Open Access
Issue
Published:
*16 November 2020*

Keywords:

Lie group machine learning, Lie group subspace orbit generation learning, quantum group learning, symplectic group learning, Lie group fiber bundle learning
Cite this article:

Lu M, Li F.
Survey on Lie Group Machine Learning.
Big Data Mining and Analytics,
2020, 3(4): 235-258.
https://doi.org/10.26599/BDMA.2020.9020011
Download citation

1607

Views

329

Downloads

Citations

32

Crossref

14

WoS

32

Scopus

0

CSCD

Lie group machine learning is recognized as the theoretical basis of brain intelligence, brain learning, higher machine learning, and higher artificial intelligence. Sample sets of Lie group matrices are widely available in practical applications. Lie group learning is a vibrant field of increasing importance and extraordinary potential and thus needs to be developed further. This study aims to provide a comprehensive survey on recent advances in Lie group machine learning. We introduce Lie group machine learning techniques in three major categories: supervised Lie group machine learning, semisupervised Lie group machine learning, and unsupervised Lie group machine learning. In addition, we introduce the special application of Lie group machine learning in image processing. This work covers the following techniques: Lie group machine learning model, Lie group subspace orbit generation learning, symplectic group learning, quantum group learning, Lie group fiber bundle learning, Lie group cover learning, Lie group deep structure learning, Lie group semisupervised learning, Lie group kernel learning, tensor learning, frame bundle connection learning, spectral estimation learning, Finsler geometric learning, homology boundary learning, category representation learning, and neuromorphic synergy learning. Overall, this survey aims to provide an insightful overview of state-of-the-art development in the field of Lie group machine learning. It will enable researchers to comprehensively understand the state of the field, identify the most appropriate tools for particular applications, and identify directions for future research.

menu

Abstract

Full text

Outline

About this article

Lie group machine learning is recognized as the theoretical basis of brain intelligence, brain learning, higher machine learning, and higher artificial intelligence. Sample sets of Lie group matrices are widely available in practical applications. Lie group learning is a vibrant field of increasing importance and extraordinary potential and thus needs to be developed further. This study aims to provide a comprehensive survey on recent advances in Lie group machine learning. We introduce Lie group machine learning techniques in three major categories: supervised Lie group machine learning, semisupervised Lie group machine learning, and unsupervised Lie group machine learning. In addition, we introduce the special application of Lie group machine learning in image processing. This work covers the following techniques: Lie group machine learning model, Lie group subspace orbit generation learning, symplectic group learning, quantum group learning, Lie group fiber bundle learning, Lie group cover learning, Lie group deep structure learning, Lie group semisupervised learning, Lie group kernel learning, tensor learning, frame bundle connection learning, spectral estimation learning, Finsler geometric learning, homology boundary learning, category representation learning, and neuromorphic synergy learning. Overall, this survey aims to provide an insightful overview of state-of-the-art development in the field of Lie group machine learning. It will enable researchers to comprehensively understand the state of the field, identify the most appropriate tools for particular applications, and identify directions for future research.

[1]

F. Z. Li, S. P. He, and X. P. Qian, Survey on lie group machine learning, (in Chinese), *Chin. J. Comput*., vol. 33, no. 7, pp. 1115-1126, 2010

[2]

F. Z. Li, X. P. Qian, L. Xie, and S. P. He, *Machine Learning Theory and Its Applications*, (in Chinese). Hefei, China: China University of Science and Technology Press, 2009.

[3]

F. Z. Li, L. Zhang, J. W. Yang, X. P. Qian, B. J. Wang, and S. P. He, *Lie Group Machine Learning*, (in Chinese). Hefei, China: China University of Science and Technology Press, 2013.

[4]

F. Z. Li, L. Zhang, and Z. Zhang, Lie Group Machine Learning, https://doi.org/10.1515/9783110499506, 2019.

[5]

T. A. Haill, An application of the lie group theory of continuous point transformations to the Vlasov-Maxwell equations (Plasma Physics), PhD dissertation, University of Illinois at Urbana-Champaign, Champaign, IL, USA, 1985.

