{"id":20162,"date":"2020-06-26T17:26:38","date_gmt":"2020-06-26T09:26:38","guid":{"rendered":"https:\/\/swarma.org\/?p=20162"},"modified":"2020-06-26T17:26:38","modified_gmt":"2020-06-26T09:26:38","slug":"%e8%bf%91%e8%b7%9d%e7%a6%bb%e6%84%9f%e6%9f%93%e4%bc%a0%e6%92%ad%e7%9a%84%e8%92%99%e7%89%b9%e5%8d%a1%e7%bd%97%e6%a8%a1%e6%8b%9f%e7%a0%94%e7%a9%b6-%e7%bd%91%e7%bb%9c%e7%a7%91%e5%ad%a6%e8%ae%ba","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=20162","title":{"rendered":"\u8fd1\u8ddd\u79bb\u611f\u67d3\u4f20\u64ad\u7684\u8499\u7279\u5361\u7f57\u6a21\u62df\u7814\u7a76 | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901239\u7bc7"},"content":{"rendered":"
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    \u7f8e\u56fd\u3001\u4e9a\u6d32\u548c\u6b27\u6d32\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6d41\u884c\u7684\u65f6\u95f4\u6570\u636e\u7cfb\u5217\u8868\u660e\uff0cSARS-CoV-2 Spike D614G \u53d8\u5f02\u5177\u6709\u9009\u62e9\u6027\uff1b<\/span><\/h2>\n<\/li>\n
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    \u58a8\u897f\u54e5\u7684\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e: \u6d41\u884c\u75c5\u7f51\u7edc\uff1b<\/span><\/h2>\n<\/li>\n
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    \u65f6\u95f4\u4f9d\u8d56\u548c\u65f6\u95f4\u72ec\u7acb\u7684 SIR \u6a21\u578b\u5e94\u7528\u4e8e\u5728\u963f\u6839\u5ef7\u3001\u5df4\u897f\u3001\u54e5\u4f26\u6bd4\u4e9a\u3001\u58a8\u897f\u54e5\u548c\u5357\u975e\u7206\u53d1\u7684\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\uff1b<\/span><\/h2>\n<\/li>\n
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     L\u00e9vy \u6f2b\u6b65\u8d85\u6269\u6563\u5f00\u59cb\u7684\u666e\u904d\u6027\u8d77\u6e90\uff1b<\/span><\/h2>\n<\/li>\n
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    <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

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

    A Monte Carlo simulation study of proximity-based infection spread<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.12212<\/span><\/p>\n

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

    S. Triambak,D. P. Mahapatra<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Recent work showed that the initial COVID-19 data from China followed a subexponential power-law increase in the number of confirmed cases. This was attributed to a combination of effective containment and mitigation measures employed as well as behaviorial changes by the population. In view of this, we performed a Monte Carlo random walk study to better understand proximity-based infectious disease spread, particularly under restrictions. Our model is found to be rather robust and reproduces the observed power-law growth without relying on any external parameters. Three growth regimes (quadratic, power-law and exponential) emerge naturally from our simulations. These results suggest that the early containment of the disease within China was close to optimal and could not have been significantly improved upon. We show that reasonable agreement with other data can be attained by incorporating small-world-like connections in the simulations. The prescribed model and its generalizations might be useful for future strategies in the midst of the present pandemic.<\/span><\/p>\n

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    \u9ad8\u6548\u8fd0\u8f93\u7269\u6d41—- \u5965\u5730\u5229<\/strong><\/span><\/p>\n

    \u57ce\u5e02\u8d27\u8fd0\u7684\u4e00\u79cd\u9014\u5f84<\/strong><\/span><\/p>\n

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

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

    Efficient Transport Logistics, An Approach for Urban Freight Transport in Austria<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.11377<\/span><\/p>\n

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

    Verena Brandst\u00e4tter,Cristina Olaverri-Monreal<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>To alleviate traffic congestion that results from the growth of e-commerce we propose an approach in the city of Linz, Austria by relying on shared distribution centers from different companies. We develop two algorithms to find out the optimal location for the hubs and calculate the shortest path between locations. Results showed that in an urban environment, the implementation of hubs results in a reduction of the number of delivery vehicles. It reduces driving distances from hub to the customers, and also benefits the drivers that need to return home every day.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u4e3a\u4e86\u51cf\u8f7b\u7535\u5b50\u5546\u52a1\u589e\u957f\u5e26\u6765\u7684\u4ea4\u901a\u5835\u585e\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u79cd\u5728\u5965\u5730\u5229\u6797\u8328\u5e02\u901a\u8fc7\u4f9d\u8d56\u4e8e\u4e0d\u540c\u516c\u53f8\u7684\u5171\u4eab\u914d\u9001\u4e2d\u5fc3\u7684\u65b9\u6cd5\u3002\u6211\u4eec\u53d1\u5c55\u4e86\u4e24\u4e2a\u7b97\u6cd5\u6765\u627e\u51fa\u67a2\u7ebd\u7684\u6700\u4f73\u4f4d\u7f6e\u548c\u8ba1\u7b97\u4f4d\u7f6e\u4e4b\u95f4\u7684\u6700\u77ed\u8def\u5f84\u3002\u7ed3\u679c\u8868\u660e\uff0c\u5728\u57ce\u5e02\u73af\u5883\u4e2d\uff0c\u5efa\u7acb\u67a2\u7ebd\u5bfc\u81f4\u8fd0\u8f93\u8f66\u8f86\u6570\u91cf\u51cf\u5c11\u3002\u5b83\u7f29\u77ed\u4e86\u4ece\u67a2\u7ebd\u5230\u5ba2\u6237\u7684\u884c\u8f66\u8ddd\u79bb\uff0c\u4e5f\u4f7f\u6bcf\u5929\u9700\u8981\u56de\u5bb6\u7684\u53f8\u673a\u53d7\u76ca\u3002<\/span><\/p>\n


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    \u4f7f\u7528\u53d8\u5316\u4e2d\u7684\u53d8\u5316\u6a21\u578b\uff0c<\/strong><\/span><\/p>\n

    \u5728\u9009\u5b9a\u7684\u6b27\u6d32\u56fd\u5bb6\u548c\u7f8e\u56fd\uff0c<\/strong><\/span><\/p>\n

    \u56fd\u5bb6\u5c01\u9501\u5bf9\u65b0\u578b\u51a0\u72b6<\/strong><\/span><\/p>\n

    \u75c5\u6bd2\u80ba\u708e\u6b7b\u4ea1\u7684\u5f71\u54cd<\/strong><\/span><\/strong><\/p>\n

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

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

    Impact of national lockdown on COVID-19 deaths in select European countries and the US using a Changes-in-Changes model<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.12251<\/span><\/p>\n

