The Impacts of China's Shadow Banking Credit Creation on the Effectiveness of Monetary Policy

This paper researches the impact that shadow banking in China has upon credit creation and the potential effectiveness of monetary policy. Using a credit creation model, we derive the effect that shadow banking has upon the money multiplier and the money supply. The model shows that shadow banking can change the money multiplier, potentially increasing it during an expansion and decreasing it during a contraction. Introducing shadow banking in a CC-LM model results in a shift of the CC and LM curves resulting in a higher equilibrium output. A vector autoregressive model is used to empirically estimate the impact of shadow banking deposits' growth rate on the growth rates of the broad money supply, GDP, and the CPI. The results show that shadow banking's credit creation function in China has a pro-cyclical characteristic, potentially reducing the money supply's controllability and increasing the difficulty in effectively regulating monetary policy. This paper introduces shadow banking into the currency creation process of traditional commercial banks, accounting for the reserve requirement ratio, the excess reserve ratio, the shadow bank leakage rate, and the reserved deduction rate. Future research can determine whether coordinating monetary policy and leverage ratio regulation mitigates the impact of shadow banking. Another area of research is how the shadow banking of non-financial companies affect monetary policy

Characteristics and Provenance Implications of Rare Earth Elements and Nd Isotope in PM2.5 in a Coastal City of Southeastern China

The source apportionment of fine particulate matters, especially PM2.5, has drawn great attention worldwide. Since rare earth elements (REEs) and Nd isotopes can serve as source tracers, in this study, the characteristics and provenance implications of REEs and Nd isotopes in PM2.5 of four seasons in Xiamen city, China, were investigated. The range of the ratios of ΣREE to PM2.5 was 1.04 × 10−5 to 8.06 × 10−4, and the mean concentration of REEs in PM2.5 were in the order of spring > autumn > winter > summer. According to the geoaccumulation index (Igeo), spring was the season in which anthropogenic sources had the greatest impact on the REEs in PM2.5. The chondrite-normalized REE distribution patterns exhibited light rare earth elements (LREEs, including La, Ce, Pr, Nd, Pm, Sm and Eu) enrichment and a flat heavy rare earth elements (HREEs, including Gd, Tb, Dy, Ho, Er, Tm, Yb and Lu) pattern. Significant negative Eu anomalies and no significant Ce anomalies were observed in the PM2.5. The results of La-Ce-Sm ternary plots indicated that the REEs in the PM2.5 might be related to both natural and anthropogenic sources. Combined with the Nd isotope, the 143Nd/144Nd versus Ce/Ce* plot further illustrated that the REEs in the PM2.5 seemed to mostly originate from multiple potential sources, in which vehicle exhaust emissions, coal burning and cement dust made a great contribution to REEs in PM2.5

Automated Model Hardening with Reinforcement Learning for On-Orbit Object Detectors with Convolutional Neural Networks

On-orbit object detection has received extensive attention in the field of artificial intelligence (AI) in space research. Deep-learning-based object-detection algorithms are often computationally intensive and rely on high-performance devices to run. However, those devices usually lack space-qualified versions, and they can hardly meet the reliability requirement if directly deployed on a satellite platform, due to software errors induced by the space environment. In this paper, we evaluated the impact of space-environment-induced software errors on object-detection algorithms through large-scale fault injection tests. Aside from silent data corruption (SDC), we propose an extended criterial SDC-0.1 to better quantify the effect of the transient faults on the object-detection algorithms. Considering that a bit-flip error could cause severe detection result corruption in many cases, we propose a novel automated model hardening with reinforcement learning (AMHR) framework to solve this problem. AMHR searches for error-sensitive kernels in a convolutional neural network (CNN) through trial and error with a deep deterministic policy gradient (DDPG) agent and has fine-grained modular-level redundancy to increase the fault tolerance of the CNN-based object detectors. Compared to other selective hardening methods, AMHR achieved the lowest SDC-0.1 rates for various detectors and could tremendously improve the mean average precision (mAP) of the SSD detector by 28.8 in the presence of multiple errors

