Classical-quantum correspondence has been an intriguing issue ever since quantum theory was proposed. The search- ing for signatures of classically nonintegrable dynamics in quantum systems comprises the interesting f...Classical-quantum correspondence has been an intriguing issue ever since quantum theory was proposed. The search- ing for signatures of classically nonintegrable dynamics in quantum systems comprises the interesting field of quantum chaos. In this short review, we shall go over recent efforts of extending the understanding of quantum chaos to relativistic cases. We shall focus on the level spacing statistics for two-dimensional massless Dirac billiards, i.e., particles confined in a closed region. We shall discuss the works for both the particle described by the massless Dirac equation （or Weyl equation） and the quasiparticle from graphene. Although the equations are the same, the boundary conditions are typically different, rendering distinct level spacing statistics.展开更多
Facial expression recognition has been a hot topic for decades,but high intraclass variation makes it challenging.To overcome intraclass variation for visual recognition,we introduce a novel fusion methodology,in whic...Facial expression recognition has been a hot topic for decades,but high intraclass variation makes it challenging.To overcome intraclass variation for visual recognition,we introduce a novel fusion methodology,in which the proposed model first extract features followed by feature fusion.Specifically,RestNet-50,VGG-19,and Inception-V3 is used to ensure feature learning followed by feature fusion.Finally,the three feature extraction models are utilized using Ensemble Learning techniques for final expression classification.The representation learnt by the proposed methodology is robust to occlusions and pose variations and offers promising accuracy.To evaluate the efficiency of the proposed model,we use two wild benchmark datasets Real-world Affective Faces Database(RAF-DB)and AffectNet for facial expression recognition.The proposed model classifies the emotions into seven different categories namely:happiness,anger,fear,disgust,sadness,surprise,and neutral.Furthermore,the performance of the proposed model is also compared with other algorithms focusing on the analysis of computational cost,convergence and accuracy based on a standard problem specific to classification applications.展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos. 11005053,11135001,and 11375074)the Air Force Office of Scientific Research (Grant No. FA9550-12-1-0095)the Office of Naval Research (Grant No. N00014-08-1-0627)
文摘Classical-quantum correspondence has been an intriguing issue ever since quantum theory was proposed. The search- ing for signatures of classically nonintegrable dynamics in quantum systems comprises the interesting field of quantum chaos. In this short review, we shall go over recent efforts of extending the understanding of quantum chaos to relativistic cases. We shall focus on the level spacing statistics for two-dimensional massless Dirac billiards, i.e., particles confined in a closed region. We shall discuss the works for both the particle described by the massless Dirac equation （or Weyl equation） and the quasiparticle from graphene. Although the equations are the same, the boundary conditions are typically different, rendering distinct level spacing statistics.
文摘Facial expression recognition has been a hot topic for decades,but high intraclass variation makes it challenging.To overcome intraclass variation for visual recognition,we introduce a novel fusion methodology,in which the proposed model first extract features followed by feature fusion.Specifically,RestNet-50,VGG-19,and Inception-V3 is used to ensure feature learning followed by feature fusion.Finally,the three feature extraction models are utilized using Ensemble Learning techniques for final expression classification.The representation learnt by the proposed methodology is robust to occlusions and pose variations and offers promising accuracy.To evaluate the efficiency of the proposed model,we use two wild benchmark datasets Real-world Affective Faces Database(RAF-DB)and AffectNet for facial expression recognition.The proposed model classifies the emotions into seven different categories namely:happiness,anger,fear,disgust,sadness,surprise,and neutral.Furthermore,the performance of the proposed model is also compared with other algorithms focusing on the analysis of computational cost,convergence and accuracy based on a standard problem specific to classification applications.