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

    Mudit Kapoor,Shamika Ravi<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>In this paper, we estimate the impact of national lockdown on COVID-19 related total and daily deaths, per million people, in select European countries. In particular, we compare countries that imposed a nationwide lockdown (Treatment group); Belgium, Denmark, France, Germany, Italy, Norway, Spain, United Kingdom (UK), and the US, to Sweden (Control group) that did not impose national lockdown using a changes-in-changes (CIC) estimation model. The key advantage of the CIC model as compared to the standard difference-in-difference model is that CIC allows for mean and variance of the outcomes to change over time in the absence of any policy intervention, and CIC accounts for endogeneity in the choice of policy intervention. Our results indicate that in contrast to Sweden, which did not impose a national lockdown, Germany, and to some extent, the US were the two countries where nationwide lockdown had a significant impact on the reduction in COVID-19 related total and daily deaths per million people. In Norway and Denmark, there was no significant impact on total and daily deaths per million people relative to Sweden. Whereas in other countries; Belgium, France, Italy, Spain, and the UK, the effect of the lockdown was in the opposite direction, that is, they experienced significantly higher COVID-19 related total and daily deaths per million people, post the lockdown as compared to Sweden. Our results suggest that the impact of nationwide lockdown on COVID-19 related total and daily deaths per million people varied from one country to another.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5728\u8fd9\u7bc7\u8bba\u6587\u4e2d\uff0c\u6211\u4eec\u4f30\u8ba1\u4e86\u56fd\u5bb6\u9632\u8303\u7981\u95ed\u5bf9\u6b27\u6d32\u67d0\u4e9b\u56fd\u5bb6\u6bcf\u5929\u6bcf\u767e\u4e07\u4eba\u4e2d\u4e0e\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6709\u5173\u7684\u603b\u6b7b\u4ea1\u4eba\u6570\u548c\u6bcf\u65e5\u6b7b\u4ea1\u4eba\u6570\u7684\u5f71\u54cd\u3002\u7279\u522b\u662f\uff0c\u6211\u4eec\u5c06\u5b9e\u65bd\u5168\u56fd\u9632\u8303\u7981\u95ed\u7684\u56fd\u5bb6(\u6cbb\u7597\u5c0f\u7ec4)\u3001\u6bd4\u5229\u65f6\u3001\u4e39\u9ea6\u3001\u6cd5\u56fd\u3001\u5fb7\u56fd\u3001\u610f\u5927\u5229\u3001\u632a\u5a01\u3001\u897f\u73ed\u7259\u3001\u82f1\u56fd(\u82f1\u56fd)\u548c\u7f8e\u56fd\u4e0e\u745e\u5178(\u63a7\u5236\u5c0f\u7ec4)\u8fdb\u884c\u4e86\u6bd4\u8f83\uff0c\u745e\u5178\u6ca1\u6709\u4f7f\u7528\u53d8\u66f4\u4f30\u8ba1\u6a21\u578b(CIC)\u5b9e\u65bd\u5168\u56fd\u9632\u8303\u7981\u95ed\u3002\u4e0e\u6807\u51c6\u5dee\u5f02\u6a21\u578b\u76f8\u6bd4\uff0cCIC \u6a21\u578b\u7684\u4e3b\u8981\u4f18\u52bf\u5728\u4e8e\uff0c\u5728\u6ca1\u6709\u4efb\u4f55\u653f\u7b56\u5e72\u9884\u7684\u60c5\u51b5\u4e0b\uff0cCIC \u5141\u8bb8\u7ed3\u679c\u7684\u5747\u503c\u548c\u65b9\u5dee\u968f\u65f6\u95f4\u800c\u53d8\u5316\uff0cCIC \u89e3\u91ca\u4e86\u653f\u7b56\u5e72\u9884\u9009\u62e9\u7684\u5185\u751f\u6027\u3002\u6211\u4eec\u7684\u7814\u7a76\u7ed3\u679c\u8868\u660e\uff0c\u4e0e\u6ca1\u6709\u5b9e\u65bd\u5168\u56fd\u9632\u8303\u7981\u95ed\u7684\u745e\u5178\u76f8\u6bd4\uff0c\u5fb7\u56fd\u548c\u5728\u67d0\u79cd\u7a0b\u5ea6\u4e0a\uff0c\u7f8e\u56fd\u662f\u5168\u56fd\u9632\u8303\u7981\u95ed\u5bf9\u51cf\u5c11\u4e0e\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6709\u5173\u7684\u603b\u6b7b\u4ea1\u4eba\u6570\u548c\u6bcf\u5929\u6bcf\u767e\u4e07\u4eba\u6b7b\u4ea1\u4eba\u6570\u6709\u663e\u8457\u5f71\u54cd\u7684\u4e24\u4e2a\u56fd\u5bb6\u3002\u5728\u632a\u5a01\u548c\u4e39\u9ea6\uff0c\u4e0e\u745e\u5178\u76f8\u6bd4\uff0c\u6bcf\u767e\u4e07\u4eba\u7684\u603b\u6b7b\u4ea1\u7387\u548c\u6bcf\u65e5\u6b7b\u4ea1\u7387\u6ca1\u6709\u663e\u8457\u5f71\u54cd\u3002\u7136\u800c\u5728\u5176\u4ed6\u56fd\u5bb6\uff0c\u6bd4\u5229\u65f6\uff0c\u6cd5\u56fd\uff0c\u610f\u5927\u5229\uff0c\u897f\u73ed\u7259\u548c\u82f1\u56fd\uff0c\u5c01\u9501\u7684\u5f71\u54cd\u662f\u76f8\u53cd\u7684\u65b9\u5411\uff0c\u4e5f\u5c31\u662f\u8bf4\uff0c\u4ed6\u4eec\u7ecf\u5386\u4e86\u660e\u663e\u66f4\u9ad8\u7684\u4e0e\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6709\u5173\u7684\u603b\u6570\u548c\u6bcf\u5929\u6bcf\u767e\u4e07\u4eba\u7684\u6b7b\u4ea1\uff0c\u6bd4\u745e\u5178\u5c01\u9501\u540e\u3002\u6211\u4eec\u7684\u7814\u7a76\u7ed3\u679c\u8868\u660e\uff0c\u5168\u56fd\u8303\u56f4\u5185\u7684\u5c01\u9501\u5bf9\u4e0e\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6709\u5173\u7684\u603b\u6b7b\u4ea1\u4eba\u6570\u548c\u6bcf\u5929\u6bcf\u767e\u4e07\u4eba\u7684\u6b7b\u4ea1\u4eba\u6570\u7684\u5f71\u54cd\u56e0\u56fd\u800c\u5f02\u3002<\/span><\/p>\n


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    \u76d1\u6d4b\u653f\u5e9c\u5e72\u9884\u4ee5\u904f\u5236<\/strong><\/span><\/p>\n

    \u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u7684\u5f71\u54cd: <\/strong><\/span><\/p>\n

    \u4e00\u4e2a\u5b9a\u91cf\u7684\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\uff1a<\/span><\/strong><\/span><\/h2>\n

    Monitoring the Impacts of Government Interventions to Contain COVID-19: A Quantitative Approach<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.12177<\/span><\/p>\n

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

    Shuo Wang,Xian Yang,Yuan Huang,Ling Li,Zhongzhao Teng,Yike Guo<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Monitoring the evolving impacts of non-pharmaceutical intervention measures requires fine-grained estimation of transmission dynamics. We propose a framework to estimate instantaneous reproduction number R_t using Bayesian inference upon a renewal process, disentangling the R_t reduction into mitigation and suppression factors for quantifying their impacts at a finer granularity. Investigating the impacts of intervention measures of European countries, the United States and Wuhan with the framework, we reveal the effects of interventions in Europe and alert that 30 states in the United States are facing resurgence risks.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u76d1\u6d4b\u975e\u836f\u7269\u5e72\u9884\u63aa\u65bd\u4e0d\u65ad\u53d8\u5316\u7684\u5f71\u54cd\u9700\u8981\u5bf9\u4f20\u64ad\u52a8\u6001\u8fdb\u884c\u7ec6\u81f4\u7684\u4f30\u8ba1\u3002\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u4e2a\u6846\u67b6\u6765\u4f30\u8ba1\u77ac\u65f6\u518d\u751f\u6570 r t\uff0c\u4f7f\u7528\u8d1d\u53f6\u65af\u63a8\u65ad\u5728\u66f4\u65b0\u8fc7\u7a0b\u4e2d\uff0c\u5c06 r t \u7ea6\u7b80\u5206\u89e3\u4e3a\u7f13\u89e3\u548c\u6291\u5236\u56e0\u7d20\uff0c\u4ee5\u4fbf\u5728\u66f4\u7ec6\u7684\u7c92\u5ea6\u4e0a\u91cf\u5316\u5b83\u4eec\u7684\u5f71\u54cd\u3002\u901a\u8fc7\u5bf9\u6b27\u6d32\u56fd\u5bb6\u3001\u7f8e\u56fd\u548c\u6b66\u6c49\u5e72\u9884\u63aa\u65bd\u5f71\u54cd\u7684\u6846\u67b6\u8c03\u67e5\uff0c\u63ed\u793a\u4e86\u6b27\u6d32\u5e72\u9884\u63aa\u65bd\u7684\u6548\u679c\uff0c\u5e76\u8b66\u793a\u7f8e\u56fd30\u4e2a\u5dde\u6b63\u9762\u4e34\u590d\u82cf\u98ce\u9669\u3002<\/span><\/p>\n


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    \u96c6\u7fa4\u5185\u548c\u96c6\u7fa4\u95f4\u8026\u5408\u5e73\u8861<\/strong><\/span><\/p>\n

    \u5bf9\u975e\u7ebf\u6027\u7f51\u7edc\u7cfb\u7edf\u6027\u80fd\u7684\u5f71\u54cd<\/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

    Impact of intra and inter-cluster coupling balance on the performance of nonlinear networked systems<\/span><\/h2>\n

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

    http:\/\/a<\/span>rxiv.org\/abs\/2006.11357<\/span><\/p>\n

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

    Jiachen Ye,Peng Ji,David Waxman,Wei Lin,Yamir Moreno<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>The dynamical and structural aspects of cluster synchronization (CS) in complex systems have been intensively investigated in recent years. Here, we study CS of dynamical systems with intra and inter-cluster couplings. We propose new metrics that describe the performance of such systems and evaluate them as a function of the strength of the couplings within and between clusters. We obtain analytical results that indicate that spectral differences between the Laplacian matrices associated with the partition between intra and inter-couplings directly affect the proposed metrics of system performance. Our results show that the dynamics of the system might exhibit an optimal balance that optimizes its performance. Our work provides new insights into the way specific symmetry properties relate to collective behavior, and could lead to new forms to increase the controllability of complex systems and to optimize their stability.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u8fd1\u5e74<\/span>\u6765\uff0c\u4eba\u4eec\u5bf9\u590d\u6742\u7cfb\u7edf\u4e2d\u7c07\u540c\u6b65\u7684\u52a8\u529b\u5b66\u548c\u7ed3\u6784\u6027\u8d28\u8fdb\u884c\u4e86\u6df1\u5165\u7684\u7814\u7a76\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u7814\u7a76\u4e86\u5177\u6709\u7c07\u5185\u548c\u7c07\u95f4\u8026\u5408\u7684\u52a8\u6001\u7cfb\u7edf\u7684 CS\u3002\u6211\u4eec\u63d0\u51fa\u65b0\u7684\u5ea6\u91cf\u6807\u51c6\u6765\u63cf\u8ff0\u8fd9\u4e9b\u7cfb\u7edf\u7684\u6027\u80fd\uff0c\u5e76\u5c06\u5b83\u4eec\u4f5c\u4e3a\u96c6\u7fa4\u5185\u90e8\u548c\u96c6\u7fa4\u4e4b\u95f4\u8026\u5408\u5f3a\u5ea6\u7684\u51fd\u6570\u6765\u8bc4\u4f30\u3002\u6211\u4eec\u5f97\u5230\u7684\u5206\u6790\u7ed3\u679c\u8868\u660e\uff0c\u8c31\u5dee\u5f02\u4e4b\u95f4\u7684\u62c9\u666e\u62c9\u65af\u77e9\u9635\u76f8\u5173\u7684\u5206\u5272\u4e4b\u95f4\u7684\u5185\u90e8\u548c\u76f8\u4e92\u8026\u5408\u76f4\u63a5\u5f71\u54cd\u7cfb\u7edf\u6027\u80fd\u7684\u5ea6\u91cf\u3002\u6211\u4eec\u7684\u7ed3\u679c\u8868\u660e\uff0c\u7cfb\u7edf\u7684\u52a8\u6001\u53ef\u80fd\u8868\u73b0\u51fa\u4e00\u4e2a\u6700\u4f73\u7684\u5e73\u8861\uff0c\u4ee5\u4f18\u5316\u5176\u6027\u80fd\u3002\u6211\u4eec\u7684\u5de5\u4f5c\u5bf9\u7279\u5b9a\u7684\u5bf9\u79f0\u6027\u8d28\u4e0e\u96c6\u4f53\u884c\u4e3a\u7684\u5173\u7cfb\u63d0\u4f9b\u4e86\u65b0\u7684\u89c1\u89e3\uff0c\u5e76\u53ef\u80fd\u5bfc\u81f4\u65b0\u7684\u5f62\u5f0f\uff0c\u4ee5\u589e\u52a0\u590d\u6742\u7cfb\u7edf\u7684\u53ef\u63a7\u6027\u548c\u4f18\u5316\u5176\u7a33\u5b9a\u6027\u3002<\/span><\/p>\n


