The World Wide Web provides a wealth of information about everything, including contemporary audio and visual art events, which are discussed on media outlets, blogs, and specialized websites alike. This information m...The World Wide Web provides a wealth of information about everything, including contemporary audio and visual art events, which are discussed on media outlets, blogs, and specialized websites alike. This information may become a robust source of real-world data, which may form the basis of an objective data-driven analysis. In this study, a methodology for collecting information about audio and visual art events in an automated manner from a large array of websites is presented in detail. This process uses cutting edge Semantic Web, Web Search and Generative AI technologies to convert website documents into a collection of structured data. The value of the methodology is demonstrated by creating a large dataset concerning audiovisual events in Greece. The collected information includes event characteristics, estimated metrics based on their text descriptions, outreach metrics based on the media that reported them, and a multi-layered classification of these events based on their type, subjects and methods used. This dataset is openly provided to the general and academic public through a Web application. Moreover, each event’s outreach is evaluated using these quantitative metrics, the results are analyzed with an emphasis on classification popularity and useful conclusions are drawn concerning the importance of artistic subjects, methods, and media.展开更多
Recurrent event data frequently occur in longitudinal studies, and it is often of interest to estimate the effects of covariates on the recurrent event rate. This paper considers a class of semiparametric transformati...Recurrent event data frequently occur in longitudinal studies, and it is often of interest to estimate the effects of covariates on the recurrent event rate. This paper considers a class of semiparametric transformation rate models for recurrent event data, which uses an additive AMen model as its covariate dependent baseline. The new models are flexible in that they allow for both additive and multiplicative covariate effects, and some covariate effects are allowed to be nonparametric and time-varying. An estimating procedure is proposed for parameter estimation, and the resulting estimators are shown to be consistent and asymptotically normal. Simulation studies and a real data analysis demonstrate that the proposed method performs well and is appropriate for practical use.展开更多
The Jiangmen Underground Neutrino Observatory（JUNO） detector is designed to determine the neutrino mass hierarchy and precisely measure oscillation parameters. The general purpose design also allows measurements of ...The Jiangmen Underground Neutrino Observatory（JUNO） detector is designed to determine the neutrino mass hierarchy and precisely measure oscillation parameters. The general purpose design also allows measurements of neutrinos from many terrestrial and non-terrestrial sources. The JUNO Event Data Model（EDM） plays a central role in the offline software system. It describes the event data entities through all processing stages for both simulated and collected data, and provides persistency via the input/output system. Also, the EDM is designed to enable flexible event handling such as event navigation, as well as the splitting of MC IBD signals and mixing of MC backgrounds. This paper describes the design, implementation and performance of the JUNO EDM.展开更多
Recurrent events data with a terminal event （e.g., death） often arise in clinical and ob- servational studies. Variable selection is an important issue in all regression analysis. In this paper, the authors first pr...Recurrent events data with a terminal event （e.g., death） often arise in clinical and ob- servational studies. Variable selection is an important issue in all regression analysis. In this paper, the authors first propose the estimation methods to select the significant variables, and then prove the asymptotic behavior of the proposed estimator. Furthermore, the authors discuss the computing algorithm to assess the proposed estimator via the linear function approximation and generalized cross validation method for determination of the tuning parameters. Finally, the finite sample estimation for the asymptotical covariance matrix is also proposed.展开更多
Objective: The reports submitted to the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) from 1997 to 2011 were reviewed to assess the gender effects on muscular adverse events induced by t...Objective: The reports submitted to the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) from 1997 to 2011 were reviewed to assess the gender effects on muscular adverse events induced by the administration of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors (statins). Methods: After the deletion of duplicated submissions and the revision of arbitrary drug names, the reports involving pravastatin, simvastatin, atorvastatin, rosuvastatin, and cerivastatin were analyzed. Data mining algorithms were applied for the quantitative detection of signals, where a signal means a drug-associated adverse event, including the proportional reporting ratio, the reporting odds ratio, the information component, and the empirical Bayes geometric mean. Myopathy, myalgia, myositis, rhabdomyolysis, and an increase in creatine phosphokinase level were focused on as the muscular adverse events. Results: The total number of reports was 3,472,494. The signal scores suggested that all 5 statins were associated with 5 muscular adverse events in both male and female patients. The scores varied among statins, but were more noteworthy for cerivastatin. Conclusion: The data strongly suggested the necessity of well-organized clinical studies on statin-associated muscular adverse events.展开更多
文摘The World Wide Web provides a wealth of information about everything, including contemporary audio and visual art events, which are discussed on media outlets, blogs, and specialized websites alike. This information may become a robust source of real-world data, which may form the basis of an objective data-driven analysis. In this study, a methodology for collecting information about audio and visual art events in an automated manner from a large array of websites is presented in detail. This process uses cutting edge Semantic Web, Web Search and Generative AI technologies to convert website documents into a collection of structured data. The value of the methodology is demonstrated by creating a large dataset concerning audiovisual events in Greece. The collected information includes event characteristics, estimated metrics based on their text descriptions, outreach metrics based on the media that reported them, and a multi-layered classification of these events based on their type, subjects and methods used. This dataset is openly provided to the general and academic public through a Web application. Moreover, each event’s outreach is evaluated using these quantitative metrics, the results are analyzed with an emphasis on classification popularity and useful conclusions are drawn concerning the importance of artistic subjects, methods, and media.
