Gutzwiller density functional calculations of the electronic structure of FeAs-based superconductors: Evidence for a three-dimensional Fermi surface

The electronic structures of FeAs-compounds strongly depend on the Fe-As bonding, which can not be described successfully by the local density approximation (LDA). Treating the multi-orbital fluctuations from $ab$-$initio$ by LDA+Gutzwiller method, we are now able to predict the correct Fe-As bond-length, and find that Fe-As bonding-strength is 30% weaker, which will explain the observed "soft phonon". The bands are narrowed by a factor of 2, and the $d_{3z^2-r^2}$ orbital is pushed up to cross the Fermi level, forming 3-dimensional Fermi surfaces, which suppress the anisotropy and the ($\pi,\pi$) nesting. The inter-orbital Hund's coupling $J$ rather than $U$ plays crucial roles to obtain these results.Comment: 4 pages, 4 figures, 1 tabl

MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering

Collaborative filtering (CF) is widely used by personalized recommendation systems, which aims to predict the preference of users with historical user-item interactions. In recent years, Graph Neural Networks (GNNs) have been utilized to build CF models and have shown promising performance. Recent state-of-the-art GNN-based CF approaches simply attribute their performance improvement to the high-order neighbor aggregation ability of GNNs. However, we observe that some powerful deep GNNs such as JKNet and DropEdge, can effectively exploit high-order neighbor information on other graph tasks but perform poorly on CF tasks, which conflicts with the explanation of these GNN-based CF research. Different from these research, we investigate the GNN-based CF from the perspective of Markov processes for distance learning with a unified framework named Markov Graph Diffusion Collaborative Filtering (MGDCF). We design a Markov Graph Diffusion Network (MGDN) as MGDCF's GNN encoder, which learns vertex representations by trading off two types of distances via a Markov process. We show the theoretical equivalence between MGDN's output and the optimal solution of a distance loss function, which can boost the optimization of CF models. MGDN can generalize state-of-the-art models such as LightGCN and APPNP, which are heterogeneous GNNs. In addition, MGDN can be extended to homogeneous GNNs with our sparsification technique. For optimizing MGDCF, we propose the InfoBPR loss function, which extends the widely used BPR loss to exploit multiple negative samples for better performance. We conduct experiments to perform detailed analysis on MGDCF. The source code is publicly available at https://github.com/hujunxianligong/MGDCF

A Conceptual Framework for Data Property Protection Based on Blockchain

Blockchain is a new decentralized infrastructure and distributed computing paradigm. The blockchain technology has the characteristics of decentralization, time series data, collective maintenance, programmable and secure. This paper addresses the needs of China Mobile\u27s digital intellectual property protection and transaction, and uses the relevant design and technology in the blockchain to propose solutions and ideas for identity authentication and traceability of China Mobile\u27s digital intellectual property transactions. Finally, the design concept of blockchain architecture based on China Mobile digital intellectual property transaction is proposed

Generating scalable entanglement of ultracold bosons in superlattices through resonant shaking

Based on a one-dimensional double-well superlattice with a unit filling of ultracold atoms per site, we propose a scheme to generate scalable entangled states in the superlattice through resonant lattice shakings. Our scheme utilizes periodic lattice modulations to entangle two atoms in each unit cell with respect to their orbital degree of freedom, and the complete atomic system in the superlattice becomes a cluster of bipartite entangled atom pairs. To demonstrate this we perform $ab \ initio$ quantum dynamical simulations using the Multi-Layer Multi-Configuration Time-Dependent Hartree Method for Bosons, which accounts for all correlations among the atoms. The proposed clusters of bipartite entanglements manifest as an essential resource for various quantum applications, such as measurement based quantum computation. The lattice shaking scheme to generate this cluster possesses advantages such as a high scalability, fast processing speed, rich controllability on the target entangled states, and accessibility with current experimental techniques.Comment: 13 pages, 3 figure

Acupuncture Mechanism and Redox Equilibrium

Oxidative stress participates in the pathological process of various diseases. Acupuncture is a component of the health care system in China that can be traced back for at least 3000 years. Recently, increased evidences indicate that acupuncture stimulation could reduce oxidative damage in organisms under pathological state, but the exact mechanism remains unclear. This review focuses on the emerging links between acupuncture and redox modulation in various disorders, such as vascular dementia, Parkinson’s disease, and hypertension, ranging from redox system, antioxidant system, anti-inflammatory system, and nervous system to signaling pathway. Although the molecular and cellular pathways studies of acupuncture effect on oxidative stress are preliminary, they represent an important step forward in the research of acupuncture antioxidative effect