Several Issues Concerning the Corruption Crimes of Private Entrepreneurs: Based on Empirical Research

Studies have shown that top leaders and department heads of privately-owned enterprises differ significantly in the types of corruption crimes and the links where crimes are committed. Therefore, corruption crimes of private entrepreneurs should be countered with the methods well targeted at the subjects of crime. In addition, penalties do not fit crimes by the conviction and penalty imposition for corruption crimes, which seriously undermine the effect of criminal penalties on crime prevention. Therefore, the sentencing range should be elaborated and other measures should be taken so that crimes and penalties will be balanced

Numerical Solutions of Optimal Risk Control and Dividend Optimization Policies under A Generalized Singular Control Formulation

This paper develops numerical methods for finding optimal dividend pay-out and reinsurance policies. A generalized singular control formulation of surplus and discounted payoff function are introduced, where the surplus is modeled by a regime-switching process subject to both regular and singular controls. To approximate the value function and optimal controls, Markov chain approximation techniques are used to construct a discrete-time controlled Markov chain with two components. The proofs of the convergence of the approximation sequence to the surplus process and the value function are given. Examples of proportional and excess-of-loss reinsurance are presented to illustrate the applicability of the numerical methods.Comment: Key words: Singular control, dividend policy, Markov chain approximation, numerical method, reinsurance, regime switchin

Realistic Full-Body Tracking from Sparse Observations via Joint-Level Modeling

To bridge the physical and virtual worlds for rapidly developed VR/AR applications, the ability to realistically drive 3D full-body avatars is of great significance. Although real-time body tracking with only the head-mounted displays (HMDs) and hand controllers is heavily under-constrained, a carefully designed end-to-end neural network is of great potential to solve the problem by learning from large-scale motion data. To this end, we propose a two-stage framework that can obtain accurate and smooth full-body motions with the three tracking signals of head and hands only. Our framework explicitly models the joint-level features in the first stage and utilizes them as spatiotemporal tokens for alternating spatial and temporal transformer blocks to capture joint-level correlations in the second stage. Furthermore, we design a set of loss terms to constrain the task of a high degree of freedom, such that we can exploit the potential of our joint-level modeling. With extensive experiments on the AMASS motion dataset and real-captured data, we validate the effectiveness of our designs and show our proposed method can achieve more accurate and smooth motion compared to existing approaches.Comment: Accepted to ICCV 2023. Project page: https://zxz267.github.io/AvatarJL