In order to discover the main causes of elevator group accidents in edge computing environment, a multi-dimensional data model of elevator accident data is established by using data cube technology, proposing and impl...In order to discover the main causes of elevator group accidents in edge computing environment, a multi-dimensional data model of elevator accident data is established by using data cube technology, proposing and implementing a method by combining classical Apriori algorithm with the model, digging out frequent items of elevator accident data to explore the main reasons for the occurrence of elevator accidents. In addition, a collaborative edge model of elevator accidents is set to achieve data sharing, making it possible to check the detail of each cause to confirm the causes of elevator accidents. Lastly the association rules are applied to find the law of elevator Accidents.展开更多
An entropy-based statistic TpE has been proposed for genomic association study for disease-susceptibility locus. The statistic TpE may be directly adopted and/or extended to quantitative-trait locus （QTL）mapping for...An entropy-based statistic TpE has been proposed for genomic association study for disease-susceptibility locus. The statistic TpE may be directly adopted and/or extended to quantitative-trait locus （QTL）mapping for quantitative traits. In this article, the statistic TpE was extended and applied to quantitative trait for association analysis of QTL by means of selective genotyping. The statistical properties （the type I error rate and the power） were examined under a range of parameters and population-sampling strategies （e.g., various genetic models, various heritabilities, and various sample-selection threshold values） by simulation studies. The results indicated that the statistic TpE is robust and powerful for genomic association study of QTL. A simulation study based on the haplotype frequencies of 10 single nucleotide polymorphisms （SNPs） of angiotensin-I converting enzyme genes was conducted to evaluate the performance of the statistic TPE for genetic association study.展开更多
The empirical likelihood is used to propose a new class of quantile estimators in the presence of some auxiliary information under positively associated samples. It is shown that the proposed quantile estimators are a...The empirical likelihood is used to propose a new class of quantile estimators in the presence of some auxiliary information under positively associated samples. It is shown that the proposed quantile estimators are asymptotically normally distributed with smaller asymptotic variances than those of the usual quantile estimators.展开更多
Reinforcement learning can be modeled as markov decision process mathematically.In consequence,the interaction samples as well as the connection relation between them are two main types of information for learning.How...Reinforcement learning can be modeled as markov decision process mathematically.In consequence,the interaction samples as well as the connection relation between them are two main types of information for learning.However,most of recent works on deep reinforcement learning treat samples independently either in their own episode or between episodes.In this paper,in order to utilize more sample information,we propose another learning system based on directed associative graph(DAG).The DAG is built on all trajectories in real time,which includes the whole connection relation of all samples among all episodes.Through planning with directed edges on DAG,we offer another perspective to estimate stateaction pair,especially for the unknowns to deep neural network(DNN)as well as episodic memory(EM).Mixed loss function is generated by the three learning systems(DNN,EM and DAG)to improve the efficiency of the parameter update in the proposed algorithm.We show that our algorithm is significantly better than the state-of-the-art algorithm in performance and sample efficiency on testing environments.Furthermore,the convergence of our algorithm is proved in the appendix and its long-term performance as well as the effects of DAG are verified.展开更多
One of the obstacles of the efficient association rule mining is theexplosive expansion of data sets since it is costly or impossible to scan large databases, esp., formultiple times. A popular solution to improve the...One of the obstacles of the efficient association rule mining is theexplosive expansion of data sets since it is costly or impossible to scan large databases, esp., formultiple times. A popular solution to improve the speed and scalability of the association rulemining is to do the algorithm on a random sample instead of the entire database. But how toeffectively define and efficiently estimate the degree of error with respect to the outcome of thealgorithm, and how to determine the sample size needed are entangling researches until now. In thispaper, an effective and efficient algorithm is given based on the PAC (Probably Approximate Correct)learning theory to measure and estimate sample error. Then, a new adaptive, on-line, fast samplingstrategy - multi-scaling sampling - is presented inspired by MRA (Multi-Resolution Analysis) andShannon sampling theorem, for quickly obtaining acceptably approximate association rules atappropriate sample size. Both theoretical analysis and empirical study have showed that the Samplingstrategy can achieve a very good speed-accuracy trade-off.展开更多
Exploration of artworks is enjoyable but often time consuming.For example,it is not always easy to discover the favorite types of unknown painting works.It is not also always easy to explore unpopular painting works w...Exploration of artworks is enjoyable but often time consuming.For example,it is not always easy to discover the favorite types of unknown painting works.It is not also always easy to explore unpopular painting works which looks similar to painting works created by famous artists.This paper presents a painting image browser which assists the explorative discovery of user-interested painting works.The presented browser applies a new multidimensional data visualization technique that highlights particular ranges of particular numeric values based on association rules to suggest cues to find favorite painting images.This study assumes a large number of painting images are provided where categorical information(e.g.,names of artists,created year)is assigned to the images.The presented system firstly calculates the feature values of the images as a preprocessing step.Then the browser visualizes the multidimensional feature values as a heatmap and highlights association rules discovered from the relationships between the feature values and categorical information.This mechanism enables users to explore favorite painting images or painting images that look similar to famous painting works.Our case study and user evaluation demonstrates the effectiveness of the presented image browser.展开更多
文摘In order to discover the main causes of elevator group accidents in edge computing environment, a multi-dimensional data model of elevator accident data is established by using data cube technology, proposing and implementing a method by combining classical Apriori algorithm with the model, digging out frequent items of elevator accident data to explore the main reasons for the occurrence of elevator accidents. In addition, a collaborative edge model of elevator accidents is set to achieve data sharing, making it possible to check the detail of each cause to confirm the causes of elevator accidents. Lastly the association rules are applied to find the law of elevator Accidents.
