1,849 research outputs found
Editors' report, November 2011
No full textAndrew A. Somogyi, Lionel D. Lewis, Adam F. Cohen, Albert Ferro, Yoon K. Loke & James M. Ritte
Editors' pick 2009
No full textA. Somogyi, Y. K. Loke, A. Ferro, L. D. Lewis, A. F. Cohen and J. M. Ritte
Decentralized Approximate Bayesian Inference for Distributed Sensor Network
No full textBayesian models provide a framework for probabilistic modelling of complex datasets. Many such models are computationally demanding, especially in the presence of large datasets. In sensor network applications, statistical (Bayesian) parameter estimation usually relies on decentralized algorithms, in which both data and computation are distributed across the nodes of the network. In this paper we propose a framework for decentralized Bayesian learning using Bregman Alternating Direction Method of Multipliers (B-ADMM).We demonstrate the utility of our framework, with Mean Field Variational Bayes (MFVB) as the primitive for distributed affine structure from motion (SfM).Peer reviewe
Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty
No full textWe propose new methods to speed up convergence of the Alternating Direction Method of Multipliers (ADMM), a common optimization tool in the context of large scale and distributed learning. The proposed method accelerates the speed of convergence by automatically deciding the constraint penalty needed for parameter consensus in each iteration. In addition, we also propose an extension of the method that adaptively determines the maximum number of iterations to update the penalty. We show that this approach effectively leads to an adaptive, dynamic network topology underlying the distributed optimization. The utility of the new penalty update schemes is demonstrated on both synthetic and real data, including an instance of the probabilistic matrix factorization task known as the structure-from-motion problem.Peer reviewe
A Knowledge Distribution Model to Support an Author in Narrative Creation
No full textAdjusting the knowledge of characters and the reader is a critical task for an author in narrative creation. Throughout a narrative, both characters and the reader experience events according to their own timelines and perspectives. They interpret information accumulated through their experience and update knowledge to the narrative-world which the author constructed. In this paper, we present a Knowledge Distribution Model which supports an author in finely controlling the knowledge of characters and the reader. Within the model, the Knowledge Structure is constructed by connecting event, information, and knowledge. The Knowledge State is evaluated as the degree of belief under the knowledge structure. We adopted a probabilistic reasoning model to calculate the knowledge state. The change in knowledge state, defined as Knowledge Flow, is visually presented to the author. We designed a GUI prototype to implement the proposed modeling process, and demonstrated the knowledge flow with an actual cinematic narrative
Distributed Probabilistic Learning for Camera Networks
No full textProbabilistic approaches to computer vision typically assume a centralized setting, with the algorithm granted access to all observed data points. However, many problems in wide-area surveillance can benefit from distributed modeling, either because of physical or computations constraints. In this work we present an approach to estimation and learning of generative probabilistic models in a distributed context. In particular, we show how traditional centralized models, such as probabilistic principal component analysis (PPCA), can be learned when the data is distributed across a network of sensors. We demonstrate the utility of this approach on the problem of distributed affine structure from motion (SfM). Our experiments suggest that the accuracy of the accuracy of the learned probabilistic structure and motion models rivals that of traditional centralized factorization methods.Technical report DCS-TR-69
Urinary leukotriene E4 as a biomarker in NSAID-exacerbated respiratory disease (N-ERD): A systematic review and meta-analysis
No full textPurpose of Review: Non-steroidal exacerbated respiratory disease (N-ERD) currently requires aspirin challenge testing for diagnosis. Urinary leukotriene E4 (uLTE4) has been extensively investigated as potential biomarker in N-ERD. We aimed to assess the usefulness of uLTE4 as a biomarker in the diagnosis of N-ERD. Recent Findings: N-ERD, formerly known as aspirin-intolerant asthma (AIA), is characterised by increased leukotriene production. uLTE4 indicates cysteinyl leukotriene production, and a potential biomarker in N-ERD. Although several studies and have examined the relationship between uLTE4 and N-ERD, the usefulness of uLTE4 as a biomarker in a clinical setting remains unclear. Findings: Our literature search identified 38 unique eligible studies, 35 were included in the meta-analysis. Meta-analysis was performed (i.e. pooled standardised mean difference (SMD) with 95% confidence intervals (95% CI)) and risk of bias assessed (implementing Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy (Cochrane DTA)). Data from 3376 subjects was analysed (1354 N-ERD, 1420 ATA, and 602 HC). uLTE4 was higher in N-ERD vs ATA (n=35, SMD 0.80; 95% CI 0.72–0.89). uLTE4 increased following aspirin challenge in N-ERD (n=12, SMD 0.56; 95% CI 0.26–0.85) but not ATA (n=8, SMD 0.12; CI−0.08–0.33). This systematic review and meta-analysis showed that uLTE4 is higher in N-ERD than ATA or HC. Likewise, people with N-ERD have greater increases in uLTE4 following aspirin challenge. However, due to the varied uLTE4 measurement and result reporting practice, clinical utility of these findings is limited. Future studies should be standardised to increase clinical significance and interpretability of the result
Challenges and directions of activity of president Yoon Suk Yeol’s administration
No full textThe purpose of the article is to describe the 2022 South Korean presidential election against the backdrop of a paradigm shift and to show the challenges and directions of Yoon Suk Yeol’s new administration. The author focuses on the research problems present in the new South Korean politics. Elements of change and continuity, which were also present in previous administrations are highlighted. In May 2022, Yoon Suk Yeol was sworn into the South Korea’s highest office. Yoon’s win in the presidential election ended a trend in which a decade of progressive rule was followed by a change to conservative rule. Since 1998, progressive and conservative presidents have alternated every two terms. The minimal difference in votes in favor of the conservative candidate reflected the divisions and social preferences of Koreans who favored a change from progressive to conservative government. The results of the 2022 presidential election revealed the polarization of South Korean society. Yoon will face a series of difficult challenges. In domestic politics, he must confront the housing crisis, widespread dissatisfaction with economic inequality, and generational tensions, among other issues. Yoon will also be challenged by the parliamentary majority currently held by the Democratic Party in the National Assembly. In foreign policy, South Korea’s new president advocates strengthening the alliance with the United States and cooperation with the Quad countries; he promises to improve relations with Japan, and to take steps toward South Korea playing a greater role in the world. In his inter-Korean policy, on the other hand, Yoon follows the traditional position of the conservatives, pledging to strengthen a policy of deterrence against acts of aggression and provocation by North Korea
Efficacy of Antiplatelet Therapy in Secondary Prevention Following Lacunar Stroke : Pooled Analysis of Randomized Trials
No full text© 2015 American Heart Association, Inc. Acknowledgements C.S. Kwok and A. Shoamanesh was involved in design, screening, study selection, data extraction, data analysis, and preparation of article. H.C. Copley was involved in screening and data extraction. P.K. Myint was involved in the design, screening, and preparation of the article. Y.K. Loke was involved in the design, study selection, data extractions, data analysis, and preparation of the article. O.R. Benavente was involved in design and preparation of the article.Peer reviewe
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