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Machine learning and applications in ultrafast photonics

Nature Photonics volume 15, pages 91–101 (2021)Cite this article

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Abstract

Recent years have seen the rapid growth and development of the field of smart photonics, where machine-learning algorithms are being matched to optical systems to add new functionalities and to enhance performance. An area where machine learning shows particular potential to accelerate technology is the field of ultrafast photonics — the generation and characterization of light pulses, the study of light–matter interactions on short timescales, and high-speed optical measurements. Our aim here is to highlight a number of specific areas where the promise of machine learning in ultrafast photonics has already been realized, including the design and operation of pulsed lasers, and the characterization and control of ultrafast propagation dynamics. We also consider challenges and future areas of research.

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Fig. 1: Overview of the main machine-learning concepts and implementations that can be used in ultrafast photonics.
Fig. 2: Illustration of machine-learning strategies for optimization and self-tuning of ultrafast fibre lasers using control of intracavity elements via a feedback loop and control algorithm.
Fig. 3: Machine-learning applications in ultrafast photonics.

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References

  1. Jordan, M. I. & Mitchell, T. M. Machine learning: trends, perspectives, and prospects. Science 349, 255–260 (2015).

    ADS  MathSciNet  MATH  Google Scholar 

  2. Mahlab, U., Shamir, J. & Caulfield, H. J. Genetic algorithm for optical pattern recognition. Opt. Lett. 16, 648–650 (1991).

    ADS  Google Scholar 

  3. Kihm, K. D. & Lyons, D. P. Optical tomography using a genetic algorithm. Opt. Lett. 21, 1327–1329 (1996).

    ADS  Google Scholar 

  4. Albert, O., Sherman, L., Mourou, G., Norris, T. B. & Vdovin, G. Smart microscope: an adaptive optics learning system for aberration correction in multiphoton confocal microscopy. Opt. Lett. 25, 52–54 (2000).

    ADS  Google Scholar 

  5. Eisenhammer, T., Lazarov, M., Leutbecher, M., Schöffel, U. & Sizmann, R. Optimization of interference filters with genetic algorithms applied to silver-based heat mirrors. Appl. Opt. 32, 6310–6315 (1993).

    ADS  Google Scholar 

  6. Martin, S., Rivory, J. & Schoenauer, M. Synthesis of optical multilayer systems using genetic algorithms. Appl. Opt. 34, 2247–2254 (1995).

    ADS  Google Scholar 

  7. Zibar, D., Wymeersch, H. & Lyubomirsky, I. Machine learning under the spotlight. Nat. Photon. 11, 749–751 (2017).

    ADS  Google Scholar 

  8. Zhou, J., Huang, B., Yan, Z. & Bünzli, J.-C. G. Emerging role of machine learning in light-matter interaction. Light Sci. Appl. 8, 84 (2019).

    ADS  Google Scholar 

  9. Nadell, C. C., Huang, B., Malof, J. M. & Padilla, W. J. Deep learning for accelerated all-dielectric metasurface design. Opt. Express 27, 27523–27535 (2019).

    ADS  Google Scholar 

  10. Malkiel, I. et al. Plasmonic nanostructure design and characterization via deep learning. Light Sci. Appl. 7, 60 (2018).

    ADS  Google Scholar 

  11. Hegde, R. S. Deep learning: a new tool for photonic nanostructure design. Nanoscale Adv. 2, 1007–1023 (2020).

    ADS  Google Scholar 

  12. Chen, C. L. et al. Deep learning in label-free cell classification. Sci. Rep. 6, 21471 (2016).

    ADS  Google Scholar 

  13. Ouyang, W., Aristov, A., Lelek, M., Hao, X. & Zimmer, C. Deep learning massively accelerates super-resolution localization microscopy. Nat. Biotechnol. 36, 460–468 (2018).

    Google Scholar 

  14. Durand, A. et al. A machine learning approach for online automated optimization of super-resolution optical microscopy. Nat. Commun. 9, 5247 (2018).

