Old-age dependency ratio(OADR)is commonly used to indicate the financial burden of population aging;increases in OADR have caused widespread concerns.To better measure the financial burden,this paper proposes a depend...Old-age dependency ratio(OADR)is commonly used to indicate the financial burden of population aging;increases in OADR have caused widespread concerns.To better measure the financial burden,this paper proposes a dependency ratio of non-labor-force population to labor-force population(NLDR).This ratio includes OADR as a special case.This paper finds that,when measured by NLDR,financial burden actually declined in five of the G7 countries during the years 2000-2014.To project future trends,labor force participation rates by age f(x)can be forecasted using the coherent LeeCarter method.This paper combines the forecasted f(x)and the population projections of the United Nations,to forecast increases of NLDR for the G7 countries between 2014 and 2050.These increases are on average less than onefifth of the increases projected for OADR.Because OADR ignores the increase of labor force participation,its description of the problem of population aging for the G7 countries in the past is unrealistic and inaccurate,and forecasts of the future based on OADR are likely to be just as unrealistic.Understanding the conditions and reasons for increases in labor force participation can provide valuable insights into the issues of population aging in China,where the remarkable increase of OADR may result in real financial burdens.One condition for labor force participation to increase could be that people remain in good health,which makes continuing to work more feasible.Other reasons for labor force participation to increase are likely to be found in government policies that encourage people to continue working longer.For China,collecting reliable data on labor force participation is also crucial.Without these data,the effects of the policies that encourage people to continue working longer cannot be detected;and therefore the policies cannot be properly developed.展开更多
Anomaly detection is an important problem in various research and application fields.Researchers design reliable schemes to provide solutions for effectively detecting anomaly points.Most of the existing anomaly detec...Anomaly detection is an important problem in various research and application fields.Researchers design reliable schemes to provide solutions for effectively detecting anomaly points.Most of the existing anomaly detection schemes are unsupervised methods,such as anomaly detection methods based on density,distance and clustering.In total,unsupervised anomaly detection methods have many limitations.For example,they cannot be well combined with prior knowledge in some anomaly detection tasks.For some nonlinear anomaly detection tasks,the modeling is complex and faces dimensional disasters,which are greatly affected by noise.Sometimes it is difficult to find abnormal events that users are interested in,and users need to customize model parameters before detection.With the wide application of deep learning technology,it has a good modeling ability to solve linear and nonlinear data relationships,but the application of deep learning technology in the field of anomaly detection has many challenges.If we regard exceptions as a supervised problem,exceptions are a few,and we usually face the problem of too few labels.To obtain a model that performs well in the anomaly detection task,it requires a high initial training set.Therefore,to solve the above problems,this paper proposes a supervised learning method with manual participation.We introduce the integrated learning model and train a supervised anomaly detection model with strong stability and high accuracy through active learning technology.In addition,this paper adopts certain strategies to maximize the accuracy of anomaly detection and minimize the cost of manual labeling.In the experimental link,we will show that our method is better than some traditional anomaly detection algorithms.展开更多
文摘Old-age dependency ratio(OADR)is commonly used to indicate the financial burden of population aging;increases in OADR have caused widespread concerns.To better measure the financial burden,this paper proposes a dependency ratio of non-labor-force population to labor-force population(NLDR).This ratio includes OADR as a special case.This paper finds that,when measured by NLDR,financial burden actually declined in five of the G7 countries during the years 2000-2014.To project future trends,labor force participation rates by age f(x)can be forecasted using the coherent LeeCarter method.This paper combines the forecasted f(x)and the population projections of the United Nations,to forecast increases of NLDR for the G7 countries between 2014 and 2050.These increases are on average less than onefifth of the increases projected for OADR.Because OADR ignores the increase of labor force participation,its description of the problem of population aging for the G7 countries in the past is unrealistic and inaccurate,and forecasts of the future based on OADR are likely to be just as unrealistic.Understanding the conditions and reasons for increases in labor force participation can provide valuable insights into the issues of population aging in China,where the remarkable increase of OADR may result in real financial burdens.One condition for labor force participation to increase could be that people remain in good health,which makes continuing to work more feasible.Other reasons for labor force participation to increase are likely to be found in government policies that encourage people to continue working longer.For China,collecting reliable data on labor force participation is also crucial.Without these data,the effects of the policies that encourage people to continue working longer cannot be detected;and therefore the policies cannot be properly developed.
基金supported by the State Grid Research Project“Study on Intelligent Analysis Technology of Abnormal Power Data Quality based on Rule Mining” (5700-202119176A-0-0-00).
文摘Anomaly detection is an important problem in various research and application fields.Researchers design reliable schemes to provide solutions for effectively detecting anomaly points.Most of the existing anomaly detection schemes are unsupervised methods,such as anomaly detection methods based on density,distance and clustering.In total,unsupervised anomaly detection methods have many limitations.For example,they cannot be well combined with prior knowledge in some anomaly detection tasks.For some nonlinear anomaly detection tasks,the modeling is complex and faces dimensional disasters,which are greatly affected by noise.Sometimes it is difficult to find abnormal events that users are interested in,and users need to customize model parameters before detection.With the wide application of deep learning technology,it has a good modeling ability to solve linear and nonlinear data relationships,but the application of deep learning technology in the field of anomaly detection has many challenges.If we regard exceptions as a supervised problem,exceptions are a few,and we usually face the problem of too few labels.To obtain a model that performs well in the anomaly detection task,it requires a high initial training set.Therefore,to solve the above problems,this paper proposes a supervised learning method with manual participation.We introduce the integrated learning model and train a supervised anomaly detection model with strong stability and high accuracy through active learning technology.In addition,this paper adopts certain strategies to maximize the accuracy of anomaly detection and minimize the cost of manual labeling.In the experimental link,we will show that our method is better than some traditional anomaly detection algorithms.
