Duality in topological superconductors and topological ferromagnetic insulators in a honeycomb lattice

The ground state of large Hubbard $U$ limit of a honeycomb lattice near half-filling is known to be a singlet $d+id$-wave superconductor. It is also known that this $d+id$ superconductor exhibits a chiral $p+ip$ pairing locally at the Dirac cone, characterized by a $2\mathbb{Z}$ topological invariant. By constructing a dual transformation, we demonstrate that this $2\mathbb{Z}$ topological superconductor is equivalent to a collection of two topological ferromagnetic insulators. As a result of the duality, the topology of the electronic structures for a $d+id$ superconductor is controllable via the change of the chemical potential by tuning the gate voltage. In particular, instead of being always a chiral superconductor, we find that the $d+id$ superconductor undergoes a topological phase transition from a chiral superconductor to a quasi-helical superconductor as the gap amplitude or the chemical potential decreases. The quasi-helical superconducting phase is found to be characterized by a topological invariant in the pseudo-spin charge sector with vanishing both the Chern number and the spin Chern number. We further elucidate the topological phase transition by analyzing the relationship between the topological invariant and the rotation symmetry. Due to the angular momentum carried by the gap function and spin-orbit interactions, we show that by placing $d+id$ superconductors in proximity to ferromagnets, varieties of chiral superconducting phases characterized by higher Chern numbers can be accessed, providing a new platform for hosting large numbers of Majorana modes at edges.Comment: 12 pages, 6 figure

Improved HAC Covariance Matrix Estimation Based on Forecast Errors

We propose computing HAC covariance matrix estimators based on one-stepahead forecasting errors. It is shown that this estimator is consistent and has smaller bias than other HAC estimators. Moreover, the tests that rely on this estimator have more accurate sizes without sacrificing its power.forecast error, HAC estimator, kernel estimator, recursive residual, robust test

Multi-Label Zero-Shot Learning with Structured Knowledge Graphs

In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance. Inspired by the way humans utilize semantic knowledge between objects of interests, we propose a framework that incorporates knowledge graphs for describing the relationships between multiple labels. Our model learns an information propagation mechanism from the semantic label space, which can be applied to model the interdependencies between seen and unseen class labels. With such investigation of structured knowledge graphs for visual reasoning, we show that our model can be applied for solving multi-label classification and ML-ZSL tasks. Compared to state-of-the-art approaches, comparable or improved performances can be achieved by our method.Comment: CVPR 201