{"id":43849,"date":"2023-07-19T22:34:43","date_gmt":"2023-07-19T14:34:43","guid":{"rendered":"https:\/\/swarma.org\/?p=43849"},"modified":"2023-07-19T22:34:43","modified_gmt":"2023-07-19T14:34:43","slug":"%e3%80%8a%e7%a5%9e%e7%bb%8f%e7%bd%91%e7%bb%9c%e7%9a%84%e7%bb%9f%e8%ae%a1%e5%9c%ba%e8%ae%ba%e3%80%8b%e5%87%ba%e7%89%88%e6%8b%9b%e5%8b%9f-%e9%9b%86%e6%99%ba%e4%bf%b1%e4%b9%90%e9%83%a8%e3%80%8c","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=43849","title":{"rendered":"\u300a\u795e\u7ecf\u7f51\u7edc\u7684\u7edf\u8ba1\u573a\u8bba\u300b\u51fa\u7248\u62db\u52df | \u96c6\u667a\u4ff1\u4e50\u90e8\u300c\u795e\u7ecf\u52a8\u529b\u5b66\u793e\u533a\u300d\u4f17\u5305\u7ffb\u8bd1\u4e66\u7c4d"},"content":{"rendered":"
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Moritz Helias \u662f\u5fb7\u56fd\u4e9a\u741b\u5de5\u4e1a\u5927\u5b66\u4e8e\u5229\u5e0c\u7814\u7a76\u4e2d\u5fc3\u7684\u7ec4\u957f\u548c\u7269\u7406\u7cfb\u7684\u52a9\u7406\u6559\u6388\u3002\u4ed6\u5728\u6c49\u5821\u5927\u5b66\u83b7\u5f97\u7406\u8bba\u56fa\u4f53\u7269\u7406\u5b66\u6587\u51ed\uff0c\u5728\u5fb7\u56fd\u5f17\u83b1\u5821\u5927\u5b66\u83b7\u5f97\u8ba1\u7b97\u795e\u7ecf\u79d1\u5b66\u535a\u58eb\u5b66\u4f4d\u3002\u968f\u540e\u5728\u65e5\u672c RIKEN Wako-Shi \u548c J\u00fclich \u7814\u7a76\u4e2d\u5fc3\u62c5\u4efb\u535a\u58eb\u540e\u804c\u4f4d\u3002\u4ed6\u7684\u4e3b\u8981\u7814\u7a76\u5174\u8da3\u662f\u795e\u7ecf\u7f51\u7edc\u52a8\u529b\u5b66\u548c\u529f\u80fd\u53ca\u5176\u4f7f\u7528\u7edf\u8ba1\u7269\u7406\u5b66\u548c\u573a\u8bba\u5de5\u5177\u7684\u5b9a\u91cf\u5206\u6790\u3002<\/span><\/p>\n


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David Dahmen \u662f\u5fb7\u56fd\u4e8e\u5229\u5e0c\u7814\u7a76\u4e2d\u5fc3\u795e\u7ecf\u79d1\u5b66\u4e0e\u533b\u5b66\u7814\u7a76\u6240\u7684\u535a\u58eb\u540e\u7814\u7a76\u5458\u3002\u4ed6\u5728\u5fb7\u56fd\u4e9a\u741b\u5de5\u4e1a\u5927\u5b66\u83b7\u5f97\u7269\u7406\u5b66\u7855\u58eb\u5b66\u4f4d\uff0c\u81f4\u529b\u4e8e\u7c92\u5b50\u7269\u7406\u5b66\u7684\u6709\u6548\u573a\u8bba\u65b9\u6cd5\u3002\u4e4b\u540e\uff0c\u4ed6\u8f6c\u5411\u8ba1\u7b97\u795e\u7ecf\u79d1\u5b66\u9886\u57df\uff0c\u5e76\u4e8e 2017 \u5e74\u83b7\u5f97\u535a\u58eb\u5b66\u4f4d\u3002\u4ed6\u7684\u7814\u7a76\u5305\u62ec\u5faa\u73af\u795e\u7ecf\u5143\u7f51\u7edc\u7684\u5efa\u6a21\u3001\u5206\u6790\u548c\u6a21\u62df\uff0c\u7279\u522b\u5173\u6ce8\u6570\u5b66\u5de5\u5177\u548c\u6a21\u62df\u6982\u5ff5\u7684\u5f00\u53d1\u548c\u77e5\u8bc6\u8f6c\u79fb\u3002\u4ed6\u7684\u4e3b\u8981\u5174\u8da3\u662f\u968f\u673a\u795e\u7ecf\u7f51\u7edc\u7684\u573a\u8bba\u65b9\u6cd5\u3001\u5faa\u73af\u7f51\u7edc\u4e2d\u7684\u76f8\u5173\u6027\u4ee5\u53ca\u5c40\u90e8\u573a\u52bf\u7684\u5efa\u6a21\u3002<\/span><\/p>\n