[6]

A. Baker, *Matrix Groups*: *An Introduction to Lie Group Theory*. Berlin, Germany: Springer, 2002.

[7]

W. Fulton and J. Harris, Representation theory: A first course, https://doi.org/10.2307/3618117, 1991.

[8]

M. Hunacek, Lie groups: An introduction through linear groups, https://doi.org/10.1017/S0025557200183561, 2008.

[9]

P. Yarlagadda, O. Ozcanli, and J. Mundy, Lie group distance based generic 3-d vehicle classification, in Proc. 2008 19th Int. Conf. on Pattern Recognition, Tampa, FL, USA, 2008.

[10]

Y. M. Lui, Advances in matrix manifolds for computer vision, *Image Vision Comput*., vol. 30, nos. 6&7, pp. 380-388, 2012.

[11]

H. Xu and F. Z. Li, The design of Su(n) classifier of lie group machine learning (LML), *J. Comput. Informat. Syst*., vol. 1, no. 4, pp. 835-841, 2005.

[12]

H. Xu and F. Z. Li, Algorithms of dynkin diagrams in lie group machine learning, *J. Commun. Comput*., vol. 4, no. 3, pp. 13-17, 2007.

[13]

H. Xu and F. Z. Li, Geometry algorisms of dynkin diagrams in lie group machine learning, *J. Nanchang Inst. Technol*., vol. 25, no. 2, pp. 74-78, 2006.

[14]

H. Xu and F. Z. Li, Lie group machine learning’s axiom hypothesizes, in Proc. 2006 IEEE Int. Conf. on Granular Computing, Atlanta, GA, USA, 2006, pp. 401-404.

[15]

F. Chen, Research and application on orbits generated algorithm of learning subspace in Lie-Group Machine Learning (LML), (in Chinese), master dissertation, Soochow University, Suzhou, China, 2007.

[16]

F. Chen and F. Z. Li, Orbits generated theory of learning subspace and its algorithm in Lie-Group machine learning (LML), *J. Suzhou Univ*. (*Nat. Sci. Ed*.), vol. 23, no. 1, pp. 61-66, 2007.

[17]

C. L. Lv, Z. K. Wu, D. Zhang, X. C. Wang, and M. Q. Zhou, 3D nose shape net for human gender and ethnicity classification, *Pattern Recognit. Lett*., vol. 126, pp. 51-57, 2019.

[18]

E. Boutellaa, O. Kerdjidj, and K. Ghanem, Covariance matrix based fall detection from multiple wearable sensors, *J. Biomed. Informat*., vol. 94, p. 103 189, 2019.

[19]

Y. Heider, K. Wang, and W. C. Sun, SO(3)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials, *Comput. Methods Appl. Mech. Eng*., vol. 363, p. 112 875, 2020.

[20]

G. Lebanon, Metric learning for text documents, *IEEE Trans. Pattern Anal. Mach. Intell*., vol. 28, no. 4, pp. 497-508, 2006.

[21]

F. Chen and F. Z. Li, Orbits generated lattice algorithm of learning subspace in Lie-group Machine Learning (LML), (in Chinese), *Comput. Eng. Appl*., vol. 43, no. 15, pp. 184-187, 2007.

[22]

N. Nasios and A. G. Bors, Kernel-based classification using quantum mechanics, *Pattern Recognit*., vol. 40, no. 3, pp. 875-889, 2007.

[23]

S. P. He and F. Z. Li, A molecular docking drug design algorithm based on quantum group, (in Chinese), *J. Nanjing Univ*. (*Nat. Sci*.), vol. 44, no. 5, pp. 512-519, 2008.

[24]

Y. M. Wu, J. Y. Hu, and X. G. Yin, Symplectic integrators of the equations of multibody system dynamics on manifolds, (in Chinese), *Adv. Mech*., vol. 32, no. 2, pp. 189-195, 2002.

[25]

K. Feng and M. Z. Qin, *Symplectic Geometric Algorithms for Hamiltonian Systems*. Berlin, Germany: Springer, 2010.

[26]

Z. X. Xu, D. Y. Zhou, and Z. C, Deng, Numerical method based on hamilton system and symplectic algorithm to differential games, *Appl. Math. Mech*., vol. 27, no. 3, pp. 341-346, 2006.

[27]

H. X. Fu and F. Z. Li, Research of the symplectic group classifier based on lie group machine learning, in Proc. 4th Int. Conf. on Fuzzy Systems and Knowledge Discovery (FSKD 2007), Haikou, China, 2007, pp. 649-655.

