The mafic dykes(dolerites)during the Early Paleozoic are widely spread in Langao-Ziyang,southern Qiling Block,and the investigation on these dykes are very important.Previous studies have mainly focused on the Siluria...The mafic dykes(dolerites)during the Early Paleozoic are widely spread in Langao-Ziyang,southern Qiling Block,and the investigation on these dykes are very important.Previous studies have mainly focused on the Silurian mafic dykes;however,research on the Earlier Paleozoic mafic dykes is relatively weak at present.Therefore,the overall understanding of the mantle source and genetic dynamic setting during the Early Paleozoic in this area is lacking.To study the accurate age and origin of the Early Paleozoic mafic dykes in Ziyang,southern Shaanxi Province,the mafic dykes from dabacunand Qinmingzhai were selected and the petrology,zircon U-Pb chronology,geochemistry,and Sr-Nd-Hf isotopes were studied.Analysis indicates that the mafic dykes studied are mainly composed of dolerite,and they are the products of the Early Ordovician(475.8-480.7 Ma).Furthermore,the dolerites belong to alkaline rock series,and they are characterized by enrichment in LREE,Rb,Ba,Sr,Nb,(87Sr/86Sr)i=0.7020-0.7050,εNd(t)=3.0-4.0),εHf(t)=4.5-12.1,176Hf/177Hf=0.282681-0.282844.This suggests that the mafic dyke were derived from the partial melting of a depleted lithospheric mantle,and the genetic process is mainly controlled by the mantle plume based on the discussion of the genetic model.Furthermore,the genetic process experienced the separation and crystallization of olivine and clinopyroxene at the same time,with little crustal contamination.展开更多
It is well-known that attribute reduction is a crucial action of rough set.The significant characteristic of attribute reduction is that it can reduce the dimensions of data with clear semantic explanations.Normally,t...It is well-known that attribute reduction is a crucial action of rough set.The significant characteristic of attribute reduction is that it can reduce the dimensions of data with clear semantic explanations.Normally,the learning performance of attributes in derived reduct is much more crucial.Since related measures of rough set dominate the whole process of identifying qualified attributes and deriving reduct,those measures may have a direct impact on the performance of selected attributes in reduct.However,most previous researches about attribute reduction take measures related to either supervised perspective or unsupervised perspective,which are insufficient to identify attributes with superior learning performance,such as stability and accuracy.In order to improve the classification stability and classification accuracy of reduct,in this paper,a novel measure is proposed based on the fusion of supervised and unsupervised perspectives:(1)in terms of supervised perspective,approximation quality is helpful in quantitatively characterizing the relationship between attributes and labels;(2)in terms of unsupervised perspective,conditional entropy is helpful in quantitatively describing the internal structure of data itself.In order to prove the effectiveness of the proposed measure,18 University of CaliforniaIrvine(UCI)datasets and 2 Yale face datasets have been employed in the comparative experiments.Finally,the experimental results show that the proposed measure does well in selecting attributes which can provide distinguished classification stabilities and classification accuracies.展开更多
基金This study was supported by the National Natural Science Foundation of China(Grant:41573022).
文摘The mafic dykes(dolerites)during the Early Paleozoic are widely spread in Langao-Ziyang,southern Qiling Block,and the investigation on these dykes are very important.Previous studies have mainly focused on the Silurian mafic dykes;however,research on the Earlier Paleozoic mafic dykes is relatively weak at present.Therefore,the overall understanding of the mantle source and genetic dynamic setting during the Early Paleozoic in this area is lacking.To study the accurate age and origin of the Early Paleozoic mafic dykes in Ziyang,southern Shaanxi Province,the mafic dykes from dabacunand Qinmingzhai were selected and the petrology,zircon U-Pb chronology,geochemistry,and Sr-Nd-Hf isotopes were studied.Analysis indicates that the mafic dykes studied are mainly composed of dolerite,and they are the products of the Early Ordovician(475.8-480.7 Ma).Furthermore,the dolerites belong to alkaline rock series,and they are characterized by enrichment in LREE,Rb,Ba,Sr,Nb,(87Sr/86Sr)i=0.7020-0.7050,εNd(t)=3.0-4.0),εHf(t)=4.5-12.1,176Hf/177Hf=0.282681-0.282844.This suggests that the mafic dyke were derived from the partial melting of a depleted lithospheric mantle,and the genetic process is mainly controlled by the mantle plume based on the discussion of the genetic model.Furthermore,the genetic process experienced the separation and crystallization of olivine and clinopyroxene at the same time,with little crustal contamination.
基金supported by the National Natural Science Foundation of China(Grant Nos.62006099,62076111)the Key Research and Development Program of Zhenjiang-Social Development(Grant No.SH2018005)+1 种基金the Natural Science Foundation of Jiangsu Higher Education(Grant No.17KJB520007)Industry-school Cooperative Education Program of the Ministry of Education(Grant No.202101363034).
文摘It is well-known that attribute reduction is a crucial action of rough set.The significant characteristic of attribute reduction is that it can reduce the dimensions of data with clear semantic explanations.Normally,the learning performance of attributes in derived reduct is much more crucial.Since related measures of rough set dominate the whole process of identifying qualified attributes and deriving reduct,those measures may have a direct impact on the performance of selected attributes in reduct.However,most previous researches about attribute reduction take measures related to either supervised perspective or unsupervised perspective,which are insufficient to identify attributes with superior learning performance,such as stability and accuracy.In order to improve the classification stability and classification accuracy of reduct,in this paper,a novel measure is proposed based on the fusion of supervised and unsupervised perspectives:(1)in terms of supervised perspective,approximation quality is helpful in quantitatively characterizing the relationship between attributes and labels;(2)in terms of unsupervised perspective,conditional entropy is helpful in quantitatively describing the internal structure of data itself.In order to prove the effectiveness of the proposed measure,18 University of CaliforniaIrvine(UCI)datasets and 2 Yale face datasets have been employed in the comparative experiments.Finally,the experimental results show that the proposed measure does well in selecting attributes which can provide distinguished classification stabilities and classification accuracies.
