Asymptotic Stability for Equilibria of Nonlinear Semiflows with Applications to Rotating Viscoelastic Rods, Part I

No Abstrac

Ownership, Incentives and Monitoring

This paper studies the effect of ownership structure on workers' incentives for investing in firm-specific human capital. Particularly, we analyse such incentivers and monitoring under employee ownership and capitalist ownership. In our model, the employee-owned firm is a firm bought by its workers who pay the competitive price. Under certain conditions, we show that the workers' investment and expected income are higher and the monitoring intensity is lower in an employee-owned firm than they are in a capitalist firm. We also show that the incentive effect of employee ownership increases as a worker's reservation wage decreases, as the monitoring cost or as the productivity uncertainty increases. Most of our results are consistent with the available empirical evidenceEmployee ownership, monitoring, incentives, property rights

Revisit of directed flow in relativistic heavy-ion collisions from a multiphase transport model

We have revisited several interesting questions on how the rapidity-odd directed flow is developed in relativistic $^{197}$Au+$^{197}$Au collisions at $\sqrt{s_{NN}}$ = 200 and 39 GeV based on a multiphase transport model. As the partonic phase evolves with time, the slope of the parton directed flow at midrapidity region changes from negative to positive as a result of the later dynamics at 200 GeV, while it remains negative at 39 GeV due to the shorter life time of the partonic phase. The directed flow splitting for various quark species due to their different initial eccentricities is observed at 39 GeV, while the splitting is very small at 200 GeV. From a dynamical coalescence algorithm with Wigner functions, we found that the directed flow of hadrons is a result of competition between the coalescence in momentum and coordinate space as well as further modifications by the hadronic rescatterings.Comment: 8 pages, 8 figures, version after major revisio

Unified Spectral Clustering with Optimal Graph

Spectral clustering has found extensive use in many areas. Most traditional spectral clustering algorithms work in three separate steps: similarity graph construction; continuous labels learning; discretizing the learned labels by k-means clustering. Such common practice has two potential flaws, which may lead to severe information loss and performance degradation. First, predefined similarity graph might not be optimal for subsequent clustering. It is well-accepted that similarity graph highly affects the clustering results. To this end, we propose to automatically learn similarity information from data and simultaneously consider the constraint that the similarity matrix has exact c connected components if there are c clusters. Second, the discrete solution may deviate from the spectral solution since k-means method is well-known as sensitive to the initialization of cluster centers. In this work, we transform the candidate solution into a new one that better approximates the discrete one. Finally, those three subtasks are integrated into a unified framework, with each subtask iteratively boosted by using the results of the others towards an overall optimal solution. It is known that the performance of a kernel method is largely determined by the choice of kernels. To tackle this practical problem of how to select the most suitable kernel for a particular data set, we further extend our model to incorporate multiple kernel learning ability. Extensive experiments demonstrate the superiority of our proposed method as compared to existing clustering approaches.Comment: Accepted by AAAI 201