{"id":9634,"date":"2016-08-12T01:00:00","date_gmt":"2016-08-11T17:00:00","guid":{"rendered":"http:\/\/swarma.org\/%swarma-paper%\/"},"modified":"2018-12-12T00:15:44","modified_gmt":"2018-12-11T16:15:44","slug":"%e6%96%87%e6%91%98%e5%a4%a7%e6%95%b0%e6%8d%ae%e5%a6%82%e4%bd%95%e9%80%a0%e6%88%90%e8%99%9a%e5%81%87%e4%bf%a1%e5%bf%83how-big-data-creates-false-confidence","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=9634","title":{"rendered":"\u6587\u6458|\u5927\u6570\u636e\u5982\u4f55\u9020\u6210\u865a\u5047\u4fe1\u5fc3(How Big Data Creates False Confidence)"},"content":{"rendered":"
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 <\/strong><\/span>  \u7b2c2\u671f\u590d\u6742\u6027\u6587\u6458\u7ffb\u8bd1\u65b0\u9c9c\u51fa\u7089\u5566<\/strong><\/span>\uff01\uff01\uff01<\/strong><\/p>\n


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\u7279\u522b\u611f\u8c22\u5085\u6e25\u6210<\/strong>\u8001\u5e08\u3001\u5510\u7490<\/strong>\u8001\u5e08\u3001\u5f20\u6c5f<\/strong>\u8001\u5e08\u4e3a\u6b64\u6b21\u7ffb\u8bd1\u505a\u4e86\u7ec6\u81f4\u7684\u5ba1\u6821\u5de5\u4f5c\uff01<\/p>\n

\u7279\u522b\u606d\u559c\u4e94\u4f4d\u7ae5\u978b\u83b7\u5f97\u4e86\u201c\u96c6\u667a\u4f18\u79c0\u8bd1\u53cb<\/strong>\u201d\u8363\u8a89\u3002\u4ed6\u4eec\u5206\u522b\u662f\uff1a<\/p>\n


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\u201c\u57fa\u4e8e\u70ed\u70b9\u5b9a\u4ef7\u7b56\u7565\u6765\u7f13\u89e3\u57ce\u5e02\u62e5\u5835\u95ee\u9898\uff08Decongestion of urban areas with hotspot-pricing\uff09\u201d<\/p>\n<\/blockquote>\n

\u8bd1\u8005 <\/span>HSAH_CHOI<\/strong>
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\u201c\u4ece\u6210\u529f\u4e2d\u7406\u6e05\u8868\u73b0\uff08Untangling performance from success\uff09\u201d<\/p>\n<\/blockquote>\n

\u8bd1\u8005 <\/span>jeffersonchou<\/strong><\/p>\n


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\u201c\u5f00\u653e\u521b\u65b02.0\u7684\u5341\u4e8c\u6761\u51c6\u5219 \uff08Twelve principles for open innovation 2.0\uff09\u201d<\/p>\n<\/blockquote>\n

\u8bd1\u8005 \u80e1\u9e4f\u535a<\/strong><\/p>\n


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\u201c\u4e00\u53ea\u540c\u65f6\u751f\u6d3b\u5728\u4e24\u4e2a\u76d2\u5b50\u4e2d\u7684\u859b\u5b9a\u8c14\u732b\uff08A Schr\u00f6dinger cat living in two boxes)\u201d<\/p>\n<\/blockquote>\n

\u8bd1\u8005 \u738b\u7ee7\u5eb7<\/strong><\/p>\n


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\u201c\u8d2b\u7a77\u4e0e\u8868\u89c2\u9057\u4f20\u53d8\u5f02\u53ca\u7cbe\u795e\u75be\u75c5\u7684\u5173\u7cfb\uff08Poverty linked to epigenetic changes and mental illness\uff09\u201d<\/p>\n<\/blockquote>\n

\u8bd1\u8005 \u674e\u5b87\u5cf0<\/strong><\/p>\n

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\u4eca\u5929\u7684\u5206\u4eab\u4e3a\u7ffb\u8bd1\u76845-10\u7bc7<\/strong>\uff0c\u5982\u4e0b\uff1a<\/span><\/p>\n


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5\u3001<\/strong><\/span>\u5927\u6570\u636e\u5982\u4f55\u9020\u6210\u865a\u5047\u4fe1\u5fc3(How Big Data Creates False Confidence) <\/span><\/strong><\/h2>\n

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\u539f\u6587\u94fe\u63a5\uff1a<\/strong><\/p>\n

http:\/\/nautil.us\/blog\/how-big-data-creates-false-confidence<\/span><\/p>\n

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(Translated by –<\/em>\u960e<\/span><\/em>\u8d6b<\/span>)<\/em><\/p>\n

