[1] S. Zu, Y. Bao, Z. Fang. Planar plasmonic chiral nanostructures. Nanoscale, 8, 3900-3905(2016).

[2] X. Yin, M. Schäferling, A. K. U. Michel, A. Tittl, M. Wuttig, T. Taubner, H. Giessen. Active chiral plasmonics. Nano Lett., 15, 4255-4260(2015).

[3] W. Li, Z. J. Coppens, L. V. Besteiro, W. Wang, A. O. Govorov, J. Valentine. Circularly polarized light detection with hot electrons in chiral plasmonic metamaterials. Nat. Commun., 6, 8379(2015).

[4] J. M. Slocik, A. O. Govorov, R. R. Naik. Plasmonic circular dichroism of peptide-functionalized gold nanoparticles. Nano Lett., 11, 701-705(2011).

[5] A. O. Govorov, Z. Fan, P. Hernandez, J. M. Slocik, R. R. Naik. Theory of circular dichroism of nanomaterials comprising chiral molecules and nanocrystals: plasmon enhancement, dipole interactions, and dielectric effects. Nano Lett., 10, 1374-1382(2010).

[6] B. M. Maoz, Y. Chaikin, A. B. Tesler, O. Bar Elli, Z. Fan, A. O. Govorov, G. Markovich. Amplification of chiroptical activity of chiral biomolecules by surface plasmons. Nano Lett., 13, 1203-1209(2013).

[7] J. R. Mejía-Salazar, O. N. Oliveira. Plasmonic biosensing. Chem. Rev., 118, 10617-10625(2018).

[8] X. T. Kong, L. V. Besteiro, Z. Wang, A. O. Govorov. Plasmonic chirality and circular dichroism in bioassembled and nonbiological systems: theoretical background and recent progress. Adv. Mater., 1801790(2018).

[9] R. Tullius, G. W. Platt, L. Khosravi Khorashad, N. Gadegaard, A. J. Lapthorn, V. M. Rotello, G. Cooke, L. D. Barron, A. O. Govorov, A. S. Karimullah, M. Kadodwala. Superchiral plasmonic phase sensitivity for fingerprinting of protein interface structure. ACS Nano, 11, 12049-12056(2017).

[10] A. Micsonai, F. Wien, L. Kernya, Y. H. Lee, Y. Goto, M. Réfrégiers, J. Kardos. Accurate secondary structure prediction and fold recognition for circular dichroism spectroscopy. Proc. Natl. Acad. Sci. USA, 112, E3095-E3103(2015).

[11] E. Hendry, T. Carpy, J. Johnston, M. Popland, R. V. Mikhaylovskiy, A. J. Lapthorn, S. M. Kelly, L. D. Barron, N. Gadegaard, M. Kadodwala. Ultrasensitive detection and characterization of biomolecules using superchiral fields. Nat. Nanotechnol., 5, 783-787(2010).

[12] X. T. Kong, L. Khosravi Khorashad, Z. Wang, A. O. Govorov. Photothermal circular dichroism induced by plasmon resonances in chiral metamaterial absorbers and bolometers. Nano Lett., 18, 2001-2008(2018).

[13] J. Hirschberg, C. D. Manning. Advances in natural language processing. Science, 349, 261-266(2015).

[14] T. Lu, K. Yun, A. Huyen. Deep neural networks for pattern recognition(2018).

[15] J. Zou, M. Huss, A. Abid, P. Mohammadi, A. Torkamani, A. Telenti. A primer on deep learning in genomics. Nat. Genet., 51, 12-18(2019).

[16] M. W. Libbrecht, W. S. Noble. Machine learning in genetics and genomics. Nat. Rev. Genet., 16, 321-332(2015).

[17] D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, Y. Chen, T. Lillicrap, F. Hui, L. Sifre, G. Van Den Driessche, T. Graepel, D. Hassabis. Mastering the game of Go without human knowledge. Nature, 550, 354-359(2017).

[18] D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, T. Lillicrap, K. Simonyan, D. Hassabis. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science, 362, 1140-1144(2018).

[19] S. Singh, A. Okun, A. Jackson. Artificial intelligence: learning to play Go from scratch. Nature, 550, 336-337(2017).

[20] W. Ma, F. Cheng, Y. Liu. Deep-learning-enabled on-demand design of chiral metamaterials. ACS Nano, 12, 6326-6334(2018).

[21] I. Malkiel, M. Mrejen, A. Nagler, U. Arieli, L. Wolf, H. Suchowski. Plasmonic nanostructure design and characterization via Deep Learning. Light Sci. Appl., 7, 60(2018).

[22] T. F. De Lima, H. T. Peng, A. N. Tait, M. A. Nahmias, H. B. Miller, B. J. Shastri, P. R. Prucnal. Machine learning with neuromorphic photonics. J. Lightwave Technol., 37, 1515-1534(2019).

