Plasmon-modulated bistable second-harmonic generation in a nonlinear microcavity coupled to a metallic nanoparticle.

Multiple nonlinear effects with second-harmonic (SH) generation and optical bistability (OB) are highly desired but rather rare due to insufficient intrinsic nonlinearity in most nonlinear media. Here, a nonlinear microcavity coupled to a metallic nanoparticle (MNP) is suggested to realize the bistable SH generation. When two counterpropagating driving fields are injected into the cavity, a traditional SH pathway is constructed via the two-photon process of fundamental-harmonic (FH) photons. In addition, we report the coexistence between the SH and OB effects under the condition that the strong excitation and the detuning management for the driving fields boost system nonlinear responses. In the presence of a control field toward the MNP, our calculation finds that the traditional and plasmon-induced SH pathways in the present system allow enhancing the SH conversion efficiency and reducing the OB thresholds simultaneously. With the control field intensity increasing, a linear growth rate for the SH efficiency maximum is achieved. Furthermore, the relative phase between the driving and control fields has profound effects on modulating the bistable SH efficiency, the bistable interval and the thresholds.

High-precision three dimensional atom localization via multiphoton quantum destructive interference.

We propose an effective scheme for high-precision three dimensional(3D) atom localization via measuring the population of excited state in a four-level atomic system driven by a probe field and three orthogonal standing-wave fields. In this scheme, the position-dependent multiphoton quantum destructive interference leads to multiphoton excitation of the excited state and enhances the fluorescence emission. We show that adjusting the frequency detuning and phase shifts associated with the standing-wave fields can modify the multiphoton quantum destructive interference and lead to a redistribution of the atoms. The maximal probability of finding the atom at the certain position in one period of the standing-wave fields can be 100% and the highest spatial precision is about 0.02λ.

Mixture of personality improved spiking actor network for efficient multi-agent cooperation.

Adaptive multi-agent cooperation with especially unseen partners is becoming more challenging in multi-agent reinforcement learning (MARL) research, whereby conventional deep-learning-based algorithms suffer from the poor new-player-generalization problem, possibly caused by not considering theory-of-mind theory (ToM). Inspired by the ToM personality in cognitive psychology, where a human can easily resolve this problem by predicting others' intuitive personality first before complex actions, we propose a biologically-plausible algorithm named the mixture of personality (MoP) improved spiking actor network (SAN). The MoP module contains a determinantal point process to simulate the formation and integration of different personality types, and the SAN module contains spiking neurons for efficient reinforcement learning. The experimental results on the benchmark cooperative overcooked task showed that the proposed MoP-SAN algorithm could achieve higher performance for the paradigms with (learning) and without (generalization) unseen partners. Furthermore, ablation experiments highlighted the contribution of MoP in SAN learning, and some visualization analysis explained why the proposed algorithm is superior to some counterpart deep actor networks.