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    \u7f8e\u56fd\u3001\u4e9a\u6d32\u548c\u6b27\u6d32\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e<\/strong><\/span><\/p>\n

    \u6d41\u884c\u7684\u65f6\u95f4\u6570\u636e\u7cfb\u5217\u8868\u660e\uff0c<\/strong><\/span><\/p>\n

    SARS-CoV-2 Spike D614G \u53d8\u5f02<\/strong><\/span><\/p>\n

    \u5177\u6709\u9009\u62e9\u6027<\/strong><\/span><\/p>\n

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

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

    Temporal data series of COVID-19 epidemics in the USA, Asia and Europe suggests a selective sweep of SARS-CoV-2 Spike D614G variant<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.11609<\/span><\/p>\n

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

    Taima N. Furuyama,Fernando Antoneli,Isabel M. V. G. Carvalho,Marcelo R. S. Briones,Luiz M. R. Janini<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>The COVID-19 pandemic started in Wuhan, China, and caused the worldwide spread of the RNA virus SARS-CoV-2, the causative agent of COVID-19. Because of its mutational rate, wide geographical distribution, and host response variance this coronavirus is currently evolving into an array of strains with increasing genetic diversity. Most variants apparently have neutral effects for disease spread and symptoms severity. However, in the viral Spike protein, which is responsible for host cell attachment and invasion, an emergent variant, containing the amino acid substitution D to G in position 614 (D614G), was suggested to increase viral infection capability. To test whether this variant has epidemiological impact, the temporal distributions of the SARS-CoV-2 samples bearing D or G at position 614 were compared in the USA, Asia and Europe. The epidemiological curves were compared at early and late epidemic stages. At early stages, where containment measures were still not fully implemented, the viral variants are supposed to be unconstrained and its growth curves might approximate the free viral dynamics. Our analysis shows that the D614G prevalence and the growth rates of COVID-19 epidemic curves are correlated in the USA, Asia and Europe. Our results suggest a selective sweep that can be explained, at least in part, by a propagation advantage of this variant, in other words, that the molecular level effects of D614G have sufficient impact on population transmission dynamics as to be detected by differences in rate coefficients of epidemic growth curves.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u75c5\u6bd2\u5927\u6d41\u884c\u59cb\u4e8e\u4e2d\u56fd\u6b66\u6c49\uff0c\u5f15\u8d77\u4e86 RNA \u75c5\u6bd2 SARS-CoV-2\u7684\u5168\u7403\u4f20\u64ad\uff0cSARS-CoV-2\u662f\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u75c5\u6bd2\u7684\u75c5\u539f\u4f53\u3002\u7531\u4e8e\u5176\u7a81\u53d8\u7387\uff0c\u5e7f\u6cdb\u7684\u5730\u7406\u5206\u5e03\u548c\u5bbf\u4e3b\u53cd\u5e94\u65b9\u5dee\uff0c\u8fd9\u79cd\u51a0\u72b6\u75c5\u6bd2\u76ee\u524d\u6b63\u5728\u6f14\u53d8\u4e3a\u4e00\u7cfb\u5217\u7684\u83cc\u682a\u589e\u52a0\u9057\u4f20\u591a\u6837\u6027\u3002\u5927\u591a\u6570\u53d8\u5f02\u663e\u7136\u5bf9\u75be\u75c5\u4f20\u64ad\u548c\u75c7\u72b6\u4e25\u91cd\u6027\u6709\u4e2d\u6027\u5f71\u54cd\u3002\u7136\u800c\uff0c\u5728\u8d1f\u8d23\u5bbf\u4e3b\u7ec6\u80de\u9644\u7740\u548c\u4fb5\u88ad\u7684\u75c5\u6bd2\u7a57\u86cb\u767d\u4e2d\uff0c\u4e00\u4e2a\u7a81\u53d8\u4f53\uff0c\u5728614\u4f4d\u70b9(D614G)\u542b\u6709\u6c28\u57fa\u9178\u66ff\u4ee3\u7684 d \u5230 g\uff0c\u63d0\u9ad8\u4e86\u75c5\u6bd2\u7684\u611f\u67d3\u80fd\u529b\u3002\u4e3a\u4e86\u68c0\u9a8c\u8fd9\u4e00\u53d8\u5f02\u662f\u5426\u5177\u6709\u6d41\u884c\u75c5\u5b66\u5f71\u54cd\uff0c\u6211\u4eec\u6bd4\u8f83\u4e86\u7f8e\u56fd\u3001\u4e9a\u6d32\u548c\u6b27\u6d32614\u4f4d\u5e26 d \u6216 g \u7684 SARS-CoV-2\u6837\u54c1\u7684\u65f6\u95f4\u5206\u5e03\u3002\u6bd4\u8f83\u4e86\u6d41\u884c\u75c5\u5b66\u65e9\u671f\u548c\u665a\u671f\u7684\u6d41\u884c\u66f2\u7ebf\u3002\u5728\u65e9\u671f\u9636\u6bb5\uff0c\u904f\u5236\u63aa\u65bd\u4ecd\u7136\u6ca1\u6709\u5b8c\u5168\u5b9e\u65bd\uff0c\u75c5\u6bd2\u53d8\u5f02\u88ab\u8ba4\u4e3a\u662f\u4e0d\u53d7\u7ea6\u675f\u7684\uff0c\u5b83\u7684\u751f\u957f\u66f2\u7ebf\u53ef\u80fd\u63a5\u8fd1\u81ea\u7531\u75c5\u6bd2\u52a8\u529b\u5b66\u3002\u6211\u4eec\u7684\u5206\u6790\u8868\u660e\uff0c\u5728\u7f8e\u56fd\u3001\u4e9a\u6d32\u548c\u6b27\u6d32\uff0cD614G \u6d41\u884c\u7387\u548c\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e \/ \u827e\u6ecb\u75c5\u6d41\u884c\u66f2\u7ebf\u7684\u589e\u957f\u7387\u662f\u76f8\u5173\u7684\u3002\u6211\u4eec\u7684\u7814\u7a76\u7ed3\u679c\u8868\u660e\uff0c\u9009\u62e9\u6027\u626b\u63cf\u81f3\u5c11\u53ef\u4ee5\u90e8\u5206\u5730\u89e3\u91ca\u8fd9\u79cd\u53d8\u5f02\u7684\u4f20\u64ad\u4f18\u52bf\uff0c\u6362\u53e5\u8bdd\u8bf4\uff0cD614G \u7684\u5206\u5b50\u6c34\u5e73\u6548\u5e94\u5bf9\u79cd\u7fa4\u4f20\u64ad\u52a8\u529b\u5b66\u6709\u8db3\u591f\u7684\u5f71\u54cd\uff0c\u53ef\u4ee5\u901a\u8fc7\u6d41\u884c\u75c5\u589e\u957f\u66f2\u7ebf\u901f\u7387\u7cfb\u6570\u7684\u5dee\u5f02\u6765\u68c0\u6d4b\u3002<\/span><\/p>\n


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    \u58a8\u897f\u54e5\u7684\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e: <\/strong><\/span><\/p>\n

    \u6d41\u884c\u75c5\u7f51\u7edc<\/strong><\/span><\/p>\n

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

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

    COVID-19 in Mexico: A Network of Epidemics<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.11635<\/span><\/p>\n

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

    Guillermo de Anda-J\u00e1uregui<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Mexico, like the rest of the world, is currently facing the The COVID-19 pandemic. Given the size of its territory, the efforts to contain the disease have involved both national and regional measures. For this work, the curves of daily new cases of each municipality reported by the federal government were compared. We found that 114 municipalities form a large network of statistically dependent epidemic phenomena. Based on the network’s modular structure, these 114 municipalities can be split into four distinct communities of coordinated epidemic phenomena. These clusters are not limited by geographical proximity. These findings can be helpful for public health officials for the evaluation of past strategies and the development of new directed interventions.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u58a8\u897f\u54e5\u548c\u4e16\u754c\u5176\u4ed6\u5730\u533a\u4e00\u6837\uff0c\u76ee\u524d\u6b63\u9762\u4e34\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6d41\u611f\u5927\u6d41\u884c\u3002\u9274\u4e8e\u5176\u9886\u571f\u9762\u79ef\u4e4b\u5927\uff0c\u63a7\u5236\u8be5\u75be\u75c5\u7684\u52aa\u529b\u6d89\u53ca\u56fd\u5bb6\u548c\u533a\u57df\u63aa\u65bd\u3002\u5bf9\u4e8e\u8fd9\u9879\u5de5\u4f5c\uff0c\u6bcf\u4e2a\u5e02\u653f\u5e9c\u62a5\u544a\u7684\u6bcf\u65e5\u65b0\u75c5\u4f8b\u66f2\u7ebf\u8fdb\u884c\u4e86\u6bd4\u8f83\u3002\u6211\u4eec\u53d1\u73b0\uff0c114\u4e2a\u57ce\u5e02\u5f62\u6210\u4e86\u4e00\u4e2a\u5e9e\u5927\u7684\u7f51\u7edc\u7edf\u8ba1\u4f9d\u8d56\u7684\u6d41\u884c\u75c5\u73b0\u8c61\u3002\u6839\u636e\u7f51\u7edc\u7684\u6a21\u5757\u7ed3\u6784\uff0c\u8fd9114\u4e2a\u57ce\u5e02\u53ef\u4ee5\u5206\u4e3a\u56db\u4e2a\u4e0d\u540c\u7684\u534f\u8c03\u6d41\u884c\u73b0\u8c61\u793e\u533a\u3002\u8fd9\u4e9b\u96c6\u7fa4\u4e0d\u53d7\u5730\u7406\u90bb\u8fd1\u6027\u7684\u9650\u5236\u3002\u8fd9\u4e9b\u53d1\u73b0\u53ef\u4ee5\u5e2e\u52a9\u516c\u5171\u536b\u751f\u5b98\u5458\u8bc4\u4f30\u8fc7\u53bb\u7684\u6218\u7565\u548c\u53d1\u5c55\u65b0\u7684\u6307\u5bfc\u5e72\u9884\u63aa\u65bd\u3002<\/span><\/p>\n