文摘An entropy-based statistic TpE has been proposed for genomic association study for disease-susceptibility locus. The statistic TpE may be directly adopted and/or extended to quantitative-trait locus （QTL）mapping for quantitative traits. In this article, the statistic TpE was extended and applied to quantitative trait for association analysis of QTL by means of selective genotyping. The statistical properties （the type I error rate and the power） were examined under a range of parameters and population-sampling strategies （e.g., various genetic models, various heritabilities, and various sample-selection threshold values） by simulation studies. The results indicated that the statistic TpE is robust and powerful for genomic association study of QTL. A simulation study based on the haplotype frequencies of 10 single nucleotide polymorphisms （SNPs） of angiotensin-I converting enzyme genes was conducted to evaluate the performance of the statistic TPE for genetic association study.
基金supported by the National Natural Science Foundation of China(11271088,11361011,11201088)the Natural Science Foundation of Guangxi(2013GXNSFAA019004,2013GXNSFAA019007,2013GXNSFBA019001)
文摘The empirical likelihood is used to propose a new class of quantile estimators in the presence of some auxiliary information under positively associated samples. It is shown that the proposed quantile estimators are asymptotically normally distributed with smaller asymptotic variances than those of the usual quantile estimators.
基金This work is supported by the National Key Research and Development Program of China,2018YFA0701603 and Natural Science Foundation of Anhui Province,2008085MF213.
文摘Reinforcement learning can be modeled as markov decision process mathematically.In consequence,the interaction samples as well as the connection relation between them are two main types of information for learning.However,most of recent works on deep reinforcement learning treat samples independently either in their own episode or between episodes.In this paper,in order to utilize more sample information,we propose another learning system based on directed associative graph(DAG).The DAG is built on all trajectories in real time,which includes the whole connection relation of all samples among all episodes.Through planning with directed edges on DAG,we offer another perspective to estimate stateaction pair,especially for the unknowns to deep neural network(DNN)as well as episodic memory(EM).Mixed loss function is generated by the three learning systems(DNN,EM and DAG)to improve the efficiency of the parameter update in the proposed algorithm.We show that our algorithm is significantly better than the state-of-the-art algorithm in performance and sample efficiency on testing environments.Furthermore,the convergence of our algorithm is proved in the appendix and its long-term performance as well as the effects of DAG are verified.
基金CAS Project of Brain and Mind Science，国家高技术研究发展计划(863计划)，国家重点基础研究发展计划(973计划)，国家自然科学基金，湖南省自然科学基金
文摘One of the obstacles of the efficient association rule mining is theexplosive expansion of data sets since it is costly or impossible to scan large databases, esp., formultiple times. A popular solution to improve the speed and scalability of the association rulemining is to do the algorithm on a random sample instead of the entire database. But how toeffectively define and efficiently estimate the degree of error with respect to the outcome of thealgorithm, and how to determine the sample size needed are entangling researches until now. In thispaper, an effective and efficient algorithm is given based on the PAC (Probably Approximate Correct)learning theory to measure and estimate sample error. Then, a new adaptive, on-line, fast samplingstrategy - multi-scaling sampling - is presented inspired by MRA (Multi-Resolution Analysis) andShannon sampling theorem, for quickly obtaining acceptably approximate association rules atappropriate sample size. Both theoretical analysis and empirical study have showed that the Samplingstrategy can achieve a very good speed-accuracy trade-off.
文摘Exploration of artworks is enjoyable but often time consuming.For example,it is not always easy to discover the favorite types of unknown painting works.It is not also always easy to explore unpopular painting works which looks similar to painting works created by famous artists.This paper presents a painting image browser which assists the explorative discovery of user-interested painting works.The presented browser applies a new multidimensional data visualization technique that highlights particular ranges of particular numeric values based on association rules to suggest cues to find favorite painting images.This study assumes a large number of painting images are provided where categorical information(e.g.,names of artists,created year)is assigned to the images.The presented system firstly calculates the feature values of the images as a preprocessing step.Then the browser visualizes the multidimensional feature values as a heatmap and highlights association rules discovered from the relationships between the feature values and categorical information.This mechanism enables users to explore favorite painting images or painting images that look similar to famous painting works.Our case study and user evaluation demonstrates the effectiveness of the presented image browser.