    ADS  Google Scholar 

  15. Palmieri, A. M. et al. Experimental neural network enhanced quantum tomography. npj Quantum Inf. 6, 20 (2020).

    ADS  Google Scholar 

  16. Zibar, D., Piels, M., Jones, R. & Schäeffer, C. G. Machine learning techniques in optical communication. J. Lightwave Technol. 34, 1442–1452 (2016).

    ADS  Google Scholar 

  17. Musumeci, F. et al. An overview on application of machine learning techniques in optical networks. IEEE Commun. Surv. Tutorials 21, 1383–1408 (2019).

    Google Scholar 

  18. Lugnan, A. et al. Photonic neuromorphic information processing and reservoir computing. APL Photon. 5, 020901 (2020).

    ADS  Google Scholar 

  19. Knox, W. H. Ultrafast technology in telecommunications. IEEE J. Sel. Top. Quantum Electron. 6, 1273–1278 (2000).

    ADS  Google Scholar 

  20. Sibbett, W., Lagatsky, A. A. & Brown, C. T. A. The development and application of femtosecond laser systems. Opt. Express 20, 6989–7001 (2012).

    ADS  Google Scholar 

  21. Sugioka, K. & Cheng, Y. Ultrafast lasers — reliable tools for advanced materials processing. Light Sci. Appl. 3, e149 (2014).

    ADS  Google Scholar 

  22. Fermann, M. E., Galvanauskas, A. & Sucha, G. Ultrafast Lasers: Technology and Applications Vol. 80 (CRC Press, 2002).

  23. Xu, C. & Wise, F. W. Recent advances in fibre lasers for nonlinear microscopy. Nat. Photon. 7, 875–882 (2013).

    ADS  Google Scholar 

  24. Grelu, P. & Akhmediev, N. Dissipative solitons for mode-locked lasers. Nat. Photon. 6, 84–92 (2012).

    ADS  Google Scholar 

  25. Richardson, D. J., Nilsson, J. & Clarkson, W. A. High power fiber lasers: current status and future perspectives. J. Opt. Soc. Am. B 27, B63–B92 (2010).

    Google Scholar 

  26. Fermann, M. E. & Hartl, I. Ultrafast fibre lasers. Nat. Photon. 7, 868–874 (2013).

    ADS  Google Scholar 

  27. Fu, X. & Kutz, N. J. High-energy mode-locked fiber lasers using multiple transmission filters and a genetic algorithm. Opt. Express 21, 6526–6537 (2013).

    ADS  Google Scholar 

  28. Fu, X., Brunton, S. L. & Kutz, J. N. Classification of birefringence in mode-locked fiber lasers using machine learning and sparse representation. Opt. Express 22, 8585–8597 (2014).

    ADS  Google Scholar 

  29. Kutz, J. N. & Brunton, S. L. Intelligent systems for stabilizing mode-locked lasers and frequency combs: machine learning and equation-free control paradigms for self-tuning optics. Nanophotonics 4, 459–471 (2015).

    Google Scholar 

  30. Baumeister, T., Brunton, S. L. & Kutz, J. N. Deep learning and model predictive control for self-tuning mode-locked lasers. J. Opt. Soc. Am. B 35, 617–626 (2018).

    ADS  Google Scholar 

  31. Andral, U. et al. Fiber laser mode locked through an evolutionary algorithm. Optica 2, 275–278 (2015).

    ADS  Google Scholar 

  32. Andral, U. et al. Toward an autosetting mode-locked fiber laser cavity. J. Opt. Soc. Am. B 33, 825–833 (2016).

    ADS  Google Scholar 

  33. Woodward, R. & Kelleher, E. Towards smart lasers: self-optimisation of an ultrafast pulse source using a genetic algorithm. Sci. Rep. 6, 37616 (2016).

    ADS  Google Scholar 

  34. Woodward, R. & Kelleher, E. Genetic algorithm-based control of birefringent filtering for self-tuning, self-pulsing fiber lasers. Opt. Lett. 42, 2952–2955 (2017).

    ADS  Google Scholar 

  35. Winters, D. G., Kirchner, M. S., Backus, S. J. & Kapteyn, H. C. Electronic initiation and optimization of nonlinear polarization evolution mode-locking in a fiber laser. Opt. Express 25, 33216–33225 (2017).

