Regional and local patterns of upper‐plate deformation in Cascadia: the importance of the down‐dip extent of locking relative to upper‐plate strength contrasts

Tectonics ◽  
2022 ◽  
Author(s):  
K.A. McKenzie ◽  
K.P. Furlong ◽  
M.W. Herman
Keyword(s):  
Plate Deformation ◽  
Local Patterns

WHAT IS THE MECHANISM OF UPPER-PLATE DEFORMATION AS A RESULT OF FLAT-SLAB SUBDUCTION?

2018 ◽  
Author(s):  
Jolante W. van Wijk ◽  
◽  
Claire A. Currie ◽  
Gary Axen
Keyword(s):  
Flat Slab ◽  
Plate Deformation ◽  
Flat Slab Subduction

Information on evolutionary age in redundancy of complex network

2019 ◽  
Vol 33 (27) ◽  
pp. 1950331
Author(s):  
Shiguo Deng ◽  
Henggang Ren ◽  
Tongfeng Weng ◽  
Changgui Gu ◽  
Huijie Yang
Keyword(s):  
Principal Component Analysis ◽  
Complex Networks ◽  
Metabolic Network ◽  
Complex Network ◽  
Cellular Networks ◽  
Topological Structure ◽  
Quantitative Measure ◽  
Principal Component ◽  
Evolutionary Information ◽  
Local Patterns

Evolutionary processes of many complex networks in reality are dominated by duplication and divergence. This mechanism leads to redundant structures, i.e. some nodes share most of their neighbors and some local patterns are similar, called redundancy of network. An interesting reverse problem is to discover evolutionary information from the present topological structure. We propose a quantitative measure of redundancy of network from the perspective of principal component analysis. The redundancy of a community in the empirical human metabolic network is negatively and closely related with its evolutionary age, which is consistent with that for the communities in the modeling protein–protein network. This behavior can be used to find the evolutionary difference stored in cellular networks.

scholarly journals Stock Market Trading Rules Discovery Based on Biclustering Method

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Yun Xue ◽  
Zhiwen Liu ◽  
Jie Luo ◽  
Zhihao Ma ◽  
Meizhen Zhang ◽  
...  
Keyword(s):  
Stock Price ◽  
Historical Data ◽  
Probability Model ◽  
Trading Rules ◽  
Normal Probability ◽  
Long Time ◽  
Local Patterns ◽  
Short Term Prediction ◽  
Stock Market Trading ◽  
The Impact

The prediction of stock market’s trend has become a challenging task for a long time, which is affected by a variety of deterministic and stochastic factors. In this paper, a biclustering algorithm is introduced to find the local patterns in the quantized historical data. The local patterns obtained are regarded as the trading rules. Then the trading rules are applied in the short term prediction of the stock price, combined with the minimum-error-rate classification of the Bayes decision theory under the assumption of multivariate normal probability model. In addition, this paper also makes use of the idea of the stream mining to weaken the impact of historical data on the model and update the trading rules dynamically. The experiment is implemented on real datasets and the results prove the effectiveness of the proposed algorithm.

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