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\u8fd9\u672c\u4e662019\u5e74\u5728arXiv.org\u5e73\u53f0\u53d1\u5e03\uff0c\u5e76\u4e8e2020\u5e74\u7531Springer\u51fa\u7248\uff1a<\/span><\/section>\n

https:\/\/link.springer.com\/book\/10.1007\/978-3-030-46444-8<\/span><\/p>\n


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\u7b2c\u4e00\u90e8<\/span>\u5206\uff081-5\u7ae0\uff09\u4ecb\u7ecd\u6982\u7387\u3001\u77e9\u3001\u7d2f\u79ef\u91cf\u53ca\u5176\u5173\u7cfb\u7b49\u7edf\u8ba1\u573a\u8bba\u4e2d\u7684\u57fa\u672c\u6982\u5ff5\uff0c\u4ee5\u7ef4\u514b\u5b9a\u7406\u4e3a\u4f8b\u8fdb\u884c\u793a\u4f8b\u8bf4\u660e\uff0c\u5e76\u56de\u987e\u7edf\u8ba1\u5b66\u8bed\u5883\u4e0b\u7684\u8d39\u66fc\u56fe\u548c\u5fae\u6270\u7406\u8bba\u3002<\/span><\/p>\n


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\u7b2c\u4e8c\u90e8\u5206\uff086-10\u7ae0\uff09\u5c06\u9759\u6001\u7684\u6982\u7387\u5206\u5e03\u6269\u5c55\u5230\u52a8\u529b\u5b66\u7cfb\u7edf\uff0c\u7279\u522b\u662f\u4f0a\u85e4\uff08Ito\uff09\u610f\u4e49\u4e0b\u7684\u968f\u673a\u5fae\u5206\u65b9\u7a0b\uff0c\u5e76\u5728Martin-Siggia-Rose-De Dominicis Janssen\u8def\u5f84\u79ef\u5206\u5f62\u5f0f\u4e2d\u5904\u7406\u3002\u5229\u7528\u65e0\u5e8f\u7cfb\u7edf\u7684\u6982\u5ff5\uff0c\u7814\u7a76\u5177\u6709\u968f\u673a\u8fde\u63a5\u7684\u7f51\u7edc\u5e76\u63a8\u5bfc\u51fa\u5176\u81ea\u6d3d\u52a8\u529b\u5b66\u5e73\u5747\u573a\u8bba\u3002\u57fa\u4e8e\u52a8\u529b\u5b66\u5e73\u5747\u573a\u8bba\u6a21\u578b\uff0c\u89e3\u91ca\u795e\u7ecf\u5143\u7f51\u7edc\u4e2d\u6da8\u843d\u7684\u7edf\u8ba1\u91cf\u3001\u6df7\u6c8c\u6216\u6709\u5e8f\u72b6\u6001\u7684\u6d8c\u73b0\uff0c\u5e76\u5728\u6a21\u578b\u4e2d\u52a0\u5165\u968f\u673a\u6027\u8fdb\u884c\u6269\u5c55\u7814\u7a76\u3002<\/span><\/p>\n


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\u4e3b\u8981\u76ee\u5f55<\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n

1.\u5f15\u8a00<\/span><\/p>\n

2.\u6982\u7387\u3001\u77e9\u3001\u7d2f\u79ef\u91cf<\/span><\/p>\n

3.\u9ad8\u65af\u5206\u5e03\u548c\u7ef4\u514b\uff08Wick\uff09\u5b9a\u7406<\/span><\/p>\n

