Surface plasmon polaritons in topological insulator

We study surface plasmon polaritons on topological insulator-vacuum interface. When the time-reversal symmetry is broken due to ferromagnetic coupling, the surface states exhibit magneto-optical Kerr effect. This effect gives rise to a novel transverse type surface plasmon polariton, besides the longitudinal type. In specific, these two types contain three different channels, corresponding to the pole of determinant of Fresnel reflection matrix. All three channels of surface plasmon polaritons display tight confinement, long lifetime and show strong light-matter coupling with a dipole emitter.Comment: 6 pages, 4 figure

A Semiblind Two-Way Training Method for Discriminatory Channel Estimation in MIMO Systems

Discriminatory channel estimation (DCE) is a recently developed strategy to enlarge the performance difference between a legitimate receiver (LR) and an unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless system. Specifically, it makes use of properly designed training signals to degrade channel estimation at the UR which in turn limits the UR's eavesdropping capability during data transmission. In this paper, we propose a new two-way training scheme for DCE through exploiting a whitening-rotation (WR) based semiblind method. To characterize the performance of DCE, a closed-form expression of the normalized mean squared error (NMSE) of the channel estimation is derived for both the LR and the UR. Furthermore, the developed analytical results on NMSE are utilized to perform optimal power allocation between the training signal and artificial noise (AN). The advantages of our proposed DCE scheme are two folds: 1) compared to the existing DCE scheme based on the linear minimum mean square error (LMMSE) channel estimator, the proposed scheme adopts a semiblind approach and achieves better DCE performance; 2) the proposed scheme is robust against active eavesdropping with the pilot contamination attack, whereas the existing scheme fails under such an attack.Comment: accepted for publication in IEEE Transactions on Communication

Crystal structure and electronic structure of quaternary semiconductors Cu2_2ZnTiSe4_4 and Cu2_2ZnTiS4_4 for solar cell absorber

We design two new I2-II-IV-VI4 quaternary semiconductors Cu$_2$ZnTiSe$_4$ and Cu$_2$ZnTiS$_4$, and systematically study the crystal and electronic structure by employing first-principles electronic structure calculations. Among the considered crystal structures, it is confirmed that the band gaps of Cu$_2$ZnTiSe$_4$ and Cu$_2$ZnTiS$_4$ originate from the full occupied Cu 3$d$ valence band and unoccupied Ti 3$d$ conducting band, and kesterite structure should be the ground state. Furthermore, our calculations indicate that Cu$_2$ZnTiSe$_4$ and Cu$_2$ZnTiS$_4$ have comparable band gaps with Cu$_2$ZnTSe$_4$ and Cu$_2$ZnTS$_4$, but almost twice larger absorption coefficient $\alpha(\omega)$. Thus, the materials are expected to be candidate materials for solar cell absorber.Comment: 4 pages, 4 figure

CD-CNN: A Partially Supervised Cross-Domain Deep Learning Model for Urban Resident Recognition

Driven by the wave of urbanization in recent decades, the research topic about migrant behavior analysis draws great attention from both academia and the government. Nevertheless, subject to the cost of data collection and the lack of modeling methods, most of existing studies use only questionnaire surveys with sparse samples and non-individual level statistical data to achieve coarse-grained studies of migrant behaviors. In this paper, a partially supervised cross-domain deep learning model named CD-CNN is proposed for migrant/native recognition using mobile phone signaling data as behavioral features and questionnaire survey data as incomplete labels. Specifically, CD-CNN features in decomposing the mobile data into location domain and communication domain, and adopts a joint learning framework that combines two convolutional neural networks with a feature balancing scheme. Moreover, CD-CNN employs a three-step algorithm for training, in which the co-training step is of great value to partially supervised cross-domain learning. Comparative experiments on the city Wuxi demonstrate the high predictive power of CD-CNN. Two interesting applications further highlight the ability of CD-CNN for in-depth migrant behavioral analysis.Comment: 8 pages, 5 figures, conferenc

Utilization methods and practice of abandoned mines and related rock mechanics under the ecological and double carbon strategy in china—a comprehensive review

Governance of abandoned mines has become a pressing issue for China. The utilization of abandoned mines is a technology that can solve the problem of governance and recreate the value of mines, which is in line with the current strategic goals of ecological protection and double carbon in China. In this paper, the various utilization models and the advances in rock mechanics of abandoned mines across the globe are summarized and reviewed. The utilization models of abandoned mines can be categorized into four aspects: Energy storage, Waste treatment, Ecological restoration, and carbon dioxide (CO2) sequestration. There are a number of applications and uses of abandoned mines, such as pumped storage, compressed air storage, salt cavern gas/oil storage construction, carbon dioxide storage and utilization, radioactive waste disposal and treatment, and tourism development. Various progress practices of abandoned mines are discussed in detail with emphasis on the national conditions of China. The basic rock mechanics problems and advances involved in the construction of the facilities related to the utilization of abandoned mines are discussed and evaluated. The establishment of relevant research and experimental platforms will contribute to the sustainable development of China’s mining industry and the improvement of clean technologies. © 2022 by the authors

Social Media Would Not Lie: Prediction of the 2016 Taiwan Election via Online Heterogeneous Data

The prevalence of online media has attracted researchers from various domains to explore human behavior and make interesting predictions. In this research, we leverage heterogeneous social media data collected from various online platforms to predict Taiwan's 2016 presidential election. In contrast to most existing research, we take a "signal" view of heterogeneous information and adopt the Kalman filter to fuse multiple signals into daily vote predictions for the candidates. We also consider events that influenced the election in a quantitative manner based on the so-called event study model that originated in the field of financial research. We obtained the following interesting findings. First, public opinions in online media dominate traditional polls in Taiwan election prediction in terms of both predictive power and timeliness. But offline polls can still function on alleviating the sample bias of online opinions. Second, although online signals converge as election day approaches, the simple Facebook "Like" is consistently the strongest indicator of the election result. Third, most influential events have a strong connection to cross-strait relations, and the Chou Tzu-yu flag incident followed by the apology video one day before the election increased the vote share of Tsai Ing-Wen by 3.66%. This research justifies the predictive power of online media in politics and the advantages of information fusion. The combined use of the Kalman filter and the event study method contributes to the data-driven political analytics paradigm for both prediction and attribution purposes

GaussianEditor: Editing 3D Gaussians Delicately with Text Instructions

Recently, impressive results have been achieved in 3D scene editing with text instructions based on a 2D diffusion model. However, current diffusion models primarily generate images by predicting noise in the latent space, and the editing is usually applied to the whole image, which makes it challenging to perform delicate, especially localized, editing for 3D scenes. Inspired by recent 3D Gaussian splatting, we propose a systematic framework, named GaussianEditor, to edit 3D scenes delicately via 3D Gaussians with text instructions. Benefiting from the explicit property of 3D Gaussians, we design a series of techniques to achieve delicate editing. Specifically, we first extract the region of interest (RoI) corresponding to the text instruction, aligning it to 3D Gaussians. The Gaussian RoI is further used to control the editing process. Our framework can achieve more delicate and precise editing of 3D scenes than previous methods while enjoying much faster training speed, i.e. within 20 minutes on a single V100 GPU, more than twice as fast as Instruct-NeRF2NeRF (45 minutes -- 2 hours).Comment: Project page: https://GaussianEditor.github.i