Constraints on the Interaction between Dark Matter and Baryons from Cooling Flow Clusters

Other nongravitational heating processes are needed to resolve the disagreement between the absence of cool gas components in the centers of galaxy clusters revealed recently by Chandra and XMM observations and the expectations of conventional radiative cooling models. Here we propose that the interaction between dark matter particles and ordinary baryonic matter may act as an alternative for the reheating of intracluster medium (ICM) in the inner regions of clusters, in which kinetic energy of dark matter is transported to ICM to balance the radiative cooling. Using the Chandra and XMM data of typical clusters, we set a useful constraint on the dark matter-baryon cross-section: $\sigma_{xp}/m_x \sim 1 \times 10^{-25}$ cm$^2$GeV$^{-1}$, where $m_x$ is the mass of dark matter particles

Universality class of the depinning transition in the two-dimensional Ising model with quenched disorder

With Monte Carlo methods, we investigate the universality class of the depinning transition in the two-dimensional Ising model with quenched random fields. Based on the short-time dynamic approach, we accurately determine the depinning transition field and both static and dynamic critical exponents. The critical exponents vary significantly with the form and strength of the random fields, but exhibit independence on the updating schemes of the Monte Carlo algorithm. From the roughness exponents $\zeta, \zeta_{loc}$ and $\zeta_s$, one may judge that the depinning transition of the random-field Ising model belongs to the new dynamic universality class with $\zeta \neq \zeta_{loc}\neq \zeta_s$ and $\zeta_{loc} \neq 1$. The crossover from the second-order phase transition to the first-order one is observed for the uniform distribution of the random fields, but it is not present for the Gaussian distribution.Comment: 16 pages, 16 figures, 3 table

Bandit-based cooperative coevolution for tackling contribution imbalance in large-scale optimization problems

This paper addresses the issue of computational resource allocation within the context of cooperative coevolution. Cooperative coevolution typically works by breaking a problem down into smaller subproblems (or components) and coevolving them in a round-robin fashion, resulting in a uniform resource allocation among its components. Despite its success on a wide range of problems, cooperative coevolution struggles to perform efficiently when its components do not contribute equally to the overall objective value. This is of crucial importance on large-scale optimization problems where such difference are further magnified. To resolve this imbalance problem, we extend the standard cooperative coevolution to a new generic framework capable of learning the contribution of each component using multi-armed bandit techniques. The new framework allocates the computational resources to each component proportional to their contributions towards improving the overall objective value. This approach results in a more economical use of the limited computational resources. We study different aspects of the proposed framework in the light of extensive experiments. Our empirical results confirm that even a simple bandit-based credit assignment scheme can significantly improve the performance of cooperative coevolution on large-scale continuous problems, leading to competitive performance as compared to the state-of-the-art algorithms