A discrete centrosymmetric （H2O）20（CH3OH）4 binary cluster was confined in the cavity of a metaMigand hybrid [Ag4（bpda）2（bpp）4＂ 14H2O.2CH3OH], （1） （where bpp = 1,3-bis（4-pyridyl）propane and H2bpda = 2,2＇...A discrete centrosymmetric （H2O）20（CH3OH）4 binary cluster was confined in the cavity of a metaMigand hybrid [Ag4（bpda）2（bpp）4＂ 14H2O.2CH3OH], （1） （where bpp = 1,3-bis（4-pyridyl）propane and H2bpda = 2,2＇-biphenyldicarboxylic acid） The novel mixed water-methanol cluster consists of one grail-shaped hexadecameric cluster, four dangling water and four hanging methanol molecules. The （H2O）16 cluster is composed of two pairs of edge-sharing （H2O）5 rings attached to one （H2O）4 core with twenty hydrogen bonds. Alternatively, the （H2O）16 cluster is structurally similar to a complicated hydrocarbon generated by undergoing [2＋2] cycloaddition of 1,2,3,4,5,6-hexahydropentalene, which reveals the resemblance between water clusters and organic compounds.展开更多
This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the“Higgs machine learning challenge 2014”data set.This unsupervised ...This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the“Higgs machine learning challenge 2014”data set.This unsupervised detection goes in this paper analysis through 4 steps:(1)selection of the most informative features from the considered data;(2)definition of the number of clusters based on the elbow criterion.The experimental results showed that the optimal number of clusters that group the considered data in an unsupervised manner corresponds to 2 clusters;(3)proposition of a new approach for hybridization of both hard and fuzzy clustering tuned with Ant Lion Optimization(ALO);(4)comparison with some existing metaheuristic optimizations such as Genetic Algorithm(GA)and Particle Swarm Optimization(PSO).By employing a multi-angle analysis based on the cluster validation indices,the confusion matrix,the efficiencies and purities rates,the average cost variation,the computational time and the Sammon mapping visualization,the results highlight the effectiveness of the improved Gustafson-Kessel algorithm optimized withALO(ALOGK)to validate the proposed approach.Even if the paper gives a complete clustering analysis,its novel contribution concerns only the Steps(1)and(3)considered above.The first contribution lies in the method used for Step(1)to select the most informative features and variables.We used the t-Statistic technique to rank them.Afterwards,a feature mapping is applied using Self-Organizing Map(SOM)to identify the level of correlation between them.Then,Particle Swarm Optimization(PSO),a metaheuristic optimization technique,is used to reduce the data set dimension.The second contribution of thiswork concern the third step,where each one of the clustering algorithms as K-means(KM),Global K-means(GlobalKM),Partitioning AroundMedoids(PAM),Fuzzy C-means(FCM),Gustafson-Kessel(GK)and Gath-Geva(GG)is optimized and tuned with ALO.展开更多
In order to realize the intelligent mechanization of the last process of the fruit industry chains,the identification of fruit packing boxes is researched.A multi-view database is established to describe the omnidirec...In order to realize the intelligent mechanization of the last process of the fruit industry chains,the identification of fruit packing boxes is researched.A multi-view database is established to describe the omnidirectional attitudes of the fruit packing boxes.In order to reduce the data redundancy caused by multi-view acquisition,a new binary multi-view kernel principal component analysis network(BMKPCANet) is built,and a multi-view recognition method of fruit packing boxes is proposed based on the BMKPCANet and support vector machine(SVM).The experimental results show that the recognition accuracy of proposed BMKPCANet is 12.82% higher than PCANet and3.51% higher than KPCANet on average.The time consumption of proposed BMKPCANet is 7.74%lower than PCANet and 29.01% lower than KPCANet on average.This work has laid a theoretical foundation for multi-view recognition of 3 D objects and has a good practical application value.展开更多