Although no one can quite agree how to define it, thegeneral idea is to find datasets so enormous that they can reveal patterns invisibleto conventional inquiry. The data are often generated by millions of real-worlduser actions, such as tweets or credit-card purchases, and they can takethousands of computers to collect, store, and analyze. To many companies andresearchers, though, the investment is worth it because the patterns can unlockinformation about anything from genetic disorders to tomorrow\u2019s stock prices.<\/em> But there\u2019s a problem: It\u2019s tempting to think that with such anincredible volume of data behind them, studies relying on big data couldn\u2019t bewrong. But the bigness of the data can imbue the results with a false sense ofcertainty. Many of them are probably bogus\u2014and the reasons why should give uspause about any research that blindly trusts big data.<\/p>\n<\/blockquote>\n

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\u867d\u7136\u5bf9\u5982\u4f55\u5b9a\u4e49\u5927\u6570\u636e\uff0c\u6ca1\u6709\u8fbe\u6210\u5341\u5206\u4e00\u81f4\u610f\u89c1\uff0c\u4f46\u662f\uff0c\u5176\u603b\u4f53\u601d\u8def\u662f\u627e\u5230\u8db3\u591f\u5e9e\u5927\u7684\u6570\u636e\u96c6\uff0c\u4ee5\u63ed\u793a\u5e38\u89c4\u8c03\u67e5\u4e0d\u53ef\u89c1\u7684\u6a21\u5f0f\u3002\u8fd9\u4e9b\u6570\u636e\u5e38\u5e38\u7531\u6570\u4ee5\u767e\u4e07\u8ba1\u73b0\u5b9e\u751f\u6d3b\u4e2d\u7684\u7528\u6237\u884c\u4e3a\u6240\u4ea7\u751f\uff0c\u5982\u53d1\u5fae\u535a\u6216\u4fe1\u7528\u5361\u8d2d\u7269\u7b49\uff0c\u4eba\u4eec\u9700\u8981\u52a8\u7528\u6570\u4ee5\u5343\u8ba1\u7684\u8ba1\u7b97\u673a\u6536\u96c6\uff0c\u50a8\u5b58\u548c\u5206\u6790\u8fd9\u4e9b\u6570\u636e\u3002\u867d\u7136\u5982\u6b64\uff0c\u5bf9\u4e8e\u8bb8\u591a\u516c\u53f8\u548c\u7814\u7a76\u4eba\u5458\u800c\u8a00\uff0c\u8fd9\u4e9b\u6295\u8d44\u8fd8\u662f\u503c\u5f97\u7684\uff0c\u56e0\u4e3a\u8fd9\u4e2a\u6a21\u5f0f\u80fd\u591f\u7834\u89e3\u4ece\u5bb6\u65cf\u9057\u4f20\u75c5\u5230\u660e\u5929\u7684\u80a1\u5e02\u4ef7\u683c\u76f8\u5173\u4fe1\u606f\u7684\u4efb\u4f55\u4e8b\u60c5\u3002\u4f46\u6709\u4e2a\u95ee\u9898\uff1a\u5b83\u4f7f\u4eba\u81ea\u7136\u5730\u8ba4\u4e3a\uff0c\u7531\u8fd9\u4e9b\u7814\u7a76\u80cc\u540e\u60ca\u4eba\u7684\u6570\u636e\u91cf\uff0c\u6240\u4ee5\u8fd9\u4e9b\u4f9d\u6258\u4e8e\u5982\u6b64\u5927\u6570\u636e\u7684\u7814\u7a76\u662f\u4e0d\u4f1a\u6709\u9519\u8bef\u7684\u3002\u4f46\u5de8\u5927\u7684\u6570\u636e\u53ef\u80fd\u4f1a\u4ee5\u865a\u5047\u7684\u786e\u5b9a\u611f\u5f71\u54cd\u7814\u7a76\u7ed3\u679c\u3002\u8bb8\u591a\u7814\u7a76\u7ed3\u679c\u53ef\u80fd\u90fd\u662f\u865a\u5047\u7684<\/span> \u2013 \u800c\u8fd9\u4e5f\u662f\u4e3a\u4ec0\u4e48\u4f7f\u6211\u4eec\u4e0d\u518d\u76f2\u76ee\u76f8\u4fe1\u5927\u6570\u636e\u7814\u7a76\u7684\u7406\u7531\u3002<\/span><\/p>\n


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6\u3001\u975e\u795e\u7ecf<\/span>\u751f\u7269\u4f53\u4e2d\u7684\u4e60\u60ef<\/span>\u6027\u5b66\u4e60<\/span>\uff1a\u6765\u81ea\u9ecf\u83cc\u7684\u8bc1<\/span>\u636e (Habituation in non-neuralorganisms: evidence from slime moulds)<\/strong> <\/strong><\/span><\/h2>\n

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\u539f\u6587<\/span><\/strong>\u94fe<\/span><\/strong>\u63a5\uff1a<\/span><\/strong><\/p>\n

http:\/\/dx.doi.org\/10.1098\/rspb.2016.0446<\/p>\n

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(Translated by – <\/em>\u9ad8\u5fb7<\/span><\/em>\u534e<\/span><\/em>\uff0c<\/span>edited by <\/em>\u5085\u6e25\u6210<\/span>)<\/em><\/p>\n

