7,216 research outputs found
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
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