Fine structure of charge-exchange spin-dipole excitations in 16^{16}O

The charge-exchange spin-dipole (SD) excitations for both $(p,n)$ and $(n,p)$ channels in $^{16}$O are investigated in the fully self-consistent random phase approximation based on the covariant density functional theory. The fine structure of SD excitations in the most up-to-date $^{16}$O($\vec p, \vec n$)$^{16}$F experiment is excellently reproduced without any readjustment in the functional. The characteristics of SD excitations are understood with the delicate balance between the $\sigma$- and $\omega$-meson fields via the exchange terms. The fine structure of SD excitations for $^{16}$O($n,p$)$^{16}$N channel is predicted for future experiments.Comment: 5 pages, 4 figure

Spin-orbit and orbit-orbit strengths for radioactive neutron-rich doubly magic nucleus 132^{132}Sn in relativistic mean field theory

Relativistic mean field (RMF) theory is applied to investigate the properties of the radioactive neutron-rich doubly magic nucleus $^{132}$Sn and the corresponding isotopes and isotones. The two-neutron and two-proton separation energies are well reproduced by the RMF theory. In particular, the RMF results agree with the experimental single-particle spectrum in $^{132}$Sn as well as the Nilsson spin-orbit parameter $C$ and orbit-orbit parameter $D$ thus extracted, but remarkably differ from the traditional Nilsson parameters. Furthermore, the present results provide a guideline for the isospin dependence of the Nilsson parameters.Comment: 4 pages, 4 figures, Phys. Rev. C in pres

Deep-neural-network solution of the ab initio nuclear structure

Predicting the structure of quantum many-body systems from the first principles of quantum mechanics is a common challenge in physics, chemistry, and material science. Deep machine learning has proven to be a powerful tool for solving condensed matter and chemistry problems, while for atomic nuclei, it is still quite challenging because of the complicated nucleon-nucleon interactions, which strongly couples the spatial, spin, and isospin degrees of freedom. By combining essential physics of the nuclear wave functions and the strong expressive power of artificial neural networks, we develop FeynmanNet, a novel deep-learning variational quantum Monte Carlo approach for \emph{ab initio} nuclear structure. We show that FeynmanNet can provide very accurate ground-state energies and wave functions for $^4$He, $^6$Li, and even up to $^{16}$O as emerging from the leading-order and next-to-leading-order Hamiltonians of pionless effective field theory. Compared to the conventional diffusion Monte Carlo approaches, which suffer from the severe inherent fermion-sign problem, FeynmanNet reaches such a high accuracy in a variational way and scales polynomially with the number of nucleons. Therefore, it paves the way to a highly accurate and efficient \emph{ab initio} method for predicting nuclear properties based on the realistic interactions between nucleons.Comment: 13 pages, 3 figure

Perturbative interpretation of relativistic symmetries in nuclei

Perturbation theory is used systematically to investigate the symmetries of the Dirac Hamiltonian and their breaking in atomic nuclei. Using the perturbation corrections to the single-particle energies and wave functions, the link between the single-particle states in realistic nuclei and their counterparts in the symmetry limits is discussed. It is shown that the limit of S-V=const and relativistic harmonic oscillator (RHO) potentials can be connected to the actual Dirac Hamiltonian by the perturbation method, while the limit of S+V=const cannot, where S and V are the scalar and vector potentials, respectively. This indicates that the realistic system can be treated as a perturbation of spin-symmetric Hamiltonians, and the energy splitting of the pseudospin doublets can be regarded as a result of small perturbation around the Hamiltonian with RHO potentials, where the pseudospin doublets are quasidegenerate.Comment: 5 pages, 4 figures, Phys. Rev. C in pres

Pseudospin symmetry: Recent progress with supersymmetric quantum mechanics

It is an interesting and open problem to trace the origin of the pseudospin symmetry in nuclear single-particle spectra and its symmetry breaking mechanism in actual nuclei. In this report, we mainly focus on our recent progress on this topic by combining the similarity renormalization group technique, supersymmetric quantum mechanics, and perturbation theory. We found that it is a promising direction to understand the pseudospin symmetry in a quantitative way.Comment: 4 pages, 1 figure, Proceedings of the XX International School on Nuclear Physics, Neutron Physics and Applications, Varna, Bulgaria, 16-22 September, 201

User Perspectives On Adoption Of A Hybrid Tagging System: A Case Of Topic Structure Of Zhihu Knowledge Community

Social tagging system has been prevalent thanks to its user-centric and flexible features. However, it suffers from its uncontrolled vocabulary and loose connection between tags. To overcome their drawbacks, a hybrid tagging system, which combines the ideas of the traditional taxonomy and social tagging, is adopting by some online knowledge communities. The top layers of the hybrid tagging system are determined by the website designer, while the bottom layers are constructed by users under certain restrictions. Due to the absence of sufficient research on user acceptance of this hybrid tagging system, cognitive factors affecting user adoption of the system is explored in this paper with topic structure of Zhihu, the famous Chinese knowledge community. An integrated model is proposed based on technology acceptance model and social cognitive theory. A survey will be conducted to empirically verify relationships between proposed constructs and actual usage. The research is expected to provide guidance for incremental improvement on a hybrid tagging system or development on new tagging systems

Tetrahedral shape of 110^{110}Zr from covariant density functional theory in 3D lattice space

Covariant density functional theory is solved in 3D lattice space by implementing the preconditioned conjugate gradient method with a filtering function (PCG-F). It considerably improves the computational efficiency compared to the previous inverse Hamiltonian method (IHM). This new method is then applied to explore the tetrahedral shape of $^{110}$Zr in the full deformation space. The ground state of $^{110}$Zr is found to have a tetrahedral shape, but the deformations $\beta_{31}$ and $\beta_{33}$ greatly soften the potential energy surface. This effect is analysed with the microscopic evolution of the single-particle levels near the Fermi surface driven by the deformation

Temperature Matrix-Based Data Placement Optimization in Edge Computing Environment

The scale of data shows an explosive growth trend, with wide use of cloud storage. However, there are challenges such as network latency and energy consumption. The emergence of edge computing brings data close to the edge of the network, making it a good supplement to cloud computing. The spatiotemporal characteristics of data have been largely ignored in studies of data placement and storage optimization. To this end, a temperature matrix-based data placement method using an improved Hungarian algorithm (TEMPLIH) is proposed in this work. A temperature matrix is used to reflect the influence of data characteristics on its placement. A data replica matrix selection algorithm based on temperature matrix (RSA-TM) is proposed to meet latency requirements. Then, an improved Hungarian algorithm based on replica matrix (IHA-RM) is proposed, which satisfies the balance among the multiple goals of latency, cost, and load balancing. Compared with other data placement strategies, experiments show that the proposed method can effectively reduce the cost of data placement while meeting user access latency requirements and maintaining a reasonable load balance between edge servers. Further improvement is discussed and the idea of regional value is proposed