    ADS  Google Scholar 

  36. Kokhanovskiy, A., Ivanenko, A., Kobtsev, S., Smirnov, S. & Turitsyn, S. Machine learning methods for control of fibre lasers with double gain nonlinear loop mirror. Sci. Rep. 9, 2916 (2019).

    ADS  Google Scholar 

  37. Meng, F. & Dudley, J. M. Towards a self-driving ultrafast fiber laser. Light Sci. Appl. 9, 26 (2020).

    ADS  Google Scholar 

  38. Pu, G., Yi, L., Zhang, L. & Hu, W. Intelligent programmable mode-locked fiber laser with a human-like algorithm. Optica 6, 362–369 (2019).

    ADS  Google Scholar 

  39. Pu, G., Yi, L., Zhang, L. & Hu, W. Genetic algorithm-based fast real-time automatic mode-locked fiber laser. IEEE Photon. Technol. Lett. 32, 7–10 (2020).

    ADS  Google Scholar 

  40. Kokhanovskiy, A. et al. Machine learning-based pulse characterization in figure-eight mode-locked lasers. Opt. Lett. 44, 3410–3413 (2019).

    ADS  Google Scholar 

  41. Pu, G. et al. Intelligent control of mode-locked femtosecond pulses by time-stretch-assisted real-time spectral analysis. Light Sci. Appl. 9, 13 (2020).

    ADS  Google Scholar 

  42. Farfan, C. A., Epstein, J. & Turner, D. B. Femtosecond pulse compression using a neural-network algorithm. Opt. Lett. 43, 5166–5169 (2018).

    ADS  Google Scholar 

  43. Finot, C., Gukov, I., Hammani, K. & Boscolo, S. Nonlinear sculpturing of optical pulses with normally dispersive fiber-based devices. Opt. Fiber Technol. 45, 306–312 (2018).

    ADS  Google Scholar 

  44. Zhang, W. Q., Afshar, S. & Monro, T. M. A genetic algorithm based approach to fiber design for high coherence and large bandwidth supercontinuum generation. Opt. Express 17, 19311–19327 (2009).

    ADS  Google Scholar 

  45. Arteaga-Sierra, F. R. et al. Supercontinuum optimization for dual-soliton based light sources using genetic algorithms in a grid platform. Opt. Express 22, 23686–23693 (2014).

    ADS  Google Scholar 

  46. Michaeli, L. & Bahabad, A. Genetic algorithm driven spectral shaping of supercontinuum radiation in a photonic crystal fiber. J. Opt. 20, 055501 (2018).

    ADS  Google Scholar 

  47. Wetzel, B. et al. Customizing supercontinuum generation via on-chip adaptive temporal pulse-splitting. Nat. Commun. 9, 4884 (2018).

    ADS  Google Scholar 

  48. Ryczkowski, P. et al. Real-time full-field characterization of transient dissipative soliton dynamics in a mode-locked laser. Nat. Photon. 12, 221–227 (2018).

    ADS  Google Scholar 

  49. Kamilov, U. S. et al. Learning approach to optical tomography. Optica 2, 517–522 (2015).

    ADS  Google Scholar 

  50. Rivenson, Y., Wu, Y. & Ozcan, A. Deep learning in holography and coherent imaging. Light Sci. Appl. 8, 85 (2019).

    ADS  Google Scholar 

  51. Borhani, N., Kakkava, E., Moser, C. & Psaltis, D. Learning to see through multimode fibers. Optica 5, 960–966 (2018).

    ADS  Google Scholar 

  52. Li, Y., Xue, Y. & Tian, L. Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media. Optica 5, 1181–1190 (2018).

    ADS  Google Scholar 

  53. Liu, T. et al. Deep learning-based super-resolution in coherent imaging systems. Sci. Rep. 9, 3926 (2019).

    ADS  Google Scholar 

  54. Krumbügel, M. A. et al. Direct ultrashort-pulse intensity and phase retrieval by frequency-resolved optical gating and a computational neural network. Opt. Lett. 21, 143–145 (1996).

