Existing blockwise empirical likelihood(BEL)method blocks the observations or their analogues,which is proven useful under some dependent data settings.In this paper,we introduce a new BEL(NBEL)method by blocking the ...Existing blockwise empirical likelihood(BEL)method blocks the observations or their analogues,which is proven useful under some dependent data settings.In this paper,we introduce a new BEL(NBEL)method by blocking the scoring functions under high dimensional cases.We study the construction of confidence regions for the parameters in spatial autoregressive models with spatial autoregressive disturbances(SARAR models)with high dimension of parameters by using the NBEL method.It is shown that the NBEL ratio statistics are asymptoticallyχ^(2)-type distributed,which are used to obtain the NBEL based confidence regions for the parameters in SARAR models.A simulation study is conducted to compare the performances of the NBEL and the usual EL methods.展开更多
Assessment of temporal and spatial variations in surface water quality is important to evaluate the health of a watershed and make necessary management decisions to control current and future pollution of receiving wa...Assessment of temporal and spatial variations in surface water quality is important to evaluate the health of a watershed and make necessary management decisions to control current and future pollution of receiving water bodies. In this work, surface water quality data for 12 physical and chemical parameters collected from 10 sampling sites in the Nenjiang River basin during the years(2012-2013) were analyzed. The results show that river water quality has significant temporal and spatial variations. Hierarchical cluster analysis(HCA) grouped 12 months into three periods(LF, MF and HF) and classified 10 monitoring sites into three regions(LP, MP and HP) based on the similarity of water quality characteristics. The principle component analysis(PCA)/factor analysis(FA) was used to recognize the factors or origins responsible for temporal and spatial water quality variations. Temporal and spatial PCA/FA revealed that the Nenjiang River water chemistry was strongly affected by rock/water interaction, hydrologic processes and anthropogenic activities. This work demonstrates that the application of HCA and PCA/FA has achieved meaningful classification based on temporal and spatial criteria.展开更多
Peta-scale high-perfomlance computing systems are increasingly built with heterogeneous CPU and GPU nodes to achieve higher power efficiency and computation throughput. While providing unprecedented capabilities to co...Peta-scale high-perfomlance computing systems are increasingly built with heterogeneous CPU and GPU nodes to achieve higher power efficiency and computation throughput. While providing unprecedented capabilities to conduct computational experiments of historic significance, these systems are presently difficult to program. The users, who are domain experts rather than computer experts, prefer to use programming models closer to their domains （e.g., physics and biology） rather than MPI and OpenME This has led the development of domain-specific programming that provides domain-specific programming interfaces but abstracts away some performance-critical architecture details. Based on experience in designing large-scale computing systems, a hybrid programming framework for scientific computing on heterogeneous architectures is proposed in this work. Its design philosophy is to provide a collaborative mechanism for domain experts and computer experts so that both domain-specific knowledge and performance-critical architecture details can be adequately exploited. Two real-world scientific applications have been evaluated on TH-IA, a peta-scale CPU-GPU heterogeneous system that is currently the 5th fastest supercomputer in the world. The experimental results show that the proposed framework is well suited for developing large-scale scientific computing applications on peta-scale heterogeneous CPU/GPU systems.展开更多
Multivariate statistical techniques,such as cluster analysis(CA),discriminant analysis(DA),principal component analysis(PCA) and factor analysis(FA),were applied to evaluate and interpret the surface water quality dat...Multivariate statistical techniques,such as cluster analysis(CA),discriminant analysis(DA),principal component analysis(PCA) and factor analysis(FA),were applied to evaluate and interpret the surface water quality data sets of the Second Songhua River(SSHR) basin in China,obtained during two years(2012-2013) of monitoring of 10 physicochemical parameters at 15 different sites.The results showed that most of physicochemical parameters varied significantly among the sampling sites.Three significant groups,highly polluted(HP),moderately polluted(MP) and less polluted(LP),of sampling sites were obtained through Hierarchical agglomerative CA on the basis of similarity of water quality characteristics.DA identified p H,F,DO,NH3-N,COD and VPhs were the most important parameters contributing to spatial variations of surface water quality.However,DA did not give a considerable data reduction(40% reduction).PCA/FA resulted in three,three and four latent factors explaining 70%,62% and 71% of the total variance in water quality data sets of HP,MP and LP regions,respectively.FA revealed that the SSHR water chemistry was strongly affected by anthropogenic activities(point sources:industrial effluents and wastewater treatment plants;non-point sources:domestic sewage,livestock operations and agricultural activities) and natural processes(seasonal effect,and natural inputs).PCA/FA in the whole basin showed the best results for data reduction because it used only two parameters(about 80% reduction) as the most important parameters to explain 72% of the data variation.Thus,this work illustrated the utility of multivariate statistical techniques for analysis and interpretation of datasets and,in water quality assessment,identification of pollution sources/factors and understanding spatial variations in water quality for effective stream water quality management.展开更多
基金Supported by the National Natural Science Foundation of China(12061017,12361055)the Research Fund of Guangxi Key Lab of Multi-source Information Mining&Security(22-A-01-01)。
文摘Existing blockwise empirical likelihood(BEL)method blocks the observations or their analogues,which is proven useful under some dependent data settings.In this paper,we introduce a new BEL(NBEL)method by blocking the scoring functions under high dimensional cases.We study the construction of confidence regions for the parameters in spatial autoregressive models with spatial autoregressive disturbances(SARAR models)with high dimension of parameters by using the NBEL method.It is shown that the NBEL ratio statistics are asymptoticallyχ^(2)-type distributed,which are used to obtain the NBEL based confidence regions for the parameters in SARAR models.A simulation study is conducted to compare the performances of the NBEL and the usual EL methods.
