Thermodynamic Properties of Rashba Spin-Orbit-Coupled Fermi Gas

We investigate the thermodynamic properties of a superfluid Fermi gas subject to Rashba spin-orbit coupling and effective Zeeman field. We adopt a T-matrix scheme that takes beyond-mean-field effects, which are important for strongly interacting systems, into account. We focus on the calculation of two important quantities: the superfluid transition temperature and the isothermal compressibility. Our calculation shows very distinct influences of the out-of-plane and the in-plane Zeeman fields on the Fermi gas. We also confirm that the in-plane Zeeman field induces a Fulde-Ferrell superfluid below the critical temperature and an exotic finite-momentum pseudo-gap phase above the critical temperature.Comment: 8 pages, 9 figure

Effective p-wave interaction and topological superfluids in s-wave quantum gases

P-wave interaction in cold atoms may give rise to exotic topological superfluids. However, the realization of p-wave interaction in cold atom system is experimentally challenging. Here we propose a simple scheme to synthesize effective $p$-wave interaction in conventional $s$-wave interacting quantum gases. The key idea is to load atoms into spin-dependent optical lattice potential. Using two concrete examples involving spin-1/2 fermions, we show how the original system can be mapped into a model describing spinless fermions with nearest neighbor p-wave interaction, whose ground state can be a topological superfluid that supports Majorana fermions under proper conditions. Our proposal has the advantage that it does not require spin-orbit coupling or loading atoms onto higher orbitals, which is the key in earlier proposals to synthesize effective $p$-wave interaction in $s$-wave quantum gases, and may provide a completely new route for realizing $p$-wave topological superfluids.Comment: 5 pages, 4 figure

Field-induced topological pair-density wave states in a multilayer optical lattice

We study the superfluid phases of a Fermi gas in a multilayer optical lattice system in the presence of out-of-plane Zeeman field, as well as spin-orbit (SO) coupling. We show that the Zeeman field combined with the SO coupling leads to exotic topological pair-density wave (PDW) phases in which different layers possess different superfluid order parameters, even though each layer experiences the same Zeeman field and the SO coupling. We elucidate the mechanism of the emerging PDW phases, and characterize their topological properties by calculating the associated Chern numbers.Comment: 7 pages, 6 figures, accepted by Phys. Rev.

Hartree-Fock-Bogoliubov Theory of Dipolar Fermi Gases

We construct a fully self-consistent Hartree-Fock-Bogoliubov theory that describes a spinless Fermi gas with long-range interaction. We apply this theory to a system of uniform dipolar fermionic polar molecules, which has attracted much attention recently, due to rapid experimental progress in achieving such systems. By calculating the anisotropic superfluid order parameter, and the critical temperature $T_{c}$, we show that, "hign $T_c$" superfluid can be achieved with a quite modest value of interaction strength for polar molecules. In addition, we also show that the presence of the Fock exchange interaction enhances superfluid pairing.Comment: 4.1 pages, 4 figure

Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features

TThe goal of our work is to discover dominant objects in a very general setting where only a single unlabeled image is given. This is far more challenge than typical co-localization or weakly-supervised localization tasks. To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Location Mining (OLM), which exploits the advantages of data mining and feature representation of pre-trained convolutional neural networks (CNNs). Specifically, we first convert the feature maps from a pre-trained CNN model into a set of transactions, and then discovers frequent patterns from transaction database through pattern mining techniques. We observe that those discovered patterns, i.e., co-occurrence highlighted regions, typically hold appearance and spatial consistency. Motivated by this observation, we can easily discover and localize possible objects by merging relevant meaningful patterns. Extensive experiments on a variety of benchmarks demonstrate that OLM achieves competitive localization performance compared with the state-of-the-art methods. We also evaluate our approach compared with unsupervised saliency detection methods and achieves competitive results on seven benchmark datasets. Moreover, we conduct experiments on fine-grained classification to show that our proposed method can locate the entire object and parts accurately, which can benefit to improving the classification results significantly