Zobrazeno 1 - 10
of 25 181
pro vyhledávání: '"Chen,Yuan"'
With the global economic integration and the high interconnection of financial markets, financial institutions are facing unprecedented challenges, especially liquidity risk. This paper proposes a liquidity coverage ratio (LCR) prediction model based
Externí odkaz:
http://arxiv.org/abs/2410.19211
This study introduces a training-free conditional diffusion model for learning unknown stochastic differential equations (SDEs) using data. The proposed approach addresses key challenges in computational efficiency and accuracy for modeling SDEs by u
Externí odkaz:
http://arxiv.org/abs/2410.03108
We present a new Deep Neural Network (DNN) architecture capable of approximating functions up to machine accuracy. Termed Chebyshev Feature Neural Network (CFNN), the new structure employs Chebyshev functions with learnable frequencies as the first h
Externí odkaz:
http://arxiv.org/abs/2409.19135
This paper takes the graph neural network as the technical framework, integrates the intrinsic connections between enterprise financial indicators, and proposes a model for enterprise credit risk assessment. The main research work includes: Firstly,
Externí odkaz:
http://arxiv.org/abs/2409.17909
Autor:
Li, Yuwei, Chen, Yuan, Ji, Shouling, Zhang, Xuhong, Yan, Guanglu, Liu, Alex X., Wu, Chunming, Pan, Zulie, Lin, Peng
Publikováno v:
IEEE Transactions on Dependable and Secure Computing, vol. 21, no. 1, pp. 168-185, Jan.-Feb. 2024
gVisor is a Google-published application-level kernel for containers. As gVisor is lightweight and has sound isolation, it has been widely used in many IT enterprises \cite{Stripe, DigitalOcean, Cloundflare}. When a new vulnerability of the upstream
Externí odkaz:
http://arxiv.org/abs/2409.13139
We consider a state-space model (SSM) parametrized by some parameter $\theta$ and aim at performing joint parameter and state inference. A popular idea to carry out this task is to replace $\theta$ by a Markov chain $(\theta_t)_{t\geq 0}$ and then to
Externí odkaz:
http://arxiv.org/abs/2409.08928
Autor:
Chen, Yuan, Xiu, Dongbin
We present a numerical method for learning the dynamics of slow components of unknown multiscale stochastic dynamical systems. While the governing equations of the systems are unknown, bursts of observation data of the slow variables are available. B
Externí odkaz:
http://arxiv.org/abs/2408.14821
Publikováno v:
Phys. Rev. A 110, 043321 (2024)
We develop a functional integral formulation for a homogeneous bosonic atomic-molecular mixture with Feshbach coupling in three-spatial dimensions. Taking phase stability into account, we establish a rich ground-state phase diagram, which features th
Externí odkaz:
http://arxiv.org/abs/2407.07422
Autor:
Tan, Zhen-Yu, Chen, Ji-Pei, Shi, Yu-Ke, Chen, Yuan, Qin, Ming-Hui, Gao, Xing-Sen, Liu, Jun-Ming
Magnetic skyrmions emerge as promising quasi-particles for encoding information in nextgeneration spintronic devices. Their innate flexibility in shape is essential for the applications although they were often ideally treated as rigid particles. In
Externí odkaz:
http://arxiv.org/abs/2407.07418
Autor:
Chen, Yuan, Xiu, Dongbin
We present a numerical method for learning unknown nonautonomous stochastic dynamical system, i.e., stochastic system subject to time dependent excitation or control signals. Our basic assumption is that the governing equations for the stochastic sys
Externí odkaz:
http://arxiv.org/abs/2406.15747