[28]

H. X. Fu, Research on symplectic group classifier in Lie group machine learning, (in Chinese), master dissertation, Soochow University, Suzhou, China, 2008.

[29]

W. W. Guan and F. Z. Li, Drug molecular design using lie group machine learning (LML), in Proc. Int. Conf. on Advanced Intelligence, Beijing, China, 2008, pp. 411-414.

[30]

O. Nechaeva, The neural network approach to automatic construction of adaptive meshes on multiply-connected domains, in Proc. Int. Joint Conf. on Neural Network, Orlando, FL, USA, 2007, pp. 1912-1917.

[31]

W. W. Guan, Research of covering algorithm in Lie Group machine learning, (in Chinese), master dissertation, Soochow University, Suzhou, China, 2009.

[32]

L. M. Lui, W. Zeng, S. T. Yau, and X. F. Gu, Shape analysis of planar objects with arbitrary topologies using conformal geometry, in Proc. 11th European Conf. on Computer Vision, Crete, Greece, 2010, pp. 672-686.

[33]

Y. Chen, F. Z. Li, and P. Zou, Multiply connected lie group covering learning algorithm for image classifi-cation, (in Chinese), *J. Front. Comput. Sci. Technol*., vol. 8, no. 9, pp. 1101-1112, 2014.

[34]

C. Yan and F. Z. Li, Path optimization algorithms for covering learning, (in Chinese), *J. Software*, vol. 26, no. 11, pp. 2781-2794, 2015.

[35]

L. H. Wu and F. Z. Li, Multiply Lie Group kernel covering learning algorithm for image classification, (in Chinese), *J. Front. Comput. Sci. Technol*., vol. 10, no. 12, pp. 1737-1743, 2016.

[36]

Y. S. Bengio, Learning deep architectures for AI, *Found. Trends Mach. Learn*., vol. 2, no. 1, pp. 1-127, 2009.

[37]

G. E. Hinton, S. Osindero, and Y. W. Teh, A fast learning algorithm for deep belief nets, *Neural Comput*., vol. 18, no. 7, pp. 1527-1554, 2006.

[38]

W. H. He and F. Z. Li, Research on Lie Group deep structure learning algorithm, (in Chinese), *J. Front. Comput. Sci. Technol*., vol. 4, no. 7, pp. 646-653, 2010.

[39]

Z. Q. Hong, Algebraic feature extraction of image for recognition, *Pattern Recognit*., vol. 24, no. 3, pp. 211-219, 1991.

[40]

Y. Tan, T. N. Tan, Y. H. Wang, and Y. C. Fang, Do singular values contain adequate information for face recognition? *Pattern Recognit*., vol. 36, no. 3, pp. 649-655, 2003.

[41]

M. D. Yang, F. Z. Li, L. Zhang, and Z. Zhang, Lie group impression for deep learning, *Informat. Process. Lett*., vol. 136, pp. 12-16, 2018.

[42]

C. Gao, F. Z. Li, and C. Shen, Research on Lie Group kernel learning algorithm, (in Chinese), *J. Front. Comput. Sci. Technol*., vol. 6, no. 11, pp. 1026-1038, 2012.

[43]

V. M. Govindu, Lie-algebraic averaging for globally consistent motion estimation, in Proc. 2004 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, Washington, DC, USA, 2004, p. 8 161 455.

[44]

R. Subbarao and P. Meer, Nonlinear mean shift for clustering over analytic manifolds, in Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, New York, NY, USA, 2006.

[45]

O. Tuzel, R. Subbarao, and P. Meer, Simultaneous multiple 3D motion estimation via mode finding on lie groups, in Proc. 10th IEEE Int. Conf. on Computer Vision, Beijing, China, 2005, pp. 18-25.

[46]

M. Moakher, Means and averaging in the group of rotations, *SIAM J. Matrix Anal. Appl*., vol. 24, no. 1, pp. 1-16, 2002.

[47]

C. Gao and F. Z. Li, Lie group means learning algorithm, (in Chinese), *Pattern Recognit. Artif. Intell*., vol. 25, no. 6, pp. 900-908, 2012.

[48]

C. Gao, Research on Lie Group mean learning algorithm and its application, (in Chinese), master dissertation, Soochow University, Suzhou, China, 2012.