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    \u6570\u636e\u9a71\u52a8\u7684\u5206\u6790: <\/strong><\/span><\/p>\n

    \u5c01\u9501\u662f\u5426\u904f\u5236\u4e86<\/strong><\/span><\/p>\n

    \u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u5728\u5370\u5ea6\u7684\u4f20\u64ad<\/strong><\/span><\/p>\n

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

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

    Did the lockdown curb the spread of COVID-19 infection rate in India: A data-driven analysis<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.12006<\/span><\/p>\n

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

    Dipankar Mondal,Siddhartha P. Chakrabarty<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>In order to analyze the effectiveness of three successive nationwide lockdown enforced in India, we present a data-driven analysis of four key parameters, reducing the transmission rate, restraining the growth rate, flattening the epidemic curve and improving the health care system. These were quantified by the consideration of four different metrics, namely, reproduction rate, growth rate, doubling time and death to recovery ratio. The incidence data of the COVID-19 (during the period of 2nd March 2020 to 31st May 2020) outbreak in India was analyzed for the best fit to the epidemic curve, making use of the exponential growth, the maximum likelihood estimation, sequential Bayesian method and estimation of time-dependent reproduction. The best fit (based on the data considered) was for the time-dependent approach. Accordingly, this approach was used to assess the impact on the effective reproduction rate. The period of pre-lockdown to the end of lockdown 3, saw a<\/span>45% reduction in the rate of effective reproduction rate. During the same period the growth rate reduced from <\/span>393% during the pre-lockdown to <\/span>33% after lockdown 3, accompanied by the average doubling time increasing form <\/span>4-<\/span>6 days to <\/span>12-<\/span>14 days. Finally, the death-to-recovery ratio dropped from <\/span>0.28 (pre-lockdown) to <\/span>0.08 after lockdown 3. In conclusion, all the four metrics considered to assess the effectiveness of the lockdown, exhibited significant favourable changes, from the pre-lockdown period to the end of lockdown 3. Analysis of the data in the post-lockdown period with these metrics will provide greater clarity with regards to the extent of the success of the lockdown.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u4e3a\u4e86\u5206\u6790\u5370\u5ea6\u8fde\u7eed\u4e09\u6b21\u5168\u56fd\u6027\u5c01\u9501\u7684\u6709\u6548\u6027\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u56db\u4e2a\u5173\u952e\u53c2\u6570\u7684\u6570\u636e\u9a71\u52a8\u5206\u6790\uff0c\u964d\u4f4e\u4f20\u64ad\u7387\uff0c\u6291\u5236\u589e\u957f\u7387\uff0c\u5e73\u7f13\u6d41\u884c\u66f2\u7ebf\u548c\u6539\u5584\u536b\u751f\u4fdd\u5065\u7cfb\u7edf\u3002\u8fd9\u4e9b\u88ab\u91cf\u5316\u7684\u8003\u8651\u56db\u4e2a\u4e0d\u540c\u7684\u6307\u6807\uff0c\u5373\u7e41\u6b96\u7387\uff0c\u751f\u957f\u7387\uff0c\u500d\u589e\u65f6\u95f4\u548c\u6b7b\u4ea1\u6062\u590d\u7387\u3002\u5bf9\u5370\u5ea6\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u7206\u53d1\u7684\u53d1\u75c5\u7387\u6570\u636e(2020\u5e743\u67082\u65e5\u81f32020\u5e745\u670831\u65e5\u671f\u95f4)\u8fdb\u884c\u4e86\u5206\u6790\uff0c\u4ee5\u5bfb\u627e\u4e0e\u75ab\u60c5\u66f2\u7ebf\u7684\u6700\u4f73\u62df\u5408\uff0c\u5229\u7528\u6307\u6570\u589e\u957f\u3001\u6700\u5927\u4f3c\u7136\u4f30\u8ba1\u3001\u5e8f\u8d2f\u8d1d\u53f6\u65af\u65b9\u6cd5\u548c\u65f6\u95f4\u4f9d\u8d56\u6027\u7e41\u6b96\u7684\u4f30\u8ba1\u3002\u6700\u4f73\u62df\u5408(\u57fa\u4e8e\u6240\u8003\u8651\u7684\u6570\u636e)\u662f\u65f6\u95f4\u76f8\u5173\u7684\u65b9\u6cd5\u3002\u56e0\u6b64\uff0c\u8fd9\u79cd\u65b9\u6cd5\u88ab\u7528\u6765\u8bc4\u4f30\u5bf9\u6709\u6548\u7e41\u6b96\u7387\u7684\u5f71\u54cd\u3002\u4ece\u7981\u95ed\u524d\u5230\u7981\u95ed3\u7ed3\u675f\u7684\u8fd9\u6bb5\u65f6\u95f4\u91cc<\/span>45% \u6709\u6548\u7e41\u6b96\u7387\u4e0b\u964d\u3002\u5728\u540c\u4e00\u65f6\u671f\uff0c\u751f\u957f\u7387\u4ece<\/span>393% \u5728\u7981\u95ed\u524d<\/span>33% \u5728\u7981\u95ed3\u53f7\u4e4b\u540e\uff0c\u4f34\u968f\u7740\u5e73\u5747\u500d\u589e\u65f6\u95f4\u7684\u5f62\u5f0f <\/span>4-<\/span>6 \u5929\u5230<\/span>12-<\/span>14 \u5929\u6700\u540e\uff0c\u6b7b\u4ea1\u4e0e\u5eb7\u590d\u7684\u6bd4\u7387\u4ece<\/span>0.28 \u5230<\/span>0.08 \u3002\u57283\u53f7\u7981\u95ed\u5ba4\u4e4b\u540e\u3002\u603b\u800c\u8a00\u4e4b\uff0c\u6240\u6709\u8bc4\u4f30\u5c01\u9501\u6709\u6548\u6027\u7684\u56db\u4e2a\u6307\u6807\uff0c\u4ece\u5c01\u9501\u524d\u671f\u5230\u5c01\u95013\u7ed3\u675f\uff0c\u90fd\u8868\u73b0\u51fa\u4e86\u663e\u8457\u7684\u6709\u5229\u53d8\u5316\u3002\u4f7f\u7528\u8fd9\u4e9b\u6307\u6807\u5bf9\u5c01\u9501\u540e\u671f\u95f4\u7684\u6570\u636e\u8fdb\u884c\u5206\u6790\uff0c\u53ef\u4ee5\u66f4\u6e05\u695a\u5730\u4e86\u89e3\u5c01\u9501\u7684\u6210\u529f\u7a0b\u5ea6\u3002<\/span><\/p>\n


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    \u68ee\u6797\u780d\u4f10\u4e0e\u4e16\u754c\u4eba\u53e3\u53ef\u6301\u7eed\u6027: <\/strong><\/span><\/p>\n

    \u5b9a\u91cf\u5206\u6790<\/strong><\/span><\/span><\/p>\n

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

    Deforestation and world population sustainability: a quantitative analysis<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.12202<\/span><\/p>\n

