Why Are Saving Rates So High in China?

In this paper, we define "The Chinese Saving Puzzle" as the persistently high national saving rate at 34-53 percent of gross domestic product (GDP) in the past three decades and a surge in the saving rate by 11 percentage points from 2000-2008. Using data from the Flow of Funds Accounts (FFA) and Urban Household Surveys (UHS) supplemented by the findings from existing studies, we analyze the sources and causes of China's high and rising saving rates in the government, corporate, and household sectors. Although the causes of China's high saving are complex, we suggest that the evolving economic, demographic, and policy trends in the internal and external environments of the Chinese economy will likely lead to a decline in national saving in the foreseeable future.demographic structure, aggregate saving, international comparison, household behavior, China

Wave breaking in the unidirectional non-local wave model

In this paper we study wave breaking in the unidirectional non-local wave model describing the motion of a collision-free plasma in a magnetic field. By analyzing the monotonicity and continuity properties of a system of the Riccati-type differential inequalities involving the extremal slopes of flows, we show a new sufficient condition on the initial data to exhibit wave breaking. Moreover, the estimates of life span and wave breaking rate are derived

Lyapunov Exponents and Phase Transitions of Born-Infeld AdS Black Holes

In this paper, we characterize the phase transitons of Born-Infeld AdS black holes in terms of Lyapunov exponents. We calculate the Lyapunov exponents for both null and timelike geodesics. It is found that black hole phase transitions can be described by multiple-valued Lyapunov exponents. And its phase diagram can be characterized by Lyapunov exponents and Hawking temperature. Besides, the change of Lyapunov exponents can be considered as order parameter, and exists a critical exponent $1/2$ near critical point.Comment: 22 pages, 21 figure

A Real-Time and Adaptive-Learning Malware Detection Method Based on API-Pair Graph

The detection of malware have developed for many years, and the appearance of new machine learning and deep learning techniques have improved the effect of detectors. However, most of current researches have focused on the general features of malware and ignored the development of the malware themselves, so that the features could be useless with the time passed as well as the advance of malware techniques. Besides, the detection methods based on machine learning are mainly static detection and analysis, while the study of real-time detection of malware is relatively rare. In this article, we proposed a new model that could detect malware real-time in principle and learn new features adaptively. Firstly, a new data structure of API-Pair was adopted, and the constructed data was trained with Maximum Entropy model, which could satisfy the goal of weighting and adaptive learning. Then a clustering was practised to filter relatively unrelated and confusing features. Moreover, a detector based on Lont Short Term Memory Network (LSTM) was devised to achieve the goal of real-time detection. Finally, a series of experiments were designed to verify our method. The experimental results showed that our model could obtain the highest accuracy of 99.07% in general tests and keep the accuracies above 97% with the development of malware; the results also proved the feasibility of our model in real-time detection through the simulation experiment, and robustness against a typical adversarial attack