基金Project(2012ZX07501002-001)supported by Major Science and Technology Program for Water Pollution Control and Treatment of the Ministry of Science and Technology,China
文摘Assessment of temporal and spatial variations in surface water quality is important to evaluate the health of a watershed and make necessary management decisions to control current and future pollution of receiving water bodies. In this work, surface water quality data for 12 physical and chemical parameters collected from 10 sampling sites in the Nenjiang River basin during the years(2012-2013) were analyzed. The results show that river water quality has significant temporal and spatial variations. Hierarchical cluster analysis(HCA) grouped 12 months into three periods(LF, MF and HF) and classified 10 monitoring sites into three regions(LP, MP and HP) based on the similarity of water quality characteristics. The principle component analysis(PCA)/factor analysis(FA) was used to recognize the factors or origins responsible for temporal and spatial water quality variations. Temporal and spatial PCA/FA revealed that the Nenjiang River water chemistry was strongly affected by rock/water interaction, hydrologic processes and anthropogenic activities. This work demonstrates that the application of HCA and PCA/FA has achieved meaningful classification based on temporal and spatial criteria.
基金Project(61170049) supported by the National Natural Science Foundation of ChinaProject(2012AA010903) supported by the National High Technology Research and Development Program of China
文摘Peta-scale high-perfomlance computing systems are increasingly built with heterogeneous CPU and GPU nodes to achieve higher power efficiency and computation throughput. While providing unprecedented capabilities to conduct computational experiments of historic significance, these systems are presently difficult to program. The users, who are domain experts rather than computer experts, prefer to use programming models closer to their domains （e.g., physics and biology） rather than MPI and OpenME This has led the development of domain-specific programming that provides domain-specific programming interfaces but abstracts away some performance-critical architecture details. Based on experience in designing large-scale computing systems, a hybrid programming framework for scientific computing on heterogeneous architectures is proposed in this work. Its design philosophy is to provide a collaborative mechanism for domain experts and computer experts so that both domain-specific knowledge and performance-critical architecture details can be adequately exploited. Two real-world scientific applications have been evaluated on TH-IA, a peta-scale CPU-GPU heterogeneous system that is currently the 5th fastest supercomputer in the world. The experimental results show that the proposed framework is well suited for developing large-scale scientific computing applications on peta-scale heterogeneous CPU/GPU systems.
基金Project (2012ZX07501002-001) supported by the Ministry of Science and Technology of China
文摘Multivariate statistical techniques,such as cluster analysis(CA),discriminant analysis(DA),principal component analysis(PCA) and factor analysis(FA),were applied to evaluate and interpret the surface water quality data sets of the Second Songhua River(SSHR) basin in China,obtained during two years(2012-2013) of monitoring of 10 physicochemical parameters at 15 different sites.The results showed that most of physicochemical parameters varied significantly among the sampling sites.Three significant groups,highly polluted(HP),moderately polluted(MP) and less polluted(LP),of sampling sites were obtained through Hierarchical agglomerative CA on the basis of similarity of water quality characteristics.DA identified p H,F,DO,NH3-N,COD and VPhs were the most important parameters contributing to spatial variations of surface water quality.However,DA did not give a considerable data reduction(40% reduction).PCA/FA resulted in three,three and four latent factors explaining 70%,62% and 71% of the total variance in water quality data sets of HP,MP and LP regions,respectively.FA revealed that the SSHR water chemistry was strongly affected by anthropogenic activities(point sources:industrial effluents and wastewater treatment plants;non-point sources:domestic sewage,livestock operations and agricultural activities) and natural processes(seasonal effect,and natural inputs).PCA/FA in the whole basin showed the best results for data reduction because it used only two parameters(about 80% reduction) as the most important parameters to explain 72% of the data variation.Thus,this work illustrated the utility of multivariate statistical techniques for analysis and interpretation of datasets and,in water quality assessment,identification of pollution sources/factors and understanding spatial variations in water quality for effective stream water quality management.
