Wei Zhao

Inverting kinetic energy impacts of high-speed meteoroids is an effective means to study the dynamics of celestial impact, thereby, evaluate the impact effects and hazards. This paper uses a Smoothed Particle Hydrodynamics method based Celestial Impact Dynamics (PySPH-CID) to simulate the ejecta and crater evolution process of meteorites colliding into Earth; and a Kernel Gaussian Mixture Ridge Regression (KGMRR) method is proposed to fit the one-to-many mapping between cause and effect of an impact with multiple sets of feasible solutions; a solution space is obtained using Gaussian Process (GP), for the formation conditions of craters after a small celestial body impacts. An inversion of colliding crater of Yilan in Heilongjiang Province, Northeast China, 46°23'03'' N and 129°18'40'' E, with 1.85km diameter, which located entirely on the cretaceous granite bedrock and preserved the remnants of two-thirds of the crater rim, was made. Assuming the impactor diameter is around 120m, preliminary experiment gives colliding cause vectors that impact velocities range between 17.8~15.0 km/s with 60.7°~66.6° obliquely incident, impactor density varying in scope of 2772~2815 kg/m3. This work provides a direct reference for effectively defending against the threat of foreign celestial bodies to the Earth, developing and transforming controlled impact technologies, as well as implementing precise and controllable impacts.

In the visual object tracking via unmanned aerial vehicle (UAV) the correlation filtering (CF) is one of the mainstream methods for the reason that optimizing filters by circulant samples facilitates the calculation. The prevalent discriminative CF (DCF) frameworks credited to a ridge regression model followed by various regularizations focus on regressing to a fixed Gaussian label, which maybe easily induce over-fitting. To these concerns, we integrate the hinge-squared loss (HSL) of structured SVM with the CF model so as to attain a dynamical label which regresses the difference between target and background samples and thus enhances the robustness in tracking. Moreover, we assume weighted channels to augment the discrimination of hinge-squared loss, thus indicating it has as good extensibility as the ridge regression in usual CF frameworks. The proposed method channel-weighted structured correlation filtering (CWSCF) has achieved excellent performance compared with other methods in mainstream UAV tracking benchmarks.

Multi-agent reinforcement learning (MARL) is a prevalent learning paradigm for solving stochastic games. In most MARL studies, agents in a game are defined as teammates or enemies beforehand, and the relationships among the agents remain fixed throughout the game. However, in real-world problems, the agent relationships are commonly unknown in advance or dynamically changing. Many multi-party interactions start off by asking: who is on my team? This question arises whether it is the first day at the stock exchange or the kindergarten. Therefore, training policies for such situations in the face of imperfect information and ambiguous identities is an important problem that needs to be addressed. In this work, we develop a novel identity detection reinforcement learning (IDRL) framework that allows an agent to dynamically infer the identities of nearby agents and select an appropriate policy to accomplish the task. In the IDRL framework, a relation network is constructed to deduce the identities of other agents by observing the behaviors of the agents. A danger network is optimized to estimate the risk of false-positive identifications. Beyond that, we propose an intrinsic reward that balances the need to maximize external rewards and accurate identification. After identifying the cooperation-competition pattern among the agents, IDRL applies one of the off-the-shelf MARL methods to learn the policy. To evaluate the proposed method, we conduct experiments on Red-10 card-shedding game, and the results show that IDRL achieves superior performance over other state-of-the-art MARL methods. Impressively, the relation network has the par performance to identify the identities of agents with top human players; the danger network reasonably avoids the risk of imperfect identification. The code to reproduce all the reported results is available online at https://github.com/MR-BENjie/IDRL.