We investigate the performances of the pairwise correlations（PCs） in different quantum networks consisting of fourwave mixers（FWMs） and beamsplitters（BSs）. PCs with quantum correlation in different quantum netwo...We investigate the performances of the pairwise correlations（PCs） in different quantum networks consisting of fourwave mixers（FWMs） and beamsplitters（BSs）. PCs with quantum correlation in different quantum networks can be verified by calculating the degree of relative intensity squeezing for any pair of all the output fields. More interestingly, the quantum correlation recovery and enhancement are present in the FWM＋BS network and the repulsion effect phenomena（signal（idler）-frequency mode cannot be quantum correlated with the other two idler（signal）-frequency modes simultaneously）between the PCs with quantum correlation are predicted in the FWM ＋ FWM and FWM ＋ FWM ＋ FWM networks. Our results presented here pave the way for the manipulation of the quantum correlation in quantum networks.展开更多
We investigate the behavior of geometric global quantum discord （GGQD） and concurrence （C） between half- spins of a mixed-three-spin （1/2, 1, 1/2） system with the Ising-XY model for which spins （1, 1/2） have t...We investigate the behavior of geometric global quantum discord （GGQD） and concurrence （C） between half- spins of a mixed-three-spin （1/2, 1, 1/2） system with the Ising-XY model for which spins （1, 1/2） have the Ising interaction and half-spins （1/2, 1/2） have both XY and the Dzyaloshinskii Moriya interactions together, under the decoherence action. A single-ion anisotropy property with coefficient ζ is assumed for the spin-integer. This system which includes an analytical Hamiltonian is considered at the front of an external homogeneous magnetic field B in thermal equilibrium. Finally, we compare GGQD and C and express some interesting phase flip reactions of the total quantum correlation and pairwise entanglement between spins （1/2, 1/2）. Generally, we conclude that the concurrence and GGQD have different behaviors under the phase flip channel.展开更多
Acute leukemia is an aggressive disease that has high mortality rates worldwide.The error rate can be as high as 40%when classifying acute leukemia into its subtypes.So,there is an urgent need to support hematologists...Acute leukemia is an aggressive disease that has high mortality rates worldwide.The error rate can be as high as 40%when classifying acute leukemia into its subtypes.So,there is an urgent need to support hematologists during the classification process.More than two decades ago,researchers used microarray gene expression data to classify cancer and adopted acute leukemia as a test case.The high classification accuracy they achieved confirmed that it is possible to classify cancer subtypes using microarray gene expression data.Ensemble machine learning is an effective method that combines individual classifiers to classify new samples.Ensemble classifiers are recognized as powerful algorithms with numerous advantages over traditional classifiers.Over the past few decades,researchers have focused a great deal of attention on ensemble classifiers in a wide variety of fields,including but not limited to disease diagnosis,finance,bioinformatics,healthcare,manufacturing,and geography.This paper reviews the recent ensemble classifier approaches utilized for acute leukemia gene expression data classification.Moreover,a framework for classifying acute leukemia gene expression data is proposed.The pairwise correlation gene selection method and the Rotation Forest of Bayesian Networks are both used in this framework.Experimental outcomes show that the classification accuracy achieved by the acute leukemia ensemble classifiers constructed according to the suggested framework is good compared to the classification accuracy achieved in other studies.展开更多
基金Project supported by the National Natural Science Foundation of China(Grants Nos.91436211,11374104,and 10974057)the Natural Science Foundation of Shanghai,China(Grant No.17ZR1442900)+5 种基金the Specialized Research Fund for the Doctoral Program of Higher Education,China(Grant No.20130076110011)the Program for Professor of Special Appointment(Eastern Scholar)at Shanghai Institutions of Higher Learning,the Program for New Century Excellent Talents in University,China(Grant No.NCET-10-0383)the Shu Guang Project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation,China(Grant No.11SG26)the Shanghai Pujiang Program,China(Grant No.09PJ1404400)the Scientific Research Foundation for the Returned Overseas Chinese Scholars,State Education Ministry,National Basic Research Program of China(Grant No.2016YFA0302103)the Program of State Key Laboratory of Advanced 207 Optical Communication Systems and Networks,China(Grant No.2016GZKF0JT003)
文摘We investigate the performances of the pairwise correlations（PCs） in different quantum networks consisting of fourwave mixers（FWMs） and beamsplitters（BSs）. PCs with quantum correlation in different quantum networks can be verified by calculating the degree of relative intensity squeezing for any pair of all the output fields. More interestingly, the quantum correlation recovery and enhancement are present in the FWM＋BS network and the repulsion effect phenomena（signal（idler）-frequency mode cannot be quantum correlated with the other two idler（signal）-frequency modes simultaneously）between the PCs with quantum correlation are predicted in the FWM ＋ FWM and FWM ＋ FWM ＋ FWM networks. Our results presented here pave the way for the manipulation of the quantum correlation in quantum networks.
文摘We investigate the behavior of geometric global quantum discord （GGQD） and concurrence （C） between half- spins of a mixed-three-spin （1/2, 1, 1/2） system with the Ising-XY model for which spins （1, 1/2） have the Ising interaction and half-spins （1/2, 1/2） have both XY and the Dzyaloshinskii Moriya interactions together, under the decoherence action. A single-ion anisotropy property with coefficient ζ is assumed for the spin-integer. This system which includes an analytical Hamiltonian is considered at the front of an external homogeneous magnetic field B in thermal equilibrium. Finally, we compare GGQD and C and express some interesting phase flip reactions of the total quantum correlation and pairwise entanglement between spins （1/2, 1/2）. Generally, we conclude that the concurrence and GGQD have different behaviors under the phase flip channel.
文摘Acute leukemia is an aggressive disease that has high mortality rates worldwide.The error rate can be as high as 40%when classifying acute leukemia into its subtypes.So,there is an urgent need to support hematologists during the classification process.More than two decades ago,researchers used microarray gene expression data to classify cancer and adopted acute leukemia as a test case.The high classification accuracy they achieved confirmed that it is possible to classify cancer subtypes using microarray gene expression data.Ensemble machine learning is an effective method that combines individual classifiers to classify new samples.Ensemble classifiers are recognized as powerful algorithms with numerous advantages over traditional classifiers.Over the past few decades,researchers have focused a great deal of attention on ensemble classifiers in a wide variety of fields,including but not limited to disease diagnosis,finance,bioinformatics,healthcare,manufacturing,and geography.This paper reviews the recent ensemble classifier approaches utilized for acute leukemia gene expression data classification.Moreover,a framework for classifying acute leukemia gene expression data is proposed.The pairwise correlation gene selection method and the Rotation Forest of Bayesian Networks are both used in this framework.Experimental outcomes show that the classification accuracy achieved by the acute leukemia ensemble classifiers constructed according to the suggested framework is good compared to the classification accuracy achieved in other studies.
