Relevance Vector Machine(RVM)is a supervised learning algorithm extended from Support Vector Machine based on the Bayesian sparsity model.Relevance Vector Machine classification suffers from theoretical limitations an...Relevance Vector Machine(RVM)is a supervised learning algorithm extended from Support Vector Machine based on the Bayesian sparsity model.Relevance Vector Machine classification suffers from theoretical limitations and computational inefficiency mainly because there is no closed-form solution for the posterior of the weight parameters.We propose two advanced Bayesian approaches for RVM classification,namely the Enhanced RVM and the Reinforced RVM,to perfect the theoretic framework of RVM and extend the algorithm to the imbalanced data problem,which has an arresting skew in data size between classes.First,the Enhanced RVM conducts a strict Bayesian sampling process instead of the approximation method in the original one to remedy its theoretic limitations,especially the nonconvergence of the iterations.Secondly,we conjecture that the hierarchical prior makes the Reinforced RVM achieve consistent estimations of the quantities of interest compared with the non-consistent estimations of the original RVM.Consistency is necessary for RVM classification since it makes the model more stable and localises the relevant vectors more accurately in the imbalanced data problem.The two-level prior also renders the Reinforced one competitive in the imbalanced data problem by building the inner connection of parameter dimensions and alloting a more vital relevance to the small class data weight parameter.The theoretic proofs and several numeric studies demonstrate the merits of our two proposed algorithms.展开更多
High-entropy metallic glass(HEMG)is a new type of metallic material with high-entropy alloy-like com-position and amorphous structure,which render HEMGs unusual glass formation behaviors and unique properties.In recen...High-entropy metallic glass(HEMG)is a new type of metallic material with high-entropy alloy-like com-position and amorphous structure,which render HEMGs unusual glass formation behaviors and unique properties.In recent years,fast research progress has been witnessed on the HEMGs,and thus a sys-tematic review is required.In this review,we first introduce the concept of the HEMGs and summarize the developed HEMGs.Then,the glass-forming ability of the HEMGs is discussed,and the general rules are proposed.Focusing on the thermal stability of HEMGs,the effect of entropy on the energy states of HEMGs and the crystallization behavior of HEMGs are discussed.Finally,the mechanical,magnetic,cat-alytic and other properties of HEMGs are presented,and the advantages and disadvantages of HEMGs are shown.This review can function as a quick guideline for overviewing the HEMG field.展开更多
The reconstruction of high-resolution sea-level variation curves in deep time based on the standard car-bonate microfacies knowledge graph(SMFKG)is of great scientific significance for exploring the Earth system evolu...The reconstruction of high-resolution sea-level variation curves in deep time based on the standard car-bonate microfacies knowledge graph(SMFKG)is of great scientific significance for exploring the Earth system evolution and predicting future sea-level and climate changes.In this study,the concepts,attri-butes,and relationships among standard carbonate microfacies(SMF)are comprehensively analyzed;an ontology layer is established and its data layer is constructed using thin-section descriptions;and finally,the SMFKG is established.Additionally,based on the knowledge graph,an application for automatically identifying SMF using identification markers and reconstructing the high-resolution relative sea-level variation curve using the SMF and facies zones is compiled.Then,all thin sections of the late Ediacaran Dengying Formation in the western margin of the Yangtze Platform are observed and described in detail,the SMF and facies zones are identified automatically,and the relative sea-level curve is recon-structed automatically using the SMFKG.The reconstruction results show that the Yangtze Platform experienced four sea-level rise and fall cycles in the late Ediacaran,of which two intense regressions led to subaerial-exposed unconformities in the interior and top of the Dengying Formation,which is highly consistent with previous research results.This shows that the high-resolution relative sea-level variation curve in deep time can be reconstructed efficiently and intelligently using the SMFKG.Additionally,in the near future,the combination of an automatic digital slide-scanning system,machine-learning techniques,and the SMFKG can achieve one-stop fully automatic SMF recognition and reconstruction of high-resolution relative sea-level variation curves in deep time,which has a high application value.展开更多
