Trust in China: A Cross-Regional Analysis

Using the cross-regional data, this paper shows that trust has a strong effect on uneven development of economy in China. As is discovered in many studies, it is found that trust affects the growth of economy, size distribution of enterprise, and FDI inflow and so on. We also find that cross-regional differences of trust in China are reflections of the regional diversities of education, marketization of economies, urbanization, population density and transportation facilities. Although not statistically significant, “too many officials” may damage social trust. The paper demonstrates that trust cannot simply be taken as a cultural heritage. The paper also argues that sustainability of further economic development of China much depends on how fast China can build trust-facilitating institution, and that the most fundamental institution for trust is the property right.Trust, Economic performance, Information Repeated game, Transaction

A fourth-order unfitted characteristic finite element method for solving the advection-diffusion equation on time-varying domains

We propose a fourth-order unfitted characteristic finite element method to solve the advection-diffusion equation on time-varying domains. Based on a characteristic-Galerkin formulation, our method combines the cubic MARS method for interface tracking, the fourth-order backward differentiation formula for temporal integration, and an unfitted finite element method for spatial discretization. Our convergence analysis includes errors of discretely representing the moving boundary, tracing boundary markers, and the spatial discretization and the temporal integration of the governing equation. Numerical experiments are performed on a rotating domain and a severely deformed domain to verify our theoretical results and to demonstrate the optimal convergence of the proposed method

Knowledge spillovers from FDI in the People's Republic of China: The role of educated labor in multinational enterprises

This paper employs a firm-level panel data set for a high-tech cluster in the People's Republic of China to examine knowledge spillovers from multinational enterprises (MNEs) to domestic firms, focusing on the role of MNEs' employment of educated workers. We find that knowledge within MNEs spills over to domestic firms in the same industry through MNEs' employment of workers with graduate-level or overseas education. We also find that Japanese MNEs contribute less to knowledge spillovers than United States MNEs. This is most likely due to the fact that Japanese MNEs in the People's Republic of China do not employ as much educated labor

MDFL: Multi-domain Diffusion-driven Feature Learning

High-dimensional images, known for their rich semantic information, are widely applied in remote sensing and other fields. The spatial information in these images reflects the object's texture features, while the spectral information reveals the potential spectral representations across different bands. Currently, the understanding of high-dimensional images remains limited to a single-domain perspective with performance degradation. Motivated by the masking texture effect observed in the human visual system, we present a multi-domain diffusion-driven feature learning network (MDFL) , a scheme to redefine the effective information domain that the model really focuses on. This method employs diffusion-based posterior sampling to explicitly consider joint information interactions between the high-dimensional manifold structures in the spectral, spatial, and frequency domains, thereby eliminating the influence of masking texture effects in visual models. Additionally, we introduce a feature reuse mechanism to gather deep and raw features of high-dimensional data. We demonstrate that MDFL significantly improves the feature extraction performance of high-dimensional data, thereby providing a powerful aid for revealing the intrinsic patterns and structures of such data. The experimental results on three multi-modal remote sensing datasets show that MDFL reaches an average overall accuracy of 98.25%, outperforming various state-of-the-art baseline schemes. The code will be released, contributing to the computer vision community

The Effects of Corporate Governance on the Innovation Performance of Chinese SMEs

We investigate the degree to which corporate governance and ownership affects the innovation performance of firms in China with a particular focus on privately owned small and medium enterprises (SMEs). We hypothesize that (1) board-related governance measures will enhance innovation because they improve monitoring and provide access to necessary resources; (2) ownership concentration initially facilitates innovation because large shareholders are more likely to commit to the long-term nature of innovation, and have the incentive to monitor managers whose time horizon may be shorter; however we argue that these effects weaken as large shareholders becomes entrenched at higher levels of concentration; and (3) hiring an external CEO will enhance innovation both by ensuring professional management of the company, and by alleviating the entrenchment possibilities associated with large shareholders. These hypotheses are tested using a unique sample of 370 mostly private and relatively small Chinese firms in Zhejiang province, for the period 2004 to 2006. The results suggest that for this sample, corporate governance and ownership affect innovation activity when measured by patenting activity, but not when measured by new product sales

A uniform preconditioner for a Newton algorithm for total-variation minimization and minimum-surface problems

Solution methods for the nonlinear partial differential equation of the Rudin-Osher-Fatemi (ROF) and minimum-surface models are fundamental for many modern applications. Many efficient algorithms have been proposed. First order methods are common. They are popular due to their simplicity and easy implementation. Some second order Newton-type iterative methods have been proposed like Chan-Golub-Mulet method. In this paper, we propose a new Newton-Krylov solver for primal-dual finite element discretization of the ROF model. The method is so simple that we just need to use some diagonal preconditioners during the iterations. Theoretically, the proposed preconditioners are further proved to be robust and optimal with respect to the mesh size, the penalization parameter, the regularization parameter, and the iterative step, essentially it is a parameter independent preconditioner. We first discretize the primal-dual system by using mixed finite element methods, and then linearize the discrete system by Newton\textquoteright s method. Exploiting the well-posedness of the linearized problem on appropriate Sobolev spaces equipped with proper norms, we propose block diagonal preconditioners for the corresponding system solved with the minimum residual method. Numerical results are presented to support the theoretical results.Comment: 22 pages, 24 figure

Finite-time lag projective synchronization of delayed fractional-order quaternion-valued neural networks with parameter uncertainties

This paper discusses a class issue of finite-time lag projective synchronization (FTLPS) of delayed fractional-order quaternion-valued neural networks (FOQVNNs) with parameter uncertainties, which is solved by a non-decomposition method. Firstly, a new delayed FOQVNNs model with uncertain parameters is designed. Secondly, two types of feedback controller and adaptive controller without sign functions are designed in the quaternion domain. Based on the Lyapunov analysis method, the non-decomposition method is applied to replace the decomposition method that requires complex calculations, combined with some quaternion inequality techniques, to accurately estimate the settling time of FTLPS. Finally, the correctness of the obtained theoretical results is testified by a numerical simulation example

Trust in China: A Cross-Regional Analysis

Using the cross-regional data, this paper shows that trust has a strong effect on uneven development of economy in China. As is discovered in many studies, it is found that trust affects the growth of economy, size distribution of enterprise, and FDI inflow and so on. We also find that cross-regional differences of trust in China are reflections of the regional diversities of education, marketization of economies, urbanization, population density and transportation facilities. Although not statistically significant, “too many officials” may damage social trust. The paper demonstrates that trust cannot simply be taken as a cultural heritage. The paper also argues that sustainability of further economic development of China much depends on how fast China can build trust-facilitating institution, and that the most fundamental institution for trust is the property right.http://deepblue.lib.umich.edu/bitstream/2027.42/39972/3/wp586.pd