[49]

B. Schölkopf, A. Smola, and K. R. Müller, Nonlinear component analysis as a kernel eigenvalue problem, *Neural Computation*, vol. 10, no. 5, pp, 1299-1319, 1998.

[50]

M. A. Aizerman, E. M. Braverman, and L. I. Rozonoer, Theoretical foundations of the potential function method in pattern recognition learning, *Automat. Remote Control*, vol. 25, no. 6, pp. 821-837, 1964.

[51]

J. Cheng, Q. S. Liu, and H. Q. Lu, Texture classification using kernel independent component analysis, in Proc. 17th Int. Conf. on Pattern Recognition, Cambridge, UK, 2004, p. 8 213 183.

[52]

S. Mika, G. Ratsch, J. Weston, B. Scholkopf, and K. R. Mullers, Fisher discriminant analysis with kernels, in Neural Networks for Signal Processing IX: Proc. 1999 IEEE Signal Processing Society Workshop, Madison, WI, USA, 1999, p. 6 497 095.

[53]

C. Y. Xu, C. Y. Lu, J. B. Gao, T. J. Wang, and S. C. Yan, Facial analysis with a lie group kernel, *IEEE Trans. Circuits Syst. Video Technol*., vol. 25, no. 7, pp. 1140-1150, 2015.

[54]

B. N. Sheehan and Y. Saad, Higher order orthogonal iteration of tensors (HOOI) and its relation to PCA and GLRAM, in Proc. SIAM Int. Conf. on Data Mining, Minneapolis, MN, USA, 2007.

[55]

J. Yang, D. Zhang, A. F. Frangi, and J. Y. Yang, Two-dimensional PCA: A new approach to appearance-based face representation and recognition, *IEEE Trans. Pattern Anal. Mach. Intell*., vol. 26, no. 1, pp. 131-137, 2004.

[56]

J. P. Ye, Generalized low rank approximations of matrices, *Mach. Learn*., vol. 61, no. 1, pp. 167-191, 2005.

[57]

M. Lu and F. Z. Li, Neighborhood-embedded tensor learning, (in Chinese), *J. Front. Comput. Sci. Technol*., vol. 11, no. 7, pp. 1102-1113, 2017.

[58]

J. T. Sun, H. J. Zeng, H. Liu, Y. C. Lu, and Y. Lu, CubeSVD: A novel approach to personalized web search, in Proc. 14th Int. Conf. on World Wide Web, Chiba, Japan, 2005.

[59]

X. F. He, D. Cai, and P. Niyogi, Tensor subspace analysis, in Proc. 18th Int. Conf. on Neural Information Processing Systems, British Columbia, Canada, 2005, pp. 499-506.

[60]

X. L. Li, Research and application on a data reduction method based on tensor field, (in Chinese), master dissertation, Soochow University, Suzhou, China, 2009.

[61]

C. T. Lu, Research on tensor learning algorithm and its application on disease prediction, (in Chinese), master dissertation, Soochow University, Suzhou, China, 2017.

[62]

J. H. Tang, X. B. Shu, Z. C. Li, Y. G. Jiang, and Q. Tian, Social anchor-unit graph regularized tensor completion for large-scale image retagging, *IEEE Trans. Pattern Anal. Mach. Intell*., vol. 41, no. 8, pp. 2027-2034, 2019.

[63]

J. Li, F. Z. Li, and L. Zhang, Image segmentation algorithm based on affine connection, (in Chinese), *J. Front. Comput. Sci. Technol*., vol. 6, no. 3, pp. 267-274, 2012.

[64]

Q. Xu, F. Z. Li, and P. Zou, The k-means algorithm based on finsler geometry, (in Chinese), *J. Univ. Sci. Technol. China*, vol. 7, pp. 570-575, 2014.

[65]

M. Chen, S. P. He, and F. Z. Li, A lie group machine learning algorithm for linear classification, (in Chinese), *Microelectron. Comput*., vol. 26, no. 10, pp. 170-173, 2009.

[66]

M. Xian and F. Z. Li, Learning algorithm based on homology boundary, (in Chinese), *Comput. Eng. Appl*., vol. 44, no. 21, pp. 192-194&204, 2008.

