{"id":21121,"date":"2020-09-24T21:33:14","date_gmt":"2020-09-24T13:33:14","guid":{"rendered":"https:\/\/swarma.org\/?p=21121"},"modified":"2020-09-24T21:33:14","modified_gmt":"2020-09-24T13:33:14","slug":"%e6%b5%81%e7%bd%91%e7%bb%9c%e4%b8%ad%e6%81%a2%e5%a4%8d%e4%b8%8e%e5%a4%b1%e6%95%88%e6%89%a9%e6%95%a3%e7%9a%84%e6%8b%93%e6%89%91%e7%90%86%e8%ae%ba-%e7%bd%91%e7%bb%9c%e7%a7%91%e5%ad%a6%e8%ae%ba","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=21121","title":{"rendered":"\u6d41\u7f51\u7edc\u4e2d\u6062\u590d\u4e0e\u5931\u6548\u6269\u6563\u7684\u62d3\u6251\u7406\u8bba | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901212\u7bc7"},"content":{"rendered":"
\n
\u96c6\u667a\u6591\u56fe\u6700\u65b0\u8bba\u6587\u680f\u76ee\uff0c\u5173\u6ce8\u590d\u6742\u7cfb\u7edf\u3001\u7f51\u7edc\u79d1\u5b66\u3001\u8ba1\u7b97\u793e\u4f1a\u79d1\u5b66\u3001\u7edf\u8ba1\u7269\u7406\u7b49\u9886\u57df\u7684\u524d\u6cbf\u8fdb\u5c55\uff0c\u6bcf\u5929\u5373\u65f6\u66f4\u65b0\u76f8\u5173\u9876\u520a\u8bba\u6587\uff0c\u548c\u6700\u65b0arXiv\u9884\u5370\u672c\u8bba\u6587\u3002<\/span>
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\u672c\u6587\u662f\u5bf9\u8fd1\u671f<\/span>arX<\/span>iv<\/span>\u8bba\u6587\u7684<\/span>\u6c47\u603b\u7f16\u8bd1\uff08by \u5f69\u4e91\u5c0f\u8bd1\uff09\u3002\u66f4\u591a\u6700\u65b0\u8bba\u6587\uff0c\u8bf7\u626b\u4e8c\u7ef4\u7801\u83b7\u53d6\u3002<\/span><\/section>\n

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\u6838\u5fc3\u901f\u9012<\/span><\/section>\n

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  • \n

    \u6d41\u7f51\u7edc\u4e2d\u6062\u590d\u4e0e\u5931\u6548\u6269\u6563\u7684\u62d3\u6251\u7406\u8bba\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u57fa\u4e8e Minkowski \u5ea6\u91cf\u7684\u65f6\u7a7a\u4eff\u771f\u6a21\u578b\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u6a21\u5757\u5316\u5141\u8bb8\u5bf9\u97f3\u4e50\u548c\u8a00\u8bed\u611f\u77e5\u65f6\u671f\u7684\u4eba\u8111\u7f51\u7edc\u8fdb\u884c\u5206\u7c7b\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u901a\u8fc7\u5177\u6709\u56fa\u5b9a\u8282\u70b9\u7684\u9009\u6c11\u6a21\u578b\u9884\u6d4b\u9009\u4e3e\u7ed3\u679c\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u5f53\u6df1\u5ea6\u5f3a\u5316\u5b66\u4e60\u9047\u5230\u8054\u90a6\u5b66\u4e60\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u7528\u4e8e\u5b66\u4e60\u56fe\u8868\u793a\u7684\u53ef\u89e3\u91ca\u3001\u7a33\u5b9a\u548c\u53ef\u6269\u5c55\u5377\u79ef\u7f51\u7edc\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u8d85\u6a21\u5bf9\u7b56\u4e2d\u7684\u6700\u4f18\u76ee\u6807\u9009\u62e9\u95ee\u9898\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u590d\u6742\u5143\u80de\u81ea\u52a8\u673a\u751f\u6210\u7684\u968f\u673a\u5c55\u5f00\u65b9\u6cd5\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u4ece\u53ef\u9006\u7ec6\u80de\u81ea\u52a8\u673a\u770b\u590d\u52a8\u529b\u5b66\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u8d85\u8d8a\u793e\u4ea4\u5a92\u4f53\u5206\u6790: \u7528\u81ea\u7ec4\u7ec7\u589e\u91cf\u5f0f\u673a\u5668\u5b66\u4e60\u7406\u89e3\u4eba\u7c7b\u884c\u4e3a\u548c\u6df1\u5c42\u60c5\u611f\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u56fe\u5f62\u5206\u7c7b\u7684\u6570\u636e\u589e\u5f3a\uff1b<\/span><\/h2>\n<\/li>\n
  • \n

    \u7f51\u7edc\u548c\u7ad9\u7ea7\u81ea\u884c\u8f66\u5171\u4eab\u7cfb\u7edf\u9884\u6d4b: \u65e7\u91d1\u5c71\u6e7e\u533a\u6848\u4f8b\u7814\u7a76\uff1b<\/span><\/h2>\n<\/li>\n<\/ul>\n


    <\/p>\n


    <\/p>\n

    \n
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    <\/p>\n

    \u6d41\u7f51\u7edc\u4e2d\u6062\u590d\u4e0e\u5931\u6548\u6269\u6563\u7684<\/strong><\/span><\/p>\n

    \u62d3\u6251\u7406\u8bba<\/strong><\/span><\/p>\n

    <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

    <\/span>
    <\/h2>\n

    \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

    Topological theory of resilience and failure spreading in flow networks<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/p>\n

    https:\/\/pattern.swarma.org\/paper?id=356cfdb8-fd54-11ea-8e01-0242ac1a000a<\/span>
    <\/p>\n

    \u4f5c\u8005\uff1a<\/span><\/strong><\/span><\/p>\n

    Franz Kaiser,Dirk Witthaut<\/span><\/p>\n


    <\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u4f9b\u5e94\u7f51\u7edc\u4e2d\u7684\u94fe\u8def\u6545\u969c\u53ef\u80fd\u5bfc\u81f4\u707e\u96be\u6027\u7684\u540e\u679c\uff0c\u4ece\u800c\u5bfc\u81f4\u7f51\u7edc\u7684\u5b8c\u5168\u5d29\u6e83\u3002\u56e0\u6b64\uff0c\u9632\u6b62\u5931\u8d25\u8513\u5ef6\u7684\u7b56\u7565\u53d7\u5230\u4e86\u5e7f\u6cdb\u7684\u8ffd\u6c42\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u5229\u7528\u7ebf\u6027\u6d41\u7f51\u7edc\u4e2d\u94fe\u8def\u5931\u6548\u7684\u751f\u6210\u6811\u516c\u5f0f\u6765\u5206\u6790\u963b\u6b62\u5931\u6548\u6269\u6563\u7684\u62d3\u6251\u7ed3\u6784\u3002\u7279\u522b\u5730\uff0c\u6211\u4eec\u5229\u7528\u57fa\u4e8e textit { Matrix tree \u5b9a\u7406}\u7684\u7535\u963b\u7f51\u7edc\u7684\u4e00\u4e2a\u7ed3\u679c\u6765\u5206\u6790\u7535\u7f51\u4e2d\u94fe\u8def\u6545\u969c\u540e\u7684\u6545\u969c\u6269\u6563\u3002\u4f7f\u7528\u94fe\u8def\u5931\u6548\u7684\u751f\u6210\u6811\u516c\u5f0f\uff0c\u6211\u4eec\u5206\u6790\u4e863\u79cd\u57fa\u4e8e\u7f51\u7edc\u62d3\u6251\u7684\u7b56\u7565\uff0c\u8fd9\u4e9b\u7b56\u7565\u5141\u8bb8\u51cf\u5c11\u5355\u94fe\u8def\u5931\u6548\u7684\u5f71\u54cd\u3002\u4e0e\u57fa\u4e8e\u964d\u4f4e\u7f51\u7edc\u8fde\u901a\u6027\u7684\u4f20\u7edf\u904f\u5236\u7b56\u7565\u76f8\u6bd4\uff0c\u6211\u4eec\u7684\u6240\u6709\u7b56\u7565\u90fd\u6ca1\u6709\u964d\u4f4e\u7f51\u683c\u4f20\u8f93\u6d41\u7684\u80fd\u529b\uff0c\u6216\u8005\u5b9e\u9645\u4e0a\u6539\u5584\u4e86\u5b83\u3002\u6211\u4eec\u7684\u7814\u7a76\u7ed3\u679c\u4e5f\u89e3\u91ca\u4e86\u4e3a\u4ec0\u4e48\u67d0\u4e9b\u8fde\u63a5\u7279\u6027\u5b8c\u5168\u6291\u5236\u4e86\u6700\u8fd1\u51fa\u7248\u7269\u4e2d\u62a5\u9053\u7684\u4efb\u4f55\u6545\u969c\u6269\u6563\u3002<\/span><\/p>\n