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

    Mauro <\/span>Bologna,Gerardo Aquino<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>In this paper we afford a quantitative analysis of the sustainability of current world population growth in relation to the parallel deforestation process adopting a statistical point of view. We consider a simplified model based on a stochastic growth process driven by a continuous time random walk, which depicts the technological evolution of human kind, in conjunction with a deterministic generalised logistic model for humans-forest interaction and we evaluate the probability of avoiding the self-destruction of our civilisation. Based on the current resource consumption rates and best estimate of technological rate growth our study shows that we have very low probability, less than 10% in most optimistic estimate, to survive without facing a catastrophic collapse.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u672c\u6587\u91c7\u7528\u7edf\u8ba1\u5b66\u7684\u89c2\u70b9\uff0c\u5bf9\u5f53\u524d\u4e16\u754c\u4eba\u53e3\u589e\u957f\u4e0e\u5e73\u884c\u6bc1\u6797\u8fc7\u7a0b\u7684\u53ef\u6301\u7eed\u6027\u8fdb\u884c\u4e86\u5b9a\u91cf\u5206\u6790\u3002\u6211\u4eec\u8003\u8651\u4e86\u4e00\u4e2a\u7b80\u5316\u7684\u6a21\u578b\uff0c\u8be5\u6a21\u578b\u57fa\u4e8e\u8fde\u7eed\u65f6\u95f4\u968f\u673a\u6e38\u8d70\u9a71\u52a8\u7684\u968f\u673a\u589e\u957f\u8fc7\u7a0b\uff0c\u63cf\u8ff0\u4e86\u4eba\u7c7b\u7684\u6280\u672f\u8fdb\u5316\uff0c\u5e76\u7ed3\u5408\u4e00\u4e2a\u786e\u5b9a\u6027\u7684\u5e7f\u4e49\u903b\u8f91\u65af\u8c1b\u6a21\u578b\u6765\u63cf\u8ff0\u4eba\u7c7b\u4e0e\u68ee\u6797\u7684\u76f8\u4e92\u4f5c\u7528\uff0c\u6211\u4eec\u8bc4\u4f30\u4e86\u907f\u514d\u4eba\u7c7b\u6587\u660e\u81ea\u6211\u6bc1\u706d\u7684\u53ef\u80fd\u6027\u3002\u57fa\u4e8e\u5f53\u524d\u7684\u8d44\u6e90\u6d88\u8017\u7387\u548c\u5bf9\u6280\u672f\u589e\u957f\u7387\u7684\u6700\u4f73\u4f30\u8ba1\uff0c\u6211\u4eec\u7684\u7814\u7a76\u8868\u660e\uff0c\u6211\u4eec\u5728\u4e0d\u9762\u4e34\u707e\u96be\u6027\u5d29\u6e83\u7684\u60c5\u51b5\u4e0b\u751f\u5b58\u7684\u6982\u7387\u975e\u5e38\u4f4e\uff0c\u6700\u4e50\u89c2\u7684\u4f30\u8ba1\u4e0d\u523010%\u3002<\/span><\/p>\n


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    \u65f6\u95f4\u4f9d\u8d56\u548c\u65f6\u95f4\u72ec\u7acb\u7684 SIR \u6a21\u578b<\/strong><\/span><\/p>\n

    \u5e94\u7528\u4e8e\u5728\u963f\u6839\u5ef7\u3001\u5df4\u897f\u3001\u54e5\u4f26\u6bd4\u4e9a\u3001<\/strong><\/span><\/p>\n

    \u58a8\u897f\u54e5\u548c\u5357\u975e\u7206\u53d1\u7684\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e<\/strong><\/span><\/p>\n

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

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

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

    Time-dependent and time-independent SIR models applied to the COVID-19 outbreak in Argentina, Brazil, Colombia, Mexico and South Africa<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.12479<\/span><\/p>\n

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

    Nana Geraldine Cabo Bizet,Dami\u00e1n Kaloni Mayorga Pe\u00f1a<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>We consider the SIR epidemiological model applied to the evolution of COVID-19 with two approaches. In the first place we fit a global SIR model, with time delay, and constant parameters throughout the outbreak, including the contagion rate. The contention measures are reflected on an effective reduced susceptible population<\/span>Neff. In the second approach we consider a time-dependent contagion rate that reflects the contention measures either through a step by step fitting process or by following an exponential decay. In this last model the population is considered the one of the country <\/span>N. In the linear region of the differential equations, when the total population <\/span>N is large the predictions are independent of <\/span>N. We apply these methodologies to study the spread of the pandemic in Argentina, Brazil, Colombia, Mexico, and South Africa for which the infection peaks are yet to be reached. In all of these cases we provide estimates for the reproduction and recovery rates. The scenario for a time varying contagion rate is optimistic, considering that reasonable measures are taken such that the reproduction factor <\/span>R0 decreases exponentially. The measured values for the recovery rate <\/span>\u03b3 are reported finding a universality of this parameter over various countries. We discuss the correspondence between the global SIR with effective population <\/span>Neff and the evolution of the time local SIR.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u8003\u8651\u7528\u4e24\u79cd\u65b9\u6cd5\u5c06 SIR \u6d41\u884c\u75c5\u5b66\u6a21\u578b\u5e94\u7528\u4e8e covid-19\u7684\u8fdb\u5316\u3002\u9996\u5148\uff0c\u6211\u4eec\u62df\u5408\u4e00\u4e2a\u5168\u5c40 SIR \u6a21\u578b\uff0c\u5177\u6709\u65f6\u95f4\u5ef6\u8fdf\uff0c\u5e76\u4e14\u5728\u6574\u4e2a\u7206\u53d1\u8fc7\u7a0b\u4e2d\u53c2\u6570\u4e0d\u53d8\uff0c\u5305\u62ec\u4f20\u67d3\u7387\u3002\u4e89\u593a\u63aa\u65bd\u53cd\u6620\u5728\u6709\u6548\u51cf\u5c11\u6613\u611f\u79cd\u7fa4\u4e0a<\/span>Neff. \u5728\u7b2c\u4e8c\u79cd\u65b9\u6cd5\u4e2d\uff0c\u6211\u4eec\u8003\u8651\u4e00\u4e2a\u4f9d\u8d56\u4e8e\u65f6\u95f4\u7684\u4f20\u67d3\u7387\uff0c\u5b83\u901a\u8fc7\u4e00\u4e2a\u9010\u6b65\u62df\u5408\u7684\u8fc7\u7a0b\u6216\u8005\u9075\u5faa\u4e00\u4e2a\u6307\u6570\u8870\u51cf\u6765\u53cd\u6620\u4e89\u8bba\u7684\u5ea6\u91cf\u3002\u5728\u6700\u540e\u4e00\u4e2a\u6a21\u578b\u4e2d\uff0c\u4eba\u53e3\u88ab\u8ba4\u4e3a\u662f\u56fd\u5bb6\u7684\u4e00\u90e8\u5206<\/span>N.\u6211\u4eec\u5e94\u7528\u8fd9\u4e9b\u65b9\u6cd5\u6765\u7814\u7a76\u8fd9\u79cd\u6d41\u884c\u75c5\u5728\u963f\u6839\u5ef7\u3001\u5df4\u897f\u3001\u54e5\u4f26\u6bd4\u4e9a\u3001\u58a8\u897f\u54e5\u548c\u5357\u975e\u7684\u4f20\u64ad\u60c5\u51b5\uff0c\u8fd9\u4e9b\u56fd\u5bb6\u7684\u611f\u67d3\u9ad8\u5cf0\u5c1a\u672a\u5230\u6765\u3002\u5728\u6240\u6709\u8fd9\u4e9b\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u63d0\u4f9b\u4e86\u590d\u5236\u548c\u6062\u590d\u7387\u7684\u4f30\u8ba1\u3002\u8003\u8651\u5230\u91c7\u53d6\u4e86\u5408\u7406\u7684\u63aa\u65bd\uff0c\u4f7f\u518d\u751f\u4ea7\u56e0\u7d20\u5f97\u4ee5\u4fdd\u7559\uff0c\u65f6\u53d8\u4f20\u67d3\u7387\u7684\u5047\u8bbe\u662f\u4e50\u89c2\u7684<\/span>R0 \u56de\u6536\u7387\u7684\u6d4b\u91cf\u503c<\/span>\u03b3 \uff0c\u672c\u6587\u62a5\u9053\u4e86\u8fd9\u4e2a\u53c2\u6570\u5728\u5404\u56fd\u7684\u666e\u9002\u6027\uff0c\u5e76\u8ba8\u8bba\u4e86\u5168\u5c40 SIR \u4e0e\u6709\u6548\u603b\u4f53\u4e4b\u95f4\u7684\u5bf9\u5e94\u5173\u7cfb<\/span>Neff \u4ee5\u53ca\u65f6\u95f4\u6f14\u53d8\u7684\u672c\u5730 SIR\u3002<\/span><\/p>\n


    <\/p>\n

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

    \n
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    <\/p>\n

    \u901a\u98ce\u51cf\u5c11\u75c5\u6bd2\u4f20\u64ad\u7684\u5fc5\u8981\u6027\u7b80\u5355\u91cf\u5316<\/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

    Necessity of ventilation for mitigating virus transmission quantified simply<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.11651<\/span><\/p>\n

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

    Eric G. Blackman,Gourab Ghoshal<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>To mitigate the SARS-CoV-2 pandemic, officials have employed social distancing and stay-at-home measures. Less attention has focused on ventilation. Effective distancing practices for open spaces may be ineffective for poorly ventilated spaces, both of which are commonly filled with turbulent air. While turbulence initially reduces the risk of infection near a virion-source, it eventually increases the exposure risk for all occupants in a space without ventilation. Here we estimate the time-scale for virions injected into a room of turbulent air to infect an occupant, distinguishing cases of low vs. high initial virion mass loads and virion-destroying vs. virion-reflecting walls. An open window typifies ventilation and we show that its minimum area needed to ensure safety depends only on the ratio of total viral load to threshold load for infection. Our order-of-magnitude estimates complement more detailed approaches.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u4e3a\u4e86\u7f13\u89e3 SARS-CoV-2\u5927\u6d41\u884c\uff0c\u5b98\u5458\u4eec\u91c7\u7528\u4e86\u793e\u4f1a\u758f\u8fdc\u548c\u5c45\u5bb6\u63aa\u65bd\u3002\u4eba\u4eec\u5f88\u5c11\u5173\u6ce8\u901a\u98ce\u95ee\u9898\u3002\u5f00\u653e\u7a7a\u95f4\u7684\u6709\u6548\u8ddd\u79bb\u505a\u6cd5\u53ef\u80fd\u5bf9\u901a\u98ce\u4e0d\u826f\u7684\u7a7a\u95f4\u65e0\u6548\uff0c\u8fd9\u4e24\u4e2a\u7a7a\u95f4\u901a\u5e38\u90fd\u5145\u6ee1\u4e86\u6e4d\u6d41\u7a7a\u6c14\u3002\u867d\u7136\u6e4d\u6d41\u6700\u521d\u4f1a\u964d\u4f4e\u75c5\u6bd2\u6e90\u9644\u8fd1\u611f\u67d3\u7684\u98ce\u9669\uff0c\u4f46\u6700\u7ec8\u4f1a\u589e\u52a0\u6ca1\u6709\u901a\u98ce\u8bbe\u5907\u7684\u7a7a\u95f4\u4e2d\u6240\u6709\u5c45\u4f4f\u8005\u7684\u66b4\u9732\u98ce\u9669\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u4f30\u8ba1\u65f6\u95f4\u5c3a\u5ea6\u7684\u75c5\u6bd2\u6ce8\u5165\u4e00\u4e2a\u623f\u95f4\u7684\u6e4d\u6d41\u7a7a\u6c14\u611f\u67d3\u4e00\u4e2a\u5c45\u4f4f\u8005\uff0c\u533a\u5206\u60c5\u51b5\u4f4e\u4e0e\u9ad8\u521d\u59cb\u75c5\u6bd2\u8d28\u91cf\u8d1f\u8377\u548c\u75c5\u6bd2\u7834\u574f\u4e0e\u75c5\u6bd2\u53cd\u5c04\u5899\u3002\u5f00\u7a97\u901a\u6c14\u662f\u5178\u578b\u7684\u901a\u6c14\u65b9\u5f0f\uff0c\u6211\u4eec\u53d1\u73b0\u786e\u4fdd\u901a\u6c14\u5b89\u5168\u6240\u9700\u7684\u6700\u5c0f\u9762\u79ef\u4ec5\u53d6\u51b3\u4e8e\u75c5\u6bd2\u8f7d\u91cf\u4e0e\u611f\u67d3\u9608\u503c\u8f7d\u91cf\u7684\u6bd4\u503c\u3002\u6211\u4eec\u7684\u6570\u91cf\u7ea7\u4f30\u8ba1\u8865\u5145\u4e86\u66f4\u8be6\u7ec6\u7684\u65b9\u6cd5\u3002<\/span><\/p>\n


