The optical performance of reflective holographic polymer-dispersed liquid crystals (H-PDLCs) is investigated as a function of sample thickness and laser exposure intensity, and, the data are analyzed in terms of a nonlocal photopolymerization model. The intensity of laser exposure is proven to have a strong influence on the reflection efficiency of H-PDLCs. We have found that the experimental results cannot be completely interpreted by the previous local diffusion model. Combined with transfer matrix analysis, a modified diffusion model with a nonlocal photopolymerization term is proposed herein, which qualitatively describes our experimental observations. The experimental data demonstrates our assertion that the nonlocal effect is strongly correlated to the exposure conditions. Under the low exposure condition, the diffusion effect is screened by this nonlocal effect, and effectively a small diffusion constant is observed. Under the high exposure condition, the nonlocal effect can be suppressed and the modified diffusion model can be deduced to the original local diffusion model. Also, within the framework of this nonlocal model, overexposure can be properly explained.
Stock price trend prediction has been a hot issue in the financial field, which has been paid much attention by both academia and industry. It is a challenging task due to the non-stationary and high volatility of the stock prices. Traditional methods for predicting stock price trends are mostly based on the historical OHLC (i.e., open, high, low, and close prices) data. However, it eliminates most of the trading information. To address this problem, in this paper, another type of stock price data, i.e., limit order books (LOBs), is used. For better exploring the relationship of the LOBs and stock price trend, inspired by the successful application of deep learning-based methods, an attention-based LSTM model is applied. The trend of stock price can be predicted by using the LOBs data of the previous day. By using the real stock price data of the China stock market, the effectiveness of the proposed model is validated by experimental results.
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