基金National Statistical Science Research Project of China,Grant/Award Number:2021LY070Association of Fundamental Computing Education in Chinese Universities,Basic Computer Education Teaching Research Project,Grant/Award Number:2022-AFCEC-217。
文摘Relevance Vector Machine(RVM)is a supervised learning algorithm extended from Support Vector Machine based on the Bayesian sparsity model.Relevance Vector Machine classification suffers from theoretical limitations and computational inefficiency mainly because there is no closed-form solution for the posterior of the weight parameters.We propose two advanced Bayesian approaches for RVM classification,namely the Enhanced RVM and the Reinforced RVM,to perfect the theoretic framework of RVM and extend the algorithm to the imbalanced data problem,which has an arresting skew in data size between classes.First,the Enhanced RVM conducts a strict Bayesian sampling process instead of the approximation method in the original one to remedy its theoretic limitations,especially the nonconvergence of the iterations.Secondly,we conjecture that the hierarchical prior makes the Reinforced RVM achieve consistent estimations of the quantities of interest compared with the non-consistent estimations of the original RVM.Consistency is necessary for RVM classification since it makes the model more stable and localises the relevant vectors more accurately in the imbalanced data problem.The two-level prior also renders the Reinforced one competitive in the imbalanced data problem by building the inner connection of parameter dimensions and alloting a more vital relevance to the small class data weight parameter.The theoretic proofs and several numeric studies demonstrate the merits of our two proposed algorithms.
基金This work is financially supported by the National Natural Science Foundation of China(Grant Nos.51871129,51571127,and 51601063).
文摘High-entropy metallic glass(HEMG)is a new type of metallic material with high-entropy alloy-like com-position and amorphous structure,which render HEMGs unusual glass formation behaviors and unique properties.In recent years,fast research progress has been witnessed on the HEMGs,and thus a sys-tematic review is required.In this review,we first introduce the concept of the HEMGs and summarize the developed HEMGs.Then,the glass-forming ability of the HEMGs is discussed,and the general rules are proposed.Focusing on the thermal stability of HEMGs,the effect of entropy on the energy states of HEMGs and the crystallization behavior of HEMGs are discussed.Finally,the mechanical,magnetic,cat-alytic and other properties of HEMGs are presented,and the advantages and disadvantages of HEMGs are shown.This review can function as a quick guideline for overviewing the HEMG field.
基金supported by the IUGS Deep-time Digital Earth(DDE)Big Science Program,National Natural Science Foundation of China(No.42050104,No.42102138 and No.U19B6003)the Open Fund(DGERA20221103)of Key Laboratory of Deep-time Geography and Environment Reconstruction and Applications of Ministry of Natural ResourcesChengdu University of Technology,China and the Open Fund(PLC20210202)of the State Key Labora-tory of Oil and Gas Reservoir Geology and Exploitation(Chengdu University of Technology,China).
文摘The reconstruction of high-resolution sea-level variation curves in deep time based on the standard car-bonate microfacies knowledge graph(SMFKG)is of great scientific significance for exploring the Earth system evolution and predicting future sea-level and climate changes.In this study,the concepts,attri-butes,and relationships among standard carbonate microfacies(SMF)are comprehensively analyzed;an ontology layer is established and its data layer is constructed using thin-section descriptions;and finally,the SMFKG is established.Additionally,based on the knowledge graph,an application for automatically identifying SMF using identification markers and reconstructing the high-resolution relative sea-level variation curve using the SMF and facies zones is compiled.Then,all thin sections of the late Ediacaran Dengying Formation in the western margin of the Yangtze Platform are observed and described in detail,the SMF and facies zones are identified automatically,and the relative sea-level curve is recon-structed automatically using the SMFKG.The reconstruction results show that the Yangtze Platform experienced four sea-level rise and fall cycles in the late Ediacaran,of which two intense regressions led to subaerial-exposed unconformities in the interior and top of the Dengying Formation,which is highly consistent with previous research results.This shows that the high-resolution relative sea-level variation curve in deep time can be reconstructed efficiently and intelligently using the SMFKG.Additionally,in the near future,the combination of an automatic digital slide-scanning system,machine-learning techniques,and the SMFKG can achieve one-stop fully automatic SMF recognition and reconstruction of high-resolution relative sea-level variation curves in deep time,which has a high application value.
