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 $T=0$ 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 $0^{++}$, $0^{--}$ and $1^{+-}$ 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 $6/g^2 \in [6.0,6.4]$, from which we estimate $M(0^{--})/M(0^{++})=2.44 \pm 0.05 \pm 0.20$ and $M(1^{+-})/M(0^{++})=1.91 \pm 0.05 \pm 0.12$.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-hydr­oxy-2-hydroxy­methyl-2-(3-meth­oxy-2-oxido­benzyl­ideneamino-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