arXiv subjects

Yuqiang Fang

Publications and source records attributed to Yuqiang Fang.

22 records · Page 2Linked to original sources

arXiv2018

Coexistence of Intrinsic Superconductivity and Topological Insulator State in Monoclinic Phase WS2

Recently, intriguing phenomena of superconductivity, type-II Weyl semimetal or quantum spin Hall states were discovered in metastable 1T'-type VIB-group transition metal dichalcogenides (TMDs). Here, we report that monoclinic phase WS2 was discovered and synthesized in our experiments. The intrinsic superconducting transition was observed in monoclinic WS2 with a transition temperature Tc of 8.8 K which is the highest among previously reported TMDs without any fine-tuning process. Intersteing, topological insulator state, defined by topological invariant Z2, was also discovered with a single Dirac cone on the surface, which is different from all topological states reported in TMDs. Further, the electronic structure was found to have a strong anisotropy by Shubnikov-de Haas oscillations and first-principles calculations. Our findings reveal that monoclinic WS2 might be a new topological superconductivity candidate with a strong anisotropy.

cond-mat.supr-con↗

arXiv2017

Structure Re-determination and Superconductivity Observation of Bulk 1T MoS2

2H MoS2 has been intensively studied because of layer-dependent electronic structures and novel physical properties. Though the metastable 1T MoS2 with the [MoS6] octahedron was observed from the microscopic area, the true crystal structure of 1T phase has not been determined strictly. Moreover, the true physical properties have not been demonstrated from experiments due to the challenge for the preparation of pure 1T MoS2 crystals. Here, we successfully synthesized the 1T MoS2 single crystals and re-determined the crystal structure of 1T MoS2 from single-crystal X-ray diffraction. 1T MoS2 crystalizes in space group P-3m1 with a cell of a = b = 3.190(3) Å and c = 5.945(6) Å. The individual MoS2 layer consists of MoS6 octahedron sharing edge with each other. More surprisingly, the bulk 1T MoS2 crystals undergo a superconducting transition of Tc = 4 K, which is the first observation of superconductivity in pure 1T MoS2 phase.

cond-mat.supr-con↗

arXiv2014

Unified Structured Learning for Simultaneous Human Pose Estimation and Garment Attribute Classification

In this paper, we utilize structured learning to simultaneously address two intertwined problems: human pose estimation (HPE) and garment attribute classification (GAC), which are valuable for a variety of computer vision and multimedia applications. Unlike previous works that usually handle the two problems separately, our approach aims to produce a jointly optimal estimation for both HPE and GAC via a unified inference procedure. To this end, we adopt a preprocessing step to detect potential human parts from each image (i.e., a set of "candidates") that allows us to have a manageable input space. In this way, the simultaneous inference of HPE and GAC is converted to a structured learning problem, where the inputs are the collections of candidate ensembles, the outputs are the joint labels of human parts and garment attributes, and the joint feature representation involves various cues such as pose-specific features, garment-specific features, and cross-task features that encode correlations between human parts and garment attributes. Furthermore, we explore the "strong edge" evidence around the potential human parts so as to derive more powerful representations for oriented human parts. Such evidences can be seamlessly integrated into our structured learning model as a kind of energy function, and the learning process could be performed by standard structured Support Vector Machines (SVM) algorithm. However, the joint structure of the two problems is a cyclic graph, which hinders efficient inference. To resolve this issue, we compute instead approximate optima by using an iterative procedure, where in each iteration the variables of one problem are fixed. In this way, satisfactory solutions can be efficiently computed by dynamic programming. Experimental results on two benchmark datasets show the state-of-the-art performance of our approach.

cs.CV↗

arXiv2014

Cross-Scale Cost Aggregation for Stereo Matching

Human beings process stereoscopic correspondence across multiple scales. However, this bio-inspiration is ignored by state-of-the-art cost aggregation methods for dense stereo correspondence. In this paper, a generic cross-scale cost aggregation framework is proposed to allow multi-scale interaction in cost aggregation. We firstly reformulate cost aggregation from a unified optimization perspective and show that different cost aggregation methods essentially differ in the choices of similarity kernels. Then, an inter-scale regularizer is introduced into optimization and solving this new optimization problem leads to the proposed framework. Since the regularization term is independent of the similarity kernel, various cost aggregation methods can be integrated into the proposed general framework. We show that the cross-scale framework is important as it effectively and efficiently expands state-of-the-art cost aggregation methods and leads to significant improvements, when evaluated on Middlebury, KITTI and New Tsukuba datasets.

cs.CV↗