Learning, defined as a change in behaviour evoked byexperience, has hitherto been investigated almost exclusively in multicellularneural organisms. Evidence for learning in non-neural multicellular organismsis scant, and only a few unequivocal reports of learning have been described insingle-celled organisms. Here we demonstrate habituation, an unmistakable formof learning, in the non-neural organism Physarumpolycephalum. In ourexperiment, using chemotaxis as the behavioural output and quinine or caffeineas the stimulus, we showed that P. polycephalum learnt to ignore quinine orcaffeine when the stimuli were repeated, but responded again when the stimuluswas withheld for a certain time. Our results meet the principle criteria thathave been used to demonstrate habituation: responsiveness decline andspontaneous recovery. To distinguish habituation from sensory adaptation ormotor fatigue, we also show stimulus specificity. Our results point to thediversity of organisms lacking neurons, which likely display a hithertounrecognized capacity for learning, and suggest that slime moulds may be anideal model system in which to investigate fundamental mechanisms underlyinglearning processes. Besides, documenting learning in non-neural organisms suchas slime moulds is centrally important to a comprehensive, phylogeneticunderstanding of when and where in the tree of life the earliest manifestationsof learning evolved.<\/em><\/p>\n<\/blockquote>\n

\u5b66\u4e60\uff0c\u4f5c\u4e3a\u7531\u7ecf\u9a8c\u6240\u8bf1\u53d1\u7684\u4e00\u79cd\u884c\u4e3a\u4e0a\u7684\u53d8\u5316\uff0c\u8fc4\u4eca\u5df2\u7ecf\u5728\u51e0\u4e4e\u6240\u6709\u7684\u591a\u7ec6\u80de\u795e\u7ecf\u751f\u7269\u4e2d\u5f97\u5230\u7814\u7a76\u3002\u4f46\u662f\uff0c\u6709\u5173\u591a\u7ec6\u80de\u975e\u795e\u7ecf\u751f\u7269\u4f53\u5b66\u4e60\u7684\u8bc1\u636e\u4ecd\u5b58\u5728\u4e0d\u8db3\uff0c\u800c\u53ea\u6709\u5c11\u6570\u786e\u5207\u7684\u62a5\u544a\u5bf9\u5355\u7ec6\u80de\u751f\u7269\u4f53\u4e2d\u7684\u5b66\u4e60\u4f5c\u4e86\u63cf\u8ff0\u3002\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u5c55\u793a\u4e86\u591a\u5934\u7ed2\u6ce1\u83cc\uff08<\/span>Physarumpolycephalum\uff09\u8fd9\u4e00\u975e\u795e\u7ecf\u751f\u7269\u4f53\u4e2d\u7684\u4e60\u60ef\u6027\u884c\u4e3a\uff0c\u5e76\u5c06\u5176\u770b\u4f5c\u662f\u4e00\u79cd\u6bcb\u5bb9\u7f6e\u7591\u7684\u5b66\u4e60\u5f62\u5f0f\u3002\u5728\u6211\u4eec\u7684\u5b9e\u9a8c\u4e2d\uff0c\u901a\u8fc7\u5c06\u8d8b\u5316\u6027\uff08<\/span>chemotaxis\uff09\u4f5c\u4e3a\u884c\u4e3a\u7684\u8f93\u51fa\u5e76\u4e14\u5c06\u594e\u5b81\uff08<\/span>quinine\uff09\u6216\u5496\u5561\u56e0\uff08<\/span>caffeine\uff09\u4f5c\u4e3a\u523a\u6fc0\u7269\uff0c\u6211\u4eec\u53d1\u73b0\uff1a\u5f53\u523a\u6fc0\u91cd\u590d\u51fa\u73b0\u65f6\uff0c\u591a\u5934\u7ed2\u6ce1\u83cc\u5b66\u4f1a\u4e86\u5ffd\u7565\u594e\u5b81\u6216\u5496\u5561\u56e0\u7684\u4f5c\u7528\u3002\u4f46\u662f\u5f53\u8fd9\u4e9b\u523a\u6fc0\u88ab\u6291\u5236\u7279\u5b9a\u7684\u65f6\u95f4\u4e4b\u540e\uff0c\u5b83\u4eec\u53c8\u80fd\u591f\u91cd\u65b0\u4f5c\u51fa\u53cd\u5e94\u3002\u6211\u4eec\u7684\u7814\u7a76\u7ed3\u679c\u7b26\u5408\u53cd\u5e94\u6027\u4e0b\u964d\uff08<\/span>responsiveness decline\uff09\u548c\u81ea\u7136\u6062\u590d\uff08<\/span>spontaneous recovery\uff09\u7b49\u88ab\u8fd0\u7528\u4e8e\u8bc1\u5b9e\u9002\u5e94\u6027\u7684\u539f\u5219\u6807\u51c6\u3002\u4e3a\u4e86\u5c06\u4e60\u60ef\u6027\u5b66\u4e60\u4e0e\u611f\u89c9\u9002\u5e94\u6216\u8fd0\u52a8\u6027\u75b2\u52b3\u76f8\u533a\u5206\uff0c\u6211\u4eec\u4e5f\u5bf9\u523a\u6fc0\u7684\u7279\u5f02\u6027\uff08<\/span>stimulus specificity\uff09\u8fdb\u884c\u4e86\u7814\u7a76\u3002\u76f8\u5173\u7684\u7ed3\u679c\u663e\u793a\uff1a\u7f3a\u4e4f\u795e\u7ecf\u5143\u7684\u751f\u7269\u4f53\u7684\u591a\u6837\u6027\uff0c\u53ef\u80fd\u8868\u73b0\u51fa\u4e00\u79cd\u81f3\u4eca\u4ecd\u672a\u88ab\u8bc6\u522b\u7684\u5b66\u4e60\u80fd\u529b\uff1b\u5e76\u4e14\u8ba4\u4e3a\uff0c\u9ecf\u83cc\u53ef\u80fd\u662f\u7528\u6765\u7814\u7a76\u5b66\u4e60\u8fc7\u7a0b\u7684\u57fa\u672c\u673a\u5236\u7684\u7406\u60f3\u6a21\u578b\u7cfb\u7edf\u3002\u6b64\u5916\uff0c\u5bf9\u8bf8\u5982\u9ecf\u83cc\u4e4b\u7c7b\u7684\u975e\u795e\u7ecf\u751f\u7269\u4f53\u5b66\u4e60\u673a\u5236\u7684\u63ed\u793a\uff0c\u5bf9\u4e8e\u5168\u9762\u3001\u7cfb\u7edf\u5730\u7406\u89e3\u751f\u547d\u4e4b\u6811\uff08<\/span>tree of life\uff09\u4e2d\u7684\u5b66\u4e60\u7684\u65e9\u671f\u8868\u73b0\u5f62\u5f0f\u5728\u4f55\u65f6\u4f55\u5730\u53d1\u751f\u7684\u6f14\u5316\u4e5f\u81f3\u5173\u91cd\u8981\u3002<\/span><\/p>\n


