In recent decades, a new type of cultural upsurge surrounding kunqu1 has arisen in Chinese language sphere, though respectively due to different reasons in China's Mainland, Hong Kong, Taiwan and other Chinese dia...In recent decades, a new type of cultural upsurge surrounding kunqu1 has arisen in Chinese language sphere, though respectively due to different reasons in China's Mainland, Hong Kong, Taiwan and other Chinese diaspora. Against the global trend of culture heritage nationalization context, via the new media platform, the performative staging of individual emotions and reverie in the market society2, the longings to redeem various alienation in a vertical modernity3, as well as the yearnings for emotional balance in a burgeoning feministic modernity, all integrate with each other and together generate a restless transforming memory for kunqu. Just like a misty veil, this complex, contentious, contradictory and long-lasting collective memory-making process blurs kunqu＇s appearance, expands its layers, and ultimately generates a cultural myth. With detailed case studies this paper aims to reflect upon the deep reasons for the kunqu myth and to probe the transformative powers of a performative space in enabling remembrance and/or forgetting.展开更多
In this work, we apply the nonlinear filtering theory to the estimation of the partially observed dynamics of anthracnose which is a phytopathology. The signal here is the inhi- bition rate and the observations are th...In this work, we apply the nonlinear filtering theory to the estimation of the partially observed dynamics of anthracnose which is a phytopathology. The signal here is the inhi- bition rate and the observations are the fruit volume and the rotted volume. We propose stochastic models based on deterministic models studied previously in the literature, in order to represent the noise introduced by uncontrolled variations on parameters and errors on the measurements. Under the assumption of Brownian noises, we prove the well-posedness of the models in either they take into account the space variable or not. The filtering problem is solved for the nonspatial model giving Zakai and Kushner- Stratonovich equations satisfied respectively by the unnormalized and the normalized conditional distribution of the signal with respect to the observations. A prevision prob- lem and a discrete filtering problem are also studied for the realistic cases of discrete and possibly incomplete observations. We illustrate the filter behavior through figures displaying the average estimation relative error and a 95% confidence region obtained after a hundred of numerical simulations with initial conditions taken randomly with respect to uniform law.展开更多
文摘In recent decades, a new type of cultural upsurge surrounding kunqu1 has arisen in Chinese language sphere, though respectively due to different reasons in China's Mainland, Hong Kong, Taiwan and other Chinese diaspora. Against the global trend of culture heritage nationalization context, via the new media platform, the performative staging of individual emotions and reverie in the market society2, the longings to redeem various alienation in a vertical modernity3, as well as the yearnings for emotional balance in a burgeoning feministic modernity, all integrate with each other and together generate a restless transforming memory for kunqu. Just like a misty veil, this complex, contentious, contradictory and long-lasting collective memory-making process blurs kunqu＇s appearance, expands its layers, and ultimately generates a cultural myth. With detailed case studies this paper aims to reflect upon the deep reasons for the kunqu myth and to probe the transformative powers of a performative space in enabling remembrance and/or forgetting.
文摘In this work, we apply the nonlinear filtering theory to the estimation of the partially observed dynamics of anthracnose which is a phytopathology. The signal here is the inhi- bition rate and the observations are the fruit volume and the rotted volume. We propose stochastic models based on deterministic models studied previously in the literature, in order to represent the noise introduced by uncontrolled variations on parameters and errors on the measurements. Under the assumption of Brownian noises, we prove the well-posedness of the models in either they take into account the space variable or not. The filtering problem is solved for the nonspatial model giving Zakai and Kushner- Stratonovich equations satisfied respectively by the unnormalized and the normalized conditional distribution of the signal with respect to the observations. A prevision prob- lem and a discrete filtering problem are also studied for the realistic cases of discrete and possibly incomplete observations. We illustrate the filter behavior through figures displaying the average estimation relative error and a 95% confidence region obtained after a hundred of numerical simulations with initial conditions taken randomly with respect to uniform law.
