Role of quark-interchange processes in evolution of mesonic matter

We divide the cross section for a meson-meson reaction into three parts. The first part is for the quark-interchange process, the second for quark-antiquark annihilation processes and the third for resonant processes. Master rate equations are established to yield time dependence of fugacities of pions, rhos, kaons and vetor kaons. The equations include cross sections for inelastic scattering of pions, rhos, kaons and vector kaons. Cross sections for quark-interchange-induced reactions, that were obtained in a potential model, are parametrized for convenient use. The number densities of pion and rho (kaon and vector kaon) are altered by quark-interchange processes in equal magnitudes but opposite signs. The master rate equations combined with the hydrodynamic equations for longitudinal and transverse expansion are solved with many sets of initial meson fugacities. Quark-interchange processes are shown to be important in the contribution of the inelastic meson-meson scattering to evolution of mesonic matter.Comment: 28 pages, 1 figure, 8 table

Triggered massive and clustered stars formation by together H II regions G38.91-0.44 and G39.30-1.04

We present the radio continuum, infrared, and CO molecular observations of infrared dark cloud (IRDC) G38.95-0.47 and its adjacent H II regions G38.91-0.44 (N74), G38.93-0.39 (N75), and G39.30-1.04. The Purple Mountain Observation (PMO) 13.7 m radio telescope was used to detect12CO J=1-0,13CO J=1-0 and C18O J=1-0 lines. The carbon monoxide (CO) molecular observations can ensure the real association between the ionized gas and the neutral material observed nearby. To select young stellar objects (YSOs) associated this region, we used the GLIMPSE I catalog. The13CO J=1-0 emission presents two large cloud clumps. The clump consistent with IRDC G38.95-0.47 shows a triangle- like shape, and has a steep integrated-intensity gradient toward H II regions G38.91-0.44 and G39.30-1.04, suggesting that the two H II regions have expanded into the IRDC. Four submillmeter continuum sources have been detected in the IRDC G38.95-0.47. Only the G038.95-00.47-M1 source with a mass of 117 Msun has outflow and infall motions, indicating a newly forming massive star. We detected a new collimated outflow in the clump compressed by G38.93-0.39. The derived ages of the three H II regions are 6.1*10^5yr, 2.5*10^5yr, and 9.0*10^5yr, respectively. In the IRDC G38.95-0.47, the significant enhancement of several Class I YSOs indicates the presence of some recently formed stars. Comparing the ages of these H II regions with YSOs (Class I sources and massive G038.95-00.47-M1 source), we suggest that YSOs may be triggered by G38.91-0.44 and G39.30-1.04 together, which supports the radiatively driven implosion model. It may be the first time that the triggered star formation has occurred in the IRDC compressed by two H II regions. The new detected outflow may be driven by a star cluster.Comment: 6 pages, 4 figures, Accepted for publication in A&

Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking

With efficient appearance learning models, Discriminative Correlation Filter (DCF) has been proven to be very successful in recent video object tracking benchmarks and competitions. However, the existing DCF paradigm suffers from two major issues, i.e., spatial boundary effect and temporal filter degradation. To mitigate these challenges, we propose a new DCF-based tracking method. The key innovations of the proposed method include adaptive spatial feature selection and temporal consistent constraints, with which the new tracker enables joint spatial-temporal filter learning in a lower dimensional discriminative manifold. More specifically, we apply structured spatial sparsity constraints to multi-channel filers. Consequently, the process of learning spatial filters can be approximated by the lasso regularisation. To encourage temporal consistency, the filter model is restricted to lie around its historical value and updated locally to preserve the global structure in the manifold. Last, a unified optimisation framework is proposed to jointly select temporal consistency preserving spatial features and learn discriminative filters with the augmented Lagrangian method. Qualitative and quantitative evaluations have been conducted on a number of well-known benchmarking datasets such as OTB2013, OTB50, OTB100, Temple-Colour, UAV123 and VOT2018. The experimental results demonstrate the superiority of the proposed method over the state-of-the-art approaches