20,995 research outputs found

    RAFEN -- Regularized Alignment Framework for Embeddings of Nodes

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    Learning representations of nodes has been a crucial area of the graph machine learning research area. A well-defined node embedding model should reflect both node features and the graph structure in the final embedding. In the case of dynamic graphs, this problem becomes even more complex as both features and structure may change over time. The embeddings of particular nodes should remain comparable during the evolution of the graph, what can be achieved by applying an alignment procedure. This step was often applied in existing works after the node embedding was already computed. In this paper, we introduce a framework -- RAFEN -- that allows to enrich any existing node embedding method using the aforementioned alignment term and learning aligned node embedding during training time. We propose several variants of our framework and demonstrate its performance on six real-world datasets. RAFEN achieves on-par or better performance than existing approaches without requiring additional processing steps.Comment: ICCS 202

    Bayesian networks for disease diagnosis: What are they, who has used them and how?

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    A Bayesian network (BN) is a probabilistic graph based on Bayes' theorem, used to show dependencies or cause-and-effect relationships between variables. They are widely applied in diagnostic processes since they allow the incorporation of medical knowledge to the model while expressing uncertainty in terms of probability. This systematic review presents the state of the art in the applications of BNs in medicine in general and in the diagnosis and prognosis of diseases in particular. Indexed articles from the last 40 years were included. The studies generally used the typical measures of diagnostic and prognostic accuracy: sensitivity, specificity, accuracy, precision, and the area under the ROC curve. Overall, we found that disease diagnosis and prognosis based on BNs can be successfully used to model complex medical problems that require reasoning under conditions of uncertainty.Comment: 22 pages, 5 figures, 1 table, Student PhD first pape

    ENABLING EFFICIENT FLEET COMPOSITION SELECTION THROUGH THE DEVELOPMENT OF A RANK HEURISTIC FOR A BRANCH AND BOUND METHOD

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    In the foreseeable future, autonomous mobile robots (AMRs) will become a key enabler for increasing productivity and flexibility in material handling in warehousing facilities, distribution centers and manufacturing systems. The objective of this research is to develop and validate parametric models of AMRs, develop ranking heuristic using a physics-based algorithm within the framework of the Branch and Bound method, integrate the ranking algorithm into a Fleet Composition Optimization (FCO) tool, and finally conduct simulations under various scenarios to verify the suitability and robustness of the developed tool in a factory equipped with AMRs. Kinematic-based equations are used for computing both energy and time consumption. Multivariate linear regression, a data-driven method, is used for designing the ranking heuristic. The results indicate that the unique physical structures and parameters of each robot are the main factors contributing to differences in energy and time consumption. improvement on reducing computation time was achieved by comparing heuristic-based search and non-heuristic-based search. This research is expected to significantly improve the current nested fleet composition optimization tool by reducing computation time without sacrificing optimality. From a practical perspective, greater efficiency in reducing energy and time costs can be achieved.Ford Motor CompanyNo embargoAcademic Major: Aerospace Engineerin

    Corporate Social Responsibility: the institutionalization of ESG

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    Understanding the impact of Corporate Social Responsibility (CSR) on firm performance as it relates to industries reliant on technological innovation is a complex and perpetually evolving challenge. To thoroughly investigate this topic, this dissertation will adopt an economics-based structure to address three primary hypotheses. This structure allows for each hypothesis to essentially be a standalone empirical paper, unified by an overall analysis of the nature of impact that ESG has on firm performance. The first hypothesis explores the evolution of CSR to the modern quantified iteration of ESG has led to the institutionalization and standardization of the CSR concept. The second hypothesis fills gaps in existing literature testing the relationship between firm performance and ESG by finding that the relationship is significantly positive in long-term, strategic metrics (ROA and ROIC) and that there is no correlation in short-term metrics (ROE and ROS). Finally, the third hypothesis states that if a firm has a long-term strategic ESG plan, as proxied by the publication of CSR reports, then it is more resilience to damage from controversies. This is supported by the finding that pro-ESG firms consistently fared better than their counterparts in both financial and ESG performance, even in the event of a controversy. However, firms with consistent reporting are also held to a higher standard than their nonreporting peers, suggesting a higher risk and higher reward dynamic. These findings support the theory of good management, in that long-term strategic planning is both immediately economically beneficial and serves as a means of risk management and social impact mitigation. Overall, this contributes to the literature by fillings gaps in the nature of impact that ESG has on firm performance, particularly from a management perspective

    Alterations to cerebral perfusion, metabolite profiles, and neuronal morphology in the hippocampus and cortex of male and female mice during chronic exposure to a high-salt diet