    <\/p>\n

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    <\/p>\n

    \n
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    <\/p>\n

    L\u00e9vy \u6f2b\u6b65\u8d85\u6269\u6563\u5f00\u59cb\u7684\u666e\u904d\u6027\u8d77\u6e90<\/strong><\/span><\/p>\n

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

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

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

    The origin of universality in the onset of superdiffusion in L\u00e9vy walks<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.11932<\/span><\/p>\n

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

    Asaf Miron<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Superdiffusion arises when complicated, correlated and noisy motion at the microscopic scale conspires to yield peculiar dynamics at the macroscopic scale. It ubiquitously appears in a variety of scenarios, spanning a broad range of scientific disciplines. The approach of superdiffusive systems towards their long-time, asymptotic behavior was recently studied using the L\u00e9vy walk of order<\/span>1<\u03b2<2, revealing a universal transition at the critical <\/span>\u03b2c=3\/2. Here, we investigate the origin of this transition and identify two crucial ingredients: a finite velocity which couples the walker’s position to time and a corresponding transition in the fluctuations of the number of walks <\/span>n completed by the walker at time <\/span>t.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5f53\u5fae\u89c2\u5c3a\u5ea6\u7684\u590d\u6742\u3001\u76f8\u5173\u548c\u5608\u6742\u7684\u8fd0\u52a8\u5728\u5b8f\u89c2\u4ea7\u751f\u5947\u7279\u7684\u52a8\u529b\u5b66\u65f6\uff0c\u5c31\u4f1a\u4ea7\u751f\u8d85\u6269\u6563\u3002\u5b83\u65e0\u5904\u4e0d\u5728\u5730\u51fa\u73b0\u5728\u5404\u79cd\u5404\u6837\u7684\u573a\u666f\u4e2d\uff0c\u8de8\u8d8a\u4e86\u5e7f\u6cdb\u7684\u79d1\u5b66\u5206\u652f\u3002\u7814\u7a76\u4e86\u8d85\u6269\u6563\u7cfb\u7edf\u957f\u65f6\u95f4\u6e10\u8fd1\u884c\u4e3a\u7684\u903c\u8fd1\u95ee\u9898<\/span>1<\u03b2<2,\u672c\u6587\u901a\u8fc7\u5bf9\u4e2d\u56fd\u4f20\u7edf\u6587\u5316\u7684\u5206\u6790\uff0c\u63ed\u793a\u4e86\u4e2d\u56fd\u4f20\u7edf\u6587\u5316\u5728\u5173\u952e\u65f6\u523b\u7684\u666e\u904d\u8f6c\u53d8\uff0c\u5e76\u5bf9\u4e2d\u56fd\u4f20\u7edf\u6587\u5316\u7684\u53d1\u5c55\u63d0\u51fa\u4e86\u5efa\u8bae<\/span>\u03b2c=3\/2.\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u7814\u7a76\u4e86\u8fd9\u79cd\u8f6c\u53d8\u7684\u8d77\u6e90\uff0c\u5e76\u786e\u5b9a\u4e86\u4e24\u4e2a\u5173\u952e\u56e0\u7d20: \u6709\u9650\u901f\u5ea6\u4e0e\u6b65\u884c\u8005\u7684\u4f4d\u7f6e\u5173\u8054\u5230\u65f6\u95f4\uff0c\u4ee5\u53ca\u76f8\u5e94\u7684\u6b65\u884c\u6b21\u6570\u6ce2\u52a8\u7684\u8f6c\u53d8<\/span>n \u7531\u884c\u9a76\u5b8c\u6210\u7684<\/span>t\u3002<\/span><\/p>\n


    <\/p>\n

    \n
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    <\/p>\n

    \n
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    \u4e09\u7ef4\u6e4d\u6d41\u4e2d\u901f\u5ea6\u73af\u6d41\u7684\u6982\u7387\u5206\u5e03<\/strong><\/span><\/p>\n

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

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

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

    Probability Distribution of Velocity Circulation in Three Dimensional Turbulence<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.12008<\/span><\/p>\n

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

    Alexander Migdal<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>We elaborate the statistical field theory of Turbulence suggested in the previous paper cite{M20a}. We clarify and simplify the basic Energy pumping equation of that theory and study mathematical properties of singular field configuration (instanton) which determine the tails of PDF for the velocity circulation around large loop<\/span>C in isotropic turbulence at highest Reynolds numbers. Explicit analytic solution is found for the Clebsch instanton in an Euler equation for a planar loop circulation problem. This solution for vorticity is has a term proportional to a delta function in normal direction to the minimal surface bounded by the loop. The smoothing of <\/span>\u03b4 functions in the vorticity in the full Navier-Stokes equations is investigated and exponential profile of smoothed singularity is found.
    The PDF for circulation is now an infinite sum of decreasing exponential terms $EXP{- n |w|}sqrt{frac{n}{|w|}}$, with <\/span>w=\u0393\u03930[C], and <\/span>\u03930[C]\u223cAC\u2212\u2212\u2212\u221a with minimal area <\/span>AC. The leading term fits with adjusted <\/span>R2=0.9999 the PDF tail found in DNS over more than six orders of magnitude. The area dependence of the ratio of the circulation moments <\/span>M8\/M6 fits with adjusted <\/span>R2=0.9996 the DNS in inertial range of square loop sizes from <\/span>100 to <\/span>500 Kolmogorov scales.
    Thus, our theory explains DNS with high degree or confidence.
    For a flat loop we derive two-dimensional integral equation for the dependence of the scale <\/span>\u03930[C] of circulation as a function of the shape of the loop (aspect ratio for rectangular loop<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5bf9\u524d\u4eba\u63d0\u51fa\u7684\u6e4d\u6d41\u7edf\u8ba1\u573a\u7406\u8bba\u8fdb\u884c\u4e86\u8be6\u7ec6\u7684\u9610\u8ff0\u3002\u9610\u660e\u548c\u7b80\u5316\u4e86\u8be5\u7406\u8bba\u7684\u57fa\u672c\u80fd\u91cf\u62bd\u8fd0\u65b9\u7a0b\uff0c\u5e76\u7814\u7a76\u4e86\u51b3\u5b9a\u5927\u56de\u8def\u5468\u56f4\u901f\u5ea6\u73af\u6d41\u6982\u7387\u5bc6\u5ea6\u51fd\u6570(PDF)\u5c3e\u90e8\u7684\u5947\u5f02\u573a\u7ed3\u6784(\u77ac\u5b50)\u7684\u6570\u5b66\u6027\u8d28<\/span>C \u5728\u5404\u5411\u540c\u6027\u6e4d\u6d41\u4e2d\u8fbe\u5230\u6700\u9ad8\u96f7\u8bfa\u6570\u3002\u7ed9\u51fa\u4e86\u5e73\u9762\u73af\u8def\u73af\u6d41\u95ee\u9898\u6b27\u62c9\u65b9\u7a0b\u4e2d\u514b\u83b1\u5e03\u65bd\u77ac\u5b50\u7684\u663e\u5f0f\u89e3\u6790\u89e3\u3002\u8fd9\u4e2a\u6da1\u91cf\u7684\u89e3\u51b3\u65b9\u6848\u662f\u6709\u4e00\u4e2a\u9879\u6210\u6b63\u6bd4\u7684\u4e09\u89d2\u51fd\u6570\u5728\u6cd5\u5411\u7684\u6700\u5c0f\u66f2\u9762\u6709\u754c\u7684\u56de\u8def\u3002\u5e73\u6ed1<\/span>\u03b4 \u7814\u7a76\u4e86\u5168\u7eb3\u7ef4\uff0d\u65af\u6258\u514b\u65af\u65b9\u7a0b\u6da1\u91cf\u4e2d\u7684\u51fd\u6570\uff0c\u53d1\u73b0\u4e86\u5149\u6ed1\u5947\u70b9\u7684\u6307\u6570\u5206\u5e03\u3002<\/span><\/p>\n