    <\/h2>\n
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    <\/p>\n<\/section>\n<\/section>\n<\/section>\n

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    <\/p>\n

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    <\/p>\n

    \u57fa\u4e8e Minkowski \u5ea6\u91cf\u7684<\/strong><\/span><\/p>\n

    \u65f6\u7a7a\u4eff\u771f\u6a21\u578b<\/strong><\/span><\/p>\n

    <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

    <\/span>
    <\/h2>\n

    \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

    Simulation model of spacetime with the Minkowski metric<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/p>\n

    https:\/\/pattern.swarma.org\/paper?id=a7e8ddca-fd64-11ea-8e01-0242ac1a000a<\/span><\/p>\n

    \u4f5c\u8005\uff1a<\/span><\/strong><\/span><\/p>\n

    Vasyliy I. Gurianov<\/span><\/p>\n


    <\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u4e2a\u65f6\u7a7a\u4eff\u771f\u6a21\u578b\u4f5c\u4e3a\u7269\u7406\u7a7a\u95f4\u7684\u79bb\u6563\u6a21\u578b\u3002\u8be5\u6a21\u578b\u57fa\u4e8e Stephen Wolfram \u7684\u601d\u60f3\uff0c\u4f7f\u7528\u975e\u6570\u503c\u6a21\u578b\u3002\u5c06\u4eff\u771f\u6a21\u578b\u63cf\u8ff0\u4e3a\u4e00\u4e2a\u672c\u4f53\u3002\u6211\u4eec\u91c7\u7528\u9762\u5411\u5bf9\u8c61\u7684\u4eff\u771f\u65b9\u6cd5\uff0c\u4f46\u662f\u8be5\u6a21\u578b\u4e5f\u9002\u7528\u4e8e\u57fa\u4e8e agent \u7684\u4eff\u771f\u3002\u6211\u4eec\u4f7f\u7528 UML2 SP (UML Scientific Profile) \uff0c\u4e00\u79cd\u9762\u5411\u5bf9\u8c61\u7684\u79d1\u5b66\u9886\u57df\u4eff\u771f\u8bed\u8a00\u3002\u672c\u6587\u4ecb\u7ecd\u4e86\u51e0\u4e2a\u8bc1\u660e\u65f6\u95f4\u81a8\u80c0\u548c\u52a8\u6001\u76f8\u5bf9\u8bba\u6548\u5e94\u7684\u5b9e\u9a8c\u3002\u5b9e\u9a8c\u7ed3\u679c\u7684\u91cd\u590d\u6027\u53ef\u4ee5\u5f97\u5230\u9a8c\u8bc1\u3002\u6211\u4eec\u5728\u672c\u6587\u4e2d\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5230\u5b58\u50a8\u5e93\u7684\u94fe\u63a5\u3002\u8be5\u6a21\u578b\u662f\u7528 Python \u5b9e\u73b0\u7684\u3002<\/span><\/p>\n


    <\/span><\/h2>\n
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    \u6a21\u5757\u5316\u5141\u8bb8\u5bf9\u97f3\u4e50\u548c\u8a00\u8bed<\/strong><\/span><\/p>\n

    \u611f\u77e5\u65f6\u671f\u7684\u4eba\u8111\u7f51\u7edc\u8fdb\u884c\u5206\u7c7b<\/strong><\/span><\/p>\n

    <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

    <\/span>
    <\/h2>\n

    \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

    Modularity allows classification of human brain networks during music and speech perception<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/p>\n

    https:\/\/pattern.swarma.org\/paper?id=c19deaee-fd5a-11ea-8e01-0242ac1a000a<\/span><\/p>\n

    \u4f5c\u8005\uff1a<\/span><\/strong><\/span><\/p>\n

    Melia E. Bonomo,Christof Karmonik,Anthony K. Brandt,J. Todd Frazier<\/span><\/p>\n


    <\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u7814\u7a76\u4f7f\u7528\u6a21\u5757\u4f5c\u4e3a\u5168\u8111\u529f\u80fd\u7f51\u7edc\u7684\u91cf\u8bcd\u3002\u5f53\u53d7\u8bd5\u8005\u542c\u5230\u4e0d\u540c\u60c5\u7eea\u548c\u6587\u5316\u719f\u6089\u7a0b\u5ea6\u7684\u542c\u89c9\u7247\u6bb5\u65f6\uff0c\u5927\u8111\u7f51\u7edc\u662f\u7531\u529f\u80fd\u6027\u78c1\u5171\u632f\u6210\u50cf\u6784\u6210\u7684\u3002\u6211\u4eec\u7684\u5206\u6790\u7ed3\u679c\u63ed\u793a\u4e86\u9ad8\u6a21\u5757\u5316\u548c\u4f4e\u6a21\u5757\u5316\u7fa4\u4f53\u7684\u57fa\u7840\u4e0a\u7684\u7f51\u7edc\u914d\u7f6e\u5728\u4e00\u4e2a\u4e3b\u9898\u7684\u559c\u7231\u7684\u6b4c\u66f2\uff0c\u8fd9\u79cd\u5206\u7c7b\u53ef\u4ee5\u9884\u6d4b\u7f51\u7edc\u91cd\u6784\u5728\u5176\u4ed6\u542c\u89c9\u7247\u6bb5\u3002\u7279\u522b\u662f\uff0c\u4f4e\u6a21\u5757\u5316\u7ec4\u7684\u53d7\u8bd5\u8005\u5728\u719f\u6089\u548c\u4e0d\u719f\u6089\u7684\u90e8\u5206\u90fd\u8868\u73b0\u51fa\u663e\u8457\u7684\u5927\u8111\u7f51\u7edc\u91cd\u6784\u3002\u76f8\u6bd4\u4e4b\u4e0b\uff0c\u9ad8\u6a21\u5757\u5316\u7684\u5927\u8111\u7f51\u7edc\u5728\u4e0d\u719f\u6089\u7684\u97f3\u4e50\u548c\u8bed\u8a00\u73af\u5883\u4e2d\u663e\u5f97\u66f4\u52a0\u5065\u58ee\uff0c\u4ec5\u4ec5\u8868\u73b0\u51fa\u663e\u8457\u7684\u53d8\u5316\u3002\u6211\u4eec\u8fd8\u53d1\u73b0\u4e24\u7ec4\u5728\u6bcf\u6bb5\u542c\u89c9\u4e2d\u6a21\u5757\u7ec4\u6210\u7684\u7a33\u5b9a\u6027\u5b58\u5728\u5dee\u5f02\u3002\u6211\u4eec\u7684\u7814\u7a76\u7ed3\u679c\u8868\u660e\uff0c\u5728\u4e0d\u540c\u7684\u542c\u89c9\u5904\u7406\u9700\u6c42\u4e0b\uff0c\u5168\u8111\u7f51\u7edc\u7684\u6a21\u5757\u6027\u5728\u7f51\u7edc\u91cd\u6784\u4e2d\u8d77\u7740\u91cd\u8981\u4f5c\u7528\uff0c\u56e0\u6b64\uff0c\u5b83\u53ef\u80fd\u6709\u52a9\u4e8e\u5728\u97f3\u4e50\u6cbb\u7597\u8fc7\u7a0b\u4e2d\u795e\u7ecf\u53ef\u5851\u6027\u7684\u4e2a\u4f53\u5dee\u5f02\u3002<\/span><\/p>\n


    <\/span><\/h2>\n
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    <\/p>\n

    \n
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    <\/p>\n

    \u901a\u8fc7\u5177\u6709\u56fa\u5b9a\u8282\u70b9\u7684<\/strong><\/span><\/p>\n

    \u9009\u6c11\u6a21\u578b\u9884\u6d4b\u9009\u4e3e\u7ed3\u679c<\/strong><\/span><\/p>\n

