Coexistence of multi-photon processes and longitudinal couplings in superconducting flux qubits

In contrast to natural atoms, the potential energies for superconducting flux qubit (SFQ) circuits can be artificially controlled. When the inversion symmetry of the potential energy is broken, we find that the multi-photon processes can coexist in the multi-level SFQ circuits. Moreover, there are not only transverse but also longitudinal couplings between the external magnetic fields and the SFQs when the inversion symmetry of potential energy is broken. The longitudinal coupling would induce some new phenomena in the SFQs. Here we will show how the longitudinal coupling can result in the coexistence of multi-photon processes in a two-level system formed by a SFQ circuit. We also show that the SFQs can become transparent to the transverse coupling fields when the longitudinal coupling fields satisfy the certain conditions. We further show that the quantum Zeno effect can also be induced by the longitudinal coupling in the SFQs. Finally we clarify why the longitudinal coupling can induce coexistence and disappearance of single- and two-photon processes for a driven SFQ, which is coupled to a single-mode quantized field.Comment: 11 pages, 6 figure

Quantum Criticality of 1D Attractive Fermi Gas

We obtain an analytical equation of state for one-dimensional strongly attractive Fermi gas for all parameter regime in current experiments. From the equation of state we derive universal scaling functions that control whole thermodynamical properties in quantum critical regimes and illustrate physical origin of quantum criticality. It turns out that the critical properties of the system are described by these of free fermions and those of mixtures of fermions with mass $m$ and $2m$. We also show how these critical properties of bulk systems can be revealed from the density profile of trapped Fermi gas at finite temperatures and can be used to determine the T=0 phase boundaries without any arbitrariness.Comment: extended version, 9 pages, 7 eps figures, corrections of few typo

Domain-adaptive Graph Attention-supervised Network for Cross-network Edge Classification

Graph neural networks (GNNs) have shown great ability in modeling graphs, however, their performance would significantly degrade when there are noisy edges connecting nodes from different classes. To alleviate negative effect of noisy edges on neighborhood aggregation, some recent GNNs propose to predict the label agreement between node pairs within a single network. However, predicting the label agreement of edges across different networks has not been investigated yet. Our work makes the pioneering attempt to study a novel problem of cross-network homophilous and heterophilous edge classification (CNHHEC), and proposes a novel domain-adaptive graph attention-supervised network (DGASN) to effectively tackle the CNHHEC problem. Firstly, DGASN adopts multi-head GAT as the GNN encoder, which jointly trains node embeddings and edge embeddings via the node classification and edge classification losses. As a result, label-discriminative embeddings can be obtained to distinguish homophilous edges from heterophilous edges. In addition, DGASN applies direct supervision on graph attention learning based on the observed edge labels from the source network, thus lowering the negative effects of heterophilous edges while enlarging the positive effects of homophilous edges during neighborhood aggregation. To facilitate knowledge transfer across networks, DGASN employs adversarial domain adaptation to mitigate domain divergence. Extensive experiments on real-world benchmark datasets demonstrate that the proposed DGASN achieves the state-of-the-art performance in CNHHEC.Comment: IEEE Transactions on Neural Networks and Learning Systems, 202

Neighbor Contrastive Learning on Learnable Graph Augmentation

Recent years, graph contrastive learning (GCL), which aims to learn representations from unlabeled graphs, has made great progress. However, the existing GCL methods mostly adopt human-designed graph augmentations, which are sensitive to various graph datasets. In addition, the contrastive losses originally developed in computer vision have been directly applied to graph data, where the neighboring nodes are regarded as negatives and consequently pushed far apart from the anchor. However, this is contradictory with the homophily assumption of networks that connected nodes often belong to the same class and should be close to each other. In this work, we propose an end-to-end automatic GCL method, named NCLA to apply neighbor contrastive learning on learnable graph augmentation. Several graph augmented views with adaptive topology are automatically learned by the multi-head graph attention mechanism, which can be compatible with various graph datasets without prior domain knowledge. In addition, a neighbor contrastive loss is devised to allow multiple positives per anchor by taking network topology as the supervised signals. Both augmentations and embeddings are learned end-to-end in the proposed NCLA. Extensive experiments on the benchmark datasets demonstrate that NCLA yields the state-of-the-art node classification performance on self-supervised GCL and even exceeds the supervised ones, when the labels are extremely limited. Our code is released at https://github.com/shenxiaocam/NCLA

Free field realization of the exceptional current superalgebra \hat{D(2,1;\a)}_k

The free-field representations of the D(2,1;\a) current superalgebra and the corresponding energy-momentum tensor are constructed. The related screening currents of the first kind are also presented.Comment: Latex file, 10 page

Hole Doping Dependence of the Coherence Length in La2−xSrxCuO4La_{2-x}Sr_xCuO_4 Thin Films

By measuring the field and temperature dependence of magnetization on systematically doped $La_{2-x}Sr_xCuO_4$ thin films, the critical current density $j_c(0)$ and the collective pinning energy $U_p(0)$ are determined in single vortex creep regime. Together with the published data of superfluid density, condensation energy and anisotropy, for the first time we derive the doping dependence of the coherence length or vortex core size in wide doping regime directly from the low temperature data. It is found that the coherence length drops in the underdoped region and increases in the overdoped side with the increase of hole concentration. The result in underdoped region clearly deviates from what expected by the pre-formed pairing model if one simply associates the pseudogap with the upper-critical field.Comment: 4 pages, 4 figure

Influence of reaction atmosphere (H2O, N2, H2, CO2, CO) on fluidized-bed fast pyrolysis of biomass using detailed tar vapor chemistry in computational fluid dynamics

Secondary pyrolysis in fluidized bed fast pyrolysis of biomass is the focus of this work. A novel computational fluid dynamics (CFD) model coupled with a comprehensive chemistry scheme (134 species and 4169 reactions, in CHEMKIN format) has been developed to investigate this complex phenomenon. Previous results from a transient three-dimensional model of primary pyrolysis were used for the source terms of primary products in this model. A parametric study of reaction atmospheres (H2O, N2, H2, CO2, CO) has been performed. For the N2 and H2O atmosphere, results of the model compared favorably to experimentally obtained yields after the temperature was adjusted to a value higher than that used in experiments. One notable deviation versus experiments is pyrolytic water yield and yield of higher hydrocarbons. The model suggests a not overly strong impact of the reaction atmosphere. However, both chemical and physical effects were observed. Most notably, effects could be seen on the yield of various compounds, temperature profile throughout the reactor system, residence time, radical concentration, and turbulent intensity. At the investigated temperature (873 K), turbulent intensity appeared to have the strongest influence on liquid yield. With the aid of acceleration techniques, most importantly dimension reduction, chemistry agglomeration, and in-situ tabulation, a converged solution could be obtained within a reasonable time (∼30 h). As such, a new potentially useful method has been suggested for numerical analysis of fast pyrolysis

Drinfeld twist and symmetric Bethe vectors of the open XYZ chain with non-diagonal boundary terms

With the help of the Drinfeld twist or factorizing F-matrix for the eight-vertex solid-on-solid (SOS) model, we find that in the F-basis provided by the twist the two sets of pseudo-particle creation operators simultaneously take completely symmetric and polarization free form. This allows us to obtain the explicit and completely symmetric expressions of the two sets of Bethe states of the model.Comment: Latex file, 25 page