    \u53d1\u884c\u91cf\u7684 PDF \u73b0\u5728\u662f\u6307\u6570\u9012\u51cf\u9879\u7684\u65e0\u7a77\u548c $EXP {-n | w | | } sqrt { frac { n }{ | w | } $\u548c<\/span>w=\u0393\u03930[C],\u53ca<\/span>\u03930[C]\u223cAC\u2212\u2212\u2212\u221a \u6700\u5c0f\u9762\u79ef<\/span>AC.\u4e3b\u8981\u7684\u672f\u8bed\u9002\u5408\u8c03\u6574<\/span>R2=0.9999 \u3002\u5728\u8d85\u8fc76\u4e2a\u6570\u91cf\u7ea7\u7684\u65f6\u95f4\u5185\u5728 DNS \u4e2d\u5b9a\u4f4d PDF\u3002\u73af\u6d41\u77e9\u6bd4\u503c\u7684\u9762\u79ef\u76f8\u5173\u6027<\/span>M8\/M6 \u4e0e\u8c03\u6574\u8fc7\u7684\u543b\u5408<\/span>R2=0.9996 \u60ef\u6027\u8303\u56f4\u5185\u7684\u6b63\u65b9\u5f62\u56de\u8def\u5c3a\u5bf8\u7684 dn<\/span>100 \u5230<\/span>500 \u67ef\u5c14\u83ab\u54e5\u6d1b\u592b\u5c3a\u5ea6\u3002\u56e0\u6b64\uff0c\u6211\u4eec\u7684\u7406\u8bba\u5bf9 DNS \u7684\u89e3\u91ca\u5177\u6709\u9ad8\u5ea6\u7684\u53ef\u4fe1\u5ea6\u3002<\/span><\/p>\n

    \u5bf9\u4e8e\u4e00\u4e2a\u5e73\u9762\u73af\uff0c\u6211\u4eec\u63a8\u5bfc\u51fa\u5c3a\u5ea6\u4f9d\u8d56\u6027\u7684\u4e8c\u7ef4\u79ef\u5206\u65b9\u7a0b<\/span>\u03930[C] \u77e9\u5f62\u73af\u7684\u957f\u5bbd\u6bd4\u3002<\/span><\/p>\n


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

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    \u9a71\u52a8\u8c10\u632f\u5b50\u7684\u52a8\u529b\u5b66\u8026\u5408\u4e86<\/strong><\/span><\/p>\n

    \u968f\u673a\u573a\u4e2d\u6210\u5bf9\u76f8\u4e92\u4f5c\u7528\u7684\u4f0a\u8f9b\u81ea\u65cb<\/strong><\/span><\/strong><\/p>\n

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


    <\/h2>\n

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

    The dynamics of a driven harmonic oscillator coupled to pairwise interacting Ising spins in random fields<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.12429<\/span><\/p>\n

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

    Paul Zech,Andreas Otto,G\u00fcnter Radons<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>In general we are interested in dynamical systems coupled to complex hysteresis. Therefore as a first step we did some investigation on the dynamics of a periodically driven damped harmonic oscillator coupled to independent Ising spins with a local quenched disorder at zero temperature in the past. Although such a system does not produce hysteresis, we showed how to characterize the dynamics of such a piecewise-smooth system, specially in case of a large number of spins [P. Zech, A. Otto, and G. Radons, Phys. Rev. E101,042217 (2020)]. In this paper we want to extend our model to spins dimers, thus spins with pairwise interaction. We will show in which cases two interacting spins can show elementary hysteresis and we will give a connection to the ac{PM}, when superpose a infinite number of spin-pairs in the thermodynamic limit. We will see, that this will lead us to a dynamical system with an additional hysteretic force in form of a play operator. By using methods from general chaos theory, piecewise-smooth system theory and statistics we will investigate the chaotic behavior of the dynamical system for a few spins and also in case of larger number of spins by calculating bifurcation diagrams, fractal dimensions and self-averaging properties. In doing so we show, how the dynamical properties of the piecewise-smooth system for a large number of spins differs from the system in its thermodynamic limit.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u4e00\u822c\u6765\u8bf4\uff0c\u6211\u4eec\u611f\u5174\u8da3\u7684\u52a8\u529b\u7cfb\u7edf\u8026\u5408\u7684\u590d\u6742\u6ede\u540e\u3002\u56e0\u6b64\uff0c\u4f5c\u4e3a\u7b2c\u4e00\u6b65\uff0c\u6211\u4eec\u505a\u4e86\u4e00\u4e9b\u52a8\u529b\u5b66\u7684\u7814\u7a76\uff0c\u5468\u671f\u6027\u9a71\u52a8\u7684\u963b\u5c3c\u8c10\u632f\u5b50\u4e0e\u72ec\u7acb\u7684\u4f0a\u8f9b\u81ea\u65cb\u8026\u5408\uff0c\u5728\u8fc7\u53bb\u7684\u96f6\u6e29\u4e0b\u5c40\u90e8\u6dec\u706b\u65e0\u5e8f\u3002\u867d\u7136\u8fd9\u6837\u4e00\u4e2a\u7cfb\u7edf\u4e0d\u4ea7\u751f\u6ede\u540e\uff0c\u6211\u4eec\u5c55\u793a\u4e86\u5982\u4f55\u523b\u753b\u8fd9\u6837\u4e00\u4e2a\u5206\u6bb5\u5149\u6ed1\u7cfb\u7edf\u7684\u52a8\u529b\u5b66\u6027\u8d28\uff0c\u7279\u522b\u662f\u5728\u5927\u91cf\u81ea\u65cb\u7684\u60c5\u51b5\u4e0b[ p. Zech\uff0ca. Otto\uff0c\u548c g. Radons\uff0cPhys\u3002101,042217(2020)]. \u5728\u8fd9\u7bc7\u8bba\u6587\u4e2d\uff0c\u6211\u4eec\u5e0c\u671b\u5c06\u6211\u4eec\u7684\u6a21\u578b\u6269\u5c55\u5230\u81ea\u65cb\u4e8c\u805a\u4f53\uff0c\u4ece\u800c\u4f7f\u81ea\u65cb\u5177\u6709\u6210\u5bf9\u76f8\u4e92\u4f5c\u7528\u3002\u6211\u4eec\u5c06\u5c55\u793a\u5728\u54ea\u4e9b\u60c5\u51b5\u4e0b\u76f8\u4e92\u4f5c\u7528\u7684\u81ea\u65cb\u53ef\u4ee5\u663e\u793a\u51fa\u57fa\u672c\u7684\u6ede\u540e\u73b0\u8c61\uff0c\u5e76\u4e14\u6211\u4eec\u5c06\u7ed9\u51fa\u4e0e ac { PM }\u7684\u8054\u7cfb\uff0c\u5f53\u65e0\u9650\u591a\u7684\u81ea\u65cb\u5bf9\u53e0\u52a0\u5728\u70ed\u529b\u5b66\u6781\u9650\u4e2d\u65f6\u3002\u6211\u4eec\u5c06\u770b\u5230\uff0c\u8fd9\u5c06\u5f15\u5bfc\u6211\u4eec\u5230\u8fbe\u4e00\u4e2a\u5e26\u6709\u989d\u5916\u7684\u6ede\u540e\u529b\u7684\u52a8\u529b\u7cfb\u7edf\u3002\u5229\u7528\u4e00\u822c\u6df7\u6c8c\u7406\u8bba\u3001\u5206\u6bb5\u5149\u6ed1\u7cfb\u7edf\u7406\u8bba\u548c\u7edf\u8ba1\u5b66\u7684\u65b9\u6cd5\uff0c\u901a\u8fc7\u8ba1\u7b97\u5206\u53c9\u56fe\u3001\u5206\u5f62\u7ef4\u6570\u548c\u81ea\u5e73\u5747\u7279\u6027\uff0c\u7814\u7a76\u4e86\u52a8\u529b\u7cfb\u7edf\u5728\u5c11\u6570\u81ea\u65cb\u548c\u5927\u81ea\u65cb\u60c5\u51b5\u4e0b\u7684\u6df7\u6c8c\u884c\u4e3a\u3002\u5728\u8fd9\u4e2a\u8fc7\u7a0b\u4e2d\uff0c\u6211\u4eec\u5c55\u793a\u4e86\u5927\u91cf\u81ea\u65cb\u7684\u5206\u6bb5\u5149\u6ed1\u7cfb\u7edf\u7684\u52a8\u529b\u5b66\u6027\u8d28\u4e0e\u5176\u81ea\u65cb\u70ed\u529b\u5b66\u6781\u9650\u7684\u4e0d\u540c\u4e4b\u5904\u3002<\/span><\/p>\n


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    \u57fa\u4e8e\u5149\u5b66\u5076\u6781\u529b\u9631\u91ca\u653e\u548c\u6355\u83b7<\/strong><\/span><\/p>\n

    \u7684\u7a7a\u6c14\u5fae\u7c92\u5feb\u901f\u8d28\u91cf\u6d4b\u5b9a\u6280\u672f<\/strong><\/span><\/p>\n

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

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

    Technique for rapid mass determination of airborne micro-particles based on release and recapture from an optical dipole force trap<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.12429<\/span><\/p>\n

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

    Gehrig Carlse,Kevin B. Borsos,Hermina C. Beica,Thomas Vacheresse,Alex Pouliot,Jorge Perez-Garcia,Andrejs Vorozcovs,Boris Barron,Shira Jackson,Louis Marmet,A. Kumarakrishnan<\/span><\/p>\n