4.\u5fae\u6270\u5c55\u5f00<\/span><\/p>\n

5.\u94fe\u63a5\u7c07\u5b9a\u7406<\/span><\/p>\n

6.\u6cdb\u51fd\u65b9\u6cd5\u7684\u9884\u5907\u77e5\u8bc6<\/span><\/p>\n

7.\u968f\u673a\u5fae\u5206\u65b9\u7a0b\u7684\u6cdb\u51fd\u63cf\u8ff0<\/span><\/p>\n

8.Ornstein-Uhlenbeck \u8fc7\u7a0b\uff1a\u81ea\u7531\u9ad8\u65af\u7406\u8bba<\/span><\/p>\n

9.\u968f\u673a\u5fae\u5206\u65b9\u7a0b\u7684\u5fae\u6270\u7406\u8bba<\/span><\/p>\n

10.\u968f\u673a\u7f51\u7edc\u7684\u52a8\u529b\u5b66\u5e73\u5747\u573a\u7406\u8bba<\/span><\/p>\n

11.\u9876\u70b9\u751f\u6210\u51fd\u6570<\/span><\/p>\n

12.\u5c06\u7d2f\u79ef\u91cf\u5c55\u5f00\u4e3a\u9876\u70b9\u51fd\u6570\u7684\u6811\u5f62\u56fe<\/span><\/p>\n

13.\u6709\u6548\u4f5c\u7528\u91cf\u7684\u5708\u56fe\u5c55\u5f00<\/span><\/p>\n

14.MSRDJ \u5f62\u5f0f\u4e2d\u7684\u5708\u56fe\u5c55\u5f00<\/span><\/p>\n


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1 Introduction<\/strong><\/span><\/p>\n

1.1 Code, Numerics, Figures<\/span><\/section>\n
References<\/span><\/section>\n

2 Probabilities, Moments, Cumulants<\/strong><\/span><\/p>\n

2.1 Probabilities, Observables, and Moments<\/span><\/section>\n
2.2 Transformation of Random Variables<\/span><\/section>\n
2.3 Cumulants<\/span><\/section>\n
2.4 Connection Between Moments and Cumulants<\/span><\/section>\n
2.5 Problems<\/span><\/section>\n
References<\/span><\/section>\n

3 Gaussian Distribution and Wick\u2019s Theorem<\/span><\/strong><\/span><\/p>\n

3.1 Gaussian Distribution<\/span><\/section>\n
3.2 Moment and Cumulant-Generating Function of a Gaussian<\/span><\/section>\n
3.3 Wick\u2019s Theorem<\/span><\/section>\n
3.4 Graphical Representation: Feynman Diagrams<\/span><\/section>\n
3.5 Appendix: Self-Adjoint Operators<\/span><\/section>\n
3.6 Appendix: Normalization of a Gaussian<\/span><\/section>\n
References<\/span><\/section>\n

4 Perturbation Expansion<\/span><\/strong><\/span><\/p>\n

4.1 Solvable Theories with Small Perturbations<\/span><\/section>\n
4.2 Special Case of a Gaussian Solvable Theory<\/span><\/section>\n
4.3 Example: Example: \u201c\u03c63 + \u03c64\u201d Theory<\/span><\/section>\n
4.4 External Sources<\/span><\/section>\n
4.5 Cancelation of Vacuum Diagrams<\/span><\/section>\n
4.6 Equivalence of Graphical Rules for n-Point Correlation and n-th Moment<\/span><\/section>\n
4.7 Example: \u201c\u03c63 + \u03c64\u201d Theory<\/span><\/section>\n
4.8 Problems<\/span><\/section>\n
References<\/span><\/section>\n

5 Linked Cluster Theorem<\/span><\/strong><\/span><\/p>\n

5.1 Introduction<\/span><\/section>\n
5.2 General Proof of the Linked Cluster Theorem<\/span><\/section>\n
5.3 External Sources\u2014Two Complimentary Views<\/span><\/section>\n
5.4 Example: Connected Diagrams of the \u201c\u03c63 + \u03c64\u201d Theory<\/span><\/section>\n
5.5 Problems<\/span><\/section>\n
Reference<\/span><\/section>\n

6 Functional Preliminaries<\/span><\/strong><\/span><\/p>\n

6.1 Functional Derivative<\/span><\/section>\n
6.1.1 Product Rule<\/span><\/section>\n
6.1.2 Chain Rule<\/span><\/section>\n
6.1.3 Special Case of the Chain Rule: Fourier Transform<\/span><\/section>\n
6.2 Functional Taylor Series<\/span><\/section>\n

