Jiangxi University of Finance and Economics

About: Jiangxi University of Finance and Economics is a education organization based out in Nanchang, China. It is known for research contribution in the topics: Fuzzy logic & China. The organization has 2865 authors who have published 3556 publications receiving 41567 citations.

Spatiotemporal differences and influencing factors of multiple cropping index in China during 1998–2012

Abstract: Raising the level of the multiple cropping index (MCI) plays a critical role in food production of China. Therefore, exploring the spatiotemporal differences and factors of the MCI in China is of important practical significance. This paper examines the trend of multiple cropping index (MCI) changes in China at the national, regional and provincial levels during 1998–2012. Based on the Theil index, this paper explores the spatiotemporal differences of the MCI in China. Additionally, a spatial econometric model is used to identify the determinants of the spatiotemporal differences of the MCI from a behavioral perspective. The results are summarized as follows: (1) From the national perspective, the MCI shows an increasing trend year by year, rising from 120.1% in 1998 to 134.26% in 2012; (2) at the regional level, the northeastern region is the fastest growing area in terms of MCI, whereas the central region is the slowest growing area. The central region has the highest MCI level, whereas the northeastern region is connected to the lowest MCI level; (3) according to the Theil index value, the differences in the MCI show a narrowing trend from 0.11 in 1998 to 0.03 in 2012, which is primarily attributed to the differences among the four regions; (4) the MCI shows differences among China’s 31 provinces, and the gap that it shows is great; and (5) the proportion of non-agricultural population has a significant negative effect on the MCI. However, the proportions of non-agricultural industry, agricultural policy, per capita operating arable land area and rural household per capita net income have a significant positive impact on the MCI. Therefore, the following policies are suggested to increase the level of China’s cultivated land MCI: transferring rural surplus labor, increasing the farmers’ income, accelerating the transfer of the use rights of arable land, developing the scale effect of land use, providing further support and benefits to farmers in less developed regions and major grain-producing areas, and strengthening the role of the national agricultural policy.

Spectral properties of the temporal evolution of brain network structure

Abstract: The temporal evolution properties of the brain network are crucial for complex brain processes. In this paper, we investigate the differences in the dynamic brain network during resting and visual stimulation states in a task-positive subnetwork, task-negative subnetwork, and whole-brain network. The dynamic brain network is first constructed from human functional magnetic resonance imaging data based on the sliding window method, and then the eigenvalues corresponding to the network are calculated. We use eigenvalue analysis to analyze the global properties of eigenvalues and the random matrix theory (RMT) method to measure the local properties. For global properties, the shifting of the eigenvalue distribution and the decrease in the largest eigenvalue are linked to visual stimulation in all networks. For local properties, the short-range correlation in eigenvalues as measured by the nearest neighbor spacing distribution is not always sensitive to visual stimulation. However, the long-range correlation in eigenvalues as evaluated by spectral rigidity and number variance not only predicts the universal behavior of the dynamic brain network but also suggests non-consistent changes in different networks. These results demonstrate that the dynamic brain network is more random for the task-positive subnetwork and whole-brain network under visual stimulation but is more regular for the task-negative subnetwork. Our findings provide deeper insight into the importance of spectral properties in the functional brain network, especially the incomparable role of RMT in revealing the intrinsic properties of complex systems.

Economic integration and stock market dynamic linkages: evidence in the context of Australia and Asia

Abstract: Countries are becoming economically integrated and it is contended that this will also lead to their financial markets becoming integrated. This contention is important since international financial market integration diminishes portfolio diversification benefits and creates contagion risk. We test this contention in this article in the context of the Australasian region. Australia and Asia have experienced very significant economic integration through a rapid growth in their bilateral trade. We utilize a battery of econometric techniques – cointegration, asymmetric generalized dynamic conditional correlations and panel regression models. As expected, we find that trade intensity significantly drives the interdependence between their stock markets in both the short run and the long run. Thus, given the ever increasing economic integration in this region, this finding implies that their stock markets face the risk of contagion, and that investors in these markets would also be confronted with the p...

Depth Estimation Using A Self-Supervised Network based on Cross-layer Feature Fusion and the Quadtree Constraint

Abstract: Depth estimation from a camera is an important task for 3D perception. Recently, without using the labeled ground truth of depth map, a self-supervised deep learning network can use relative pose to synthesize the target image from the reference image, and the photometric error between synthesized reference image and real one is used as self-supervisory signal. In this paper, we propose a novel self-supervised depth estimation network, which takes advantage of the quadtree constraint to optimize the depth estimation network. Based on the quadtree constraint, the photometric loss and depth loss of quadtree are proposed. In order to solve the problem that multiple depth values in repeated structures and uniform texture regions can cause relatively low photometric loss, we use quadtree-based photometric loss, which calculates the averaged photometric loss in quadtree blocks instead of the pixel-wise loss. For the problem of imbalanced depth distribution, we use quadtree depth loss, which constrains the depth inconsistency within quadtree blocks. The depth estimation network is composed of deep fusion module and cross-layer feature fusion module, which can better extract the feature information of RGB image and sparse keypoints depths, and makes full use of the detail information of the shallow feature map and the semantic information of the deep feature map to enrich the feature information extraction. Experimental results demonstrate that our method outperforms the state-of-the-art approaches of depth estimation.