    ADS  Google Scholar 

  55. Nicholson, J., Omenetto, F., Funk, D. J. & Taylor, A. Evolving FROGS: phase retrieval from frequency-resolved optical gating measurements by use of genetic algorithms. Opt. Lett. 24, 490–492 (1999).

    ADS  Google Scholar 

  56. Shu, S. F. Evolving ultrafast laser information by a learning genetic algorithm combined with a knowledge base. IEEE Photon. Technol. Lett. 18, 379–381 (2006).

    ADS  Google Scholar 

  57. Zahavy, T. et al. Deep learning reconstruction of ultrashort pulses. Optica 5, 666–673 (2018).

    ADS  Google Scholar 

  58. Kleinert, S., Tajalli, A., Nagy, T. & Morgner, U. Rapid phase retrieval of ultrashort pulses from dispersion scan traces using deep neural networks. Opt. Lett. 44, 979–982 (2019).

    ADS  Google Scholar 

  59. Xiong, W. et al. Deep learning of ultrafast pulses with a multimode fiber. APL Photonics 5, 096106 (2020).

    ADS  Google Scholar 

  60. Rivenson, Y., Zhang, Y., Günaydın, H., Teng, D. & Ozcan, A. Phase recovery and holographic image reconstruction using deep learning in neural networks. Light Sci. Appl. 7, 17141 (2018).

    Google Scholar 

  61. Sinha, A., Lee, J., Li, S. & Barbastathis, G. Lensless computational imaging through deep learning. Optica 4, 1117–1125 (2017).

    ADS  Google Scholar 

  62. Wu, Y. et al. Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery. Optica 5, 704–710 (2018).

    ADS  Google Scholar 

  63. Goy, A., Arthur, K., Li, S. & Barbastathis, G. Low photon count phase retrieval using deep learning. Phys. Rev. Lett. 121, 243902 (2018).

    ADS  Google Scholar 

  64. Sanchez-Gonzalez, A. et al. Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning. Nat. Commun. 8, 15461 (2017).

    ADS  Google Scholar 

  65. White, J. & Chang, Z. Attosecond streaking phase retrieval with neural network. Opt. Express 27, 4799–4807 (2019).

    ADS  Google Scholar 

  66. Raissi, M. Deep hidden physics models: deep learning of nonlinear partial differential equations. J. Mach. Learn. Res. 19, 932–955 (2018).

    MathSciNet  MATH  Google Scholar 

  67. Raissi, M., Perdikaris, P. & Karniadakis, G. E. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686–707 (2019).

    ADS  MathSciNet  MATH  Google Scholar 

  68. Brunton, S. L., Proctor, J. L. & Kutz, J. N. Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proc. Natl Acad. Sci. USA 113, 3932–3937 (2016).

    ADS  MathSciNet  MATH  Google Scholar 

  69. Jiang, J. & Lai, Y.-C. Model-free prediction of spatiotemporal dynamical systems with recurrent neural networks: role of network spectral radius. Phys. Rev. Res. 1, 033056 (2019).

    Google Scholar 

  70. Salmela, L. et al. Predicting ultrafast nonlinear dynamics in fibre optics with a recurrent neural network. Preprint at https://arxiv.org/abs/2004.14126 (2020).

  71. Lapre, C. et al. Real-time characterization of spectral instabilities in a mode-locked fibre laser exhibiting soliton-similariton dynamics. Sci. Rep. 9, 13950 (2019).

    ADS  Google Scholar 

  72. Dudley, J. M., Genty, G., Mussot, A., Chabchoub, A. & Dias, F. Rogue waves and analogies in optics and oceanography. Nat. Rev. Phys. 1, 675–689 (2019).

    Google Scholar 

  73. Närhi, M. et al. Real-time measurements of spontaneous breathers and rogue wave events in optical fibre modulation instability. Nat. Commun. 7, 13675 (2016).

    ADS  Google Scholar 

  74. Tikan, A., Bielawski, S., Szwaj, C., Randoux, S. & Suret, P. Single-shot measurement of phase and amplitude by using a heterodyne time-lens system and ultrafast digital time-holography. Nat. Photon. 12, 228–234 (2018).

