Dynamics of neutrino-driven winds: inclusion of accurate weak interaction rates in strong magnetic fields

Solving Newtonian steady-state wind equations with accurate weak interaction rates and magnetic fields (MFs) of young neutron stars considered, we study the dynamics and nucleosynthesis of neutrino-driven winds (NDWs) from proto neutron stars (PNSs). For a typical 1.4 M$_{\odot}$ PNS model, we find the nucleosynthesis products are closely related to the luminosity of neutrinos and anti-neutrinos. The lower the luminosity is, the larger effect to the NDWs caused by weak interactions and MFs is. At a high anti-neutrino luminosity of typically $8\times 10^{51}$ erg s$^{-1}$, neutrinos and anti-neutrinos dominate the processes in a NDW and the MFs hardly change the wind's properties. While at a low anti-neutrino luminosity of $10^{51}$ erg s$^{-1}$ at the late stage of a NDW, the mass of product and nucleosynthesis are changed significantly in the strong MFs, they are less important than those in the early stage when the anti-neutrino luminosity is high. Therefore for the most models considered for the NDWs from PNSs, based on our calculations the influences of MFs and the net weak interactions on the nucleosynthesis is not significant.Comment: 8 pages, 3 figures, accepted for publication in RA

Low Resolution Face Recognition in Surveillance Systems

In surveillance systems, the captured facial images are often very small and different from the low-resolution images down-sampled from high-resolution facial images. They generally lead to low performance in face recog-nition. In this paper, we study specific scenarios of face recognition with surveillance cameras. Three important factors that influence face recognition performance are investigated: type of cameras, distance between the ob-ject and camera, and the resolution of the captured face images. Each factor is numerically investigated and analyzed in this paper. Based on these observations, a new approach is proposed for face recognition in real sur-veillance environment. For a raw video sequence captured by a surveillance camera, image pre-processing tech-niques are employed to remove the illumination variations for the enhancement of image quality. The face im-ages are further improved through a novel face image super-resolution method. The proposed approach is proven to significantly improve the performance of face recognition as demonstrated by experiments

A Sarsa( λ

Solving reinforcement learning problems in continuous space with function approximation is currently a research hotspot of machine learning. When dealing with the continuous space problems, the classic Q-iteration algorithms based on lookup table or function approximation converge slowly and are difficult to derive a continuous policy. To overcome the above weaknesses, we propose an algorithm named DFR-Sarsa(λ) based on double-layer fuzzy reasoning and prove its convergence. In this algorithm, the first reasoning layer uses fuzzy sets of state to compute continuous actions; the second reasoning layer uses fuzzy sets of action to compute the components of Q-value. Then, these two fuzzy layers are combined to compute the Q-value function of continuous action space. Besides, this algorithm utilizes the membership degrees of activation rules in the two fuzzy reasoning layers to update the eligibility traces. Applying DFR-Sarsa(λ) to the Mountain Car and Cart-pole Balancing problems, experimental results show that the algorithm not only can be used to get a continuous action policy, but also has a better convergence performance