    <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

    <\/span>
    <\/h2>\n

    \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

    Forecasting elections results via the voter model with stubborn nodes<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/p>\n

    https:\/\/pattern.swarma.org\/paper?id=ea710f8c-fd55-11ea-8e01-0242ac1a000a<\/span><\/p>\n

    \u4f5c\u8005\uff1a<\/span><\/strong><\/span><\/p>\n

    Antoine Vendeville,Benjamin Guedj,Shi Zhou<\/span><\/p>\n


    <\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u7684\u65b9\u6cd5\u6765\u9884\u6d4b\u7ed3\u679c\u7684\u9009\u4e3e\u53ea\u4f7f\u7528\u5b98\u65b9\u7684\u7ed3\u679c\u4ee5\u524d\u7684\u3002\u5b83\u662f\u57fa\u4e8e\u5177\u6709\u987d\u56fa\u8282\u70b9\u7684\u9009\u6c11\u6a21\u578b\uff0c\u5e76\u4f7f\u7528\u6211\u4eec\u4ee5\u524d\u5de5\u4f5c\u4e2d\u53d1\u5c55\u7684\u7406\u8bba\u7ed3\u679c\u3002\u6211\u4eec\u5173\u6ce8\u82f1\u56fd\u4fdd\u5b88\u515a\u548c\u5de5\u515a\u4ee5\u53ca\u7f8e\u56fd\u5171\u548c\u515a\u548c\u6c11\u4e3b\u515a\u7684\u666e\u9009\u7968\u6570\u3002\u6211\u4eec\u80fd\u591f\u5bf9\u6a21\u578b\u53c2\u6570\u8fdb\u884c\u65f6\u95f4\u6f14\u53d8\u4f30\u8ba1\uff0c\u5e76\u4f7f\u7528\u8fd9\u4e9b\u6765\u9884\u6d4b\u5728\u4efb\u4f55\u9009\u4e3e\u4e2d\u6bcf\u4e2a\u653f\u515a\u7684\u9009\u7968\u4efd\u989d\u3002\u5e73\u5747\u7edd\u5bf9\u8bef\u5dee\u4e3a4.74% \u3002\u4f5c\u4e3a\u4e00\u4e2a\u526f\u4ea7\u54c1\uff0c\u6211\u4eec\u7684\u53c2\u6570\u4f30\u8ba1\u63d0\u4f9b\u4e86\u5bf9\u653f\u6cbb\u524d\u666f\u6709\u610f\u4e49\u7684\u6d1e\u5bdf\u529b\uff0c\u544a\u8bc9\u6211\u4eec\u6709\u591a\u5c11\u9009\u6c11\u5f3a\u70c8\u652f\u6301\u548c\u53cd\u5bf9\u88ab\u8003\u8651\u7684\u653f\u515a\u3002<\/span><\/p>\n


    <\/span><\/h2>\n
    \n
    \n
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    <\/p>\n

    \n
    \n
    <\/p>\n

    \u5f53\u6df1\u5ea6\u5f3a\u5316\u5b66\u4e60\u9047\u5230\u8054\u90a6\u5b66\u4e60<\/strong><\/span><\/p>\n

    <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

    <\/span>
    <\/h2>\n

    \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

    When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/p>\n

    https:\/\/pattern.swarma.org\/paper?id=e937737c-fd55-11ea-8e01-0242ac1a000a<\/span><\/p>\n

    \u4f5c\u8005\uff1a<\/span><\/strong><\/span><\/p>\n

    Shuai Yu,Xu Chen,Zhi Zhou,Xiaowen Gong,Di Wu<\/span><\/p>\n


    <\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u8d85\u5bc6\u96c6\u8fb9\u7f18\u8ba1\u7b97(ultra-density edge computing\uff0cUDEC)\u5177\u6709\u5de8\u5927\u7684\u6f5c\u529b\uff0c\u7279\u522b\u662f\u57285G \u65f6\u4ee3\uff0c\u4f46\u5b83\u76ee\u524d\u7684\u89e3\u51b3\u65b9\u6848\u4ecd\u7136\u9762\u4e34\u6311\u6218\uff0c\u5982: i)\u7f3a\u4e4f\u5bf9\u591a\u4e2a5G \u8d44\u6e90(\u5982\u8ba1\u7b97\u3001\u901a\u4fe1\u3001\u5b58\u50a8\u548c\u670d\u52a1\u8d44\u6e90)\u7684\u6709\u6548\u5229\u7528; ii)\u4f4e\u5f00\u9500\u5378\u8f7d\u51b3\u7b56\u548c\u8d44\u6e90\u5206\u914d\u7b56\u7565; iii)\u9690\u79c1\u548c\u5b89\u5168\u4fdd\u62a4\u65b9\u6848\u3002\u56e0\u6b64\uff0c\u6211\u4eec\u9996\u5148\u63d0\u51fa\u4e86\u4e00\u4e2a\u667a\u80fd\u8d85\u5bc6\u96c6\u8fb9\u7f18\u8ba1\u7b97(I-UDEC)\u6846\u67b6\uff0c\u8be5\u6846\u67b6\u5c06\u533a\u5757\u94fe\u548c\u4eba\u5de5\u667a\u80fd(AI)\u96c6\u6210\u52305G \u8d85\u5bc6\u96c6\u8fb9\u7f18\u8ba1\u7b97\u7f51\u7edc\u4e2d\u3002\u9996\u5148\uff0c\u6211\u4eec\u5c55\u793a\u4e86\u6846\u67b6\u7684\u4f53\u7cfb\u7ed3\u6784\u3002\u7136\u540e\uff0c\u4e3a\u4e86\u5b9e\u73b0\u5b9e\u65f6\u3001\u4f4e\u5f00\u9500\u7684\u5378\u8f7d\u51b3\u7b56\u548c\u8d44\u6e90\u5206\u914d\u7b56\u7565\uff0c\u6211\u4eec\u8bbe\u8ba1\u4e86\u4e00\u79cd\u65b0\u7684\u53cc\u65f6\u95f4\u5c3a\u5ea6\u6df1\u5ea6\u5f3a\u5316\u5b66\u4e60(textit {2Ts-DRL })\u65b9\u6cd5\uff0c\u5206\u522b\u7531\u4e00\u4e2a\u5feb\u65f6\u95f4\u5c3a\u5ea6\u548c\u4e00\u4e2a\u6162\u65f6\u95f4\u5c3a\u5ea6\u7684\u5b66\u4e60\u8fc7\u7a0b\u7ec4\u6210\u3002\u5176\u4e3b\u8981\u76ee\u6807\u662f\u901a\u8fc7\u8054\u5408\u4f18\u5316\u8ba1\u7b97\u5378\u8f7d\u3001\u8d44\u6e90\u5206\u914d\u548c\u670d\u52a1\u7f13\u5b58\u653e\u7f6e\uff0c\u4f7f\u5378\u8f7d\u603b\u5ef6\u65f6\u548c\u7f51\u7edc\u8d44\u6e90\u4f7f\u7528\u6700\u5c0f\u3002\u6211\u4eec\u8fd8\u5229\u7528\u8054\u90a6\u5b66\u4e60(FL)\u4ee5\u5206\u5e03\u5f0f\u65b9\u5f0f\u8bad\u7ec3 textit {2Ts-DRL }\u6a21\u578b\uff0c\u4ee5\u4fdd\u62a4\u8fb9\u7f18\u8bbe\u5907\u7684\u6570\u636e\u9690\u79c1\u3002\u4eff\u771f\u7ed3\u679c\u9a8c\u8bc1\u4e86 I-UDEC \u6846\u67b6\u4e0b\u6587\u672c{2Ts-DRL }\u548c FL \u7684\u6709\u6548\u6027\uff0c\u8bc1\u660e\u4e86\u672c\u6587\u63d0\u51fa\u7684\u7b97\u6cd5\u53ef\u4ee5\u5c06\u4efb\u52a1\u6267\u884c\u65f6\u95f4\u51cf\u5c1131.87%\u3002<\/span><\/p>\n


    <\/span><\/h2>\n
    \n
    \n
    \n


    <\/p>\n

    \n
    \n
    <\/p>\n

    \u7528\u4e8e\u5b66\u4e60\u56fe\u8868\u793a\u7684\u53ef\u89e3\u91ca\u3001<\/strong><\/span><\/p>\n

    \u7a33\u5b9a\u548c\u53ef\u6269\u5c55\u5377\u79ef\u7f51\u7edc<\/strong><\/span><\/p>\n