[23] Z. S. Ballard, D. Shir, A. Bhardwaj, S. Bazargan, S. Sathianathan, A. Ozcan. Computational sensing using low-cost and mobile plasmonic readers designed by machine learning. ACS Nano, 11, 2266-2274(2017).

[24] N. Dordević, J. S. Beckwith, M. Yarema, O. Yarema, A. Rosspeintner, N. Yazdani, J. Leuthold, E. Vauthey, V. Wood. Machine learning for analysis of time-resolved luminescence data. ACS Photon., 5, 4888-4895(2018).

[25] L. Pilozzi, F. A. Farrelly, G. Marcucci, C. Conti. Machine learning inverse problem for topological photonics. Commun. Phys., 1, 57(2018).

[26] K. Yao, R. Unni, Y. Zheng. Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale. Nanophotonics, 8, 339-366(2019).

[27] Z. Liu, D. Zhu, S. P. Rodrigues, K. T. Lee, W. Cai. Generative model for the inverse design of metasurfaces. Nano Lett., 18, 6570-6576(2018).

[28] I. Sajedian, J. Kim, J. Rho. Finding the optical properties of plasmonic structures by image processing using a combination of convolutional neural networks and recurrent neural networks. Microsyst. Nanoeng., 5, 27(2019).

[29] Z. Liu, D. Zhu, K. T. Lee, A. S. Kim, L. Raju, W. Cai. Compounding meta-atoms into metamolecules with hybrid artificial intelligence techniques. Adv. Mater., 32, 1-7(2020).

[30] W. Ma, F. Cheng, Y. Xu, Q. Wen, Y. Liu. Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy. Adv. Mater., 31, 1901111(2019).

[31] P. R. Wiecha, O. L. Muskens. Deep learning meets nanophotonics: a generalized accurate predictor for near fields and far fields of arbitrary 3D nanostructures. Nano Lett., 20, 329-338(2019).

[32] H. Peng, S. Thomson, N. A. Smith. Deep multitask learning for semantic dependency parsing(2017).

[33] W. Huang, G. Song, H. Hong, K. Xie. Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans. Intell. Transp. Syst., 15, 2191-2201(2014).

[34] X. Gibert, V. M. Patel, R. Chellappa. Deep multitask learning for railway track inspection. IEEE Trans. Intell. Transp. Syst., 18, 153-164(2017).

[35] D. C. Luvizon, D. Picard, H. Tabia. 2D/3D pose estimation and action recognition using multitask deep learning. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 5137-5146(2018).

[36] A. Maurer, M. Pontil, B. Romera-Paredes. The benefit of multitask representation learning. J. Mach. Learn. Res., 17, 2853-2884(2016).

[37] H. B. Lee, E. Yang, S. J. Hwang. Deep asymmetric multi-task feature learning(2018).

[38] D. Dong, H. Wu, W. He, D. Yu, H. Wang. Multi-task learning for multiple language translation. 53rd Annual Meeting of the Association for Computational Linguistics and 7th International Joint Conference on Natural Language Processing, 1, 1723-1732(2015).

[39] S. Liu, S. J. Pan, Q. Ho. Distributed multi-task relationship learning. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Part F1296, 937-946(2017).

[40] P. B. Johnson, R. W. Christy. Optical constants of the noble metals. Phys. Rev. B, 6, 4370-4379(1972).

[41] P. Yu, L. V. Besteiro, Y. Huang, J. Wu, L. Fu, H. H. Tan, C. Jagadish, G. P. Wiederrecht, A. O. Govorov, Z. Wang. Broadband metamaterial absorbers. Adv. Opt. Mater., 7, 1800995(2018).

[42] P. Yu, J. Wu, E. Ashalley, A. Govorov, Z. Wang. Dual-band absorber for multispectral plasmon-enhanced infrared photodetection. J. Phys. D, 49, 365101(2016).

[43] P. Yu, L. V. Besteiro, J. Wu, Y. Huang, Y. Wang, A. O. Govorov, Z. Wang. Metamaterial perfect absorber with unabated size-independent absorption. Opt. Express, 26, 20471-20480(2018).

[44] W. Li, J. Valentine. Metamaterial perfect absorber based hot electron photodetection. Nano Lett., 14, 3510-3514(2014).

[45] H. Zhu, F. Yi, E. Cubukcu. Plasmonic metamaterial absorber for broadband manipulation of mechanical resonances. Nat. Photonics, 10, 709-714(2016).

[46] K. A. Bachman, J. J. Peltzer, P. D. Flammer, T. E. Furtak, R. T. Collins, R. E. Hollingsworth. Spiral plasmonic nanoantennas as circular polarization transmission filters. Opt. Express, 20, 1308-1319(2012).

[47] B. Frank, X. Yin, M. Schäferling, J. Zhao, S. M. Hein, P. V. Braun, H. Giessen. Large-area 3D chiral plasmonic structures. ACS Nano, 7, 6321-6329(2013).