Design of High-Performance and General-Purpose Satellite Management Unit Based on Rad-Hard Multi-Core SoCand Linux

Since deep space exploration tasks, such as space gravitational wave detection, put forward increasingly higher requirements for the satellite platform, the scale and complexity of the satellite management unit (SMU) software are also increasing, and the trend of intelligentization is showing. It is difficult for the traditional SMU based on single-core system on chip (SoC) to meet the various requirements brought by the above trends. This paper presents a high-performance general-purpose SMU design. Based on rad-hard multi-core SoC, we configure and tailor Linux, and design an SMU software architecture with three modes. It has the characteristics of high performance, high reliability, general purpose and scalability, which can meet the needs of the SMU of future complex satellites. Finally, through the application experiment in the background of the space gravitational wave detection project, the performance and application prospect of our proposed SMU are demonstrated

GNSS Signal Compression Acquisition Algorithm Based on Sensing Matrix Optimization

Due to the sparsity of GNSS signal in the correlation domain, compressed sensing theory is considered to be a promising technology for GNSS signal acquisition. However, the detection probability of the traditional compression acquisition algorithm is low under low signal-to-noise ratio (SNR) conditions. This paper proposes a GNSS compression acquisition algorithm based on sensing matrix optimization. The Frobenius norm of the difference between Gram matrix and an approximate equiangular tight frame (ETF) matrix is taken as the objective function, and the modified conjugate gradient method is adopted to reduce the mutual coherence between the measurement matrix and the sparse basis. Theoretical analysis and simulation results show that the proposed algorithm can significantly improve the detection probability compared with the existing compression acquisition algorithms under the same SNR

GNSS Signal Acquisition Algorithm Based on Two-Stage Compression of Code-Frequency Domain

The recently-emerging compressed sensing (CS) theory makes GNSS signal processing at a sub-Nyquist rate possible if it has a sparse representation in certain domain. The previously proposed code-domain compression acquisition algorithms have high computational complexity and low acquisition accuracy under high dynamic conditions. In this paper, a GNSS signal acquisition algorithm based on two-stage compression of the code-frequency domain is proposed. The algorithm maps the incoming signal of the same interval to multiple carrier frequency bins and overlaps the mapped signal that belongs to the same code phase. Meanwhile, the code domain compression is introduced to the preprocessed signal, replacing circular correlation with compressed reconstruction to obtain Doppler frequency and code phase. Theoretical analyses and simulation results show that the proposed algorithm can improve the frequency search accuracy and reduce the computational complexity by about 50% in high dynamics

SegDetector: A Deep Learning Model for Detecting Small and Overlapping Damaged Buildings in Satellite Images

Buildings bear much of the damage from natural disasters, and determining the extent of this damage is of great importance to post-disaster emergency relief. The application of deep learning to satellite remote sensing imagery has become more and more mature in monitoring natural disasters, but there are problems such as the small pixel scale of targets and overlapping targets that hinder the effectiveness of the model. Based on the SegFormer semantic segmentation model, this study proposes the SegDetector model for difficult detection of small-scale targets and overlapping targets in target detection tasks. By changing the calculation method of the loss function, the detection of overlapping samples is improved and the time-consuming non-maximum-suppression (NMS) algorithm is discarded, and the horizontal and rotational detection of buildings can be easily and conveniently implemented. In order to verify the effectiveness of the SegDetector model, the xBD dataset, which is a dataset for assessing building damage from satellite imagery, was transformed and tested. The experiment results show that the SegDetector model outperforms the state-of-the-art (SOTA) models such as you-only-look-once (YOLOv3, v4, v5) in the xBD dataset with F1: 0.71, Precision: 0.63, and Recall: 0.81. At the same time, the SegDetector model has a small number of parameters and fast detection capability, making it more practical for deployment