    <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

    <\/span>
    <\/h2>\n

    \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/h2>\n

    Explainab<\/span>le, Stable, and Scalable Graph Convolutional Networks for Learning Graph Representation<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/p>\n

    https:\/\/pattern.swarma.org\/paper?id=e8008372-fd55-11ea-8e01-0242ac1a000a<\/span><\/p>\n

    \u4f5c\u8005\uff1a<\/span><\/strong><\/span><\/p>\n

    Ping-En Lu,Cheng-Shang Chang<\/span><\/p>\n


    <\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5c06\u56fe\u4e2d\u7684\u8282\u70b9\u6620\u5c04\u5230\u6b27\u6c0f\u7a7a\u95f4\u4e2d\u7684\u5411\u91cf\u7684\u7f51\u7edc\u5d4c\u5165\u95ee\u9898\u5bf9\u4e8e\u89e3\u51b3\u56fe\u4e2d\u7684\u51e0\u4e2a\u91cd\u8981\u4efb\u52a1\u662f\u975e\u5e38\u6709\u7528\u7684\u3002\u8fd1\u5e74\u6765\uff0c\u4eba\u4eec\u63d0\u51fa\u4e86\u56fe\u795e\u7ecf\u7f51\u7edc\u6765\u89e3\u51b3\u8fd9\u4e00\u95ee\u9898\u3002\u7136\u800c\uff0c\u5927\u591a\u6570\u5d4c\u5165\u7b97\u6cd5\u548c gnn \u96be\u4ee5\u89e3\u91ca\uff0c\u5e76\u4e14\u4e0d\u80fd\u5f88\u597d\u5730\u4f38\u7f29\u4ee5\u5904\u7406\u6570\u767e\u4e07\u4e2a\u8282\u70b9\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u4ece\u4e00\u4e2a\u65b0\u7684\u89d2\u5ea6\u6765\u5904\u7406\u8fd9\u4e2a\u95ee\u9898\uff0c\u57fa\u4e8e\u4e09\u4e2a\u7ea6\u675f\u4f18\u5316\u95ee\u9898\u7684\u7b49\u4ef7\u6027: \u7f51\u7edc\u5d4c\u5165\u95ee\u9898\uff0c\u6a21\u5757\u77e9\u9635\u5728\u91c7\u6837\u56fe\u4e2d\u7684\u8ff9\u6700\u5927\u5316\u95ee\u9898\uff0c\u4ee5\u53ca\u91c7\u6837\u56fe\u4e2d\u6a21\u5757\u77e9\u9635\u7684\u77e9\u9635\u5206\u89e3\u95ee\u9898\u3002\u8fd9\u4e09\u4e2a\u95ee\u9898\u7684\u6700\u4f18\u89e3\u662f\u6a21\u5757\u5316\u77e9\u9635\u7684\u4e3b\u8981\u7279\u5f81\u5411\u91cf\u3002\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e9b\u95ee\u9898\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e24\u79cd\u7279\u6b8a\u7c7b\u578b\u7684\u56fe\u5377\u79ef\u7f51\u7edc\u7b97\u6cd5: (i)\u805a\u7c7b\u4f5c\u4e3a\u7279\u5f81\u5d4c\u5165\u7684 gcaf-GCN (caf-GCN)\u548c(ii)\u7403\u5f62 GCN\u3002\u8fd9\u4e24\u79cd\u7b97\u6cd5\u90fd\u662f\u7a33\u5b9a\u7684\u8ff9\u6700\u5927\u5316\u7b97\u6cd5\uff0c\u5e76\u4e14\u5b83\u4eec\u80fd\u591f\u5f88\u597d\u5730\u903c\u8fd1\u4e3b\u8981\u7279\u5f81\u5411\u91cf\u3002\u6b64\u5916\uff0c\u8fd8\u6709\u7a00\u758f\u56fe\u7684\u7ebf\u6027\u65f6\u95f4\u5b9e\u73b0\u3002\u9664\u4e86\u89e3\u51b3\u7f51\u7edc\u5d4c\u5165\u95ee\u9898\uff0c\u8fd9\u4e24\u4e2a\u63d0\u51fa\u7684 GCNs \u90fd\u80fd\u591f\u6267\u884c\u964d\u7ef4\u3002\u4e3a\u4e86\u8bc4\u4f30\u6211\u4eec\u63d0\u51fa\u7684 GCNs\uff0c\u6211\u4eec\u8fdb\u884c\u4e86\u5404\u79cd\u5b9e\u9a8c\uff0c\u7ed3\u679c\u8868\u660e\u6211\u4eec\u63d0\u51fa\u7684 GCNs \u6bd4\u51e0\u4e4e\u6240\u6709\u7684\u57fa\u7ebf\u65b9\u6cd5\u90fd\u8981\u597d\u3002\u6b64\u5916\uff0cCAFE-GCN \u53ef\u4ee5\u4ece\u6807\u8bb0\u7684\u6570\u636e\u4e2d\u83b7\u76ca\uff0c\u5e76\u5728\u5404\u79cd\u6027\u80fd\u6307\u6807\u65b9\u9762\u6709\u5de8\u5927\u7684\u6539\u8fdb\u3002<\/span><\/p>\n


    <\/span><\/p>\n


    <\/span><\/h2>\n

    <\/section>\n
    \n
    \n
    \n
    \n
    \n
    <\/p>\n

    \u8d85\u6a21\u5bf9\u7b56\u4e2d\u7684\u6700\u4f18\u76ee\u6807\u9009\u62e9\u95ee\u9898<\/strong><\/span><\/p>\n

    <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

    <\/span>
    <\/h2>\n

    \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

    Optimal Targeting in Super-Modular Games<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong>
    <\/span><\/h2>\n

    https:\/\/pattern.swarma.org\/paper?id=3e701d6e-fc9b-11ea-8c95-0242ac1a000a<\/span><\/p>\n

    \u4f5c\u8005\uff1a<\/span><\/strong><\/span><\/p>\n

    Giacomo Como,St\u00e9phane Durand,Fabio Fagnani<\/span><\/p>\n


    <\/p>\n

    \u6458\u8981<\/span><\/strong>\uff1a<\/span><\/strong>\u7814\u7a76\u4e86\u5177\u6709\u4e8c\u5143\u884c\u52a8\u548c\u6709\u9650\u591a\u4e2a\u53c2\u4e0e\u4eba\u7684\u8d85\u6a21\u5bf9\u7b56\u7684\u6700\u4f18\u76ee\u6807\u9009\u62e9\u95ee\u9898\u3002\u8003\u8651\u7684\u95ee\u9898\u5728\u4e8e\u9009\u62e9\u4e00\u4e2a\u6700\u5c0f\u89c4\u6a21\u7684\u73a9\u5bb6\u5b50\u96c6\uff0c\u8fd9\u6837\uff0c\u5f53\u8fd9\u4e9b\u73a9\u5bb6\u7684\u884c\u52a8\u88ab\u8feb\u8fbe\u5230\u4e00\u4e2a\u53ef\u63a7\u7684\u503c\uff0c\u800c\u5176\u4ed6\u73a9\u5bb6\u88ab\u7559\u4e0b\u6765\u91cd\u590d\u8fdb\u884c\u4e00\u4e2a\u6700\u4f73\u53cd\u5e94\u52a8\u4f5c\u65f6\uff0c\u7cfb\u7edf\u5c06\u6536\u655b\u5230\u6e38\u620f\u7684\u6700\u5927\u7eb3\u4ec0\u5747\u8861\u70b9\u3002\u6211\u4eec\u7684\u4e3b\u8981\u8d21\u732e\u5728\u4e8e\u8bc1\u660e\u4e86\u8be5\u95ee\u9898\u662f np \u5b8c\u5168\u7684\uff0c\u5e76\u4e14\u63d0\u51fa\u4e86\u4e00\u79cd\u6709\u6548\u7684\u8fed\u4ee3\u7b97\u6cd5\uff0c\u8be5\u7b97\u6cd5\u5177\u6709\u53ef\u8bc1\u660e\u7684\u6536\u655b\u6027\u3002\u8be6\u7ec6\u8ba8\u8bba\u4e86\u7f51\u7edc\u534f\u8c03\u535a\u5f08\u7684\u7279\u6b8a\u60c5\u51b5\u53ca\u5176\u4e0e\u51dd\u805a\u529b\u6982\u5ff5\u7684\u5173\u7cfb\u3002\u6700\u540e\uff0c\u6211\u4eec\u901a\u8fc7\u6a21\u62df\u8bc1\u660e\u4e86\u6211\u4eec\u7684\u65b9\u6cd5\u76f8\u5bf9\u4e8e\u57fa\u4e8e\u7ecf\u5178\u7f51\u7edc\u4e2d\u5fc3\u6027\u5ea6\u91cf\u7684\u6734\u7d20\u542f\u53d1\u5f0f\u65b9\u6cd5\u7684\u4f18\u52bf\u3002<\/span><\/p>\n


