What appears irrelevant or negligible to readers of one cultural tradition may be seminal and indispensable to those of another. This article studies a prominent Chinese mode of living—the earnest pursuit of the aesthetic qualities of life—to help bridge the “impasses of noncommunication” in cross-cultural understanding. It constructs the working concept of “the aesthetic dimension of life” from Chinese formative thoughts before it applies the concept to the reading of “Forever by Your Side,” a “short-short story” by a contemporary peasant writer in China. The discussion focuses on how the ethical and the aesthetic are mutually entailing and how aspirations for the aesthetic in life affect human actions in general and ordinary people’s everyday choices and behavior in particular. Approaching the story from Chinese cultural tradition and analyzing it with Chinese conceptual paradigms, this article offers an in-depth understanding of contemporary China in its civilizational context and shows a way to overcome obstacles of cross-cultural communication.
We describe a new technique for the classification of motor imagery electroencephalogram (EEG) recordings. The technique is based on a time-frequency analysis of EEG signals, regarding the relations between the EEG data obtained from the C3/C4 electrodes, the features were reduced according the Fisher distance. This reduced feature set is finally fed to a linear discriminant for classification. The algorithm was applied to 3 subjects, the classification performance of the proposed algorithm varied between 70% and 93.1%; across subjects.
Conditional nonlinear optimal perturbation (CNOP) is the initial perturbation that has the largest nonlinear evolution at prediction time for initial perturbations satisfying certain physical constraint condition. It does not only represent the optimal precursor of certain weather or climate event, but also stand for the initial error which has largest effect on the prediction uncertainties at the prediction time. In sensitivity and stability analyses of fluid motion, CNOP also describes the most unstable (or most sensitive) mode. CNOP has been used to estimate the upper bound of the prediction error. These physical characteristics of CNOP are examined by applying respectively them to ENSO predictability studies and ocean's thermohaline circulation (THC) sensitivity analysis. In ENSO predictability studies, CNOP, rather than linear singular vector (LSV), represents the initial patterns that evolve into ENSO events most potentially, i.e. the optimal precursors for ENSO events. When initial perturbation is considered to be the initial error of ENSO, CNOP plays the role of the initial error that has largest effect on the prediction of ENSO. CNOP also derives the upper bound of prediction error of ENSO events. In the THC sensitivity and stability studies, by calculating the CNOP (most unstable perturbation) of THC, it is found that there is an asymmetric nonlinear response of ocean's THC to the finite amplitude perturbations. Finally, attention is paid to the feasibility of CNOP in more complicated model. It is shown that in a model with higher dimensions, CNOP can be computed successfully. The corresponding optimization algorithm is also shown to be efficient.
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