7 Functional Formulation of Stochastic Differential Equations<\/span><\/strong><\/span><\/p>\n

7.1 Stochastic Differential Equations<\/span><\/section>\n
7.2 Onsager\u2013Machlup Path Integral<\/span><\/section>\n
7.3 Martin Martin\u2013Siggia\u2013Rose-De Dominicis\u2013Janssen (MSRDJ)  Path Integral<\/span><\/section>\n
7.4 Moment-Generating Functional<\/span><\/section>\n
7.5 Response Function in the MSRDJ Formalism<\/span><\/section>\n
References<\/span><\/section>\n

8 Ornstein\u2013Uhlenbeck Process: The Free Gaussian Theory<\/span><\/strong><\/span><\/p>\n

8.1 Definition<\/span><\/section>\n
8.2 Propagators in Time Domain<\/span><\/section>\n
8.3 Propagators in Fourier Domain<\/span><\/section>\n
References<\/span><\/section>\n

9 Perturbation Theory for Stochastic Differential Equations<\/span><\/strong><\/span><\/p>\n

9.1 Vanishing Moments of Response Fields<\/span><\/section>\n
9.2 Feynman Rules for SDEs in Time Domain and Frequency Domain<\/span><\/section>\n
9.3 Diagrams with More Than a Single External Leg<\/span><\/section>\n
9.4 Appendix: Unitary Fourier Transform<\/span><\/section>\n
9.5 Appendix: Vanishing Response Loops<\/span><\/section>\n
9.6 Problems<\/span><\/section>\n
References<\/span><\/section>\n

10 Dynamic Mean-Field Theory for Random Networks<\/span><\/strong><\/span><\/p>\n

10.1 The Notion of a Mean-Field Theory<\/span><\/section>\n
10.2 Definition of the Model and Generating Functional<\/span><\/section>\n
10.3 Self-averaging Observables<\/span><\/section>\n
10.4 Average over the Quenched Disorder<\/span><\/section>\n
10.5 Stationary Statistics: Self-consistent Autocorrelation as a Particle in a Potential<\/span><\/section>\n
10.6 Transition to Chaos<\/span><\/section>\n
10.7 Assessing Chaos by a Pair of Identical Systems<\/span><\/section>\n
10.8 Schr\u00f6dinger Equation for the Maximum Lyapunov Exponent<\/span><\/section>\n
10.9 Condition for Transition to Chaos<\/span><\/section>\n
10.10 Problems<\/span><\/section>\n
References<\/span><\/section>\n

