Decision-making and control with metasurface-based diffractive neural networks

The ultimate goal of artificial intelligence is to mimic the human brain to perform decision-making and control directly from high-dimensional sensory input. All-optical diffractive neural networks provide a promising solution for implementing artificial intelligence with high-speed and low-power consumption. To date, most of the reported diffractive neural networks focus on single or multiple tasks that do not involve interaction with the environment, such as object recognition and image classification. In contrast, the networks that can perform decision-making and control, to our knowledge, have not been developed yet. Here, we propose using deep reinforcement learning to implement diffractive neural networks that imitate human-level decision-making and control capability. Such networks allow for finding optimal control policies through interaction with the environment and can be readily realized with the dielectric metasurfaces. The superior performances of these networks are verified by engaging three types of classic games, Tic-Tac-Toe, Super Mario Bros., and Car Racing, and achieving the same or even higher levels comparable to human players. Our work represents a solid step of advancement in diffractive neural networks, which promises a fundamental shift from the target-driven control of a pre-designed state for simple recognition or classification tasks to the high-level sensory capability of artificial intelligence. It may find exciting applications in autonomous driving, intelligent robots, and intelligent manufacturing

Phylogenetic analysis, structural evolution and functional divergence of the 12-oxo-phytodienoate acid reductase gene family in plants

BACKGROUND: The 12-oxo-phytodienoic acid reductases (OPRs) are enzymes that catalyze the reduction of double-bonds in α, β-unsaturated aldehydes or ketones and are part of the octadecanoid pathway that converts linolenic acid to jasmonic acid. In plants, OPRs belong to the old yellow enzyme family and form multigene families. Although discoveries about this family in Arabidopsis and other species have been reported in some studies, the evolution and function of multiple OPRs in plants are not clearly understood. RESULTS: A comparative genomic analysis was performed to investigate the phylogenetic relationship, structural evolution and functional divergence among OPR paralogues in plants. In total, 74 OPR genes were identified from 11 species representing the 6 major green plant lineages: green algae, mosses, lycophytes, gymnosperms, monocots and dicots. Phylogenetic analysis showed that seven well-conserved subfamilies exist in plants. All OPR genes from green algae were clustered into a single subfamily, while those from land plants fell into six other subfamilies, suggesting that the events leading to the expansion of the OPR family occurred in land plants. Further analysis revealed that lineage-specific expansion, especially by tandem duplication, contributed to the current OPR subfamilies in land plants after divergence from aquatic plants. Interestingly, exon/intron structure analysis showed that the gene structures of OPR paralogues exhibits diversity in intron number and length, while the intron positions and phase were highly conserved across different lineage species. These observations together with the phylogenetic tree revealed that successive single intron loss, as well as indels within introns, occurred during the process of structural evolution of OPR paralogues. Functional divergence analysis revealed that altered functional constraints have occurred at specific amino acid positions after diversification of the paralogues. Most notably, significant functional divergence was also found in all pairs, except for the II/IV, II/V and V/VI pairs. Strikingly, analysis of the site-specific profiles established by posterior probability revealed that the positive-selection sites and/or critical amino acid residues for functional divergence are mainly distributed in α-helices and substrate binding loop (SBL), indicating the functional importance of these regions for this protein family. CONCLUSION: This study highlights the molecular evolution of the OPR gene family in all plant lineages and indicates critical amino acid residues likely relevant for the distinct functional properties of the paralogues. Further experimental verification of these findings may provide valuable information on the OPRs' biochemical and physiological functions

Phase-change nonlocal metasurfaces for dynamic wavefront manipulation

Recent advances in nonlocal metasurfaces have enabled unprecedented success in shaping the wavefront of light with spectral selectivity, offering new solutions for many emerging nanophotonics applications. The ability to tune both the spectral and spatial properties of such a novel class of metasurfaces is highly desirable, but the dynamic nonvolatile control remains elusive. Here, we demonstrate active narrowband wavefront manipulation by harnessing quasi-bound states in the continuum (quasi-BICs) in phase-change nonlocal metasurfaces. The proof-of-principle metasurfaces made of Sb$_2$S$_3$ allow for nonvolatile, reversible, and tunable spectral control over wavefront and switchable spatial response at a given wavelength. The design principle mainly builds upon the combination of the geometry phase of quasi-BICs and the dynamic tunability of phase-change meta-atoms to tailor the spatial response of light at distinct resonant wavelengths. By tuning the crystallization level of Sb$_2$S$_3$ meta-atoms, the dynamic nonlocal wavefront-shaping functionalities of beam steering, 1D, and 2D focusing are achieved. Furthermore, we demonstrate tunable holographic imaging with active spectral selectivity using our phase-change nonlocal metasurface. This work represents a critical advance towards developing integrated dynamic nonlocal metasurface for future augmented and virtual reality wearables