[48] J. M. Linnanto, J. E. I. Korppi-Tommola. Investigation on chlorosomal antenna geometries: tube, lamella and spiral-type self-aggregates. Photosynth. Res., 96, 227-245(2008).

[49] T. Kan, A. Isozaki, N. Kanda, N. Nemoto, K. Konishi, M. Kuwata-Gonokami, K. Matsumoto, I. Shimoyama. Spiral metamaterial for tunable circular dichroism. IEEE International Conference on Micro Electro Mechanical Systems (MEMS), 701-704(2013).

[50] G. Li, S. Zhang, T. Zentgraf. Nonlinear photonic metasurfaces. Nat. Rev. Mater., 2, 17010(2017).

[51] T. Cao, L. Zhang, R. E. Simpson, C. Wei, M. J. Cryan. Strongly tunable circular dichroism in gammadion chiral phase-change metamaterials. Opt. Express, 21, 27841-27851(2013).

[52] C. Zhang, Z. Q. Li, X. Yang, Z. Chen, Z. Wang. Controlling third harmonic generation with gammadion-shaped chiral metamaterials. AIP Adv., 6, 125014(2016).

[53] S. Wu, P. P. Qu, J. Q. Liu, D. D. Lei, K. Y. Zhang, S. T. Zhao, Y. Y. Zhu. Giant circular dichroism and its reversal in solid and inverse plasmonic gammadion-shaped structures. Opt. Express, 24, 27763-27770(2016).

[54] Y. Z. Cheng, M. L. Huang, H. R. Chen, Y. J. Zhou, X. S. Mao, R. Z. Gong. Influence of the geometry of a gammadion stereo-structure chiral metamaterial on optical properties. J. Mod. Opt., 64, 1487-1494(2017).

[55] Y. Wang, J. Deng, G. Wang, T. Fu, Y. Qu, Z. Zhang. Plasmonic chirality of L-shaped nanostructure composed of two slices with different thickness. Opt. Express, 24, 2307-2317(2016).

[56] Y. Yang, T. M. Hospedales. Deep multi-task representation learning: a tensor factorisation approach(2017).

[57] M. Rei. Semi-supervised multitask learning for sequence labeling. 55th Annual Meeting of the Association for Computational Linguist (ACL), 2121-2130(2017).

[58] I. Misra, A. Shrivastava, A. Gupta, M. Hebert. Cross-stitch networks for multi-task learning(2016).

[59] B. Neyshabur, S. Bhojanapalli, D. McAllester, N. Srebro. Exploring generalization in deep learning(2017).

[60] A. Y. Ng. Feature selection, L₁ vs. L₂ regularization, and rotational invariance. 21st International Conference on Machine Learning, 615-622(2004).

[61] R. Cipolla, Y. Gal, A. Kendall. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 7482-7491(2018).

[62] D. P. Kingma, J. L. Ba. Adam: a method for stochastic optimization. 3rd International Conference on Learning Representations—Conference Track Proceedings, 1-15(2015).

[63] J. Yao, M. Xu, Z. Yan, W. Wu, C. Yang. Supramolecular photochirogenesis with cyclodextrins. Chin. J. Org. Chem., 34, 26-35(2014).

[64] T. Li, H. G. Park, H. S. Lee, S. H. Choi. Circular dichroism study of chiral biomolecules conjugated with silver nanoparticles. Nanotechnology, 15, S660-S663(2004).

[65] J. X. Bao, N. Liu, H. W. Tian, Q. Wang, T. J. Cui, W. X. Jiang, S. Zhang, T. Cao. Chirality enhancement using Fabry–Pérot-like cavity. Research, 2020, 7873581(2020).

[66] L. Mao, K. Liu, S. Zhang, T. Cao. Extrinsically 2D-chiral metamirror in near-infrared region. ACS Photon., 7, 375-383(2020).

[67] A. O. Govorov. Plasmon-induced circular dichroism of a chiral molecule in the vicinity of metal nanocrystals. Application to various geometries. J. Phys. Chem. C, 115, 7914-7923(2011).

[68] Y. Tang, A. E. Cohen. Optical chirality and its interaction with matter. Phys. Rev. Lett., 104, 163901(2010).

[69] C. Gilroy, S. Hashiyada, K. Endo, A. S. Karimullah, L. D. Barron, H. Okamoto, Y. Togawa, M. Kadodwala. Roles of superchirality and interference in chiral plasmonic biodetection. J. Phys. Chem. C, 123, 15195-15203(2019).

[70] Y. Zhao, A. N. Askarpour, L. Sun, J. Shi, X. Li, A. Alù. Chirality detection of enantiomers using twisted optical metamaterials. Nat. Commun., 8, 6(2017).

[71] T. J. Davis, E. Hendry. Superchiral electromagnetic fields created by surface plasmons in nonchiral metallic nanostructures. Phys. Rev. B, 87, 085405(2013).