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Magnitude is one of the fundamental parameters of earthquake and also one of the essential information for earthquake early warning (EEW). Rapidly and accurately determining the high-or low-magnitude event is important for mitigating the damage of earthquake hazard. To address the problem of predicting whether an earthquake event is high magnitude ($M \ge5.5$) or low magnitude ($M < 5.5$), this letter proposes a machine learning magnitude classification framework (MCFrame) using single station data, which consists of feature extraction module and magnitude classifier, and we train the feature extraction module of MCFrame to learn the characteristics of high-magnitude ($M \ge5.5$) and low-magnitude ($M < 5.5$) earthquake events, using strong-motion records collected from the Japanese Kyoshin network (K-NET) seismic network. Then, the extracted features are used as an input of magnitude classifier to classify earthquake magnitudes. Meanwhile, we analyze the impact of three different magnitude classifiers on the performance of MCFrame, which include the deep neural network (DNN), support vector machine (SVM), and random forest (RF). We show that within 5 s after the P-wave arrival, for these three different classifiers, the magnitude classification accuracy of MCFrame is close, and the MCFrame proposed in this work has a better performance than baseline models for magnitude classification. Additionally, for the MCFrame, the accuracy of high-magnitude events ($M \ge5.5$) is more than 90%, and the accuracy of low-magnitude events ($M < 5.5$) is more than 99%. These results indicate that the MCFrame proposed in this work is significant for EEW. [ABSTRACT FROM AUTHOR]

Si optical phase shifters are key components for efficient phase modulation utilizing carrier modulation. Among many structures for carrier modulation in them, a semiconductor-insulator-semiconductor (SIS) capacitor can achieve high phase modulation efficiency because of its large carrier accumulation. However, due to the limitation caused by relatively small plasma dispersion in Si, its size and voltage for the $\pi $ phase shift are large to integrate into conventional CMOS technology. To enhance its phase modulation efficiency, material engineering of a SIS capacitor is indispensable. Although semiconductor engineering has been proved to realize efficient phase modulation, there are few reports on the insulator engineering of a SIS capacitor for efficient phase modulation. Therefore, we have introduced new functionalities for a SIS optical phase shifter using insulator engineering. In this paper, we have investigated the charge enhancement effect in a SIS capacitor with ferroelectric (FE) materials utilized by the negative capacitance (NC) effect. We will discuss the design of an NC SIS optical phase modulator to enhance this effect for a new efficient phase modulation scheme. [ABSTRACT FROM AUTHOR]