We present CCD photometric observations of an eclipsing binary in the direction of the open cluster Praesepe using the 2 m telescope at IUCAA Girawali Observatory, India. Though the system was classified as an eclipsi...We present CCD photometric observations of an eclipsing binary in the direction of the open cluster Praesepe using the 2 m telescope at IUCAA Girawali Observatory, India. Though the system was classified as an eclipsing binary by Pepper et al., detailed investigations have been lacking. The photometric solutions using the Wilson-Devinney code suggest that it is a W-type W UMa system and, interestingly, the system parameters are similar to another contact binary system SW Lac.展开更多
With the increasing number of detected exoplanet samples, the statistical properties of planetary systems have become much clearer. In this review, we sum- marize the major statistical results that have been revealed ...With the increasing number of detected exoplanet samples, the statistical properties of planetary systems have become much clearer. In this review, we sum- marize the major statistical results that have been revealed mainly by radial velocity and transiting observations, and try to interpret them within the scope of the classical core-accretion scenario of planet formation, especially in the formation of different orbital architectures for planetary systems around main sequence stars. Based on the different possible formation routes for different planet systems, we tentatively classify them into three major catalogs： hot Jupiter systems, standard systems and distant giant planet systems. The standard systems can be further categorized into three sub-types under different circumstances： solar-like systems, hot Super-Earth systems, and sub- giant planet systems. We also review the theory of planet detection and formation in binary systems as well as planets in star clusters.展开更多
The probability of default(PD) is the key element in the New Basel Capital Accord and the most essential factor to financial institutions' risk management.To obtain good PD estimation,practitioners and academics h...The probability of default(PD) is the key element in the New Basel Capital Accord and the most essential factor to financial institutions' risk management.To obtain good PD estimation,practitioners and academics have put forward numerous default prediction models.However,how to use multiple models to enhance overall performance on default prediction remains untouched.In this paper,a parametric and non-parametric combination model is proposed.Firstly,binary logistic regression model(BLRM),support vector machine(SVM),and decision tree(DT) are used respectively to establish models with relatively stable and high performance.Secondly,in order to make further improvement to the overall performance,a combination model using the method of multiple discriminant analysis(MDA) is constructed.In this way,the coverage rate of the combination model is greatly improved,and the risk of miscarriage is effectively reduced.Lastly,the results of the combination model are analyzed by using the K-means clustering,and the clustering distribution is consistent with a normal distribution.The results show that the combination model based on parametric and non-parametric can effectively enhance the overall performance on default prediction.展开更多
基金supported by the National Natural Science Foundation of China(50971063,21053001)the Natural Science Foundation of Fujian Province(2011J01047,2010J01042)+1 种基金the Scientific Research Foundation for the Returned Overseas Chinese Scholars,Education of Ministrythe Startup Package Funding of Huaqiao University (10BS210)
文摘A discrete centrosymmetric （H2O）20（CH3OH）4 binary cluster was confined in the cavity of a metaMigand hybrid [Ag4（bpda）2（bpp）4＂ 14H2O.2CH3OH], （1） （where bpp = 1,3-bis（4-pyridyl）propane and H2bpda = 2,2＇-biphenyldicarboxylic acid） The novel mixed water-methanol cluster consists of one grail-shaped hexadecameric cluster, four dangling water and four hanging methanol molecules. The （H2O）16 cluster is composed of two pairs of edge-sharing （H2O）5 rings attached to one （H2O）4 core with twenty hydrogen bonds. Alternatively, the （H2O）16 cluster is structurally similar to a complicated hydrocarbon generated by undergoing [2＋2] cycloaddition of 1,2,3,4,5,6-hexahydropentalene, which reveals the resemblance between water clusters and organic compounds.