    <\/span><\/h2>\n
    \n
    \n
    \n


    <\/p>\n

    \n
    \n
    <\/p>\n

    \u590d\u6742\u5143\u80de\u81ea\u52a8\u673a\u751f\u6210\u7684\u968f\u673a\u5c55\u5f00\u65b9\u6cd5<\/strong><\/span><\/p>\n

    <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

    <\/span>
    <\/h2>\n

    \u539f\u6587\u6807\u9898<\/span><\/strong>\uff1a<\/span><\/h2>\n

    Random Expansion Method for the Generation of Complex Cellular Automata<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong>
    <\/span><\/p>\n

    https:\/\/pattern.swarma.org\/paper?id=ac2d263a-fc96-11ea-8c95-0242ac1a000a<\/span><\/h2>\n

    \u4f5c\u8005\uff1a<\/span><\/strong><\/span><\/p>\n

    Juan Carlos Seck-Tuoh-Mora,Norberto Hernandez-Romero,Joselito Medina-Marin,Genaro J. Martinez,Irving Barragan-Vite<\/span><\/p>\n


    <\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5143\u80de\u81ea\u52a8\u673a\u4e2d\u590d\u6742\u884c\u4e3a\u7684\u51fa\u73b0\u662f\u8fd1\u5e74\u6765\u5e7f\u6cdb\u53d1\u5c55\u7684\u4e00\u4e2a\u9886\u57df\uff0c\u5176\u76ee\u7684\u662f\u751f\u6210\u548c\u5206\u6790\u4ea7\u751f\u5468\u671f\u6027\u80cc\u666f\u4e0b\u76f8\u4e92\u4f5c\u7528\u7684\u7a7a\u95f4\u79fb\u52a8\u6a21\u5f0f\u6216\u6ed1\u7fd4\u673a\u7684\u5143\u80de\u81ea\u52a8\u673a\u3002\u901a\u5e38\uff0c\u8fd9\u79cd\u7c7b\u578b\u7684\u81ea\u52a8\u673a\u662f\u901a\u8fc7\u8be6\u5c3d\u7684\u641c\u7d22\u6216\u4ed4\u7ec6\u6784\u5efa\u6f14\u5316\u89c4\u5219\u6765\u53d1\u73b0\u7684\u3002\u5728\u672c\u7814\u7a76\u4e2d\uff0c\u5229\u7528\u968f\u673a\u751f\u6210\u7684\u6837\u672c\uff0c\u83b7\u5f97\u4e86\u5177\u6709\u590d\u6742\u884c\u4e3a\u7684\u5143\u80de\u81ea\u52a8\u673a\u7684\u89c4\u8303\u3002\u7279\u522b\u662f\uff0c\u5b83\u63d0\u51fa\u4e86\u4e00\u4e2a n \u4e2a\u72b6\u6001\u7684\u7ec6\u80de\u81ea\u52a8\u673a\u5e94\u8be5\u968f\u673a\u6307\u5b9a\uff0c\u7136\u540e\u6269\u5c55\u5230\u53e6\u4e00\u4e2a\u72b6\u6001\u6570\u66f4\u591a\u7684\u81ea\u52a8\u673a\uff0c\u8fd9\u6837\u539f\u59cb\u7684\u81ea\u52a8\u673a\u4f5c\u4e3a\u5468\u671f\u6027\u7684\u80cc\u666f\u8fd0\u884c\uff0c\u5176\u4e2d\u989d\u5916\u7684\u72b6\u6001\u670d\u52a1\u4e8e\u5b9a\u4e49\u6ed1\u7fd4\u673a\u3002\u6b64\u5916\uff0c\u672c\u7814\u7a76\u8fd8\u5bf9\u8fd9\u79cd\u65b9\u6cd5\u8fdb\u884c\u4e86\u8bf4\u660e\u3002\u5728\u6b64\u57fa\u7840\u4e0a\uff0c\u5229\u7528\u4e0d\u540c\u72b6\u6001\u7684\u5747\u573a\u8fd1\u4f3c\u548c\u5c40\u90e8\u71b5\u6d4b\u5ea6\uff0c\u7814\u7a76\u4e86\u590d\u6742\u5143\u80de\u81ea\u52a8\u673a\u7684\u968f\u673a\u5b9a\u4e49\u65b9\u6cd5\u3002\u7528\u9057\u4f20\u7b97\u6cd5\u5bf9\u8fd9\u4e2a\u89c4\u8303\u8fdb\u884c\u4e86\u6539\u8fdb\uff0c\u4ee5\u83b7\u5f97\u5177\u6709\u66f4\u9ad8\u590d\u6742\u5ea6\u7684\u6807\u672c\u3002\u5229\u7528\u8fd9\u79cd\u65b9\u6cd5\uff0c\u53ef\u4ee5\u751f\u6210\u5177\u6709\u6570\u767e\u4e2a\u72b6\u6001\u7684\u590d\u6742\u81ea\u52a8\u673a\uff0c\u8bc1\u660e\u968f\u673a\u5b9a\u4e49\u7684\u591a\u72b6\u6001\u5c40\u90e8\u4ea4\u4e92\u53ef\u4ee5\u6784\u9020\u590d\u6742\u6027\u3002<\/span><\/p>\n


    <\/span><\/h2>\n
    \n
    \n
    \n


    <\/p>\n

    \n
    \n
    <\/p>\n

    \u4ece\u53ef\u9006\u7ec6\u80de\u81ea\u52a8\u673a\u770b\u590d\u52a8\u529b\u5b66<\/strong><\/span><\/p>\n

    <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

    <\/span>
    <\/h2>\n

    \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

    On complex dynamics from reversible cellular automata<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong>
    <\/span><\/p>\n

    https:\/\/pattern.swarma.org\/paper?id=ab903636-fc96-11ea-8c95-0242ac1a000a<\/span><\/h2>\n

    \u4f5c\u8005\uff1a<\/span><\/strong><\/span><\/p>\n

    Juan Carlos Seck-Tuoh-Mora,Genaro J. Martinez,Norberto Hernandez-Romero,Joselito Medina-Marin,Irving Barragan-Vite<\/h2>\n


