of the whole is composed of the representation of its parts through different hierarchies. I show that human vision has impressive abilities to recognize heavily occluded natural objects, and the human behavioral performance is better explained by compositional models rather than standard deep convolutional neural networks. In addition, I also show that human vision can rapidly and robustly extract information about spatial relationships between human body parts and discriminate three-dimensional non-rigid human poses even from a mere glance. Lastly, I show that there exists a distributed cortical network that encodes compositional pose representations with different view invariance and depth sensitivity, and the difference in these neural representations might be driven by the diversity of the supported behavior tasks. Taken together, this thesis demonstrates that human vision manifests great robustness even in these challenging visual tasks, and that the hierarchical, compositional framework may be one of the underlying principles supporting such robustness

    Geometric Data Analysis: Advancements of the Statistical Methodology and Applications

    Get PDF
    Data analysis has become fundamental to our society and comes in multiple facets and approaches. Nevertheless, in research and applications, the focus was primarily on data from Euclidean vector spaces. Consequently, the majority of methods that are applied today are not suited for more general data types. Driven by needs from fields like image processing, (medical) shape analysis, and network analysis, more and more attention has recently been given to data from non-Euclidean spaces–particularly (curved) manifolds. It has led to the field of geometric data analysis whose methods explicitly take the structure (for example, the topology and geometry) of the underlying space into account. This thesis contributes to the methodology of geometric data analysis by generalizing several fundamental notions from multivariate statistics to manifolds. We thereby focus on two different viewpoints. First, we use Riemannian structures to derive a novel regression scheme for general manifolds that relies on splines of generalized Bézier curves. It can accurately model non-geodesic relationships, for example, time-dependent trends with saturation effects or cyclic trends. Since Bézier curves can be evaluated with the constructive de Casteljau algorithm, working with data from manifolds of high dimensions (for example, a hundred thousand or more) is feasible. Relying on the regression, we further develop a hierarchical statistical model for an adequate analysis of longitudinal data in manifolds, and a method to control for confounding variables. We secondly focus on data that is not only manifold- but even Lie group-valued, which is frequently the case in applications. We can only achieve this by endowing the group with an affine connection structure that is generally not Riemannian. Utilizing it, we derive generalizations of several well-known dissimilarity measures between data distributions that can be used for various tasks, including hypothesis testing. Invariance under data translations is proven, and a connection to continuous distributions is given for one measure. A further central contribution of this thesis is that it shows use cases for all notions in real-world applications, particularly in problems from shape analysis in medical imaging and archaeology. We can replicate or further quantify several known findings for shape changes of the femur and the right hippocampus under osteoarthritis and Alzheimer's, respectively. Furthermore, in an archaeological application, we obtain new insights into the construction principles of ancient sundials. Last but not least, we use the geometric structure underlying human brain connectomes to predict cognitive scores. Utilizing a sample selection procedure, we obtain state-of-the-art results

    Attractive User Interface Elements : Measurement and prediction

    Get PDF
    The years 2020–2021 mark a time when the global population was encountered by a world-wide pandemic. The lockdown had devastating consequences on many industries and individuals, and the emergence of global economies into the postpandemic recovery has only just begun. However, as people adapted to the pandemic by embracing a mobile lifestyle, industries that employed graphical user interfaces as a means of human-computer interaction saw tremendous growth, exceeding everyone’s expectations despite predictions of a slowdown. One example is the mobile apps and games markets, touted as the fastest growing marketplaces worldwide. At the moment, the impact of the mobile economy is undeniably high, and it does not show signs of stalling. As we look ahead and start the 'return to physical', we can see new mobile habits take shape in our everyday life. Today, people conduct most daily functions via graphical user interfaces, due to the increasing technology-mediated nature of all human praxis, such as socializing, work, education, and entertainment. The interaction is realized on various different platforms, be they on desktop, mobile devices, VR or (smart) TVs. Although user interfaces themselves are not novel, their role is more significant now than anyone could have imagined only a few decades ago. Attractive visual designs in user interfaces have proven to enhance many aspects concerning usability, sense of pleasure and trust, but evaluating aesthetics is challenging due to the subjective nature of user perception. Although several theories and measurement instruments have been developed in order to assess and design pleasing user interfaces, the measures remain scattered. Therefore, the aim of this dissertation is to expand knowledge on how the visual aesthetics of graphical user interfaces can be modelled, evaluated, and assessed. Through four studies, this dissertation provides an overview of the state-of-theart in the literature of measurement instruments of visual aesthetics for graphical user interfaces. The dimensions of aesthetic perception that emerge in the context of user interface elements are also examined and introduced by developing a scale for measuring perceptions. As engaging and intuitive imagery has become one of the most valuable assets in today’s attention economy, the studies also observe individual user perceptions of different demographic groups and their relationships on aesthetic qualities to determine how they predict the success of graphical elements. The publications employ methodology ranging from a systematic literature review to sophisticated, quantitative statistical modelling methods to accurately identify and address each of the described phenomena by standardized means. The findings provided by this dissertation greatly contribute to existing literature on the measurement and prediction of visually pleasing graphical user interfaces both practically and theoretically. Advancing knowledge and guidelines in this fast-paced field requires assessment from a wide perspective, including the observation of prior work, and the adaptation of measures to the modern economy by highlighting user behavior and preferences. This is particularly important in the milieu of the increasingly growing prevalence of graphical user interfaces that will continue shaping our lives in ways unimaginable
    corecore