From the perspective of supply chain finance empowered by blockchain technology,this paper proposes a new theoretical model of bank run embedding a two-echelon supply chain.Through theoretical derivation and numerical examples,we explain the endogenous mechanism of supply chain finance and the application of blockchain technology affecting bank run risk.Our model demonstrates that supply chain finance and blockchain technology affect bank run risk through banks'loan screening efforts.Blockchain-enabled supply chain finance has an asymmetrical effect on banks'loan screening efforts,which leads to a similar effect on alleviating informa-tion asymmetry between banks and enterprises.Furthermore,there may be an inher-ent conflict between the role of blockchain-enabled supply chain finance in mitigating credit rationing for SMEs and maintaining financial stability.When blockchain tech-nology alleviates the information asymmetry between banks and enterprises,and the probability of high-quality non-core enterprises is at a high level,applying blockchain to supply chain finance will improve the credit availability of non-core enterprises and lead to a higher risk of bank runs.For this reason,the regulatory authorities can resolve this conflict by appropriately tightening capital regulatory constraints.
In this paper, the problem of extended dissipative sliding mode control for discrete-time nonlinear singular systems are addressed. First, the T-S fuzzy polynomial singular systems are constructed to represent nonlinear singular systems with parameter uncertainties. A different delay-dependent sliding mode surface is built based on the dynamic event-triggered mechanism. Then, the sliding mode control law is obtained, which guarantees the reachability of the closed-loop system. The admissibility with extended dissipativity is established by SOS. Finally, a numerical example is given to demonstrate the correctness of the proposed strategy.
Multi-task learning (MTL) improves the performance achieved on each task by exploiting the relevant information between tasks. At present, most of the mainstream deep MTL models are based on hard parameter sharing mechanisms, which can reduce the risk of model overfitting. However, negative knowledge transfer may occur, which hinders the performance improvement achieved for each task. In this paper, for situations when multiple tasks are jointly trained, we propose the adaptive hard parameter sharing method. On the basis of the adaptive hard parameter sharing method, the number of nodes in the network is dynamically updated by setting a continuous gradient difference-based sign threshold and a warm-up training iteration threshold through the relationships between the parameters and the loss function. After each task fully utilizes the shared information, adaptive nodes are used to further optimize each task, reducing the impact of negative migration. By using simulation studies and instance analyses, we demonstrate theoretical proof that the performance of the proposed method is better than that of the competing method.
Zonoid depth, as a well-known ordering tool, has been widely used in multivariate analysis. However, since its depth value vanishes outside the convex hull of the data cloud, it suffers from the so-called `outside problem', which consequently hinders its many practical applications, e.g., classification. In this note, we propose a new class of \emph{$L_q$-norm zonoid depths}, which has no such problem. Examples are also provided to illustrate their contours.
Singular two-point boundary value problems arise in a variety of applied mathematics and physics.It is a classical problem and many researchers have done a lot of research work on this issue.In this paper,we apply cubic B-spline to explore the numerical solutions of a class of singular two-point boundary value problems.The paper method is primarily based on the super convergence in approximating second-order derivative values at the knots by the combination of second-order derivative values of cubic B-spline.The paper proposed cubic B-spline possesses super con-vergence in approximating the first-order derivative/second-order derivative of given function and thus the approximation order of our method reaches fourth order.Some numerical experiments are provided to demonstrate the effectiveness of our method compared to the other existing methods.