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7\u3001\u4f17\u5305\u89e3\u51b3\u57ce\u91cc\u7684\u7f57\u5bbe\u6c49<\/span>\u6548\u5e94<\/span>(Crowdsourcing the Robin Hood effect in cities) <\/strong><\/span>\"\u96c6\u667a\"<\/h2>\n

\u539f\u6587<\/span><\/strong>\u94fe<\/span><\/strong>\u63a5\uff1a<\/span><\/strong><\/p>\n

http:\/\/arxiv.org\/abs\/1604.08394<\/p>\n

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(Translated by – F7<\/em>\uff0c<\/span>edited by <\/em>\u5085\u6e25\u6210<\/span>)<\/em><\/p>\n

Socioeconomic inequalities in cities are embedded inspace and result in neighborhood effects, whose harmful consequences haveproved very hard to counterbalance efficiently by planning policies alone.Considering redistribution of money flows as a first step toward improvedspatial equity, we study a bottom-up approach that would rely on a slightevolution of shopping mobility practices. Building on a database of anonymizedcredit card transactions in Madrid and Barcelona, we quantify the mobilityeffort required to reach a reference situation where commercial income isevenly shared among neighborhoods. The redirections of shopping trips preservekey properties of human mobility, including travel distances. Surprisingly, forboth cities only a small fraction (<\/em>\u223c5%) of trips need to be altered to reach equity situations,improving even other sustainability indicators. The method could be implementedin mobile applications that would assist individuals in reshaping theirshopping practices, to promote the spatial redistribution of opportunities inthe city.<\/em><\/p>\n<\/blockquote>\n