    <\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u590d\u6742\u6027\u4e00\u76f4\u662f\u7ec6\u80de\u81ea\u52a8\u673a\u4e2d\u53cd\u590d\u51fa\u73b0\u7684\u7814\u7a76\u8bfe\u9898\uff0c\u56e0\u4e3a\u5b83\u4eec\u4ee3\u8868\u4e86\u4ece\u7b80\u5355\u7684\u5c40\u90e8\u76f8\u4e92\u4f5c\u7528\u4e2d\u4ea7\u751f\u590d\u6742\u884c\u4e3a\u7684\u7cfb\u7edf\u3002\u4ee5\u5f80\u7684\u5927\u91cf\u7814\u7a76\u5df2\u7ecf\u63d0\u51fa\u4e86\u590d\u6742\u7ec6\u80de\u81ea\u52a8\u673a\u7684\u5b9e\u4f8b\uff0c\u4f46\u662f\uff0c\u5927\u591a\u6570\u63d0\u51fa\u7684\u65b9\u6cd5\u90fd\u662f\u57fa\u4e8e\u4ed4\u7ec6\u7684\u641c\u7d22\u6216\u7ec6\u81f4\u7684\u8fdb\u5316\u89c4\u5219\u7684\u6784\u5efa\u3002\u672c\u6587\u4ecb\u7ecd\u4e86\u57fa\u4e8e\u53ef\u9006\u5143\u80de\u81ea\u52a8\u673a\u7684\u590d\u6742\u884c\u4e3a\u7684\u51fa\u73b0\u3002\u7279\u522b\u5730\uff0c\u672c\u6587\u8868\u660e\u53ef\u9006\u7ec6\u80de\u81ea\u52a8\u673a\u662f\u4e00\u4e2a\u9002\u5f53\u7684\u6846\u67b6\uff0c\u53ef\u4ee5\u83b7\u5f97\u53ea\u589e\u52a0\u65b0\u7684\u968f\u673a\u72b6\u6001\u7684\u590d\u6742\u884c\u4e3a\u3002\u5b9e\u9a8c\u7ed3\u679c\u8868\u660e\uff0c\u53ef\u9006\u5143\u80de\u81ea\u52a8\u673a\u968f\u673a\u589e\u52a0\u72b6\u6001\u7684\u6bd4\u4f8b\u6bd4\u53ef\u9006\u5143\u80de\u81ea\u52a8\u673a\u7684\u539f\u59cb\u72b6\u6001\u6570\u589e\u52a0\u7ea62\u500d\uff0c\u53ef\u4ee5\u83b7\u5f97\u590d\u6742\u6027\u3002\u56e0\u6b64\uff0c\u53ef\u4ee5\u83b7\u5f97\u5177\u6709\u51e0\u5341\u79cd\u72b6\u6001\u7684\u590d\u6742\u7ec6\u80de\u81ea\u52a8\u673a\u3002\u590d\u6742\u6027\u901a\u5e38\u6765\u81ea\u53ef\u9006\u7684\u7ec6\u80de\u81ea\u52a8\u673a\uff0c\u4ee5\u53ca\u4f7f\u7528\u8fdb\u5316\u89c4\u5219\u4e2d\u7684\u72b6\u6001\u6392\u5217\u6216\u884c\u5217\u6392\u5217\u7b49\u5176\u4ed6\u64cd\u4f5c\u3002\u53ef\u9006\u6027\u662f\u5b9e\u73b0\u7ec6\u80de\u81ea\u52a8\u673a\u4e2d\u590d\u6742\u884c\u4e3a\u7684\u4e00\u79cd\u6709\u6548\u7ed3\u6784\u3002
    <\/span><\/p>\n


    <\/span><\/h2>\n
    \n
    \n
    \n


    <\/p>\n

    \n
    \n
    <\/p>\n

    \u8d85\u8d8a\u793e\u4ea4\u5a92\u4f53\u5206\u6790: <\/strong><\/span><\/p>\n

    \u7528\u81ea\u7ec4\u7ec7\u589e\u91cf\u5f0f\u673a\u5668\u5b66\u4e60<\/strong><\/span><\/p>\n

    \u7406\u89e3\u4eba\u7c7b\u884c\u4e3a\u548c\u6df1\u5c42\u60c5\u611f<\/strong><\/span><\/p>\n

    <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

    <\/span>
    <\/h2>\n

    \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

    Beyond Social Media Analytics: Understanding Human Behaviour and Deep Emotion using Self Structuring Incremental Machine Learning<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong>
    <\/span><\/p>\n

    https:\/\/pattern.swarma.org\/paper?id=f40f5850-fc8d-11ea-8c95-0242ac1a000a<\/span><\/h2>\n

    \u4f5c\u8005\uff1a<\/span><\/strong><\/span><\/p>\n

    Tharindu Bandaragoda<\/span><\/h2>\n


    <\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u8fd9\u7bc7\u8bba\u6587\u53d1\u5c55\u4e86\u4e00\u4e2a\u793e\u4f1a\u6570\u636e\u6982\u5ff5\u6846\u67b6\uff0c\u8ba4\u4e3a\u793e\u4f1a\u6570\u636e\u4ee3\u8868\u4e86\u4eba\u7c7b\u793e\u4f1a\u884c\u4e3a\u3001\u9700\u6c42\u548c\u8ba4\u77e5\u7b49\u7ea7\u7684\u8868\u5c42\uff0c\u7528\u4e8e\u5c06\u793e\u4f1a\u6570\u636e\u8f6c\u5316\u4e3a\u4fdd\u5b58\u793e\u4f1a\u884c\u4e3a\u53ca\u5176\u56e0\u679c\u5173\u7cfb\u7684\u8868\u5c42\u3002\u57fa\u4e8e\u8fd9\u4e2a\u6846\u67b6\uff0c\u6784\u5efa\u4e86\u4e24\u4e2a\u5e73\u53f0\uff0c\u4ee5\u4ece\u5feb\u8282\u594f\u548c\u6162\u8282\u594f\u7684\u793e\u4f1a\u6570\u636e\u4e2d\u83b7\u53d6\u6d1e\u5bdf\u529b\u3002\u5bf9\u4e8e\u5feb\u8282\u594f\uff0c\u4e00\u4e2a\u81ea\u6211\u7ed3\u6784\u548c\u5728\u7ebf\u673a\u673a\u5668\u5b66\u4e60\u6280\u672f\u88ab\u5f00\u53d1\u6765\u81ea\u52a8\u6355\u6349\u7a81\u51fa\u7684\u4e3b\u9898\u548c\u76f8\u5e94\u7684\u52a8\u6001\u968f\u7740\u65f6\u95f4\u7684\u63a8\u79fb\u3002\u5f00\u53d1\u4e86\u4e00\u79cd\u4e8b\u4ef6\u68c0\u6d4b\u6280\u672f\uff0c\u5229\u7528\u6570\u91cf\u548c\u60c5\u7eea\u7b49\u591a\u9879\u6307\u6807\uff0c\u81ea\u52a8\u76d1\u6d4b\u5df2\u786e\u5b9a\u7684\u4e3b\u9898\u8def\u5f84\uff0c\u4ee5\u53d1\u73b0\u793e\u4f1a\u884c\u4e3a\u7684\u91cd\u5927\u6ce2\u52a8\u3002\u8fd9\u4e2a\u5e73\u53f0\u4f7f\u7528\u4e86\u4e24\u4e2a\u5927\u578b\u6570\u636e\u96c6\uff0c\u5305\u542b\u8d85\u8fc7100\u4e07\u6761 tweets\u3002\u5206\u5f00\u7684\u4e3b\u9898\u8def\u5f84\u4ee3\u8868\u4e86\u6bcf\u4e2a\u5b9e\u4f53\u7684\u5173\u952e\u4e3b\u9898\uff0c\u5e76\u4e0e\u4e3b\u9898\u4e00\u81f4\u6027\u63aa\u65bd\u4fdd\u6301\u4e00\u81f4\u3002\u5df2\u786e\u5b9a\u7684\u4e8b\u4ef6\u4e0e\u65b0\u95fb\u62a5\u9053\u7684\u5f53\u4ee3\u4e8b\u4ef6\u76f8\u5bf9\u5e94\u3002\u5176\u6b21\uff0c\u9488\u5bf9\u6162\u8282\u594f\u7684\u793e\u4f1a\u6570\u636e\uff0c\u5f00\u53d1\u4e86\u4e00\u5957\u65b0\u7684\u673a\u5668\u5b66\u4e60\u548c\u81ea\u7136\u8bed\u8a00\u5904\u7406\u6280\u672f\uff0c\u4ee5\u81ea\u52a8\u83b7\u53d6\u4e2a\u4eba\u81ea\u6211\u62ab\u9732\u7684\u4fe1\u606f\uff0c\u5982\u4eba\u53e3\u7edf\u8ba1\u3001\u60c5\u611f\u548c\u4e2a\u4eba\u4e8b\u4ef6\u7684\u65f6\u95f4\u8f74\u3002\u8fd9\u4e2a\u5e73\u53f0\u662f\u5728\u4ece\u5728\u7ebf\u652f\u6301\u5c0f\u7ec4\u6536\u96c6\u7684\u8d85\u8fc7400\u4e07\u7bc7\u6587\u7ae0\u7684\u5927\u6587\u96c6\u4e0a\u8fdb\u884c\u6d4b\u8bd5\u7684\u3002\u8fd9\u4e00\u7814\u7a76\u8fdb\u4e00\u6b65\u6269\u5c55\u5230\u5c06\u4e0e\u524d\u5217\u817a\u764c\u76f8\u5173\u7684\u5728\u7ebf\u652f\u6301\u5c0f\u7ec4\u8ba8\u8bba\u8f6c\u5316\u4e3a\u4e00\u4e2a\u591a\u7ef4\u8868\u5f81\uff0c\u5e76\u8c03\u67e5\u4e86\u60a3\u8005(\u548c\u5408\u4f5c\u4f19\u4f34)\u81ea\u6211\u62ab\u9732\u7684\u751f\u6d3b\u8d28\u91cf\u4e0e\u65f6\u95f4\u3001\u4eba\u53e3\u7edf\u8ba1\u5b66\u548c\u4e34\u5e8a\u56e0\u7d20\u7684\u5173\u7cfb\u3002\u8fd9\u4e2a\u6269\u5c55\u5e73\u53f0\u7684\u80fd\u529b\u5df2\u7ecf\u901a\u8fc7\u4ece10\u4e2a\u524d\u5217\u817a\u764c\u5728\u7ebf\u652f\u6301\u5c0f\u7ec4\u6536\u96c6\u7684\u6587\u672c\u8bed\u6599\u5e93\u5f97\u5230\u4e86\u5c55\u793a\uff0c\u8be5\u5c0f\u7ec4\u5305\u62ec609,960\u4e2a\u524d\u5217\u817a\u764c\u8ba8\u8bba\u548c22,233\u4e2a\u60a3\u8005\u3002<\/span><\/p>\n


