Stable nontrivial Z2 topology in ultrathin Bi (111) films: a first-principles study

Recently, there have been intense efforts in searching for new topological insulator (TI) materials. Based on first-principles calculations, we find that all the ultrathin Bi (111) films are characterized by a nontrivial Z2 number independent of the film thickness, without the odd-even oscillation of topological triviality as commonly perceived. The stable nontrivial Z2 topology is retained by the concurrent band gap inversions at multiple time-reversal-invariant k-points and associated with the intermediate inter-bilayer coupling of the multi-bilayer Bi film. Our calculations further indicate that the presence of metallic surface states in thick Bi(111) films can be effectively removed by surface adsorption.Comment: 5 pages, 3 figure

Joint Channel-and-Data Estimation for Large-MIMO Systems with Low-Precision ADCs

The use of low precision (e.g., 1-3 bits) analog-to-digital convenors (ADCs) in very large multiple-input multiple-output (MIMO) systems is a technique to reduce cost and power consumption. In this context, nevertheless, it has been shown that the training duration is required to be {\em very large} just to obtain an acceptable channel state information (CSI) at the receiver. A possible solution to the quantized MIMO systems is joint channel-and-data (JCD) estimation. This paper first develops an analytical framework for studying the quantized MIMO system using JCD estimation. In particular, we use the Bayes-optimal inference for the JCD estimation and realize this estimator utilizing a recent technique based on approximate message passing. Large-system analysis based on the replica method is then adopted to derive the asymptotic performances of the JCD estimator. Results from simulations confirm our theoretical findings and reveal that the JCD estimator can provide a significant gain over conventional pilot-only schemes in the quantized MIMO system.Comment: 7 pages, 4 figure

A Novel Counterfactual Data Augmentation Method for Aspect-Based Sentiment Analysis

Aspect-based-sentiment-analysis (ABSA) is a fine-grained sentiment evaluation task, which analyzes the emotional polarity of the evaluation aspects. Generally, the emotional polarity of an aspect exists in the corresponding opinion expression, whose diversity has great impact on model's performance. To mitigate this problem, we propose a novel and simple counterfactual data augmentation method to generate opinion expressions with reversed sentiment polarity. In particular, the integrated gradients are calculated to locate and mask the opinion expression. Then, a prompt combined with the reverse expression polarity is added to the original text, and a Pre-trained language model (PLM), T5, is finally was employed to predict the masks. The experimental results shows the proposed counterfactual data augmentation method performs better than current augmentation methods on three ABSA datasets, i.e. Laptop, Restaurant, and MAMS.Comment: Camera-ready for ACML 202