6 research outputs found

    Symmetry transformation of nonlinear optical current of tilted Weyl nodes and application to ferromagnetic MnBi2Te4

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    A Weyl node is characterized by its chirality and tilt. We develop a theory of how nnth order nonlinear optical conductivity behaves under transformations of anisotropic tensor and tilt, which clarify how chirality-dependent and -independent parts of optical conductivity transform under the reversal of tilt and chirality. Built on this theory, we propose ferromagnetic MnBi2Te4 as a magnetoelectrically regulated, terahertz optical device, by magnetoelectrically switching the chirality-dependent and -independent dc photocurrents. These results are useful for creating nonlinear optical devices based on topological Weyl semimetals

    Combining Enterprise Knowledge Graph and News Sentiment Analysis for Stock Price Prediction

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    Many state of the art methods analyze sentiments in news to predict stock price. When predicting stock price movement, the correlation between stocks is a factor that can’t be ignored because correlated stocks could cause co-movement. Traditional methods of measuring the correlation between stocks are mostly based on the similarity between corresponding stock price data, while ignoring the business relationships between companies, such as shareholding, cooperation and supply-customer relationships. To solve this problem, this paper proposes a new method to calculate the correlation by using the enterprise knowledge graph embedding that systematically considers various types of relationships between listed stocks. Further, we employ Gated Recurrent Unit (GRU) model to combine the correlated stocks’ news sentiment, the focal stock’s news sentiment and the focal stock’s quantitative features to predict the focal stock’s price movement. Results show that our method has an improvement of 8.1% compared with the traditional method

    Ultra Dual-Path Compression For Joint Echo Cancellation And Noise Suppression

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    Echo cancellation and noise reduction are essential for full-duplex communication, yet most existing neural networks have high computational costs and are inflexible in tuning model complexity. In this paper, we introduce time-frequency dual-path compression to achieve a wide range of compression ratios on computational cost. Specifically, for frequency compression, trainable filters are used to replace manually designed filters for dimension reduction. For time compression, only using frame skipped prediction causes large performance degradation, which can be alleviated by a post-processing network with full sequence modeling. We have found that under fixed compression ratios, dual-path compression combining both the time and frequency methods will give further performance improvement, covering compression ratios from 4x to 32x with little model size change. Moreover, the proposed models show competitive performance compared with fast FullSubNet and DeepFilterNet. A demo page can be found at hangtingchen.github.io/ultra_dual_path_compression.github.io/.Comment: Accepted by Interspeech 202
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