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    Excess dietary salt reduces resting cerebral blood flow (CBF) and vascular reactivity, which can limit the fueling of neuronal metabolism. It is hitherto unknown whether metabolic derangements induced by high-salt-diet (HSD) exposure during adulthood are reversed by reducing salt intake. In this study, male and female mice were fed an HSD from 9 to 16 months of age, followed by a normal-salt diet (ND) thereafter until 23 months of age. Controls were continuously fed either ND or HSD. CBF and metabolite profiles were determined longitudinally by arterial spin labeling magnetic resonance imaging and magnetic resonance spectroscopy, respectively. HSD reduced cortical and hippocampal CBF, which recovered after dietary salt normalization, and affected hippocampal but not cortical metabolite profiles. Compared to ND, HSD increased hippocampal glutamine and phosphocreatine levels and decreased creatine and choline levels. Dietary reversal only allowed recovery of glutamine levels. Histology analyses revealed that HSD reduced the dendritic arborization and spine density of cortical and hippocampal neurons, which were not recovered after dietary salt normalization. We conclude that sustained HSD exposure throughout adulthood causes permanent structural and metabolic alterations to the mouse brain that are not fully normalized by lowering dietary salt during aging

    The potential value of Notch1 and DLL1 in the diagnosis and prognosis of patients with active TB

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    ObjectivesThe Notch signaling pathway has been implicated in the pathogenesis of active tuberculosis (TB), and Th1-type cell-mediated immunity is essential for effective control of mycobacterial infection. However, it remains unclear whether Notch signaling molecules (Notch1, DLL1, and Hes1) and Th1-type factors (T-bet and IFN-Îł) can serve as biomarkers for tracking the progression of active TB at different stages along with peripheral blood white blood cell (WBC) parameters.MethodsA total of 60 participants were enrolled in the study, including 37 confirmed TB patients (mild (n=17), moderate/severe (n=20)) and 23 healthy controls. The mRNA expression of Notch1, DLL1, Hes1, T-bet and IFN-Îł in the peripheral blood mononuclear cells (PBMCs) of the subjects was measured by RT-qPCR, then analyzed for differences. Receiver Operating Characteristic curve (ROC) was used to assess the effectiveness of each factor as a biomarker in identifying lung injury.ResultsWe found that mRNA expression levels of Notch1, DLL1, and Hes1 were upregulated in active TB patients, with higher levels observed in those with moderate/severe TB than those with mild TB or without TB. In contrast, mRNA levels of T-bet and IFN-Îł were downregulated and significantly lower in mild and moderate/severe cases. Furthermore, the combiROC analysis of IFN-Îł and the percentage of lymphocytes (L%) among WBC parameters showed superior discriminatory ability compared to other factors for identifying individuals with active TB versus healthy individuals. Notably, Notch pathway molecules were more effective than Th1-type factors and WBC parameters in differentiating mild and moderate/severe cases of active TB, particularly in the combiROC model that included Notch1 and Hes1.ConclusionsOur study demonstrated that Notch1, Hes1, IFN-Îł, and L% can be used as biomarkers to identify different stages of active TB patients and to monitor the effectiveness of treatment

    Perfect is the enemy of test oracle

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    Automation of test oracles is one of the most challenging facets of software testing, but remains comparatively less addressed compared to automated test input generation. Test oracles rely on a ground-truth that can distinguish between the correct and buggy behavior to determine whether a test fails (detects a bug) or passes. What makes the oracle problem challenging and undecidable is the assumption that the ground-truth should know the exact expected, correct, or buggy behavior. However, we argue that one can still build an accurate oracle without knowing the exact correct or buggy behavior, but how these two might differ. This paper presents SEER, a learning-based approach that in the absence of test assertions or other types of oracle, can determine whether a unit test passes or fails on a given method under test (MUT). To build the ground-truth, SEER jointly embeds unit tests and the implementation of MUTs into a unified vector space, in such a way that the neural representation of tests are similar to that of MUTs they pass on them, but dissimilar to MUTs they fail on them. The classifier built on top of this vector representation serves as the oracle to generate "fail" labels, when test inputs detect a bug in MUT or "pass" labels, otherwise. Our extensive experiments on applying SEER to more than 5K unit tests from a diverse set of open-source Java projects show that the produced oracle is (1) effective in predicting the fail or pass labels, achieving an overall accuracy, precision, recall, and F1 measure of 93%, 86%, 94%, and 90%, (2) generalizable, predicting the labels for the unit test of projects that were not in training or validation set with negligible performance drop, and (3) efficient, detecting the existence of bugs in only 6.5 milliseconds on average.Comment: Published in ESEC/FSE 202

    Emotional complexity of fan-controlled comments: Affective labor of fans of high-popularity Chinese stars

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    In China, fan participation in media production is becoming more mainstream and diverse, and fan groups themselves are developing perceptible emotional attributes; thus, studies on affective labor involving fans are gradually increasing in number. Fan-controlled comments are a feature of fan culture that has received much attention due to their rapid growth and influence. This study uses sentiment analysis and keyword analysis to examine the main types of “emotions” felt by today's fans of highly popular stars and classifies them into four categories: idols, fan communities, the self, and the outside world. Both positive and negative emotions coexist. The study found that fans engage in this kind of obligatory affective labor, creating second-hand exchanges for personal spiritual enrichment, and focusing more on building and expressing emotions. In addition, as affective laborers, they gain a sense of belonging to a fan community and form group symbols because of their shared emotions and concerns. Throughout the process of controlling comments, the time and energy of the fan groups are consumed, their emotions are controlled, and their behavior is restrained; however, the immediate purpose they want to achieve is not achieved. What seems to be an active choice is a trap of alienated labor, bound, and controlled by forces

