6,207 research outputs found
A thermodynamically consistent quasi-particle model without density-dependent infinity of the vacuum zero point energy
In this paper, we generalize the improved quasi-particle model proposed in J.
Cao et al., [ Phys. Lett. B {\bf711}, 65 (2012)] from finite temperature and
zero chemical potential to the case of finite chemical potential and zero
temperature, and calculate the equation of state (EOS) for (2+1) flavor Quantum
Chromodynamics (QCD) at zero temperature and high density. We first calculate
the partition function at finite temperature and chemical potential, then go to
the limit and obtain the equation of state (EOS) for cold and dense QCD,
which is important for the study of neutron stars. Furthermore, we use this EOS
to calculate the quark-number density, the energy density, the quark-number
susceptibility and the speed of sound at zero temperature and finite chemical
potential and compare our results with the corresponding ones in the existing
literature
Glueball Masses from Hamiltonian Lattice QCD
We calculate the masses of the , and glueballs from
QCD in 3+1 dimensions using an eigenvalue equation method for Hamiltonian
lattice QCD developed and described elsewhere by the authors. The mass ratios
become approximately constants in the coupling region ,
from which we estimate and
.Comment: 12 pages, Latex, figures to be sent upon reques
Carbon Nanostructures Production by AC Arc Discharge Plasma Process at Atmospheric Pressure
Carbon nanostructures have received much attention for a wide range of applications. In this paper, we produced carbon nanostructures by decomposition of benzene using AC arc discharge plasma process at atmospheric pressure. Discharge was carried out at a voltage of 380 V, with a current of 6 A–20 A. The products were characterized by scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HRTEM), powder X-ray diffraction (XRD), and Raman spectra. The results show that the products on the inner wall of the reactor and the sand core are nanoparticles with 20–60 nm diameter, and the products on the electrode ends are nanoparticles, agglomerate carbon particles, and multiwalled carbon nanotubes (MWCNTs). The maximum yield content of carbon nanotubes occurs when the arc discharge current is 8 A. Finally, the reaction mechanism was discussed
(Dimethyl sulfoxide-κO)[3-hydrÂoxy-2-hydroxyÂmethyl-2-(3-methÂoxy-2-oxidoÂbenzylÂideneamino-κ2 O 2,N)propanolato-κO]dioxomolybdenum(VI). Corrigendum
Corrigendum to Acta Cryst. (2006), E62, m1994–m1996
Unsupervised Domain Adaptation via Discriminative Manifold Propagation
Unsupervised domain adaptation is effective in leveraging rich information
from a labeled source domain to an unlabeled target domain. Though deep
learning and adversarial strategy made a significant breakthrough in the
adaptability of features, there are two issues to be further studied. First,
hard-assigned pseudo labels on the target domain are arbitrary and error-prone,
and direct application of them may destroy the intrinsic data structure.
Second, batch-wise training of deep learning limits the characterization of the
global structure. In this paper, a Riemannian manifold learning framework is
proposed to achieve transferability and discriminability simultaneously. For
the first issue, this framework establishes a probabilistic discriminant
criterion on the target domain via soft labels. Based on pre-built prototypes,
this criterion is extended to a global approximation scheme for the second
issue. Manifold metric alignment is adopted to be compatible with the embedding
space. The theoretical error bounds of different alignment metrics are derived
for constructive guidance. The proposed method can be used to tackle a series
of variants of domain adaptation problems, including both vanilla and partial
settings. Extensive experiments have been conducted to investigate the method
and a comparative study shows the superiority of the discriminative manifold
learning framework.Comment: To be published in IEEE Transactions on Pattern Analysis and Machine
Intelligenc
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