We consider a bistable mesoscopic chemical reaction system and calculate entropy produc- tion along the dominant pathway during nonequilibrium phase transition. Using probability generating function method and eikonal...We consider a bistable mesoscopic chemical reaction system and calculate entropy produc- tion along the dominant pathway during nonequilibrium phase transition. Using probability generating function method and eikonal approximation, we first convert the chemical master equation into the classical Hamilton-Jacobi equation, and then find the dominant pathways between two steady states in the phase space by calculating zero-energy trajectories. We find that entropy productions are related to the actions of the forward and backward dominant pathways. At the coexistence point where the stabilities of the two steady states are equiv alent, both the system entropy change and the medium entropy change are zero; whereas at non-coexistence point both of them are nonzero.展开更多
Human action recognition has become one of the most active research topics in human-computer interaction and artificial intelligence, and has attracted much attention. Here, we employ a low-cost optical sensor Kinect ...Human action recognition has become one of the most active research topics in human-computer interaction and artificial intelligence, and has attracted much attention. Here, we employ a low-cost optical sensor Kinect to capture the action information of the human skeleton. We then propose a two-level hierarchical human action recognition model with self-selection classifiers via skeleton data. Especially different optimal classifiers are selected by probability voting mechanism and 10 times 10-fold cross validation at different coarse grained levels. Extensive simulations on a well-known open dataset and results demonstrate that our proposed method is efficient in human action recognition, achieving 94.19%the average recognition rate and 95.61% the best rate.展开更多
文摘We consider a bistable mesoscopic chemical reaction system and calculate entropy produc- tion along the dominant pathway during nonequilibrium phase transition. Using probability generating function method and eikonal approximation, we first convert the chemical master equation into the classical Hamilton-Jacobi equation, and then find the dominant pathways between two steady states in the phase space by calculating zero-energy trajectories. We find that entropy productions are related to the actions of the forward and backward dominant pathways. At the coexistence point where the stabilities of the two steady states are equiv alent, both the system entropy change and the medium entropy change are zero; whereas at non-coexistence point both of them are nonzero.
基金Supported by the National Nature Science Foundation of China under Grant Nos.11475003,61603003,and 11471093the Key Project of Cultivation of Leading Talents in Universities of Anhui Province under Grant No.gxfxZD2016174+2 种基金Funds of Integration of Cloud Computing and Big DataInnovation of Science and Technology of Ministry of Education of China under Grant No.2017A09116Anhui Provincial Department of Education Outstanding Top-Notch Talent-Funded Project under Grant No.gxbjZD26
文摘Human action recognition has become one of the most active research topics in human-computer interaction and artificial intelligence, and has attracted much attention. Here, we employ a low-cost optical sensor Kinect to capture the action information of the human skeleton. We then propose a two-level hierarchical human action recognition model with self-selection classifiers via skeleton data. Especially different optimal classifiers are selected by probability voting mechanism and 10 times 10-fold cross validation at different coarse grained levels. Extensive simulations on a well-known open dataset and results demonstrate that our proposed method is efficient in human action recognition, achieving 94.19%the average recognition rate and 95.61% the best rate.
