Coupling the valley degree of freedom to antiferromagnetic order

Conventional electronics are based invariably on the intrinsic degrees of freedom of an electron, namely, its charge and spin. The exploration of novel electronic degrees of freedom has important implications in both basic quantum physics and advanced information technology. Valley as a new electronic degree of freedom has received considerable attention in recent years. In this paper, we develop the theory of spin and valley physics of an antiferromagnetic honeycomb lattice. We show that by coupling the valley degree of freedom to antiferromagnetic order, there is an emergent electronic degree of freedom characterized by the product of spin and valley indices, which leads to spin-valley dependent optical selection rule and Berry curvature-induced topological quantum transport. These properties will enable optical polarization in the spin-valley space, and electrical detection/manipulation through the induced spin, valley and charge fluxes. The domain walls of an antiferromagnetic honeycomb lattice harbors valley-protected edge states that support spin-dependent transport. Finally, we employ first principles calculations to show that the proposed optoelectronic properties can be realized in antiferromagnetic manganese chalcogenophosphates (MnPX_3, X = S, Se) in monolayer form.Comment: 6 pages, 5 figure

Matching Natural Language Sentences with Hierarchical Sentence Factorization

Semantic matching of natural language sentences or identifying the relationship between two sentences is a core research problem underlying many natural language tasks. Depending on whether training data is available, prior research has proposed both unsupervised distance-based schemes and supervised deep learning schemes for sentence matching. However, previous approaches either omit or fail to fully utilize the ordered, hierarchical, and flexible structures of language objects, as well as the interactions between them. In this paper, we propose Hierarchical Sentence Factorization---a technique to factorize a sentence into a hierarchical representation, with the components at each different scale reordered into a "predicate-argument" form. The proposed sentence factorization technique leads to the invention of: 1) a new unsupervised distance metric which calculates the semantic distance between a pair of text snippets by solving a penalized optimal transport problem while preserving the logical relationship of words in the reordered sentences, and 2) new multi-scale deep learning models for supervised semantic training, based on factorized sentence hierarchies. We apply our techniques to text-pair similarity estimation and text-pair relationship classification tasks, based on multiple datasets such as STSbenchmark, the Microsoft Research paraphrase identification (MSRP) dataset, the SICK dataset, etc. Extensive experiments show that the proposed hierarchical sentence factorization can be used to significantly improve the performance of existing unsupervised distance-based metrics as well as multiple supervised deep learning models based on the convolutional neural network (CNN) and long short-term memory (LSTM).Comment: Accepted by WWW 2018, 10 page

Disorder-free sputtering method on graphene

Deposition of various materials onto graphene without causing any disorder is highly desirable for graphene applications. Especially, sputtering is a versatile technique to deposit various metals and insulators for spintronics, and indium tin oxide to make transparent devices. However, the sputtering process causes damage to graphene because of high energy sputtered atoms. By flipping the substrate and using a high Ar pressure, we demonstrate that the level of damage to graphene can be reduced or eliminated in dc, rf, and reactive sputtering processes

Evaluation of the stress singularities of plane V-notches in bonded dissimilar materials

According to the linear theory of elasticity, there exists a combination of different orders of stress singularity at a V-notch tip of bonded dissimilar materials. The singularity reflects a strong stress concentration near the sharp V-notches. In this paper, a new way is proposed in order to determine the orders of singularity for two-dimensional V-notch problems. Firstly, on the basis of an asymptotic stress field in terms of radial coordinates at the V-notch tip, the governing equations of the elastic theory are transformed into an eigenvalue problem of ordinary differential equations (ODEs) with respect to the circumferential coordinate h around the notch tip. Then the interpolating matrix method established by the first author is further developed to solve the general eigenvalue problem. Hence, the singularity orders of the V-notch problem are determined through solving the corresponding ODEs by means of the interpolating matrix method. Meanwhile, the associated eigenvectors of the displacement and stress fields near the V-notches are also obtained. These functions are essential in calculating the amplitude of the stress field described as generalized stress intensity factors of the V-notches. The present method is also available to deal with the plane V-notch problems in bonded orthotropic multi-material. Finally, numerical examples are presented to illustrate the accuracy and the effectiveness of the method

Brown adipocytes can display a mammary basal myoepithelial cell phenotype in vivo

This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB13030000) and the CAS-Novonordisk Foundation, as well as grants from the ‘1000 talents’ recruitment program, and a ‘Great-wall professorship’ from the CAS-Novonordisk Foundation all to JRS. We are grateful to all the members of Molecular Energetics Group for their support and discussion of the results. We would like to thank the Center for Biological Imaging from Institute of Biophysics Chinese Academy of Sciences and Professor Zhaohui Wang's Lab from Institute of Genetics and Developmental Biology Chinese Academy of Sciences for confocal microscopy and the Center for Developmental Biology from Institute of Genetics and Developmental Biology Chinese Academy of Sciences and Dr. Jai from Core Facility for Protein Research from Institute of Biophysics Chinese Academy of Sciences for flow cytometry. We are grateful to Dr. Kuang from Purdue University and Dr. Zhu from Chinese Academy of Medical Sciences Peking Union Medical College for the kind donation of Myf5-Cre mice and Dr. Wolfrum from the Institute of Food Nutrition and Health at the ETH Zurich for the kind donation of the Ucp1-DTR mice. Xun Huang provided valuable comments on previous versions of the manuscript.Peer reviewedPublisher PD