11 Vertex-Generating Function<\/span><\/strong><\/span><\/p>\n

  11.1 Motivating Example for the Expansion Around a Non-vanishingMean Value<\/span><\/p>\n

  11.2 Legendre Transform and Definition of the Vertex-Generating Function<\/span><\/p>\n

  11.3 Perturbation Expansion of<\/span><\/p>\n

  11.4 Generalized One-line Irreducibility<\/span><\/p>\n

  11.5 Example<\/span><\/p>\n

  11.6 Vertex Functions in the Gaussian Case<\/span><\/p>\n

  11.7 Example: Vertex Functions of the \u201c\u03c63 + \u03c64\u201d-Theory<\/span><\/p>\n

  11.8 Appendix: Explicit Cancelation Until Second Order<\/span><\/p>\n

  11.9 Appendix: Convexity of W<\/span><\/p>\n

  11.10 Appendix: Legendre Transform of a Gaussian<\/span><\/p>\n

  11.11 Problems<\/span><\/p>\n

  References<\/span><\/p>\n

12 Expansion of Cumulants into Tree Diagrams of Vertex Functions<\/span><\/strong><\/span><\/p>\n

  12.1 Definition of Vertex Functions<\/span><\/p>\n

  12.2 Self-energy or Mass Operator<\/span><\/p>\n

13 Loopwise Expansion of the Effective Action<\/strong><\/span><\/p>\n

  13.1 Motivation and Tree-Level Approximation<\/span><\/p>\n

  13.2 Counting the Number of Loops<\/span><\/p>\n

  13.3 Loopwise Expansion of the Effective Action: Higher Number of Loops<\/span><\/p>\n

  13.4 Example: \u03c63 + \u03c64-Theory<\/span><\/p>\n

  13.5 Appendix: Equivalence of Loopwise Expansion and Infinite Resummation<\/span><\/p>\n

  13.6 Appendix: Interpretation of  as Effective Action<\/span><\/p>\n

  13.7 Appendix: Loopwise Expansion of Self-consistency Equation<\/span><\/p>\n

  13.8 Problems<\/span><\/p>\n

  References<\/span><\/p>\n

14 Loopwise Expansion in the MSRDJ Formalism<\/span><\/strong><\/span><\/p>\n

  14.1 Intuitive Approach<\/span><\/p>\n

  14.2 Loopwise Corrections to the Effective Equation of Motion<\/span><\/p>\n

  14.3 Corrections to the Self-energy and Self-consistency<\/span><\/p>\n

  14.4 Self-energy Correction to the Full Propagator<\/span><\/p>\n

  14.5 Self-consistent One-Loop<\/span><\/p>\n

  14.6 Appendix: Solution by Fokker\u2013Planck Equation<\/span><\/p>\n

  References<\/span><\/p>\n

15 Nomenclature<\/span><\/strong><\/span><\/p>\n


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Moritz Helias\u548cDavid Dahmen\u4e24\u4f4d\u5177\u6709\u4e0d\u540c\u5b66\u79d1\u80cc\u666f\u7684\u79d1\u5b66\u5bb6\u64b0\u5199\u672c\u4e66\u7684\u76ee\u6807\u662f\u5728\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u4ecb\u7ecd\u7edf\u8ba1\u573a\u8bba\u7684\u5f62\u5f0f<\/span>\u53d1\u5c55\uff0c\u4f7f<\/span>\u8bfb\u8005\u80fd\u591f\u7406\u89e3\u4e0a\u8ff0\u5de5\u4f5c\u5e76\u5c06\u5b83\u4eec\u6269\u5c55\u5230\u7406\u8bba\u795e\u7ecf\u79d1\u5b66\u4e2d\u51fa\u73b0\u7684\u65b0\u95ee\u9898\u3002\u76ee\u6807\u8bfb\u8005\u662f\u7269\u7406\u5b66\u5bb6\u3001\u6570\u5b66\u5bb6\u3001\u8ba1\u7b97\u673a\u79d1\u5b66\u5bb6\u3001\u795e\u7ecf\u79d1\u5b66\u5bb6\u548c\u60f3\u8fdb\u5165\u8be5\u9886\u57df\u7684\u8de8\u5b66\u79d1\u7814\u7a76\u8005\uff0c\u4e5f\u53ef\u4ee5\u4f5c\u4e3a\u9ad8\u5e74\u7ea7\u672c\u79d1\u751f\u548c\u7814\u7a76\u751f\u7684\u4e00\u5b66\u671f\u7684\u4e3b\u8981\u6559\u6750\u3002<\/span><\/p>\n


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\u96c6\u667a\u4ff1\u4e50\u90e8\u795e\u7ecf\u52a8\u529b\u5b66\u793e\u533a\uff08600+\u4eba\uff0c\u7855\u535a\u6bd4\u4f8b80%+\uff09\u7ec4\u7ec7\u7ffb\u8bd1\u4e86\u300aStatistical Field Theory for Neural Networks\u300b\uff08\u795e\u7ecf\u7f51\u7edc\u7684\u7edf\u8ba1\u573a\u8bba\uff09\u4e00\u4e66\u3002\u73b0\u62db\u52df\u5bf9\u672c\u4e66\u611f\u5174\u8da3\u7684\u51fa\u7248\u793e\u5bf9\u672c\u4e66\u8fdb\u884c\u51fa\u7248\uff0c\u4e5f\u6b22\u8fce\u6709\u610f\u5411\u57fa\u4e8e\u672c\u4e66\u5185\u5bb9\u5f00\u53d1\u8bfe\u7a0b\u7b49\u76f8\u5173\u5185\u5bb9\u7684\u673a\u6784\u4e00\u8d77\u57fa\u4e8e\u672c\u4e66\u5185\u5bb9\u8fdb\u884c\u7b56\u5212<\/strong>\u3002\u7b56\u5212\u5185\u5bb9\u5305\u62ec\u4f46\u4e0d\u9650\u4e8e\u7ebf\u4e0a\/\u7ebf\u4e0b\u8bfe\u7a0b\u3001\u57f9\u8bad\u7b49\u3002<\/span><\/p>\n


