An Empirical Study of Intra-day Stock Return Volatility

It is well known that microstructure noise could have substantial impact on volatility estimation of high frequency asset returns. The Two Scale Realized Volatility (TSRV) estimator makes use of all the available data and at the same time corrects the effect of market microstructure noise. In this study, 30-minute TSRV series is constructed from tick-by-tick Dow Jones 30 stock prices. Our results show that the 30-minute volatility estimate series has the stylized characteristics, including volatility clustering, long memory and displaying U-shape within the day. Also, the volatility for stocks during earning announcement period is significantly higher than that in non-announcement period. This phenomenon is particularly striking at the opening hour of the announcement day. Time series model is built on the periodic and long memory features with rolling window size of one month. We forecast the out-of-sample 30-minute volatility one day ahead based on Semi-parametric Fractional Autoregressive model and modified HAR-RV linear regression model

Traffic prediction for 5G: A deep learning approach based on lightweight hybrid attention networks

The maturity of 5G technology provides a guarantee for increasingly large communication networks, while the resources required for communication and computation are also increasing, and reasonable resource allocation can improve the efficiency of network communication and reduce the consumption of communication resources. Existing deep learning methods have been able to predict network traffic to a certain extent, so as to solve the communication efficiency and resource consumption problems in the field of integrated sensing, communication and computation (ISCC) through rational resource allocation. However, the following problems still exist: (1) The feature learning ability of the prediction model is insufficient, and the prediction accuracy needs to be improved. (2) Powerful and complex deep learning methods lead to an increase in the prediction cost of the model. To address these problems, this paper proposes a deep learning method based on a lightweight hybrid attention network. In order to capture the key features of 5G data more effectively, an efficient hybrid attention mechanism (EHA) is proposed. After this attention is applied to convolution, the key information can be well enhanced. We use depthwise separable convolution in feature extraction, which greatly improves the efficiency of lightweight convolution layer (LC) in feature extraction. Combined with the efficient hybrid attention mechanism (EHA), the proposed model has better lightweight properties. Experimental results show that the model proposed in this paper has lower RMSE and MAE values on the three datasets, as well as fewer parameters and computational effort compared to the baseline scheme.</p

Blurriness curves corresponding to Fig. 3.

<p>Blurriness curves corresponding to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101866#pone-0101866-g003" target="_blank">Fig. 3</a>.</p

EER values obtained from palmprint images with different blurriness using different methods: (a) HOG and VO–HOG, (b) RHOG and VO–RHOG, and (c) WRHOG and VO–WRHOG.

<p>EER values obtained from palmprint images with different blurriness using different methods: (a) HOG and VO–HOG, (b) RHOG and VO–RHOG, and (c) WRHOG and VO–WRHOG.</p

Blurred Palmprint Recognition Based on Stable-Feature Extraction Using a Vese–Osher Decomposition Model

<div><p>As palmprints are captured using non-contact devices, image blur is inevitably generated because of the defocused status. This degrades the recognition performance of the system. To solve this problem, we propose a stable-feature extraction method based on a Vese–Osher (VO) decomposition model to recognize blurred palmprints effectively. A Gaussian defocus degradation model is first established to simulate image blur. With different degrees of blurring, stable features are found to exist in the image which can be investigated by analyzing the blur theoretically. Then, a VO decomposition model is used to obtain structure and texture layers of the blurred palmprint images. The structure layer is stable for different degrees of blurring (this is a theoretical conclusion that needs to be further proved via experiment). Next, an algorithm based on weighted robustness histogram of oriented gradients (WRHOG) is designed to extract the stable features from the structure layer of the blurred palmprint image. Finally, a normalized correlation coefficient is introduced to measure the similarity in the palmprint features. We also designed and performed a series of experiments to show the benefits of the proposed method. The experimental results are used to demonstrate the theoretical conclusion that the structure layer is stable for different blurring scales. The WRHOG method also proves to be an advanced and robust method of distinguishing blurred palmprints. The recognition results obtained using the proposed method and data from two palmprint databases (PolyU and Blurred–PolyU) are stable and superior in comparison to previous high-performance methods (the equal error rate is only 0.132%). In addition, the authentication time is less than 1.3 s, which is fast enough to meet real-time demands. Therefore, the proposed method is a feasible way of implementing blurred palmprint recognition.</p></div

Traffic prediction for 5G: A deep learning approach based on lightweight hybrid attention networks

The maturity of 5G technology provides a guarantee for increasingly large communication networks, while the resources required for communication and computation are also increasing, and reasonable resource allocation can improve the efficiency of network communication and reduce the consumption of communication resources. Existing deep learning methods have been able to predict network traffic to a certain extent, so as to solve the communication efficiency and resource consumption problems in the field of integrated sensing, communication and computation (ISCC) through rational resource allocation. However, the following problems still exist: (1) The feature learning ability of the prediction model is insufficient, and the prediction accuracy needs to be improved. (2) Powerful and complex deep learning methods lead to an increase in the prediction cost of the model. To address these problems, this paper proposes a deep learning method based on a lightweight hybrid attention network. In order to capture the key features of 5G data more effectively, an efficient hybrid attention mechanism (EHA) is proposed. After this attention is applied to convolution, the key information can be well enhanced. We use depthwise separable convolution in feature extraction, which greatly improves the efficiency of lightweight convolution layer (LC) in feature extraction. Combined with the efficient hybrid attention mechanism (EHA), the proposed model has better lightweight properties. Experimental results show that the model proposed in this paper has lower RMSE and MAE values on the three datasets, as well as fewer parameters and computational effort compared to the baseline scheme.</p

Illustration of palmprint image preprocessing.

<p>Illustration of palmprint image preprocessing.</p

2822117.mov

optical reconstruction from different perspective

The image degradation model.

<p>The image degradation model.</p

2822118.mov

the video of motion parallax when the hogel size is 1c