Silicon-based nonlinear metasurfaces were implemented only with third-order nonlinearity due to the crystal centrosymmetry and the efficiencies are considerably low, which hinders their practical applications with low-power lasers. Here, we propose to integrate a two-dimensional GaSe flake onto a silicon metasurface to assist high-efficiency second-order nonlinear processes, including second-harmonic generation (SHG) and sum-frequency generation (SFG). By resonantly pumping the integrated GaSe-metasurface, which supports a Fano resonance, the obtained SHG is about two-orders of magnitude 1 arXiv:1904.06027v2 [physics.optics] 22 Apr 2019 stronger than the third-harmonic generation from the bare silicon metasurface. In addition, thanks to the resonant field enhancement and GaSe's strong second-order nonlinearity, SHG of the integrated structure could be excited successfully with a lowpower continuous-wave laser, which makes it possible to further implement SFG. The high-efficiency second-order nonlinear processes assisted by two-dimensional materials present potentials to expand silicon metasurface's functionalities in nonlinear regime.
Although the value of using static code attributes to learn defect predictor has been widely debated, there is no doubt that software defect predictions can effectively improve software quality and testing efficiency. Many data mining methods have already been introduced into defect predictions. We noted there have several versions of defect predictor based on Naïve Bayes theory, and analyzed their difference estimation method and algorithm complexity. We found the best one which is Multi-variants Gauss Naïve Bayes (MvGNB) by performing prediction performance evaluation, and we compared this model with decision tree learner J48. Experiment results on the benchmarking data sets of MDP made us believe that MvGNB would be useful for defect predictions.
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