    <\/span><\/h2>\n
    \n
    \n
    \n


    <\/p>\n

    \n
    \n
    <\/p>\n

    \u56fe\u5f62\u5206\u7c7b\u7684\u6570\u636e\u589e\u5f3a<\/strong><\/span><\/p>\n

    <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

    <\/span>
    <\/h2>\n

    \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

    Data Augmentation for Graph Classification<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong>
    <\/span><\/p>\n

    https:\/\/pattern.swarma.org\/paper?id=52f9465c-fc8c-11ea-8c95-0242ac1a000a<\/span><\/h2>\n

    \u4f5c\u8005\uff1a<\/span><\/strong><\/span><\/p>\n

    Jiajun Zhou,Jie Shen,Qi Xuan<\/span><\/h2>\n


    <\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u56fe\u5f62\u5206\u7c7b\u5728\u836f\u7269\u5206\u7c7b\u3001\u6bd2\u6027\u68c0\u6d4b\u3001\u86cb\u767d\u8d28\u5206\u6790\u7b49\u65b9\u9762\u6709\u7740\u91cd\u8981\u7684\u4f5c\u7528\u3002\u7136\u800c\uff0c\u7531\u4e8e\u57fa\u51c6\u6570\u636e\u96c6\u89c4\u6a21\u7684\u9650\u5236\uff0c\u4f7f\u5f97\u56fe\u5206\u7c7b\u6a21\u578b\u5bb9\u6613\u9677\u5165\u8fc7\u5ea6\u62df\u5408\u548c\u4e0d\u591f\u6cdb\u5316\u7684\u5c40\u9762\u3002\u9488\u5bf9\u8fd9\u4e00\u95ee\u9898\uff0c\u6211\u4eec\u5f15\u5165\u4e86\u56fe\u7684\u6570\u636e\u6269\u5145\uff0c\u5e76\u63d0\u51fa\u4e86\u4e24\u79cd\u542f\u53d1\u5f0f\u7b97\u6cd5: \u968f\u673a\u6620\u5c04\u548c\u6a21\u4f53\u76f8\u4f3c\u6027\u6620\u5c04\uff0c\u901a\u8fc7\u5bf9\u56fe\u7684\u7ed3\u6784\u8fdb\u884c\u542f\u53d1\u5f0f\u4fee\u6539\uff0c\u4e3a\u5c0f\u89c4\u6a21\u57fa\u51c6\u6570\u636e\u96c6\u751f\u6210\u66f4\u591a\u7684\u5f31\u6807\u8bb0\u6570\u636e\u3002\u6b64\u5916\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u4e2a\u901a\u7528\u7684\u6a21\u578b\u6f14\u5316\u6846\u67b6 M-Evolve\uff0c\u5b83\u7ed3\u5408\u4e86\u56fe\u589e\u5f3a\u3001\u6570\u636e\u8fc7\u6ee4\u548c\u6a21\u578b\u518d\u8bad\u7ec3\u6765\u4f18\u5316\u9884\u8bad\u7ec3\u7684\u56fe\u5206\u7c7b\u5668\u3002\u57286\u4e2a\u57fa\u51c6\u6570\u636e\u96c6\u4e0a\u8fdb\u884c\u7684\u5b9e\u9a8c\u8868\u660e\uff0c\u5728\u5bf9\u5c0f\u89c4\u6a21\u57fa\u51c6\u6570\u636e\u96c6\u8fdb\u884c\u8bad\u7ec3\u65f6\uff0cM-Evolve \u65b9\u6cd5\u53ef\u4ee5\u5e2e\u52a9\u73b0\u6709\u7684\u56fe\u5f62\u5206\u7c7b\u6a21\u578b\u51cf\u5c11\u8fc7\u5ea6\u62df\u5408\uff0c\u5e76\u4f7f\u56fe\u5f62\u5206\u7c7b\u4efb\u52a1\u7684\u51c6\u786e\u7387\u5e73\u5747\u63d0\u9ad83-12%\u3002<\/span><\/p>\n


    <\/span><\/h2>\n
    \n
    \n
    \n


    <\/p>\n

    \n
    \n
    <\/p>\n

    \u7f51\u7edc\u548c\u7ad9\u7ea7\u81ea\u884c\u8f66\u5171\u4eab\u7cfb\u7edf\u9884\u6d4b:<\/strong><\/span><\/p>\n

    \u65e7\u91d1\u5c71\u6e7e\u533a\u6848\u4f8b\u7814\u7a76<\/strong><\/span><\/p>\n

    <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


    <\/span><\/h2>\n

    \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

    Network and Station-Level Bike-Sharing System Prediction: A San Francisco Bay Area Case Study<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong>
    <\/span><\/h2>\n

    https:\/\/pattern.swarma.org\/paper?id=d7728210-fc8a-11ea-8c95-0242ac1a000a<\/span><\/h2>\n

    \u4f5c\u8005\uff1a<\/span><\/strong><\/span><\/p>\n

    Huthaifa I. Ashqar,Mohammed Elhenawy,Hesham A. Rakha,Mohammed Almannaa,Leanna House<\/span><\/h2>\n


    <\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u672c\u6587\u5c06\u673a\u5668\u5b66\u4e60\u5e94\u7528\u4e8e\u65e7\u91d1\u5c71\u6e7e\u533a\u81ea\u884c\u8f66\u5171\u4eab\u7cfb\u7edf\uff0c\u5728\u7f51\u7edc\u548c\u8f66\u7ad9\u4e24\u4e2a\u5c42\u6b21\u4e0a\u5f00\u53d1\u4e86<\/span>\u81ea\u884c\u8f66\u53ef\u7528\u6027\u5efa\u6a21\u6a21\u578b\u3002\u5728\u7ad9\u70b9\u4e00\u7ea7\u8c03\u67e5\u4e1a\u52a1\u652f\u52a9\u7cfb\u7edf\u662f\u4e00\u4e2a\u5168\u9762\u7684\u95ee\u9898\uff0c\u5c06\u4e3a\u51b3\u7b56\u8005\u3001\u89c4\u5212\u8005\u548c\u64cd\u4f5c\u8005\u63d0\u4f9b\u6240\u9700\u7684\u8be6\u7ec6\u7a0b\u5ea6\uff0c\u4ee5\u4f5c\u51fa\u91cd\u8981\u7684\u9009\u62e9\u548c\u7ed3\u8bba\u3002\u6211\u4eec\u4f7f\u7528\u968f\u673a\u68ee\u6797\u548c\u6700\u5c0f\u4e8c\u4e58\u56de\u5f52\u7b97\u6cd5\u4f5c\u4e3a\u5355\u53d8\u91cf\u56de\u5f52\u7b97\u6cd5\u6765\u5efa\u6a21\u53ef\u7528\u81ea\u884c\u8f66\u7684\u6570\u91cf\u5728\u8f66\u7ad9\u7ea7\u522b\u3002\u5bf9\u4e8e\u591a\u5143\u56de\u5f52\uff0c\u6211\u4eec\u91c7\u7528\u504f\u6700\u5c0f\u4e8c\u4e58\u56de\u5f52(Partial Least-Squares Regression\uff0cPLSR)\u6765\u51cf\u5c11\u6240\u9700\u7684\u9884\u6d4b\u6a21\u578b\uff0c\u5e76\u5728\u7f51\u7edc\u5c42\u6b21\u4e0a\u518d\u73b0\u7cfb\u7edf\u4e2d\u4e0d\u540c\u7ad9\u70b9\u7684\u65f6\u7a7a\u76f8\u4e92\u4f5c\u7528\u3002\u867d\u7136\u5355\u53d8\u91cf\u6a21\u578b\u7684\u9884\u6d4b\u8bef\u5dee\u7565\u4f4e\uff0c\u4f46\u6211\u4eec\u53d1\u73b0\u591a\u53d8\u91cf\u6a21\u578b\u7684\u7ed3\u679c\u5bf9\u4e8e\u7f51\u7edc\u7ea7\u9884\u6d4b\u662f\u6709\u5e0c\u671b\u7684\uff0c\u7279\u522b\u662f\u5728\u6709\u76f8\u5bf9\u5927\u91cf\u7a7a\u95f4\u76f8\u5173\u7684\u7ad9\u70b9\u7684\u7cfb\u7edf\u4e2d\u3002\u6b64\u5916\uff0c\u7ad9\u7ea7\u5206\u6790\u7684\u7ed3\u679c\u8868\u660e\uff0c\u4eba\u53e3\u4fe1\u606f\u548c\u5176\u4ed6\u73af\u5883\u53d8\u91cf\u662f\u6a21\u578b\u81ea\u884c\u8f66\u5728\u751f\u6001\u7cfb\u7edf\u670d\u52a1\u533a\u7684\u91cd\u8981\u56e0\u7d20\u3002\u6211\u4eec\u8fd8\u8bc1\u660e\uff0c\u53ef\u7528\u7684\u81ea\u884c\u8f66\u6a21\u578b\u5728\u7ad9\u7ea7\u65f6\u95f4 t \u6709\u4e00\u4e2a\u663e\u7740\u7684\u5f71\u54cd\uff0c\u81ea\u884c\u8f66\u8ba1\u6570\u6a21\u578b\u3002\u53f0\u7ad9\u76f8\u90bb\u65f6\u95f4\u548c\u9884\u62a5\u65f6\u95f4\u662f\u663e\u8457\u7684\u9884\u62a5\u56e0\u5b50\uff0c15\u5206\u949f\u662f\u6700\u6709\u6548\u7684\u9884\u62a5\u65f6\u95f4\u3002<\/span><\/p>\n


