230 research outputs found
Fine structure of charge-exchange spin-dipole excitations in O
The charge-exchange spin-dipole (SD) excitations for both and
channels in 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 O()F experiment is excellently reproduced without any readjustment in
the functional. The characteristics of SD excitations are understood with the
delicate balance between the - and -meson fields via the
exchange terms. The fine structure of SD excitations for
O()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 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 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 Sn as well as
the Nilsson spin-orbit parameter and orbit-orbit parameter 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 He, Li, and even up to
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
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
Tetrahedral shape of 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 Zr in the full
deformation space. The ground state of Zr is found to have a
tetrahedral shape, but the deformations and 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
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