\u57ce\u5e02\u4e2d\u7684\u793e\u4f1a\u7ecf\u6d4e\u5b66\u7684\u4e0d\u5e73\u7b49\u5185\u5d4c\u4e8e\u5730\u7406\u7a7a\u95f4\u4e4b\u4e2d\uff0c\u5e76\u5c06\u5bfc\u81f4\u90bb\u91cc\u6548\u5e94\uff08<\/span>neighborhood effects\uff09\uff0c\u5176\u5f0a\u7aef\u5df2\u7ecf\u88ab\u8bc1\u660e\u5355\u7528\u8ba1\u5212\u6027\u653f\u7b56\u662f\u5f88\u96be\u53bb\u6709\u6548\u5e73\u8861\u89e3\u51b3\u7684\u3002\u8003\u8651\u5230\u8d44\u91d1\u6d41\u7684\u518d\u5206\u914d\u53ef\u4ee5\u4f5c\u4e3a\u6539\u5584\u4e0d\u5e73\u7b49\u7684\u7684\u7b2c\u4e00\u6b65\uff0c\u6211\u4eec\u7814\u7a76\u63d0\u51fa\u4e86\u4e00\u4e2a\u81ea\u4e0b\u800c\u4e0a\u7684\u89e3\u51b3\u65b9\u6cd5\uff0c\u8be5\u65b9\u6cd5\u9700\u8981\u4e00\u4e9b\u8d2d\u7269\u6d41\u52a8\u6027\u4e0a\u7684\u6539\u53d8\u3002\u901a\u8fc7\u5efa\u7acb\u9a6c\u5fb7\u91cc\u548c\u5df4\u585e\u7f57\u90a3\u7684\u533f\u540d\u4fe1\u7528\u5361\u4ea4\u6613\u6570\u636e\u5e93\uff0c\u6211\u4eec\u91cf\u5316\u4e86\u8fbe\u5230\u4e00\u4e2a\u5728\u90bb\u91cc\u4e4b\u95f4\u5546\u4e1a\u6536\u5165\u6bd4\u8f83\u5747\u7b49\u7684\u53c2\u8003\u6c34\u5e73\u6240\u9700\u8981\u7684\u79fb\u52a8\u8017\u8d39\u91cf\u3002\u8d2d\u7269\u51fa\u884c\u7684\u91cd\u65b0\u5b9a\u5411\u4fdd\u5b58\u4e86\u4eba\u7c7b\u79fb\u52a8\u7684\u4e00\u4e9b\u5173\u952e\u6027\u8d28\uff0c\u5305\u62ec\u79fb\u52a8\u7684\u8ddd\u79bb\u3002\u4ee4\u4eba\u60ca\u5947\u7684\u662f\uff0c\u8fd9\u4e24\u4e2a\u57ce\u5e02\u90fd\u53ea\u9700\u8981\u975e\u5e38\u5c11\uff08\u5927\u7ea6<\/span>5%\uff09\u7684\u79fb\u52a8\u6539\u53d8\u5c31\u80fd\u8fbe\u5230\u8f83\u4e3a\u5e73\u7b49\u7684\u6c34\u5e73\uff0c\u540c\u65f6\u751a\u81f3\u4e5f\u6539\u8fdb\u4e86\u5176\u4ed6\u7684\u53ef\u6301\u7eed\u6027\u6307\u6807\u3002\u8be5\u65b9\u6cd5\u53ef\u4ee5\u5728\u79fb\u52a8\u5e94\u7528\u7a0b\u5e8f\u4e2d\u5b9e\u73b0\uff0c\u5e76\u4e14\u5c06\u6709\u52a9\u4e8e\u4e2a\u4eba\u91cd\u5851\u8d2d\u7269\u4e60\u60ef\uff0c\u4ee5\u4fc3\u8fdb\u57ce\u5e02\u5185\u7684\u673a\u4f1a\u5728\u7a7a\u95f4\u4e0a\u7684\u91cd\u65b0\u5206\u5e03\u3002<\/span><\/p>\n


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8\u3001\u7efc\u5408\u590d\u6742\u7f51\u7edc\u548c\u6570\u636e\u6316\u6398\uff1a\u539f\u56e0\u548c\u65b9\u6cd5<\/span> (Combining complex networksand data mining: why and how) <\/span><\/strong><\/span><\/h2>\n

\"\u96c6\u667a\"
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\u539f\u6587\u94fe\u63a5\uff1a<\/strong><\/p>\n

http:\/\/arxiv.org\/abs\/1604.08816<\/p>\n

\n

(Translated by <\/em>\u8521\u5609\u6587\uff0c<\/span> edited by <\/em>\u5085\u6e25\u6210<\/span>)<\/em><\/p>\n

The increasing power of computer technology does notdispense with the need to extract meaningful information out of data sets ofever growing size, and indeed typically exacerbates the complexity of thistask. To tackle this general problem, two methods have emerged, atchronologically different times, that are now commonly used in the scientificcommunity: data mining and complex network theory. Not only do complex networkanalysis and data mining share the same general goal, that of extractinginformation from complex systems to ultimately create a new compactquantifiable representation, but they also often address similar problems too.In the face of that, a surprisingly low number of researchers turn out toresort to both methodologies. One may then be tempted to conclude that thesetwo fields are either largely redundant or totally antithetic. The startingpoint of this review is that this state of affairs should be put down tocontingent rather than conceptual differences, and that these two fields can infact advantageously be used in a synergistic manner. An overview of both fieldsis first provided, some fundamental concepts of which are illustrated. Avariety of contexts in which complex network theory and data mining have be usedin a synergistic manner are then presented. Contexts in which the appropriateintegration of complex networks metrics can lead to improved classificationrates with respect to classical data mining algorithms and, conversely,contexts in which data mining can be used to tackle important issues in complexnetwork theory applications are illustrated. Finally, ways to achieve a tighterintegration between complex networks and data mining, and open lines ofresearch are discussed.<\/em><\/p>\n<\/blockquote>\n

