Terahertz parametric amplification as a reporter of exciton condensate dynamics.

Condensates are a hallmark of emergence in quantum materials such as superconductors and charge density waves. Excitonic insulators are an intriguing addition to this library, exhibiting spontaneous condensation of electron-hole pairs. However, condensate observables can be obscured through parasitic coupling to the lattice. Here we employ nonlinear terahertz spectroscopy to disentangle such obscurants through measurement of the quantum dynamics. We target Ta2NiSe5, a putative room-temperature excitonic insulator in which electron-lattice coupling dominates the structural transition (Tc = 326 K), hindering identification of excitonic correlations. A pronounced increase in the terahertz reflectivity manifests following photoexcitation and exhibits a Bose-Einstein condensation-like temperature dependence well below the Tc, suggesting an approach to monitor the exciton condensate dynamics. Nonetheless, dynamic condensate-phonon coupling remains as evidenced by peaks in the enhanced reflectivity spectrum at select infrared-active phonon frequencies, indicating that parametric reflectivity enhancement arises from phonon squeezing. Our results highlight that coherent dynamics can drive parametric stimulated emission.

Rice Blast Disease Recognition Using a Deep Convolutional Neural Network.

Rice disease recognition is crucial in automated rice disease diagnosis systems. At present, deep convolutional neural network (CNN) is generally considered the state-of-the-art solution in image recognition. In this paper, we propose a novel rice blast recognition method based on CNN. A dataset of 2906 positive samples and 2902 negative samples is established for training and testing the CNN model. In addition, we conduct comparative experiments for qualitative and quantitatively analysis in our evaluation of the effectiveness of the proposed method. The evaluation results show that the high-level features extracted by CNN are more discriminative and effective than traditional hand-crafted features including local binary patterns histograms (LBPH) and Haar-WT (Wavelet Transform). Moreover, quantitative evaluation results indicate that CNN with Softmax and CNN with support vector machine (SVM) have similar performances, with higher accuracy, larger area under curve (AUC), and better receiver operating characteristic (ROC) curves than both LBPH plus an SVM as the classifier and Haar-WT plus an SVM as the classifier. Therefore, our CNN model is a top performing method for rice blast disease recognition and can be potentially employed in practical applications.

Phototunable Dielectric Huygens' Metasurfaces.

Conventional dielectric metasurfaces achieve their properties through geometrical tuning and consequently are static. Although some unique properties are demonstrated, the usefulness for realistic applications is thus inherently limited. Here, control of the resonant eigenmodes supported by Huygens' metasurface (HMS) absorbers through optical excitation is proposed and demonstrated. An intensity transmission modulation depth of 99.93% is demonstrated at 1.03 THz, with an associated phase change of greater than π/2 rad. Coupled mode theory and S-parameter simulations are used to elucidate the mechanism underlying the dynamics of the metasurface and it is found that the tuning is primarily governed by modification of the magnetic dipole-like odd eigenmode, which both lifts the degeneracy, and eliminates critical coupling. The dynamic HMS demonstrates wide tunability and versatility which is not limited to the spectral range demonstrated, offering a new path for reconfigurable metasurface applications.