Qiusheng Li

Bio: Qiusheng Li is an academic researcher from City University of Hong Kong. The author has contributed to research in topics: Wind speed & Wind tunnel. The author has an hindex of 47, co-authored 429 publications receiving 8830 citations. Previous affiliations of Qiusheng Li include Chinese Ministry of Education & Guangzhou University.

Crustal structure of the southern Dabie ultrahigh‐pressure orogen and Yangtze foreland from deep seismic reflection profiling

Abstract: A new 140-km-long seismic reflection profile provides a high-resolution crustal-scale image of the southern Dabieshan high-pressure (HP) metamorphic belt and the Yangtze foreland fold-and-thrust belt. The seismic image of the stacked section shows that the southern Dabieshan metamorphic terrane and Yangtze foreland belt are separated by a large north-dipping fault. In the foreland the upper crust is dominated by a series of folds and thrusts formed during the collisional stage in the mid-Triassic; it was reworked by crustal extension resulting in the formation of a late Jurassic and Cretaceous red-bed basin. The southern Dabieshan profile shows stacked crustal slabs developed along the margin of the collisional orogenic belt. The Moho reflectors at 10–11 s (∼30–33 km) are seismically prominent and segmented by a number of south-verging thrusts that were probably developed by foreland-directed thrusting of the deeply subducted continental crust during exhumation. The seismic reflection profile suggests that structures related to the Triassic–Jurassic subduction and exhumation of the Yangtze plate are preserved despite the severe crustal extension superimposed during the late Mesozoic and Cenozoic.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Nonlinear Time Series Analysis.

Abstract: : This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)