Partial Information Differential Games for Mean-Field SDEs

This paper is concerned with non-zero sum differential games of mean-field stochastic differential equations with partial information and convex control domain. First, applying the classical convex variations, we obtain stochastic maximum principle for Nash equilibrium points. Subsequently, under additional assumptions, verification theorem for Nash equilibrium points is also derived. Finally, as an application, a linear quadratic example is discussed. The unique Nash equilibrium point is represented in a feedback form of not only the optimal filtering but also expected value of the system state, throughout the solutions of the Riccati equations.Comment: 7 page

A PRELIMINARY STUDY OF ROOT-TO-SHOOT REGENERATION BY ECTOPIC EXPRESSION OF WUS IN ARABIDOPSIS THALIANA ROOTS

Master'sMASTER OF SCIENC

Optimal Dividend Payments for the Piecewise-Deterministic Poisson Risk Model

This paper considers the optimal dividend payment problem in piecewise-deterministic compound Poisson risk models. The objective is to maximize the expected discounted dividend payout up to the time of ruin. We provide a comparative study in this general framework of both restricted and unrestricted payment schemes, which were only previously treated separately in certain special cases of risk models in the literature. In the case of restricted payment scheme, the value function is shown to be a classical solution of the corresponding HJB equation, which in turn leads to an optimal restricted payment policy known as the threshold strategy. In the case of unrestricted payment scheme, by solving the associated integro-differential quasi-variational inequality, we obtain the value function as well as an optimal unrestricted dividend payment scheme known as the barrier strategy. When claim sizes are exponentially distributed, we provide easily verifiable conditions under which the threshold and barrier strategies are optimal restricted and unrestricted dividend payment policies, respectively. The main results are illustrated with several examples, including a new example concerning regressive growth rates.Comment: Key Words: Piecewise-deterministic compound Poisson model, optimal stochastic control, HJB equation, quasi-variational inequality, threshold strategy, barrier strateg

Underwater dual manipulators-Part II: Kinematics analysis and numerical simulation

1104-1112This paper introduces dual-arm underwater manipulators mounted on an autonomous underwater vehicle (AUV), which can accomplish the underwater handling task. Firstly, the mechanical structure of the dual-arm system is briefly introduced, wherein each 4-DOF manipulator has an additional grasping function. In addition, the kinematics model of the manipulator is derived by using the improved D-H method. Secondly, the working space of the underwater dual-arm system is analyzed, which is obtained by using Monte Carlo method. The cubic polynomial interpolation and the five polynomial interpolation trajectory planning methods are compared in the joint space. Finally, with the help of the Robotics Toolbox software, the numerical test is conducted to verify the functions of the underwater dual-arm manipulator system

Numerical study of the fluid fracturing mechanism of granite at the mineral grain scale

Hydraulic fracturing is an essential technique for reservoir stimulation in the process of deep energy exploitation. Granite is composed of different rock-forming minerals and exhibits obvious heterogeneity at the mesoscale, which affects the strength and deformation characteristics of rocks and controls the damage and failure processes. Therefore, in this paper, based on the discrete element fluid-solid coupling algorithm and multiple parallel bond-grain based model (Multi Pb-GBM), a numerical model of a granite hydraulic fracturing test is established to study the evolution of hydraulic fractures in crystalline granite under different ground stress conditions. The main conclusions are as follows. The crack propagation of hydraulic fractures in granite is determined by the in situ stress state, crystal size, and mineral distribution, and the ground stress is the main controlling factor. The final fracture mode affects the maximum principal stress and shear stress, and the generation of cracks changes the distribution of the stress field. The hydraulic fracturing initiation pressure decreases with decreasing crystal size. The influence of the crystal size on the crack inclination angle is mainly reflected in local areas, and the general trend of the fissure dip angle distribution is along the direction of the maximum in situ stress. This study not only has important theoretical significance for clarifying the propagation mechanism of hydraulic fractures but also provides a theoretical basis for deep reservoir reconstruction and energy extraction

Genetic algorithm based optimization for terahertz time-domain adaptive sampling

We propose a genetic algorithm (GA) based method to improve the sampling efficiency in THz time domain spectroscopy (THz-TDS). For a typical time domain THz signal, most information are contained in a short region of the pulse which needs to be densely sampled, while the other regions fluctuating around zero can be represented by fewer points. Based on this clustering feature of the THz signal, we can use much fewer sampling points and optimize the distribution by using a GA to achieve an accurate scanning in less time. Both reflection and transmission measurements were conducted to experimentally verify the performance. The measurement results show that the sampling time can be greatly reduced while maintaining very high accuracy both in the time-domain and frequency-domain compared with a high-resolution step scan. This method significantly improves the measurement efficiency. It can be easily adapted to most THz-TDS systems equipped with a mechanical delay stage for fast detection and THz imaging

Boosting Adversarial Attacks by Leveraging Decision Boundary Information

Due to the gap between a substitute model and a victim model, the gradient-based noise generated from a substitute model may have low transferability for a victim model since their gradients are different. Inspired by the fact that the decision boundaries of different models do not differ much, we conduct experiments and discover that the gradients of different models are more similar on the decision boundary than in the original position. Moreover, since the decision boundary in the vicinity of an input image is flat along most directions, we conjecture that the boundary gradients can help find an effective direction to cross the decision boundary of the victim models. Based on it, we propose a Boundary Fitting Attack to improve transferability. Specifically, we introduce a method to obtain a set of boundary points and leverage the gradient information of these points to update the adversarial examples. Notably, our method can be combined with existing gradient-based methods. Extensive experiments prove the effectiveness of our method, i.e., improving the success rate by 5.6% against normally trained CNNs and 14.9% against defense CNNs on average compared to state-of-the-art transfer-based attacks. Further we compare transformers with CNNs, the results indicate that transformers are more robust than CNNs. However, our method still outperforms existing methods when attacking transformers. Specifically, when using CNNs as substitute models, our method obtains an average attack success rate of 58.2%, which is 10.8% higher than other state-of-the-art transfer-based attacks