Regional Finance and Regional Disparities in China

China’s growth has been spectacularly high and persistent over the last few decades. However, there have been regular expressions of concern about the uneven distribution of the benefits across regions and, at times, it has been asserted that the regional distribution of available investment funds has played an important role – national financial institutions (mainly state-owned banks) have redirected deposits from the inland to loans to large institutions in the more prosperous coastal regions. At the same time, smaller regionally-focussed institutions are likely to improve the distribution of funds. We use a panel data set disaggregated by province for the years 1986 to 2004 to test these propositions. We employ recent panel unit roots and cointegration tests using data for state-owned bank loans as well as loans by rural credit cooperatives. We find that financial disparities are related to output disparities, that this relationship is positive, that it is stronger for rural credit cooperatives than for state-owned banks and that this relationship is causal in both the long and short runs. A reduction in financial disparities can be expected to lead a narrowing of output disparities in the short run and in the long run with the effect being larger for rural credit cooperatives than for state-owned commercial banks.regional disparities, panel econometrics, regional finance, China

A machine-learning-based tool for last closed magnetic flux surface reconstruction on tokamak

Nuclear fusion power created by tokamak devices holds one of the most promising ways as a sustainable source of clean energy. One main challenge research field of tokamak is to predict the last closed magnetic flux surface (LCFS) determined by the interaction of the actuator coils and the internal tokamak plasma. This work requires high-dimensional, high-frequency, high-fidelity, real-time tools, further complicated by the wide range of actuator coils input interact with internal tokamak plasma states. In this work, we present a new machine learning model for reconstructing the LCFS from the Experimental Advanced Superconducting Tokamak (EAST) that learns automatically from the experimental data of EAST. This architecture can check the control strategy design and integrate it with the tokamak control system for real-time magnetic prediction. In the real-time modeling test, our approach achieves over 99% average similarity in LCFS reconstruction of the entire discharge process. In the offline magnetic reconstruction, our approach reaches over 93% average similarity

Research of Simulation in Character Animation Based on Physics Engine

Computer 3D character animation essentially is a product, which is combined with computer graphics and robotics, physics, mathematics, and the arts. It is based on computer hardware and graphics algorithms and related sciences rapidly developed new technologies. At present, the mainstream character animation technology is based on the artificial production of key technologies and capture frames based on the motion capture device technology. 3D character animation is widely used not only in the production of film, animation, and other commercial areas but also in virtual reality, computer-aided education, flight simulation, engineering simulation, military simulation, and other fields. In this paper, we try to study physics based character animation to solve these problems such as poor real-time interaction that appears in the character, low utilization rate, and complex production. The paper deeply studied the kinematics, dynamics technology, and production technology based on the motion data. At the same time, it analyzed ODE, PhysX, Bullet, and other variety of mainstream physics engines and studied OBB hierarchy bounding box tree, AABB hierarchical tree, and other collision detection algorithms. Finally, character animation based on ODE is implemented, which is simulation of the motion and collision process of a tricycle

Domain-Guided Conditional Diffusion Model for Unsupervised Domain Adaptation

Limited transferability hinders the performance of deep learning models when applied to new application scenarios. Recently, Unsupervised Domain Adaptation (UDA) has achieved significant progress in addressing this issue via learning domain-invariant features. However, the performance of existing UDA methods is constrained by the large domain shift and limited target domain data. To alleviate these issues, we propose DomAin-guided Conditional Diffusion Model (DACDM) to generate high-fidelity and diversity samples for the target domain. In the proposed DACDM, by introducing class information, the labels of generated samples can be controlled, and a domain classifier is further introduced in DACDM to guide the generated samples for the target domain. The generated samples help existing UDA methods transfer from the source domain to the target domain more easily, thus improving the transfer performance. Extensive experiments on various benchmarks demonstrate that DACDM brings a large improvement to the performance of existing UDA methods.Comment: Work in progres

Mega-lake formation in the eastern Hetao Basin, China, during marine isotope stages 7 and 5: A comparison of quartz and feldspar luminescence dating

Paleo-lacustrine sediments indicate that a mega-lake developed in the Hetao Basin (HTB), suggesting dramatic changes in the geomorphology and surface process of the Yellow River and surrounding regions. However, the formation time of the mega Hetao paleo-lake (HTPL) in the HTB remains unclear. In this study, a set of 7.6-m-thick lacustrine sediments in the middle of the HTB is dated by both quartz optically stimulated luminescence (OSL) and K-feldspar (KF) post-infrared infrared stimulated luminescence (p-IR IRSL, pIRIR). The results are as follows. 1) The quartz OSL ages do not increase with depth as the KF pIRIR ages displayed but are saturated at ca. 80–100 ka with the consequence of age underestimation. The influence of signal saturation may occur below 96 Gy (ca. 32 ka) in this region. Thus, the KF pIRIR ages are chosen for geomorphological evolution reconstruction. 2) These ages suggest the level of the HTPL exceeded 1033 m asl twice—at the early stages of MIS 7 (∼255–245 ka) and MIS 5 (∼130–120 ka). However, the exact scale of the HTPL and its relationship with the Jilantai paleo-lake is unclear. 3) This chronology suggests strong linkage between periodic formation of the HTPL and glacial-scale climatic change. We therefore propose an alternative hypothesis to interpret these processes: the Hobq dunefield expanded eastward to dam the narrow Jin-Shaan Gorge during the glacial periods when the flow of the Yellow River was weak. Then, in the beginning of the interglacial periods, the basin was filled by abruptly resumed runoff in response to increased glacial meltwater and precipitation on the northeastern Tibetan Plateau. Once the lake spilt out, the loose dune dam was quickly breached

The Influence of an EPS Concrete Buffer Layer Thickness on Debris Dams Impacted by Massive Stones in the Debris Flow

The failure of debris dams impacted by the massive stones in a debris flow represents a difficult design problem. Reasonable materials selection and structural design can effectively improve the resistance impact performance of debris dams. Based on the cushioning properties of expanded polystyrene (EPS) concrete, EPS concrete as a buffer layer poured on the surface of a rigid debris dam was proposed. A three-dimensional numerical calculation model of an EPS concrete buffer layer/rigid debris dam was established. The single-factor theory revealed change rules for the thickness of the buffer layer concerning the maximal impact force of the rigid debris dam surface through numerical simulation. Moreover, the impact force-time/history curves under different calculation conditions for the rigid debris dam surface were compared. Simulation results showed that the EPS concrete buffer layer can not only effectively extend the impact time of massive stones affecting the debris dam but also reduce the impact force of the rigid debris dam caused by massive stones in the debris flow. The research results provide theoretical guidance for transferring the energy of the massive stone impact, creating a structural design and optimizing debris dams

Spin excitations and the Fermi surface of superconducting FeS

High-temperature superconductivity occurs near antiferromagnetic instabilities and nematic state. Debate remains on the origin of nematic order in FeSe and its relation with superconductivity. Here, we use transport, neutron scatter- ing and Fermi surface measurements to demonstrate that hydro-thermo grown superconducting FeS, an isostructure of FeSe, is a tetragonal paramagnet without nematic order and with a quasiparticle mass significantly reduced from that of FeSe. Only stripe-type spin excitation is observed up to 100 meV. No direct coupling between spin excitation and superconductivity in FeS is found, suggesting that FeS is less correlated and the nematic order in FeSe is due to competing checkerboard and stripe spin fluctuations.Comment: 11 pages, 4 page