\u8ba1\u7b97\u673a\u6280\u672f\u7684\u53d1\u5c55\u8ddf\u4e0d\u4e0a\u6570\u636e\u5c3a\u5bf8\u7684\u6025\u5267\u589e\u957f\uff0c\u8fd9\u4f7f\u5f97\u4ece\u5176\u4e2d\u63d0\u53d6\u6709\u6548\u4fe1\u606f\u7684\u590d\u6742\u7a0b\u5ea6\u8d8a\u6765\u8d8a\u9ad8\u3002\u4e3a\u4e86\u5904\u7406\u8fd9\u7c7b\u95ee\u9898\uff0c\u4e24\u79cd\u65b9\u6cd5\u5728\u79d1\u5b66\u754c\u4e2d\u76f8\u7ee7\u8131\u9896\u800c\u51fa\uff1a\u6570\u636e\u6316\u6398\u548c\u590d\u6742\u7f51\u7edc\u3002\u8fd9\u4e24\u79cd\u65b9\u6cd5\u4e0d\u4f46\u6709\u7740\u76f8\u540c\u7684\u76ee\u6807\uff0c\u5373\u7528\u4e00\u4e2a\u62bd\u8c61\u7684\u53ef\u91cf\u5316\u7279\u5f81\u6765\u5bf9\u590d\u6742\u7cfb\u7edf\u8fdb\u884c\u8868\u793a\uff0c\u800c\u4e14\u8fd9\u4e24\u79cd\u65b9\u6cd5\u5e38\u5e38\u9762\u5bf9\u7684\u662f\u76f8\u540c\u7684\u95ee\u9898\u3002\u4f46\u662f\u5f88\u5c11\u6709\u7814\u7a76\u8005\u540c\u65f6\u4f7f\u7528\u8fd9\u4e24\u79cd\u65b9\u6cd5\u8bba\u3002\u4e00\u4e9b\u4eba\u503e\u5411\u4e8e\u8ba4\u4e3a\u8fd9\u4e24\u79cd\u65b9\u6cd5\u4ea4\u53c9\u90e8\u5206\u592a\u591a\uff0c\u6216\u8005\u8ba4\u4e3a\u5b83\u4eec\u5b8c\u5168\u5bf9\u7acb\u3002\u8fd9\u7bc7\u7efc\u8ff0\u7684\u51fa\u53d1\u70b9\u662f\uff0c\u9762\u5bf9\u8fd9\u79cd\u72b6\u51b5\uff0c\u6211\u4eec\u5e94\u8be5\u5bfb\u6c42\u7edf\u4e00\uff0c\u800c\u4e0d\u662f\u6982\u5ff5\u4e0a\u7684\u5dee\u5f02\uff0c\u4e8b\u5b9e\u4e0a\uff0c\u8fd9\u4e24\u4e2a\u9886\u57df\u53ef\u4ee5\u6709\u6548\u534f\u540c\u3002\u6211\u4eec\u9996\u5148\u5bf9\u8fd9\u4e24\u4e2a\u9886\u57df\u8fdb\u884c\u7efc\u8ff0\uff0c\u5148\u4ecb\u7ecd\u4e86\u4e00\u4e9b\u57fa\u7840\u6982\u5ff5\uff0c\u7136\u540e\u8fdb\u4e00\u6b65\u5c55\u793a\u4e86\u5982\u4f55\u7efc\u5408\u4f7f\u7528\u590d\u6742\u7f51\u7edc\u548c\u6570\u636e\u6316\u6398\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u5c55\u793a\u4e86\u4e00\u4e9b\u590d\u6742\u7f51\u7edc\u7684\u7279\u5b9a\u5ea6\u91cf\u4f5c\u4e3a\u7279\u5f81\u53ef\u4ee5\u63d0\u9ad8\u5206\u7c7b\u95ee\u9898\u7684\u51c6\u786e\u7387\uff0c\u53cd\u8fc7\u6765\u770b\u6570\u636e\u6316\u6398\u4e5f\u53ef\u4ee5\u5e2e\u52a9\u5904\u7406\u590d\u6742\u7f51\u7edc\u4e2d\u5e94\u7528\u95ee\u9898\u3002\u6700\u540e\u6211\u4eec\u63a2\u8ba8\u4e86\u7d27\u5bc6\u7ed3\u5408\u590d\u6742\u7f51\u7edc\u548c\u6570\u636e\u6316\u6398\u7684\u65b9\u6cd5\u4ee5\u53ca\u4e0e\u4e4b\u76f8\u5173\u7684\u7814\u7a76\u601d\u8def\u3002<\/span><\/p>\n


