3,568 research outputs found

    Applicability of Relativistic Point-Coupling Models to Neutron Star Physics

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    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

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    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

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    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

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    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

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    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 3^3He-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 ν=1/2\nu = 1/2.Comment: 3 pages, 5 figure
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