    Fully-Autonomous, Vision-based Traffic Signal Control: from Simulation to Reality

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    Ineffective traffic signal control is one of the major causes of congestion in urban road networks. Dynamically changing traffic conditions and live traffic state estimation are fundamental challenges that limit the ability of the existing signal infrastructure in rendering individualized signal control in real-time. We use deep reinforcement learning (DRL) to address these challenges. Due to economic and safety constraints associated training such agents in the real world, a practical approach is to do so in simulation before deployment. Domain randomisation is an effective technique for bridging the reality gap and ensuring effective transfer of simulation-trained agents to the real world. In this paper, we develop a fully-autonomous, vision-based DRL agent that achieve adaptive signal control in the face of complex, imprecise, and dynamic traffic environments. Our agent uses live visual data (i.e. a stream of real-time RGB footage) from an intersection to extensively perceive and subsequently act upon the traffic environment. Employing domain randomisation, we examine our agent’s generalisation capabilities under varying traffic conditions in both the simulation and the real-world environments. In a diverse validation set independent of training data, our traffic control agent reliably adapted to novel traffic situations and demonstrated a positive transfer to previously unseen real intersections despite being trained entirely in simulation

    Message Journal, Issue 5: COVID-19 SPECIAL ISSUE Capturing visual insights, thoughts and reflections on 2020/21 and beyond...

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    If there is a theme running through the Message Covid-19 special issue, it is one of caring. Of our own and others’ resilience and wellbeing, of friendship and community, of students, practitioners and their futures, of social justice, equality and of doing the right thing. The veins of designing with care run through the edition, wide and deep. It captures, not designers as heroes, but those with humble views, exposing the need to understand a diversity of perspectives when trying to comprehend the complexity that Covid-19 continues to generate. As graphic designers, illustrators and visual communicators, contributors have created, documented, written, visualised, reflected, shared, connected and co-created, designed for good causes and re-defined what it is to be a student, an academic and a designer during the pandemic. This poignant period in time has driven us, through isolation, towards new rules of living, and new ways of working; to see and map the world in a different light. A light that is uncertain, disjointed, and constantly being redefined. This Message issue captures responses from the graphic communication design community in their raw state, to allow contributors to communicate their experiences through both their written and visual voice. Thus, the reader can discern as much from the words as the design and visualisations. Through this issue a substantial number of contributions have focused on personal reflection, isolation, fear, anxiety and wellbeing, as well as reaching out to community, making connections and collaborating. This was not surprising in a world in which connection with others has often been remote, and where ‘normal’ social structures of support and care have been broken down. We also gain insight into those who are using graphic communication design to inspire and capture new ways of teaching and learning, developing themselves as designers, educators, and activists, responding to social justice and to do good; gaining greater insight into society, government actions and conspiracy. Introduction: Victoria Squire - Coping with Covid: Community, connection and collaboration: James Alexander & Carole Evans, Meg Davies, Matthew Frame, Chae Ho Lee, Alma Hoffmann, Holly K. Kaufman-Hill, Joshua Korenblat, Warren Lehrer, Christine Lhowe, Sara Nesteruk, Cat Normoyle & Jessica Teague, Kyuha Shim. - Coping with Covid: Isolation, wellbeing and hope: Sadia Abdisalam, Tom Ayling, Jessica Barness, Megan Culliford, Stephanie Cunningham, Sofija Gvozdeva, Hedzlynn Kamaruzzaman, Merle Karp, Erica V. P. Lewis, Kelly Salchow Macarthur, Steven McCarthy, Shelly Mayers, Elizabeth Shefrin, Angelica Sibrian, David Smart, Ane Thon Knutsen, Isobel Thomas, Darryl Westley. - Coping with Covid: Pedagogy, teaching and learning: Bernard J Canniffe, Subir Dey, Aaron Ganci, Elizabeth Herrmann, John Kilburn, Paul Nini, Emily Osborne, Gianni Sinni & Irene Sgarro, Dave Wood, Helena Gregory, Colin Raeburn & Jackie Malcolm. - Coping with Covid: Social justice, activism and doing good: Class Action Collective, Xinyi Li, Matt Soar, Junie Tang, Lisa Winstanley. - Coping with Covid: Society, control and conspiracy: Diana BĂźrhală, Maria Borțoi, Patti Capaldi, TĂąnia A. Cardoso, Peter Gibbons, Bianca Milea, Rebecca Tegtmeyer, Danne Wo
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