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9\u3001\u7814\u7a76\u591a\u90e8\u95e8<\/span>(Multi-sector)\u534f\u8c03\u53ca\u81ea\u7ec4\u7ec7\u7684\u4e00\u4e2a\u6f14\u5316\u535a\u5f08\u7406\u8bba\u65b9\u6cd5<\/span> (An Evolutionary GameTheoretic Approach to Multi-Sector Coordination and Self-Organization) <\/strong><\/span><\/h2>\n

\"\u96c6\u667a\"
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\u539f\u6587\u94fe\u63a5\uff1a<\/strong><\/p>\n

http:\/\/dx.doi.org\/10.3390\/e18040152<\/p>\n

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(Translated by –<\/em>\u9e9f<\/span><\/em>\u51e4\u5170<\/span><\/em>\u8349<\/span>-ATC-ABM-Canton)<\/em><\/p>\n

Coordination games provide ubiquitous interactionparadigms to frame human behavioral features, such as information transmission,conventions and languages as well as socio-economic processes and institutions.By using a dynamical approach, such as Evolutionary Game Theory (EGT), one isable to follow, in detail, the self-organization process by which a populationof individuals coordinates into a given behavior. Real socio-economicscenarios, however, often involve the interaction between multiple co-evolvingsectors, with specific options of their own, that call for generalized and moresophisticated mathematical frameworks. In this paper, we explore a general EGTapproach to deal with coordination dynamics in which individuals from multiplesectors interact. Starting from a two-sector, consumer\/producer scenario, weinvestigate the effects of including a third co-evolving sector that we callpublic. We explore the changes in the self-organization process of all sectors,given the feedback that this new sector imparts on the other two.<\/em><\/p>\n<\/blockquote>\n

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\u534f\u8c03\u535a\u5f08\u4e3a\u6211\u4eec\u7814\u7a76\u4eba\u7c7b\u884c\u4e3a\u7279\u5f81\uff0c\u4f8b\u5982\u4fe1\u606f\u7684\u4ea4\u6362\u4f20\u8f93\u3001\u4e60\u4fd7\u548c\u8bed\u8a00\u3001\u793e\u4f1a\u7ecf\u6d4e\u8fc7\u7a0b\u548c\u5404\u79cd\u793e\u4f1a\u673a\u6784\u7684\u5f62\u6210\uff0c\u63d0\u4f9b\u4e86\u4e00\u79cd\u901a\u7528\u7684\u76f8\u4e92\u4f5c\u7528\u8303\u5f0f\u3002\u800c\u901a\u8fc7\u8bf8\u5982\u6f14\u5316\u535a\u5f08\u8fd9\u6837\u7684\u52a8\u6001\u65b9\u6cd5\uff0c\u6211\u4eec\u80fd\u591f\u4ece\u7ec6\u8282\u4e0a\u4e86\u89e3\u5927\u91cf\u4e2a\u4f53\u901a\u8fc7\u76f8\u4e92\u534f\u4f5c\u800c\u5f62\u6210\u4e00\u79cd\u7279\u5b9a\u884c\u4e3a\u7684\u81ea\u7ec4\u7ec7\u8fc7\u7a0b\u3002<\/span><\/p>\n

\u7136\u800c\uff0c\u5728\u771f\u5b9e\u7684\u793e\u4f1a\u7ecf\u6d4e\u60c5\u5f62\u4e2d\uff0c\u4f17\u591a\u5177\u6709\u81ea\u5df1\u72ec\u7279\u89c2\u5ff5\u7684\u90e8\u95e8\uff08<\/span>Multi-sector\uff09\u4f1a\u5171\u540c\u6f14\u5316\u3001\u5171\u540c\u53d1\u5c55\uff0c\u8fd9\u5c31\u9700\u8981\u6784\u5efa\u66f4\u52a0\u666e\u9002\u548c\u7cbe\u5999\u7684\u6570\u5b66\u6a21\u578b\u3002\u5728\u672c\u6587\u4e2d\uff0c\u7b14\u8005\u5c1d\u8bd5\u4f7f\u7528\u4e00\u79cd\u4e00\u822c\u7684\u6f14\u5316\u535a\u5f08\u6a21\u578b\u6765\u5904\u7406\u534f\u8c03\u52a8\u529b\u5b66\uff0c\u5176\u4e2d\u4e2a\u4f53\u6765\u81ea\u591a\u4e2a\u90e8\u95e8\u5e76\u53ef\u4ee5\u76f8\u4e92\u4f5c\u7528\u3002\u4ece\u4e24\u4e2a\u90e8\u7c7b\uff08\u6d88\u8d39\u8005\uff0f\u751f\u4ea7\u8005\uff09\u5f00\u59cb\uff0c\u6211\u4eec\u7814\u7a76\u516c\u4f17\u90e8\u95e8\u52a0\u5165\u4e4b\u540e\u7684\u5f71\u54cd\uff0c\u516c\u4f17\u4f5c\u4e3a\u7b2c\u4e09\u4e2a\u90e8\u95e8\u4e5f\u4e00\u540c\u6f14\u8fdb\u3002\u5728\u7ed9\u5b9a\u8fd9\u4e2a\u65b0\u52a0\u5165\u90e8\u95e8\u5bf9\u53e6\u5916\u4e24\u4e2a\u90e8\u95e8\u7684\u5f71\u54cd\u7684\u53cd\u9988\u4e4b\u540e\uff0c\u6211\u4eec\u63a2\u7a76\u4e86\u4e09\u4e2a\u90e8\u95e8\u81ea\u7ec4\u7ec7\u8fdb\u7a0b\u4e2d\u7684\u6240\u6709\u53d8\u5316\u3002<\/span><\/p>\n


