Multicolor Graphene Nanoribbon/Semiconductor Nanowire Heterojunction Light-Emitting Diodes

We report novel graphene nanoribbon (GNR)/semiconductor nanowire (SNW) heterojunction light-emitting diodes (LEDs) for the first time. The GNR and SNW have a face-to-face contact structure, which has the merit of bigger active region. ZnO, CdS, and CdSe NWs were employed in our case. At forward biases, the GNR/SNW heterjunction LEDs could emit light with wavelengths varying from ultraviolet (380 nm) to green (513 nm) to red (705 nm), which were determined by the band-gaps of the involved SNWs. The mechanism of light emitting for the GNR/SNW heterojunction LED was discussed. Our approach can easily be extended to other semiconductor nano-materials. Moreover, our achievement opens the door to next-generation display technologies, including portable, "see-through", and conformable products.Comment: 12 pages, 4 figure

Model Hamiltonian for topological Kondo insulator SmB6

Starting from the kp method in combination with first-principles calculations, we systematically derive the effective Hamiltonians that capture the low energy band structures of recently discovered topological Kondo insulator SmB6. Using these effective Hamiltonians we can obtain both the energy dispersion and the spin texture of the topological surface states, which can be detected by further experiments.Comment: 6 pages, 4 figure

Quantum anomalous Hall effect and related topological electronic states

Over a long period of exploration, the successful observation of quantized version of anomalous Hall effect (AHE) in thin film of magnetically-doped topological insulator completed a quantum Hall trio---quantum Hall effect (QHE), quantum spin Hall effect (QSHE), and quantum anomalous Hall effect (QAHE). On the theoretical front, it was understood that intrinsic AHE is related to Berry curvature and U(1) gauge field in momentum space. This understanding established connection between the QAHE and the topological properties of electronic structures characterized by the Chern number. With the time reversal symmetry broken by magnetization, a QAHE system carries dissipationless charge current at edges, similar to the QHE where an external magnetic field is necessary. The QAHE and corresponding Chern insulators are also closely related to other topological electronic states, such as topological insulators and topological semimetals, which have been extensively studied recently and have been known to exist in various compounds. First-principles electronic structure calculations play important roles not only for the understanding of fundamental physics in this field, but also towards the prediction and realization of realistic compounds. In this article, a theoretical review on the Berry phase mechanism and related topological electronic states in terms of various topological invariants will be given with focus on the QAHE and Chern insulators. We will introduce the Wilson loop method and the band inversion mechanism for the selection and design of topological materials, and discuss the predictive power of first-principles calculations. Finally, remaining issues, challenges and possible applications for future investigations in the field will be addressed.Comment: Review Article published in , and update

Topological Nodal Line Semimetal and Dirac Semimetal State in Antiperovskite Cu3_3PdN

Based on first-principles calculation and effective model analysis, we propose that the cubic antiperovskite material Cu$_3$PdN can host a three-dimensional (3D) topological nodal line semimetal state when spin-orbit coupling (SOC) is ignored, which is protected by coexistence of time-reversal and inversion symmetry. There are three nodal line circles in total due to the cubic symmetry. "Drumhead"-like surface flat bands are also derived. When SOC is included, each nodal line evolves into a pair of stable 3D Dirac points as protected by C$_4$ crystal symmetry. This is remarkably distinguished from the Dirac semimetals known so far, such as Na$_3$Bi and Cd$_3$As$_2$, both having only one pair of Dirac points. Once C$_4$ symmetry is broken, the Dirac points are gapped and the system becomes a strong topological insulator with (1;111) Z$_2$ indices.Comment: 6 pages, 4 figure

Feedforward Sequential Memory Networks: A New Structure to Learn Long-term Dependency

In this paper, we propose a novel neural network structure, namely \emph{feedforward sequential memory networks (FSMN)}, to model long-term dependency in time series without using recurrent feedback. The proposed FSMN is a standard fully-connected feedforward neural network equipped with some learnable memory blocks in its hidden layers. The memory blocks use a tapped-delay line structure to encode the long context information into a fixed-size representation as short-term memory mechanism. We have evaluated the proposed FSMNs in several standard benchmark tasks, including speech recognition and language modelling. Experimental results have shown FSMNs significantly outperform the conventional recurrent neural networks (RNN), including LSTMs, in modeling sequential signals like speech or language. Moreover, FSMNs can be learned much more reliably and faster than RNNs or LSTMs due to the inherent non-recurrent model structure.Comment: 11 pages, 5 figure

