Income Disparity and Economic Growth: Evidence from China

This paper carries out a pilot empirical study on how income inequality affects growth and the macro economy by means of incorporating panel data information into a macro-econometric model. China is used as the pilot field. Provincial urban and rural household data are used to construct inequality measures, which are then used to augment household consumption equations in the ADB China model. Model simulations are performed to study the effect of inequality on GDP growth and its sectoral components. Results show that inequality is a robust explanatory variable of consumption and that the way inequality develops over time carries certain negative consequences on GDP and sectoral growth.Income inequality, Growth, Econometric model, China

A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images

In line with the development of Industry 4.0, surface defect detection/anomaly detection becomes a topical subject in the industry field. Improving efficiency as well as saving labor costs has steadily become a matter of great concern in practice, where deep learning-based algorithms perform better than traditional vision inspection methods in recent years. While existing deep learning-based algorithms are biased towards supervised learning, which not only necessitates a huge amount of labeled data and human labor, but also brings about inefficiency and limitations. In contrast, recent research shows that unsupervised learning has great potential in tackling the above disadvantages for visual industrial anomaly detection. In this survey, we summarize current challenges and provide a thorough overview of recently proposed unsupervised algorithms for visual industrial anomaly detection covering five categories, whose innovation points and frameworks are described in detail. Meanwhile, publicly available datasets for industrial anomaly detection are introduced. By comparing different classes of methods, the advantages and disadvantages of anomaly detection algorithms are summarized. Based on the current research framework, we point out the core issue that remains to be resolved and provide further improvement directions. Meanwhile, based on the latest technological trends, we offer insights into future research directions. It is expected to assist both the research community and industry in developing a broader and cross-domain perspective

Study of the Counter Anions in the Host-Guest Chemistry of Cucurbit[8]uril and 1-Ethyl-1′-benzyl-4,4′-bipyridinium

A series of 1-ethyl-1′-benzyl-4,4′-bipyridinium compounds with different counter anions (BEV-X2, where the X is Cl, Br, I, PF6, ClO4) were synthesized. By using of NMR, MS, electrochemistry, Na2S2O4-induced redox chemistry, and UV-Vis, the role of the different counter anions in the host-guest chemistry of cucurbit[8]uril (CB[8]) was studied for the first time. The result demonstrated that BEV-X2 can form a 1 : 1 host-guest complex with CB[8] in water. Theoretical calculation further suggested that the viologen region was threaded through the cavity of CB[8], while the corresponding counter anions were located outside the cavity. Some difference can be observed on UV-Vis titration and Na2S2O4-induced redox chemistry, which showed that the counter anions have some effect on the host-guest chemistry. All these provide new insights into CB[8] host-guest system

A Macroeconometric Model of the Chinese Economy

This paper describes a quarterly macroeconometric model of the Chinese economy. The model comprises household consumption, investment, government, trade, production, prices, money, and employment blocks. The equilibrium-correction form is used for all the behavioral equations and the general→simple dynamic specification approach is adopted. Great efforts have been made to achieve the best possible blend of standard long-run theories, country-specific institutional features and short-run dynamics in data. The tracking performance of the model is evaluated. Forecasting and empirical investigation of a number of topical macroeconomic issues utilizing model simulations have shown the model to be immensely useful.Macroeconometric model, Chinese economy, Forecasts, Simulations

Application-Driven AI Paradigm for Human Action Recognition

Human action recognition in computer vision has been widely studied in recent years. However, most algorithms consider only certain action specially with even high computational cost. That is not suitable for practical applications with multiple actions to be identified with low computational cost. To meet various application scenarios, this paper presents a unified human action recognition framework composed of two modules, i.e., multi-form human detection and corresponding action classification. Among them, an open-source dataset is constructed to train a multi-form human detection model that distinguishes a human being's whole body, upper body or part body, and the followed action classification model is adopted to recognize such action as falling, sleeping or on-duty, etc. Some experimental results show that the unified framework is effective for various application scenarios. It is expected to be a new application-driven AI paradigm for human action recognition