3,568 research outputs found
Applicability of Relativistic Point-Coupling Models to Neutron Star Physics
Comparing with a wide range of covariant energy density functional models
based on the finite-range meson-exchange representation, the relativistic
mean-field models with the zero-range contact interaction, namely the
relativistic point-coupling models, are still infrequent to be utilized in
establishing nuclear equation of state (EoS) and investigating neutron star
properties, although comprehensive applications and achievements of them in
describing many nuclear properties both in ground and exited states are mature.
In this work, the EoS of neutron star matter is established constructively in
the framework of the relativistic point-coupling models to study neutron star
physics. Taking two selected functionals DD-PC1 and PC-PK1 as examples, nuclear
symmetry energies and several neutron star properties including proton
fractions, mass-radius relations, the core-crust transition density, the
fraction of crustal moment of inertia and dimensionless tidal deformabilities
are discussed. A suppression of pressure of neutron star matter found in the
functional PC-PK1 at high densities results in the difficulty of its prediction
when approaching to the maximum mass of neutron stars. In addition, the
divergences between two selected functionals in describing neutron star
quantities mentioned above are still large, ascribing to the less constrained
behavior of these functionals at high densities. Then it is expected that the
constraints on the dense matter EoS from precise and massive modern
astronomical observations, such as the tidal-deformabilities taken from
gravitational-wave events, would be essential to improve the parameterizing of
the relativistic point-coupling models.Comment: To appear in the AIP Proceedings of the Xiamen-CUSTIPEN Workshop on
the EOS of Dense Neutron-Rich Matter in the Era of Gravitational Wave
Astronomy, Jan. 3-7, Xiamen, Chin
Improving Image Restoration with Soft-Rounding
Several important classes of images such as text, barcode and pattern images
have the property that pixels can only take a distinct subset of values. This
knowledge can benefit the restoration of such images, but it has not been
widely considered in current restoration methods. In this work, we describe an
effective and efficient approach to incorporate the knowledge of distinct pixel
values of the pristine images into the general regularized least squares
restoration framework. We introduce a new regularizer that attains zero at the
designated pixel values and becomes a quadratic penalty function in the
intervals between them. When incorporated into the regularized least squares
restoration framework, this regularizer leads to a simple and efficient step
that resembles and extends the rounding operation, which we term as
soft-rounding. We apply the soft-rounding enhanced solution to the restoration
of binary text/barcode images and pattern images with multiple distinct pixel
values. Experimental results show that soft-rounding enhanced restoration
methods achieve significant improvement in both visual quality and quantitative
measures (PSNR and SSIM). Furthermore, we show that this regularizer can also
benefit the restoration of general natural images.Comment: 9 pages, 6 figure
Progress on genetic polymorphism associated with diabetic retinopathy
Diabetic retinopathy(DR)is one of the most serious complications of diabetes, as the second general blindness disease in the world currently. The development of procedures for prevention and treatment of DR is one of the most important problems that should be solved currently. A lot of researches show that the development of DR is determined by genetics. The current research advance in DR relevant gene is reviewed in this article
Globalized distributionally robust optimization with multi core sets
It is essential to capture the true probability distribution of uncertain
data in the distributionally robust optimization (DRO). The uncertain data
presents multimodality in numerous application scenarios, in the sense that the
probability density function of the uncertain data has two or more modes (local
maximums). In this paper, we propose a globalized distributionally robust
optimization framework with multiple core sets (MGDRO) to handle the multimodal
data. This framework captures the multimodal structure via a penalty function
composed of the minimum distances from the random vector to all core sets.
Under some assumptions, the MGDRO model can be reformulated as tractable
semi-definite programs for both moment-based and metric-based ambiguity sets.
We applied the MGDRO models to a multi-product newswendor problem with
multimodal demands. The numerical results turn out that the MGDRO models
outperform traditional DRO models and other multimodal models greatly
Bogoliubov Hamiltonian as Derivative of Dirac Hamiltonian via Braid Relation
In this paper we discuss a new type of 4-dimensional representation of the
braid group. The matrices of braid operations are constructed by q-deformation
of Hamiltonians. One is the Dirac Hamiltonian for free electron with mass m,
the other, which we find, is related to the Bogoliubov Hamiltonian for
quasiparticles in He-B with the same free energy and mass being m/2. In the
process, we choose the free q-deformation parameter as a special value in order
to be consistent with the anyon description for fractional quantum Hall effect
with .Comment: 3 pages, 5 figure
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