    ADS  Google Scholar 

  75. Närhi, M. et al. Machine learning analysis of extreme events in optical fibre modulation instability. Nat. Commun. 9, 4923 (2018).

    ADS  Google Scholar 

  76. Salmela, L., Lapre, C., Dudley, J. M. & Genty, G. Machine learning analysis of rogue solitons in supercontinuum generation. Sci. Rep. 10, 9596 (2020).

    ADS  Google Scholar 

  77. Amil, P., Soriano, M. C. & Masoller, C. Machine learning algorithms for predicting the amplitude of chaotic laser pulses. Chaos 29, 113111 (2019).

    ADS  MathSciNet  Google Scholar 

  78. Cunillera, A., Soriano, M. C. & Fischer, I. Cross-predicting the dynamics of an optically injected single-mode semiconductor laser using reservoir computing. Chaos 29, 113113 (2019).

    ADS  MathSciNet  Google Scholar 

  79. Vlachas, P. et al. Backpropagation algorithms and reservoir computing in recurrent neural networks for the forecasting of complex spatiotemporal dynamics. Neural Netw. 126, 191–217 (2020).

    Google Scholar 

  80. Pathak, J., Hunt, B., Girvan, M., Lu, Z. & Ott, E. Model-free prediction of large spatiotemporally chaotic systems from data: a reservoir computing approach. Phys. Rev. Lett. 120, 024102 (2018).

    ADS  Google Scholar 

  81. Teğin, U. et al. Controlling spatiotemporal nonlinearities in multimode fibers with deep neural networks. APL Photon. 5, 030804 (2020).

    ADS  Google Scholar 

  82. Comin, A. & Hartschuh, A. Efficient optimization of SHG hotspot switching in plasmonic nanoantennas using phase-shaped laser pulses controlled by neural networks. Opt. Express 26, 33678–33686 (2018).

    ADS  Google Scholar 

  83. Diddams, S. A. The evolving optical frequency comb. J. Opt. Soc. Am. B 27, B51–B62 (2010).

    Google Scholar 

  84. Assion, A. et al. Control of chemical reactions by feedback-optimized phase-shaped femtosecond laser pulses. Science 282, 919–922 (1998).

    ADS  Google Scholar 

  85. Bartels, R. et al. Shaped-pulse optimization of coherent emission of high-harmonic soft X-rays. Nature 406, 164–166 (2000).

    ADS  Google Scholar 

  86. Herek, J. L., Wohlleben, W., Cogdell, R. J., Zeidler, D. & Motzkus, M. Quantum control of energy flow in light harvesting. Nature 417, 533–535 (2002).

    ADS  Google Scholar 

  87. Davies, R. & Kasper, M. Adaptive optics for astronomy. Annu. Rev. Astron. Astrophys. 50, 305–351 (2012).

    ADS  Google Scholar 

  88. Florentin, R. et al. Shaping the light amplified in a multimode fiber. Light Sci. Appl. 6, e16208 (2017).

    Google Scholar 

  89. Florentin, R., Kermene, V., Desfarges-Berthelemot, A. & Barthelemy, A. Space-time adaptive control of femtosecond pulses amplified in a multimode fiber. Opt. Express 26, 10682–10690 (2018).

    ADS  Google Scholar 

  90. Liu, B. & Weiner, A. M. Space–time focusing in a highly multimode fiber via optical pulse shaping. Opt. Lett. 43, 4675–4678 (2018).

    ADS  Google Scholar 

  91. Hughes, T. W., Minkov, M., Williamson, I. A. D. & Fan, S. Adjoint method and inverse design for nonlinear nanophotonic devices. ACS Photon. 5, 4781–4787 (2018).

    Google Scholar 

  92. Goodfellow, I. J. et al. Generative adversarial nets. Adv. Neural Inf. Process. Syst. 3, 2672–2680 (2014).

    Google Scholar 

  93. Subramaniam, A., Wong, M. L., Borker, R. D., Nimmagadda, S. & Lele, S. K. Turbulence enrichment using physics-informed generative adversarial networks. Preprint at https://arxiv.org/abs/2003.01907 (2020).