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10\u3001\u4f5c\u4e3a<\/span>\u4e00\u79cd\u6c14\u5019\u4fe1\u53f7\u5173\u952e\u6ee4<\/span>\u6ce2\u5668\u7684\u6cb3\u6d41\u7f51\u7edc<\/span>\u81ea\u7ec4\u7ec7\u8fc7<\/span>\u7a0b (Self-organization of riverchannels as a critical filter on climate signals) <\/strong><\/span><\/h2>\n

\"\u96c6\u667a\"
<\/span><\/strong><\/p>\n

\u539f\u6587\u94fe\u63a5\uff1a<\/strong><\/span><\/p>\n

http:\/\/science.sciencemag.org\/content\/352\/6286\/694?et_rid=35379714&et_cid=466541<\/span><\/p>\n

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(Translated by <\/span><\/em>Cicely<\/em>)<\/em><\/o:p><\/span><\/p>\n

Large floods should seemingly influence the depth and width of rivers. Phillips and Jerolmack, however, suggest that the self-organization of bedrock river channels blunts the impact of extreme rainfall events. River channel geometries from a wide range of course-grained rivers across the United States show that larger floods have very limited additional impact on channel geometry. River channel sculpting does increase as flood size increases, but the effect is most pronounced for moderate floods. This relationship may explain the long-term stability of rivers across shifts in climate.<\/span><\/em><\/p>\n<\/blockquote>\n

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\u5927\u6d2a\u6c34\u4f3c\u4e4e\u5f71\u54cd\u7740\u6cb3\u6d41\u7684\u6df1\u5ea6\u548c\u5bbd\u5ea6\uff0c\u800c<\/span>”Phillips”\u548c<\/span>”Jerolmack”\u540c\u65f6\u4e5f\u8ba4\u4e3a\u57fa\u5ca9\u6cb3\u9053\u81ea\u7ec4\u7ec7\u51cf\u5f31\u4e86\u6781\u7aef\u964d\u6c34\u4e8b\u4ef6\u7684\u5f71\u54cd\u3002\u5168\u7f8e\u5927\u8303\u56f4\u7684\u7c97\u7c92\u5ea6\u6cb3\u6d41\u6cb3\u9053\u7684\u51e0\u4f55\u5f62\u72b6\u663e\u793a\uff0c\u5373\u4fbf\u662f\u66f4\u5927\u7684\u6d2a\u6c34\u5bf9\u5176\u989d\u5916\u7684\u5f71\u54cd\u4e5f\u975e\u5e38\u6709\u9650\u3002\u5f53\u6d2a\u6c34\u89c4\u6a21\u589e\u52a0\u65f6\uff0c\u6cb3\u9053\u7684\u6210\u5f62\u7684\u786e\u4e5f\u968f\u4e4b\u589e\u52a0\uff0c\u800c\u8fd9\u79cd\u5f71\u54cd\u5bf9\u4e8e\u4e2d\u7b49\u5f3a\u5ea6\u7684\u6d2a\u6c34\u8868\u73b0\u5f97\u6700\u4e3a\u660e\u663e\u3002\u8fd9\u4e00\u5173\u8054\u6216\u8bb8\u53ef\u4ee5\u5bf9\u5904\u5728\u6c14\u5019\u53d8\u5316\u4e2d\u7684\u6cb3\u6d41\u7684\u957f\u671f\u7a33\u5b9a\u6027\u505a\u51fa\u89e3\u91ca\u3002<\/span><\/p>\n


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\u957f\u6309\u8bc6\u522b\u4e8c\u7ef4\u7801\uff0c\u5173\u6ce8\u96c6\u667a\u4ff1\u4e50\u90e8\uff0c<\/span><\/p>\n

\u8ba9\u6211\u4eec\u79bb\u79d1\u5b66\u63a2\u7d22\u66f4\u8fd1\u4e00\u6b65\u3002<\/span><\/p>\n


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\u59cb\u53d1\u4e8e\u5fae\u4fe1\u516c\u4f17\u53f7\uff1a \u96c6\u667a\u4ff1\u4e50\u90e8 <\/p>\n<\/blockquote>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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