[67]

F. Aiolli and A. Sperduti, A re-weighting strategy for improving margins, *Artif. Intell*., vol. 137, nos. 1&2, pp. 197-216, 2002.

[68]

J. Canny, A computational approach to edge detection, *IEEE Trans. Pattern Anal. Mach. Intell*., vol. PAMI-8, no. 6, pp. 679-698, 1986.

[69]

M. M. Zhao and F. Z. Li, Neighborhood homology learning algorithm, (in Chinese), *CAAI Trans. Intell. Syst*., vol. 9, no. 3, pp. 336-342, 2014.

[70]

A. Perperidis, K. Dhaliwal, S. McLaughlin, and T. Vercauteren, Image computing for fibre-bundle endomicroscopy: A review, *Med. Image Anal*., vol. 62, p. 101 620, 2020.

[71]

N. Sochen, R. Kimmel, and R. Malladi, A general framework for low level vision, *IEEE Trans. Image Process*., vol. 7, no. 3, pp. 310-318, 1998.

[72]

D. W. Pearson, Approximating vertical vector fields for feedforward neural networks, *Appl. Math. Lett*., vol. 9, no. 2, pp. 61-64, 1996.

[73]

J. H. Chao and J. Kim, A fibre bundle model of surfaces and its generalization, in Proc. 17th Int. Conf. on Pattern Recognition, Cambridge, UK, 2004, p. 8 221 516.

[74]

L. L. Zhou and F. Z. Li, Research on mapping mechanism of learning expression, in Proc. 5th Int. Conf. on Rough Set and Knowledge Technology, Beijing, China, 2010, pp. 298-303.

[75]

T. R. Gao, The diffusion geometry of fibre bundles: Horizontal diffusion maps, *Appl. Comput. Harmonic Anal*., .

[76]

A. Asperti and G. Longo, *Categories Types and Structures*: *An Introduction to Category Theory for the Working Computer Scientist*. Cambridge, MA, USA: MIT Press, 1991.

[77]

G. Muhiuddin, *Basic Concepts of Category Theory and Its Applications*. LAP LAMBERT Academic Publishing, 2018.

[78]

X. X. Xu, F. Z. Li, L. Zhang, and Z. Zhang, The category representation of machine learning algorithm, (in Chinese), *J. Comput. Res. Dev*., vol. 54, no. 11, pp. 2567-2575, 2017.

[79]

M. Lu, L. Zhang, X. J. Zhao, and F. Z. Li, Constrained neighborhood preserving concept factorization for data representation, *Know. Based Syst*., vol. 102, pp. 127-139, 2016.

[80]

H. X. Xu, A semi-supervised learning algorithm based on Lie Group and application, (in Chinese), master dissertation, Soochow University, Suzhou, China, 2009.

[81]

W. He, Research on the covering algorithm of machine learning and its application, (in Chinese), master dissertation, Soochow University, Suzhou, China, 2011.

[82]

M. X. Dong, Spectral estimation of image features manifold learning method, (in Chinese), master dissertation, Soochow University, Suzhou, China, 2012.

[83]

M. D. Yang, F. Z. Li, and L. Zhang, Advances in the study of lie group machine learning in recent ten years, (in Chinese), *Chin. J. Comput*., vol. 38, no. 7, pp. 1337-1356, 2015.

[84]

Y. J. Huang and F. Z. Li, Isospectral manifold learning algorithm, (in Chinese), *J. Softw*., vol. 24, no. 11, pp. 2656-2666, 2013.

[85]

Y. J. Huang and F. Z. Li, Fast learning algorithm of spectral manifold, (in Chinese), *J. Front. Comput. Sci. Technol*., vol. 8, no. 6, pp. 735-742, 2014.

[86]

Y. Ren, Y. N. Wang, and J. Zhu, Spectral learning for supervised topic models, *IEEE Trans. Pattern Anal. Mach. Intell*., vol. 40, no. 3, pp. 726-739, 2018.

[87]

D. C. Tao, X. L. Li, X. D. Wu, W. M. Hu, and S. J. Maybank, Supervised tensor learning, *Knowl. Inf. Syst*., vol. 13, no. 1, pp. 1-42, 2007.

[88]

W. Fei, Y. N. Liu, and Y. T. Zhuang, Tensor-based transductive learning for multimodality video semantic concept detection, *IEEE Trans. Multimed*., vol. 11, no. 5, pp. 868-878, 2009.