  94. Van Rullen, R. & Thorpe, S. J. Rate coding versus temporal order coding: what the retinal ganglion cells tell the visual cortex. Neural Comput. 13, 1255–1283 (2001).

    MATH  Google Scholar 

  95. Diamond, A., Schmuker, M. & Nowotny, T. An unsupervised neuromorphic clustering algorithm. Biol. Cybern. 113, 423–437 (2019).

    MathSciNet  MATH  Google Scholar 

  96. Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361, 1004–1008 (2018).

    ADS  MathSciNet  MATH  Google Scholar 

  97. Shen, Y. et al. Deep learning with coherent nanophotonic circuits. Nat. Photon. 11, 441–446 (2017).

    ADS  Google Scholar 

  98. Fang, M. Y.-S., Manipatruni, S., Wierzynski, C., Khosrowshahi, A. & DeWeese, M. R. Design of optical neural networks with component imprecisions. Opt. Express 27, 14009–14029 (2019).

    ADS  Google Scholar 

  99. Young, S. R., Rose, D. C., Karnowski, T. P., Lim, S.-H. & Patton, R. M. Optimizing deep learning hyper-parameters through an evolutionary algorithm. In Proc. Workshop on Machine Learning in High-Performance Computing Environments 1–5 (ACM, 2015).

  100. Penkovsky, B., Larger, L. & Brunner, D. Efficient design of hardware-enabled reservoir computing in FPGAs. J. Appl. Phys. 124, 162101 (2018).

    ADS  Google Scholar 

  101. Klein, A., Falkner, S., Bartels, S., Hennig, P. & Hutter, F. Fast Bayesian hyperparameter optimization on large datasets. Electron. J. Stat. 11, 4945–4968 (2017).

    MathSciNet  MATH  Google Scholar 

  102. Antonik, P., Marsal, N., Brunner, D. & Rontani, D. Bayesian optimisation of large-scale photonic reservoir computers. Preprint at https://arxiv.org/abs/2004.02535 (2020).

  103. Meng, F., Lapre, C., Billet, C., Genty, G. & Dudley, J. M. Instabilities in a dissipative soliton-similariton laser using a scalar iterative map. Opt. Lett. 45, 1232–1235 (2020).

    ADS  Google Scholar 

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Acknowledgements

G.G. acknowledges the Academy of Finland (318082, 333949, Flagship PREIN 320165). L.S. acknowledges the Faculty of Engineering and Natural Sciences graduate school of Tampere University. J.M.D. and D.B. were supported by the EUR EIPHI and I-SITE BFC projects (contracts ANR-17-EURE-0002 and ANR-15-IDEX- 0003). D.B. also acknowledges funding from the Volkswagen Foundation and from the French Agence Nationale de la Recherche (ANR-19-CE24-0006-02). The work of S.K.T. and A.K. was supported by the Russian Science Foundation (grant number 17-72-30006). S.K.T. acknowledges the support of the EPSRC project TRANSNET. The work of S.K. was supported by the Russian Foundation for Basic Research grant number 18-29-20025.

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Authors and Affiliations

  1. Laboratory of Photonics, Tampere University, Tampere, Finland

    Goëry Genty & Lauri Salmela

  2. Institut FEMTO-ST, Université Bourgogne Franche-Comté CNRS UMR 6174, Besançon, France

    John M. Dudley & Daniel Brunner

  3. Division of Laser Physics and Innovative Technologies, Novosibirsk State University, Novosibirsk, Russia

    Alexey Kokhanovskiy, Sergei Kobtsev & Sergei K. Turitsyn

  4. Aston Institute of Photonic Technologies, Aston University, Birmingham, UK

    Sergei K. Turitsyn

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  1. Goëry Genty
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Genty, G., Salmela, L., Dudley, J.M. et al. Machine learning and applications in ultrafast photonics. Nat. Photonics 15, 91–101 (2021). https://doi.org/10.1038/s41566-020-00716-4

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