文摘This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the“Higgs machine learning challenge 2014”data set.This unsupervised detection goes in this paper analysis through 4 steps:(1)selection of the most informative features from the considered data;(2)definition of the number of clusters based on the elbow criterion.The experimental results showed that the optimal number of clusters that group the considered data in an unsupervised manner corresponds to 2 clusters;(3)proposition of a new approach for hybridization of both hard and fuzzy clustering tuned with Ant Lion Optimization(ALO);(4)comparison with some existing metaheuristic optimizations such as Genetic Algorithm(GA)and Particle Swarm Optimization(PSO).By employing a multi-angle analysis based on the cluster validation indices,the confusion matrix,the efficiencies and purities rates,the average cost variation,the computational time and the Sammon mapping visualization,the results highlight the effectiveness of the improved Gustafson-Kessel algorithm optimized withALO(ALOGK)to validate the proposed approach.Even if the paper gives a complete clustering analysis,its novel contribution concerns only the Steps(1)and(3)considered above.The first contribution lies in the method used for Step(1)to select the most informative features and variables.We used the t-Statistic technique to rank them.Afterwards,a feature mapping is applied using Self-Organizing Map(SOM)to identify the level of correlation between them.Then,Particle Swarm Optimization(PSO),a metaheuristic optimization technique,is used to reduce the data set dimension.The second contribution of thiswork concern the third step,where each one of the clustering algorithms as K-means(KM),Global K-means(GlobalKM),Partitioning AroundMedoids(PAM),Fuzzy C-means(FCM),Gustafson-Kessel(GK)and Gath-Geva(GG)is optimized and tuned with ALO.
基金Supported by the National Natural Science Foundation of China(No.52075306).
文摘In order to realize the intelligent mechanization of the last process of the fruit industry chains,the identification of fruit packing boxes is researched.A multi-view database is established to describe the omnidirectional attitudes of the fruit packing boxes.In order to reduce the data redundancy caused by multi-view acquisition,a new binary multi-view kernel principal component analysis network(BMKPCANet) is built,and a multi-view recognition method of fruit packing boxes is proposed based on the BMKPCANet and support vector machine(SVM).The experimental results show that the recognition accuracy of proposed BMKPCANet is 12.82% higher than PCANet and3.51% higher than KPCANet on average.The time consumption of proposed BMKPCANet is 7.74%lower than PCANet and 29.01% lower than KPCANet on average.This work has laid a theoretical foundation for multi-view recognition of 3 D objects and has a good practical application value.
文摘We present CCD photometric observations of an eclipsing binary in the direction of the open cluster Praesepe using the 2 m telescope at IUCAA Girawali Observatory, India. Though the system was classified as an eclipsing binary by Pepper et al., detailed investigations have been lacking. The photometric solutions using the Wilson-Devinney code suggest that it is a W-type W UMa system and, interestingly, the system parameters are similar to another contact binary system SW Lac.
基金supported by the National Natural Science Foundation of China (Nos. 10833001, 10925313, 11078001 and 11003010)Fundamental Research Funds for the Central Universities (No. 1112020102)the Research Fund for the Doctoral Program of Higher Education of China (Nos. 20090091110002 and 20090091120025)
文摘With the increasing number of detected exoplanet samples, the statistical properties of planetary systems have become much clearer. In this review, we sum- marize the major statistical results that have been revealed mainly by radial velocity and transiting observations, and try to interpret them within the scope of the classical core-accretion scenario of planet formation, especially in the formation of different orbital architectures for planetary systems around main sequence stars. Based on the different possible formation routes for different planet systems, we tentatively classify them into three major catalogs： hot Jupiter systems, standard systems and distant giant planet systems. The standard systems can be further categorized into three sub-types under different circumstances： solar-like systems, hot Super-Earth systems, and sub- giant planet systems. We also review the theory of planet detection and formation in binary systems as well as planets in star clusters.
基金supported by the National Natural Science Foundation of China Key Project under Grant No.70933003the National Natural Science Foundation of China under Grant Nos.70871109 and 71203247
文摘The probability of default(PD) is the key element in the New Basel Capital Accord and the most essential factor to financial institutions' risk management.To obtain good PD estimation,practitioners and academics have put forward numerous default prediction models.However,how to use multiple models to enhance overall performance on default prediction remains untouched.In this paper,a parametric and non-parametric combination model is proposed.Firstly,binary logistic regression model(BLRM),support vector machine(SVM),and decision tree(DT) are used respectively to establish models with relatively stable and high performance.Secondly,in order to make further improvement to the overall performance,a combination model using the method of multiple discriminant analysis(MDA) is constructed.In this way,the coverage rate of the combination model is greatly improved,and the risk of miscarriage is effectively reduced.Lastly,the results of the combination model are analyzed by using the K-means clustering,and the clustering distribution is consistent with a normal distribution.The results show that the combination model based on parametric and non-parametric can effectively enhance the overall performance on default prediction.
