Rethinking PRL: A Multiscale Progressively Residual Learning Network for Inverse Halftoning

Image inverse halftoning is a classic image restoration task, aiming to recover continuous-tone images from halftone images with only bilevel pixels. Because the halftone images lose much of the original image content, inverse halftoning is a classic ill-problem. Although existing inverse halftoning algorithms achieve good performance, their results lose image details and features. Therefore, it is still a challenge to recover high-quality continuous-tone images. In this paper, we propose an end-to-end multiscale progressively residual learning network (MSPRL), which has a UNet architecture and takes multiscale input images. To make full use of different input image information, we design a shallow feature extraction module to capture similar features between images of different scales. We systematically study the performance of different methods and compare them with our proposed method. In addition, we employ different training strategies to optimize the model, which is important for optimizing the training process and improving performance. Extensive experiments demonstrate that our MSPRL model obtains considerable performance gains in detail restoration

Role of exports and FDI in economic growth: Comparative study of three northeast Asian economies

Master'sMASTER OF SCIENCE (MANAGEMENT

An Approach on the Evaluation of LNG Tank Container Transportation Safety

As a clean energy source, liquefied natural gas (LNG) has been widely accepted all around the world. As away to transport LNG, tank container transportation is becoming more and more popular. However, how to carry outsafety management for the whole transportation process of tank container is a problem troubling the whole industry.Therefore, this paper proposes a model based on the Recurrent Neural Networks(RNN) to evaluate the safetyperformance. First, find the factors affecting the safety of LNG transport by sea and construct an index system. Next,design a questionnaire and get scores from supporting experts. Then, this paper utilize the trained RNN to judge the safetystatue of LNG tank transportation. Through the comparison of training results and the final score got from experts, theresult shows that the MAE is negligible and prove the effectiveness of the RNN. Finally, a case study was conducted.From the analysis of the training results, it is known that enterprise safety management plays an important role intransportation safety and a better safety management systemwill greatly reduce the probability of accidents and improvethe transportation safety

Matrix GARCH Model: Inference and Application

Matrix-variate time series data are largely available in applications. However, no attempt has been made to study their conditional heteroskedasticity that is often observed in economic and financial data. To address this gap, we propose a novel matrix generalized autoregressive conditional heteroskedasticity (GARCH) model to capture the dynamics of conditional row and column covariance matrices of matrix time series. The key innovation of the matrix GARCH model is the use of a univariate GARCH specification for the trace of conditional row or column covariance matrix, which allows for the identification of conditional row and column covariance matrices. Moreover, we introduce a quasi maximum likelihood estimator (QMLE) for model estimation and develop a portmanteau test for model diagnostic checking. Simulation studies are conducted to assess the finite-sample performance of the QMLE and portmanteau test. To handle large dimensional matrix time series, we also propose a matrix factor GARCH model. Finally, we demonstrate the superiority of the matrix GARCH and matrix factor GARCH models over existing multivariate GARCH-type models in volatility forecasting and portfolio allocations using three applications on credit default swap prices, global stock sector indices, and future prices

Research on The Offensive Characteristics of La Liga Team Based on Social Network Analysis

To explore the difference of social network parameters between the network of passing before scoring and the network of passing before missing the goal, and to explore the correlation between social network parameters and team performance, this paper establishes the offensive pass network of 20 teams in the La Liga from 2017 to 2018, and 11 social network parameters are calculated. The Pearson correlation test is used to explore the linear correlation between 11 social network parameters and team performance. The results show that the linear correlation between the network parameters of passing before scoring and team performance is stronger than the network parameters of passing before missing the goal. According to the results, we can provide reliable and effective information to the football coaches to help improve the performance of football matches