基金supported by National Natural Science Foundation of China (Grant Nos. 11301545, 11501578 and 11501579)
文摘Recurrent event data frequently occur in longitudinal studies, and it is often of interest to estimate the effects of covariates on the recurrent event rate. This paper considers a class of semiparametric transformation rate models for recurrent event data, which uses an additive AMen model as its covariate dependent baseline. The new models are flexible in that they allow for both additive and multiplicative covariate effects, and some covariate effects are allowed to be nonparametric and time-varying. An estimating procedure is proposed for parameter estimation, and the resulting estimators are shown to be consistent and asymptotically normal. Simulation studies and a real data analysis demonstrate that the proposed method performs well and is appropriate for practical use.
基金Supported by Joint Large-Scale Scientific Facility Funds of the NSFC and CAS(U1532258)the Program for New Century Excellent Talents in University(NCET-13-0342)+1 种基金the Shandong Natural Science Funds for Distinguished Young Scholar(JQ201402)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA10010900)
文摘The Jiangmen Underground Neutrino Observatory（JUNO） detector is designed to determine the neutrino mass hierarchy and precisely measure oscillation parameters. The general purpose design also allows measurements of neutrinos from many terrestrial and non-terrestrial sources. The JUNO Event Data Model（EDM） plays a central role in the offline software system. It describes the event data entities through all processing stages for both simulated and collected data, and provides persistency via the input/output system. Also, the EDM is designed to enable flexible event handling such as event navigation, as well as the splitting of MC IBD signals and mixing of MC backgrounds. This paper describes the design, implementation and performance of the JUNO EDM.
文摘Recurrent events data with a terminal event （e.g., death） often arise in clinical and ob- servational studies. Variable selection is an important issue in all regression analysis. In this paper, the authors first propose the estimation methods to select the significant variables, and then prove the asymptotic behavior of the proposed estimator. Furthermore, the authors discuss the computing algorithm to assess the proposed estimator via the linear function approximation and generalized cross validation method for determination of the tuning parameters. Finally, the finite sample estimation for the asymptotical covariance matrix is also proposed.
文摘Objective: The reports submitted to the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) from 1997 to 2011 were reviewed to assess the gender effects on muscular adverse events induced by the administration of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors (statins). Methods: After the deletion of duplicated submissions and the revision of arbitrary drug names, the reports involving pravastatin, simvastatin, atorvastatin, rosuvastatin, and cerivastatin were analyzed. Data mining algorithms were applied for the quantitative detection of signals, where a signal means a drug-associated adverse event, including the proportional reporting ratio, the reporting odds ratio, the information component, and the empirical Bayes geometric mean. Myopathy, myalgia, myositis, rhabdomyolysis, and an increase in creatine phosphokinase level were focused on as the muscular adverse events. Results: The total number of reports was 3,472,494. The signal scores suggested that all 5 statins were associated with 5 muscular adverse events in both male and female patients. The scores varied among statins, but were more noteworthy for cerivastatin. Conclusion: The data strongly suggested the necessity of well-organized clinical studies on statin-associated muscular adverse events.
