Relativistic Brueckner-Hartree-Fock theory for neutron drops

Neutron drops confined in an external field are studied in the framework of relativistic Brueckner-Hartree-Fock theory using the bare nucleon-nucleon interaction. The ground state energies and radii of neutron drops with even numbers from $N = 4$ to $N=50$ are calculated and compared with results obtained from other nonrelativistic \textit{ab initio} calculations and from relativistic density functional theory. Special attention has been paid to the magic numbers and to the sub-shell closures. The single-particle energies are investigated and the monopole effect of the tensor force on the evolutions of the spin-orbit and the pseudospin-orbit splittings is discussed. The results provide interesting insight of neutron rich systems and can form an important guide for future density functionals.Comment: 31 pages, 12 figure

Effects of tensor forces in nuclear spin-orbit splittings from ab initio calculations

A systematic and specific pattern due to the effects of the tensor forces is found in the evolution of spin-orbit splittings in neutron drops. This result is obtained from relativistic Brueckner-Hartree-Fock theory using the bare nucleon-nucleon interaction. It forms an important guide for future microscopic derivations of relativistic and nonrelativistic nuclear energy density functionals.Comment: 14 pages, 3 figure

Fully self-consistent relativistic Brueckner-Hartree-Fock theory for finite nuclei

Starting from the relativistic form of the Bonn potential as a bare nucleon-nucleon interaction, the full Relativistic Brueckner-Hartree-Fock (RBHF) equations are solved for finite nuclei in a fully self-consistent basis. This provides a relativistic ab initio calculation of the ground state properties of finite nuclei without any free parameters and without three-body forces. The convergence properties for the solutions of these coupled equations are discussed in detail at the example of the nucleus $^{16}$O. The binding energies, radii, and spin-orbit splittings of the doubly magic nuclei $^{4}$He, $^{16}$O, and $^{40}$Ca are calculated and compared with the earlier RBHF calculated results in a fixed Dirac Woods-Saxon basis and other non-relativistic ab initio calculated results based on pure two-body forces.Comment: 22 pages, 13 figure

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

Adversarial Purification of Information Masking

Adversarial attacks meticulously generate minuscule, imperceptible perturbations to images to deceive neural networks. Counteracting these, adversarial purification methods seek to transform adversarial input samples into clean output images to defend against adversarial attacks. Nonetheless, extent generative models fail to effectively eliminate adversarial perturbations, yielding less-than-ideal purification results. We emphasize the potential threat of residual adversarial perturbations to target models, quantitatively establishing a relationship between perturbation scale and attack capability. Notably, the residual perturbations on the purified image primarily stem from the same-position patch and similar patches of the adversarial sample. We propose a novel adversarial purification approach named Information Mask Purification (IMPure), aims to extensively eliminate adversarial perturbations. To obtain an adversarial sample, we first mask part of the patches information, then reconstruct the patches to resist adversarial perturbations from the patches. We reconstruct all patches in parallel to obtain a cohesive image. Then, in order to protect the purified samples against potential similar regional perturbations, we simulate this risk by randomly mixing the purified samples with the input samples before inputting them into the feature extraction network. Finally, we establish a combined constraint of pixel loss and perceptual loss to augment the model's reconstruction adaptability. Extensive experiments on the ImageNet dataset with three classifier models demonstrate that our approach achieves state-of-the-art results against nine adversarial attack methods. Implementation code and pre-trained weights can be accessed at \textcolor{blue}{https://github.com/NoWindButRain/IMPure}

Predicting miRNA-disease associations based on multi-view information fusion

MicroRNAs (miRNAs) play an important role in various biological processes and their abnormal expression could lead to the occurrence of diseases. Exploring the potential relationships between miRNAs and diseases can contribute to the diagnosis and treatment of complex diseases. The increasing databases storing miRNA and disease information provide opportunities to develop computational methods for discovering unobserved disease-related miRNAs, but there are still some challenges in how to effectively learn and fuse information from multi-source data. In this study, we propose a multi-view information fusion based method for miRNA-disease association (MDA)prediction, named MVIFMDA. Firstly, multiple heterogeneous networks are constructed by combining the known MDAs and different similarities of miRNAs and diseases based on multi-source information. Secondly, the topology features of miRNAs and diseases are obtained by using the graph convolutional network to each heterogeneous network view, respectively. Moreover, we design the attention strategy at the topology representation level to adaptively fuse representations including different structural information. Meanwhile, we learn the attribute representations of miRNAs and diseases from their similarity attribute views with convolutional neural networks, respectively. Finally, the complicated associations between miRNAs and diseases are reconstructed by applying a bilinear decoder to the combined features, which combine topology and attribute representations. Experimental results on the public dataset demonstrate that our proposed model consistently outperforms baseline methods. The case studies further show the ability of the MVIFMDA model for inferring underlying associations between miRNAs and diseases

Tris(ethyl­enediamine-κ2 N,N′)cobalt(III) aqua­tris­(oxalato-κ2 O 1,O 2)indate(III)

In the cation of the title compound, [Co(C2H8N2)3][In(C2O4)3(H2O)], the CoIII atom is coordinated by six N atoms from three ethyl­enediamine mol­ecules. The CoIII—N bond lengths lie in the range 1.956 (4)–1.986 (4) Å. In the anion, the InIII atom is seven-coordinated by six O atoms from three oxalate ligands and by a water mol­ecule. The cations and anions are linked by extensive O—H⋯O and N—H⋯O hydrogen bonds, forming a supermolecular network