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    <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n


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    \u5982\u679c\u4f60\u7ecf\u5e38\u5173\u6ce8\u590d\u6742\u7cfb\u7edf\u548c\u8de8\u5b66\u79d1\u7814\u7a76\u8fdb\u5c55\uff0c\u662f\u7f51\u7edc\u8bba\u6587\u901f\u9012\u680f\u76ee\u7684\u957f\u671f\u8bfb\u8005\uff0c<\/span>\u6b22\u8fce\u626b\u63cf\u96c6\u667a\u5c0f\u52a9\u624b\uff0c\u5411\u6211\u4eec\u53cd\u9988\u4f60\u7684\u9700\u6c42<\/strong>\u3002\u6b22\u8fce\u63d0\u51fa\u5b9d\u8d35\u610f\u89c1\uff0c\u6211\u4eec\u4e00\u8d77\u6765\u63a2\u7d22\u8ffd\u8e2a\u524d\u6cbf\u8fdb\u5c55\u7684\u66f4\u597d\u65b9\u5f0f\u3002<\/span><\/p>\n


    <\/p>\n

    \u53c2\u4e0e\u8bba\u6587\u901f\u9012\u7528\u6237\u8c03\u7814\u540e\uff0c\u6211\u4eec\u5c06\u9080\u8bf7\u4f60\u52a0\u5165\u96c6\u667a\u7684\u79d1\u7814\u6587\u732e\u4ea4\u6d41\u7fa4<\/strong>\u3002<\/span><\/p>\n


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    \u8fd1\u671f\u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u9012<\/strong><\/span><\/strong><\/p>\n

    <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


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    <\/strong><\/p>\n

    \u7ade\u4e89 AI: \u7ade\u4e89\u53cd\u9988\u5982\u4f55\u5f71\u54cd\u673a\u5668\u5b66\u4e60 | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901213\u7bc7<\/strong><\/a>
    <\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

    \u7ecf\u6d4e\u4e0e\u75be\u75c5\u4f20\u64ad: COVID 19\u7684\u91cd\u542f\u673a\u5236 | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901211\u7bc7<\/span><\/strong><\/a>
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    \u7528\u4e8e\u4e09\u7ef4\u591a\u76ee\u6807\u8ddf\u8e2a\u7684\u56fe\u5f62\u795e\u7ecf\u7f51\u7edc | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901234\u7bc7<\/strong><\/a>
    <\/p>\n

    \u201c\u7c89\u4e1d\u7ecf\u6d4e\u201d\u5bf9\u4e2d\u56fd\u6d41\u884c\u97f3\u4e50\u4ea7\u4e1a\u6709\u7528\u5417? | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901211\u7bc7<\/strong><\/a>
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    \u65b0\u51a0\u75c5\u6bd2\u5927\u6d41\u884c\u4e2d\u7684Twitter\u56fd\u5bb6\u5f62\u8c61\uff1a\u4ee5\u4e2d\u56fd\u4e3a\u4f8b | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901211\u7bc7<\/strong><\/a>
    <\/p>\n

    \u52a0\u5165\u96c6\u667a\uff0c\u4e00\u8d77\u590d\u6742\uff01<\/strong><\/span><\/a><\/section>\n


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    \u96c6\u667a\u4ff1\u4e50\u90e8QQ\u7fa4\uff5c877391004<\/span><\/strong><\/p>\n

    \u5546\u52a1\u5408\u4f5c\u53ca\u6295\u7a3f\u8f6c\u8f7d\uff5cswarma@swarma.org
    <\/span><\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/h2>\n

    \u25c6 <\/span>\u25c6 <\/span>\u25c6<\/span><\/span><\/strong><\/strong><\/h1>\n

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    \u641c\u7d22\u516c\u4f17\u53f7\uff1a\u96c6\u667a\u4ff1\u4e50\u90e8<\/span><\/strong><\/span><\/p>\n


    <\/p>\n

    \u52a0\u5165\u201c\u6ca1\u6709\u56f4\u5899\u7684\u7814\u7a76\u6240\u201d<\/span><\/strong><\/span><\/p>\n

    \n

    <\/p>\n<\/section>\n

    \u8ba9\u82f9\u679c\u7838\u5f97\u66f4\u731b\u70c8\u4e9b\u5427\uff01<\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


    <\/p>\n

    \ud83d\udc47\u70b9\u51fb\u201c\u9605\u8bfb\u539f\u6587\u201d\uff0c\u4e86\u89e3\u66f4\u591a\u6700\u65b0arxiv\u8bba\u6587<\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/h2><\/div>\n","protected":false},"excerpt":{"rendered":"

    \u96c6\u667a\u6591\u56fe\u6700\u65b0\u8bba\u6587\u680f\u76ee\uff0c\u5173\u6ce8\u590d\u6742\u7cfb\u7edf\u3001\u7f51\u7edc\u79d1\u5b66\u3001\u8ba1\u7b97\u793e\u4f1a\u79d1\u5b66\u3001\u7edf\u8ba1\u7269\u7406\u7b49\u9886\u57df\u7684\u524d\u6cbf\u8fdb\u5c55\uff0c\u6bcf\u5929\u5373\u65f6\u66f4\u65b0\u76f8\u5173\u9876\u520a\u8bba\u6587\uff0c\u548c\u6700\u65b0arXiv\u9884\u5370\u672c\u8bba\u6587\u3002 \u672c\u6587\u662f\u5bf9\u8fd1\u671farXiv\u8bba\u6587\u7684\u6c47\u603b\u7f16\u8bd1\uff08by \u5f69\u4e91\u5c0f\u8bd1\uff09\u3002\u66f4\u591a\u6700\u65b0\u8bba\u6587\uff0c\u8bf7\u626b\u4e8c\u7ef4\u7801\u83b7\u53d6\u3002 \u6838\u5fc3\u901f\u9012 \u6d41\u7f51\u7edc\u4e2d\u6062\u590d\u4e0e\u5931\u6548\u6269\u6563\u7684\u62d3\u6251\u7406\u8bba\uff1b \u57fa\u4e8e Minkowski…<\/p>\n","protected":false},"author":1,"featured_media":21120,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"special":[],"_links":{"self":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/21121"}],"collection":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=21121"}],"version-history":[{"count":0,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/21121\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/media\/21120"}],"wp:attachment":[{"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=21121"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=21121"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=21121"},{"taxonomy":"special","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fspecial&post=21121"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}