Competitive Inner-Imaging Squeeze and Excitation for Residual Network

Residual networks, which use a residual unit to supplement the identity mappings, enable very deep convolutional architecture to operate well, however, the residual architecture has been proved to be diverse and redundant, which may leads to low-efficient modeling. In this work, we propose a competitive squeeze-excitation (SE) mechanism for the residual network. Re-scaling the value for each channel in this structure will be determined by the residual and identity mappings jointly, and this design enables us to expand the meaning of channel relationship modeling in residual blocks. Modeling of the competition between residual and identity mappings cause the identity flow to control the complement of the residual feature maps for itself. Furthermore, we design a novel inner-imaging competitive SE block to shrink the consumption and re-image the global features of intermediate network structure, by using the inner-imaging mechanism, we can model the channel-wise relations with convolution in spatial. We carry out experiments on the CIFAR, SVHN, and ImageNet datasets, and the proposed method can challenge state-of-the-art results.Comment: Code is available at https://github.com/scut-aitcm/Competitive-Inner-Imaging-SENe

Magnetisms in pp-type monolayer gallium chalcogenides (GaSe, GaS)

Magnetisms in $p$-type monolayer GaX (X=S,Se) is investigated by performing density-functional calculations. Due to the large density of states near the valence band edge, these monolayer semiconductors are ferromagnetic within a small range of hole doping. The intrinsic Ga vacancies can promote local magnetic moment while Se vacancies cannot. Magnetic coupling between vacancy-induced local moments is ferromagnetic and surprisingly long-range. The results indicate that magnetization can be induced by hole doping and can be tuned by controlled defect generation.Comment: 5 page

Automatic construction of Chinese herbal prescription from tongue image via CNNs and auxiliary latent therapy topics

The tongue image provides important physical information of humans. It is of great importance for diagnoses and treatments in clinical medicine. Herbal prescriptions are simple, noninvasive and have low side effects. Thus, they are widely applied in China. Studies on the automatic construction technology of herbal prescriptions based on tongue images have great significance for deep learning to explore the relevance of tongue images for herbal prescriptions, it can be applied to healthcare services in mobile medical systems. In order to adapt to the tongue image in a variety of photographic environments and construct herbal prescriptions, a neural network framework for prescription construction is designed. It includes single/double convolution channels and fully connected layers. Furthermore, it proposes the auxiliary therapy topic loss mechanism to model the therapy of Chinese doctors and alleviate the interference of sparse output labels on the diversity of results. The experiment use the real world tongue images and the corresponding prescriptions and the results can generate prescriptions that are close to the real samples, which verifies the feasibility of the proposed method for the automatic construction of herbal prescriptions from tongue images. Also, it provides a reference for automatic herbal prescription construction from more physical information.Comment: 17 pages, 10 figure

TopoTag: A Robust and Scalable Topological Fiducial Marker System

Fiducial markers have been playing an important role in augmented reality (AR), robot navigation, and general applications where the relative pose between a camera and an object is required. Here we introduce TopoTag, a robust and scalable topological fiducial marker system, which supports reliable and accurate pose estimation from a single image. TopoTag uses topological and geometrical information in marker detection to achieve higher robustness. Topological information is extensively used for 2D marker detection, and further corresponding geometrical information for ID decoding. Robust 3D pose estimation is achieved by taking advantage of all TopoTag vertices. Without sacrificing bits for higher recall and precision like previous systems, TopoTag can use full bits for ID encoding. TopoTag supports tens of thousands unique IDs and easily extends to millions of unique tags resulting in massive scalability. We collected a large test dataset including in total 169,713 images for evaluation, involving in-plane and out-of-plane rotation, image blur, different distances and various backgrounds, etc. Experiments on the dataset and real indoor and outdoor scene tests with a rolling shutter camera both show that TopoTag significantly outperforms previous fiducial marker systems in terms of various metrics, including detection accuracy, vertex jitter, pose jitter and accuracy, etc. In addition, TopoTag supports occlusion as long as the main tag topological structure is maintained and allows for flexible shape design where users can customize internal and external marker shapes. Code for our marker design/generation, marker detection, and dataset are available at http://herohuyongtao.github.io/research/publications/topo-tag/.Comment: Accepted to TVC

A Synthetic Approach for Recommendation: Combining Ratings, Social Relations, and Reviews

Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized choices. Online social networks and user-generated content provide diverse sources for recommendation beyond ratings, which present opportunities as well as challenges for traditional RSs. Although social matrix factorization (Social MF) can integrate ratings with social relations and topic matrix factorization can integrate ratings with item reviews, both of them ignore some useful information. In this paper, we investigate the effective data fusion by combining the two approaches, in two steps. First, we extend Social MF to exploit the graph structure of neighbors. Second, we propose a novel framework MR3 to jointly model these three types of information effectively for rating prediction by aligning latent factors and hidden topics. We achieve more accurate rating prediction on two real-life datasets. Furthermore, we measure the contribution of each data source to the proposed framework.Comment: 7 pages, 8 figure