[89]

X. L. Liu, T. J. Guo, L. F. He, and X. W. Yang, A low-rank approximation-based transductive support tensor machine for semisupervised classification, *IEEE Trans. Image Process*., vol. 24, no. 6, pp. 1825-1838, 2015.

[90]

W. M. Hu, J. Gao, J. L. Xing, C. Zhang, and S. Maybank, Semi-supervised tensor-based graph embedding learning and its application to visual discriminant tracking, *IEEE Trans. Pattern Anal. Mach. Intell*., vol. 39, no. 1, pp. 172-188, 2017.

[91]

T. Huynh-The, C. H. Hua, T. T. Ngo, and D. S. Kim, Image representation of pose-transition feature for 3D skeleton-based action recognition, *Inf. Sci*., vol. 513, pp. 112-126, 2020.

[92]

R. Vemulapalli, F. Arrate, and R. Chellappa, Human action recognition by representing 3D skeletons as points in a lie group, in Proc. 2014 IEEE Conf. on Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014, p. 14 632 342.

[93]

Z. Liu, C. Y. Zhang, and Y. L. Tian, 3D-based deep convolutional neural network for action recognition with depth sequences, *Image Vision Comput*., vol. 55, pp. 93-100, 2016.

[94]

X. Y. Cai, W. G. Zhou, and H. Q. Li, Attribute mining for scalable 3D human action recognition, in Proc. 23rd ACM Int. Conf. on Multimedia, Brisbane, Australia, 2015, pp. 1075-1078.

[95]

H. L. Zhang, P. Zhong, J. L. He, and C. X. Xia, Combining depth-skeleton feature with sparse coding for action recognition, *Neurocomputing*, vol. 230, pp. 417-426, 2017.

[96]

J. C. Núñez, R. Cabido, J. J. Pantrigo, A. S. Montemayor, and J. F. Vélez, Convolutional neural networks and long short-term memory for skeleton-based human activity and hand gesture recognition, *Pattern Recognit*., vol. 76, pp. 80-94, 2018.

[97]

G. G. Demisse, D. Aouada, and B. Ottersten, Deformation based curved shape representation, *IEEE Trans. Pattern Anal. Mach. Intell*., vol. 40, no. 6, pp. 1338-1351, 2018.

[98]

C. Von Tycowicz, F. Ambellan, A. Mukhopadhyay, and S. Zachow, An efficient riemannian statistical shape model using differential coordinates: With application to the classification of data from the osteoarthritis initiative, *Med. Image Anal*., vol. 43, pp. 1-9, 2018.

[99]

M. Hayat, M. Bennamoun, and A. A. El-Sallam, An RGB-D based image set classification for robust face recognition from Kinect data, *Neurocomputing*, vol. 171, pp. 889-900, 2016.

[100]

M. Yin, Z. Z. Wu, D. M. Shi, J. B. Gao, and S. L. Xie, Locally adaptive sparse representation on riemannian manifolds for robust classification, *Neurocomputing*, vol. 310, pp. 69-76, 2018.

[101]

T. Wang, M. N. Qiao, Y. Chen, J. Chen, A. C. Zhu, and H. Snoussi, Video feature descriptor combining motion and appearance cues with length-invariant characteristics, *Optik*, vol. 157, pp. 1143-1154, 2018.

[102]

J. M. Dong, Y. X. Peng, S. H. Ying, and Z. Y. Hu, Lietricp: An improvement of trimmed iterative closest point algorithm, *Neurocomputing*, vol. 140, pp. 67-76, 2014.

[103]

S. H. Ying, Y. W. Wang, Z. J. Wen, and Y. P. Lin, Nonlinear 2D shape registration via thin-plate spline and lie group representation, *Neurocomputing*, vol. 195, pp. 129-136, 2016.

Publication history

Copyright

Acknowledgements

Rights and permissions

Received: 14 May 2020

Accepted: 09 June 2020

Published:
16 November 2020

Issue date: December 2020

© The authors 2020

This work was supported by the National Key Research and Development Program (Nos. 2018YFA0701700 and 2018YFA0701701) and Scientific Research Foundation for Advanced Talents (No. jit-b-202045). The authors thank all of the reviewers for their valuable comments.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).