KEYWORDS: Terahertz radiation, Polarization, Wavefronts, Poincaré sphere, Silicon, Scattering, Scanning electron microscopy, Optical simulations, Control systems, Chemical elements
Dynamically controlling terahertz (THz) wavefronts in a designable fashion is highly desired in practice. However, available methods working at microwave frequencies do not work well in the THz regime due to lacking suitable tunable elements with submicrometer sizes. Here, instead of locally controlling individual meta-atoms in a THz metasurface, we show that rotating different layers (each exhibiting a particular phase profile) in a cascaded metadevice at different speeds can dynamically change the effective Jones-matrix property of the whole device, thus enabling extraordinary manipulations on the wavefront and polarization characteristics of a THz beam impinging on the device. After illustrating our strategy based on model calculations, we experimentally demonstrate two proof-of-concept metadevices, each consisting of two carefully designed all-silicon transmissive metasurfaces exhibiting different phase profiles. Rotating two metasurfaces inside the fabricated devices at different speeds, we experimentally demonstrate that the first metadevice can efficiently redirect a normally incident THz beam to scan over a wide solid-angle range, while the second one can dynamically manipulate both the wavefront and polarization of a THz beam. Our results pave the way to achieving dynamic control of THz beams, which is useful in many applications, such as THz radar, and bio- and chemical sensing and imaging.
Existing community conflict prediction models usually use a single unidirectional LSTM network to process graph and word embeddings simultaneously. However,there is no temporal coherence between graph and word embeddings. And their importance for prediction is different. A community conflict prediction method based on spliced bidirectional LSTM is proposed. Firstly, two bidirectional LSTMs are utilized to process graph and word embeddings respectively to break temporal dependency. Secondly, the hidden states of the two bidirectional LSTMs are weighted. Finally, the weighted hidden states are spliced and fed into subsequent layers of the neural network to predict conflicts. Experimental results show that this method can improve the AUC value to 0.733 on the Reddit dataset, and reduce the number of iterations of training.
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