Valley excitons in two-dimensional semiconductors

Monolayer group-VIB transition metal dichalcogenides have recently emerged as a new class of semiconductors in the two-dimensional limit. The attractive properties include: the visible range direct band gap ideal for exploring optoelectronic applications; the intriguing physics associated with spin and valley pseudospin of carriers which implies potentials for novel electronics based on these internal degrees of freedom; the exceptionally strong Coulomb interaction due to the two-dimensional geometry and the large effective masses. The physics of excitons, the bound states of electrons and holes, has been one of the most actively studied topics on these two-dimensional semiconductors, where the excitons exhibit remarkably new features due to the strong Coulomb binding, the valley degeneracy of the band edges, and the valley dependent optical selection rules for interband transitions. Here we give a brief overview of the experimental and theoretical findings on excitons in two-dimensional transition metal dichalcogenides, with focus on the novel properties associated with their valley degrees of freedom.Comment: Topical review, published online on National Science Review in Jan 201

Integrated optical devices based on broadband epsilon-near-zero meta-atoms

We verify the feasibility of the proposed theoretical strategy for designing the broadband near-zero permittivity (ENZ) metamaterial at optical frequency range with numerical simulations. In addition, the designed broadband ENZ stack are used as meta-atoms to build functional nanophotonic devices with extraordinary properties, including an ultranarrow electromagnetic energy tunneling channel and an ENZ concave focusing lens.Comment: 3 pages, 3 figure

Can you forgive? It depends on how happy you are

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This paper examined how individual group status and happiness influence forgiveness. In Study 1, happiness was treated as a trait difference: highly happy people, compared with very unhappy people, were found to be more willing to forgive murderers. More important, an interaction effect between happiness and group status on forgiveness was found, that is, highly happy people tended to be more forgiving when either ingroup or outgroup mem- bers were killed; unhappy people, however, tended to be less forgiving about murder when ingroup rather than outgroup members were killed. In Study 2, happiness was treated as an emotional state difference: happiness, rather than sadness, was found to bring greater forgiveness. Moreover, consistent with the interaction effect displayed in Study 1, happy participants tended to forgive more when ingroup or outgroup members were hurt; sad partici- pants tended to forgive less when ingroup members rather than outgroup members were hurt. Implications for connections between happiness, group membership, and forgiveness are discussed

Reduction of Second-Order Network Systems with Structure Preservation

This paper proposes a general framework for structure-preserving model reduction of a secondorder network system based on graph clustering. In this approach, vertex dynamics are captured by the transfer functions from inputs to individual states, and the dissimilarities of vertices are quantified by the H2-norms of the transfer function discrepancies. A greedy hierarchical clustering algorithm is proposed to place those vertices with similar dynamics into same clusters. Then, the reduced-order model is generated by the Petrov-Galerkin method, where the projection is formed by the characteristic matrix of the resulting network clustering. It is shown that the simplified system preserves an interconnection structure, i.e., it can be again interpreted as a second-order system evolving over a reduced graph. Furthermore, this paper generalizes the definition of network controllability Gramian to second-order network systems. Based on it, we develop an efficient method to compute H2-norms and derive the approximation error between the full-order and reduced-order models. Finally, the approach is illustrated by the example of a small-world network

PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume

We present a compact but effective CNN model for optical flow, called PWC-Net. PWC-Net has been designed according to simple and well-established principles: pyramidal processing, warping, and the use of a cost volume. Cast in a learnable feature pyramid, PWC-Net uses the cur- rent optical flow estimate to warp the CNN features of the second image. It then uses the warped features and features of the first image to construct a cost volume, which is processed by a CNN to estimate the optical flow. PWC-Net is 17 times smaller in size and easier to train than the recent FlowNet2 model. Moreover, it outperforms all published optical flow methods on the MPI Sintel final pass and KITTI 2015 benchmarks, running at about 35 fps on Sintel resolution (1024x436) images. Our models are available on https://github.com/NVlabs/PWC-Net.Comment: CVPR 2018 camera ready version (with github link to Caffe and PyTorch code