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\u9ec4\u6d77\u5e73\uff0c<\/span><\/strong>\u4e2d\u5c71\u5927\u5b66\u7269\u7406\u5b66\u9662\u6559\u6388\u3002\u672c\u79d1\u6bd5\u4e1a\u4e8e\u4e2d\u5c71\u5927\u5b66\u7406\u5de5\u5b66\u9662\uff0c\u535a\u58eb\u6bd5\u4e1a\u4e8e\u4e2d\u56fd\u79d1\u5b66\u9662\u7406\u8bba\u7269\u7406\u7814\u7a76\u6240\uff0c\u968f\u540e\u5728\u9999\u6e2f\u79d1\u6280\u5927\u5b66\u7269\u7406\u7cfb\u3001\u4e1c\u4eac\u5de5\u4e1a\u5927\u5b66\u8ba1\u7b97\u667a\u80fd\u7cfb (2012\u5e74\u83b7\u65e5\u672c\u5b66\u672f\u632f\u5174\u4f1a\u8d44\u52a9) \u4ee5\u53ca\u65e5\u672c\u7406\u5316\u5b66\u7814\u7a76\u6240 (RIKEN) \u8111\u79d1\u5b66\u4e2d\u5fc3\u4ece\u4e8b\u7edf\u8ba1\u7269\u7406\u4e0e\u673a\u5668\u5b66\u4e60\u3001 \u795e\u7ecf\u8ba1\u7b97\u4ea4\u53c9\u7684\u57fa\u7840\u7406\u8bba\u7814\u7a76\uff0c2017\u5e74\u56e0\u5728\u65e0\u76d1\u7763\u5b66\u4e60\u65b9\u9762\u7684\u7814\u7a76\u83b7\u5f97 RIKEN \u6770\u51fa\u7814\u7a76\u5956\u3002<\/span>\u4e8e2018\u5e74\u5165\u9009\u4e2d\u5c71\u5927\u5b66\u767e\u4eba\u8ba1\u5212\uff0c\u5728\u7269\u7406\u5b66\u9662\u7ec4\u5efa\u4e86\u201c\u7269\u7406\u3001\u673a\u5668\u4e0e\u667a\u80fd\u201d \uff08PMI\uff09\u7814\u7a76\u5c0f\u7ec4\uff0c\u4e13\u6ce8\u4e8e\u5404\u79cd\u795e\u7ecf\u8ba1\u7b97\u7684\u7406\u8bba\u57fa\u7840\uff0c\u957f\u671f\u76ee\u6807\u662f\u4f7f\u7528\u57fa\u4e8e\u7269\u7406\u7684\u8fd1\u4f3c\u6765\u63ed\u793a\u673a\u5668\u53ca\u5927\u8111\u667a\u80fd\u7684\u57fa\u672c\u539f\u7406\u3002<\/span><\/h2>\n

PMI Lab\uff1ahttps:\/\/www.labxing.com\/hphuang2018\u3002<\/span><\/p>\n

\u4e2a\u4eba\u4e3b\u9875\uff1ahttps:\/\/pattern.swarma.org\/master\/319<\/span><\/p>\n


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\u4e2a\u4eba\u4e3b\u9875\uff1ahttps:\/\/pattern.swarma.org\/user\/19903<\/span><\/section>\n

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\u8bfe\u7a0b\u8be6\u60c5\uff1a<\/span><\/span><\/p>\n

\u9ec4\u6d77\u5e73\uff1a\u795e\u7ecf\u7f51\u7edc\u7684\u7edf\u8ba1\u529b\u5b66\u8bfe\u7a0b | 50\u4eba\u514d\u8d39\u540d\u989d<\/a><\/span><\/strong><\/span><\/p>\n


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\u8be6\u60c5\u8bf7\u89c1\uff1a<\/span>
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500+\u795e\u7ecf\u52a8\u529b\u5b66\u793e\u533a\u6210\u5458\uff0c\u9080\u4f60\u5171\u540c\u70b9\u4eae\u66f4\u591a\u8111\u79d1\u5b66\u7814\u7a76\u7684\u5c9b\u5c7f<\/strong><\/a><\/p>\n


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