    <\/p>\n

    Abstract<\/span><\/strong>\uff1a<\/span><\/strong>We describe a new method for the rapid determination of the mass of particles confined in a free-space optical dipole-force trap. The technique relies on direct imaging of drop-and-restore experiments without the need for a vacuum environment. In these experiments, the trapping light is rapidly shuttered with an acousto-optic modulator causing the particle to be released from and subsequently recaptured by the trapping force. The trajectories of both the falls and restorations, imaged using a high-speed CMOS sensor, are combined to determine the particle mass. We corroborate these measurements using an analysis of position autocorrelation functions of the trapped particles. We report a statistical uncertainty of less than 2% for masses on the order of<\/span>5\u00d710\u221214 kg using a data acquisition time of approximately 90 seconds.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u672c\u6587\u4ecb\u7ecd\u4e86\u4e00\u79cd\u5feb\u901f\u6d4b\u5b9a\u81ea\u7531\u7a7a\u95f4\u5149\u5b66\u5076\u6781\u529b\u9631\u4e2d\u7c92\u5b50\u8d28\u91cf\u7684\u65b0\u65b9\u6cd5\u3002\u8be5\u6280\u672f\u4f9d\u8d56\u4e8e\u76f4\u63a5\u6210\u50cf\u7684\u8dcc\u843d\u548c\u6062\u590d\u5b9e\u9a8c\uff0c\u800c\u4e0d\u9700\u8981\u4e00\u4e2a\u771f\u7a7a\u73af\u5883\u3002\u5728\u8fd9\u4e9b\u5b9e\u9a8c\u4e2d\uff0c\u6355\u83b7\u5149\u88ab\u58f0\u5149\u8c03\u5236\u5668\u5feb\u901f\u5173\u95ed\uff0c\u7c92\u5b50\u88ab\u91ca\u653e\u51fa\u6765\uff0c\u968f\u540e\u88ab\u6355\u83b7\u529b\u91cd\u65b0\u6355\u83b7\u3002\u5229\u7528\u4e00\u4e2a\u9ad8\u901f CMOS \u4f20\u611f\u5668\u6210\u50cf\uff0c\u5c06\u8dcc\u843d\u548c\u4fee\u590d\u7684\u8f68\u8ff9\u7ed3\u5408\u8d77\u6765\uff0c\u4ee5\u786e\u5b9a\u7c92\u5b50\u7684\u8d28\u91cf\u3002\u6211\u4eec\u901a\u8fc7\u5206\u6790\u56da\u7981\u7c92\u5b50\u7684\u4f4d\u7f6e\u81ea\u76f8\u5173\u51fd\u6570\u6765\u8bc1\u5b9e\u8fd9\u4e9b\u6d4b\u91cf\u7ed3\u679c\u3002\u6211\u4eec\u62a5\u544a\u7684\u7edf\u8ba1\u4e0d\u786e\u5b9a\u6027\u5c0f\u4e8e2% \u7684\u7fa4\u4f17\u7684\u79e9\u5e8f<\/span>5\u00d710\u221214 kg\uff0c\u4f7f\u7528\u5927\u7ea690\u79d2\u7684\u6570\u636e\u91c7\u96c6\u65f6\u95f4\u3002<\/span><\/p>\n


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    \u6ed1\u52a8\u5206\u6790\u4e2d\u7684\u534a\u67d4\u6027\u805a\u5408\u7269:<\/strong><\/span><\/p>\n

    \u51f9\u5165\u8f6c\u53d8\uff0c\u8f6c\u6362\u548c\u6d3b\u6027\u7684\u4f5c\u7528<\/strong><\/span><\/p>\n

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

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

    Semiflexible polymer in a gliding assay: reentrant transition, role of turnover and activity<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.11603<\/span><\/p>\n

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

    Amir Shee,Nisha Gupta,Abhishek Chudhuri,Debasish Chaudhuri<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>We consider a model of an extensible semiflexible filament moving in two dimensions on a motility assay of motor proteins represented explicitly as active harmonic linkers. Their heads bind stochastically to polymer segments within a capture radius, and extend along the filament in a directed fashion before detaching. Both the extension and detachment rates are load-dependent and generate an active drive on the filament. The filament undergoes a first order phase transition from open chain to spiral conformations and shows a reentrant behavior in both the active extension and the turnover, defined as the ratio of attachment-detachment rates. Associated with the phase transition, the size and shape of the polymer changes non-monotonically, and the relevant autocorrelation functions display double-exponential decay. The corresponding correlation times show a maximum signifying the dominance of spirals. The orientational dynamics captures the rotation of spirals, and its correlation time decays with activity as a power law.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u8003\u8651\u4e00\u4e2a\u53ef\u6269\u5c55\u7684\u534a\u67d4\u6027\u706f\u4e1d\u5728\u4e8c\u7ef4\u8fd0\u52a8\u7684\u6a21\u578b\u4e0a\u7684\u8fd0\u52a8\u6d4b\u5b9a\u7684\u9a6c\u8fbe\u86cb\u767d\u663e\u5f0f\u8868\u793a\u4e3a\u6d3b\u8dc3\u7684\u8c10\u6ce2\u8fde\u63a5\u5668\u3002\u5b83\u4eec\u7684\u5934\u968f\u673a\u5730\u7ed1\u5728\u6355\u83b7\u534a\u5f84\u8303\u56f4\u5185\u7684\u805a\u5408\u7269\u73af\u8282\u4e0a\uff0c\u5728\u5206\u79bb\u4e4b\u524d\u6cbf\u7740\u7ec6\u4e1d\u5b9a\u5411\u5ef6\u4f38\u3002\u4f38\u5c55\u901f\u7387\u548c\u5206\u79bb\u901f\u7387\u90fd\u4e0e\u8f7d\u8377\u6709\u5173\uff0c\u5e76\u5728\u706f\u4e1d\u4e0a\u4ea7\u751f\u4e3b\u52a8\u9a71\u52a8\u3002\u4e1d\u72b6\u4f53\u7ecf\u5386\u4e86\u4ece\u5f00\u94fe\u5230\u87ba\u65cb\u6784\u8c61\u7684\u4e00\u7ea7\u76f8\u53d8\u8fc7\u7a0b\uff0c\u5728\u6709\u6548\u6269\u5c55\u548c\u5468\u8f6c\u8fc7\u7a0b\u4e2d\u90fd\u8868\u73b0\u51fa\u91cd\u5165\u884c\u4e3a\uff0c\u8fd9\u79cd\u884c\u4e3a\u88ab\u5b9a\u4e49\u4e3a\u9644\u7740-\u5206\u79bb\u7387\u7684\u6bd4\u503c\u3002\u805a\u5408\u7269\u7684\u5c3a\u5bf8\u548c\u5f62\u72b6\u968f\u76f8\u53d8\u5448\u975e\u5355\u8c03\u53d8\u5316\uff0c\u76f8\u5e94\u7684\u81ea\u76f8\u5173\u51fd\u6570\u5448\u53cc\u6307\u6570\u8870\u51cf\u3002\u76f8\u5e94\u7684\u76f8\u5173\u65f6\u95f4\u663e\u793a\u4e86\u87ba\u65cb\u5360\u4e3b\u5bfc\u5730\u4f4d\u7684\u6700\u5927\u503c\u3002\u5b9a\u5411\u52a8\u529b\u5b66\u6355\u83b7\u87ba\u65cb\u65cb\u8f6c\uff0c\u5176\u76f8\u5173\u65f6\u95f4\u968f\u6d3b\u52a8\u5448\u5e42\u5f8b\u8870\u51cf\u3002<\/span><\/p>\n


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    \u5f02\u5e38\u7684\u8840\u5c0f\u677f\u8fd0\u8f93\u548c\u8102\u80aa\u5c3e\u5206\u5e03<\/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

    Anomalous Platelet Transport & Fat-Tailed Distributions<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.11755<\/span><\/p>\n

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

    Christos Kotsalos,Karim Zouaoui Boudjeltia,Ritabrata Dutta,Jonas Latt,Bastien Chopard<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>The transport of platelets in blood is commonly assumed to obey an advection-diffusion equation. Here we propose a disruptive view, by showing that the random part of their velocity is governed by a fat-tailed probability distribution, usually referred to as a L’evy flight. Although for small spatio-temporal scales, it is hard to distinguish it from the generally accepted “red blood cell enhanced” Brownian motion, for larger systems this effect is dramatic as the standard approach may underestimate the flux of platelets by several orders of magnitude, compromising in particular the validity of current platelet function tests.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u8840\u5c0f\u677f\u5728\u8840\u6db2\u4e2d\u7684\u8fd0\u8f93\u901a\u5e38\u88ab\u8ba4\u4e3a\u9075\u5faa\u4e00\u4e2a\u5e73\u6d41-\u6269\u6563\u65b9\u7a0b\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u4e2a\u7834\u574f\u6027\u7684\u89c2\u70b9\uff0c\u901a\u8fc7\u5c55\u793a\u5b83\u4eec\u901f\u5ea6\u7684\u968f\u673a\u90e8\u5206\u662f\u7531\u539a\u5c3e\u6982\u7387\u5206\u5e03\u63a7\u5236\u7684\uff0c\u901a\u5e38\u88ab\u79f0\u4e3a l\u2018 evy \u98de\u884c\u3002\u867d\u7136\u5bf9\u4e8e\u5c0f\u7684\u65f6\u7a7a\u5c3a\u5ea6\uff0c\u5f88\u96be\u533a\u5206\u5b83\u4e0e\u666e\u904d\u63a5\u53d7\u7684\u201c\u7ea2\u7ec6\u80de\u589e\u5f3a\u201d\u5e03\u6717\u8fd0\u52a8\uff0c\u5bf9\u4e8e\u8f83\u5927\u7684\u7cfb\u7edf\uff0c\u8fd9\u79cd\u6548\u5e94\u662f\u620f\u5267\u6027\u7684\uff0c\u56e0\u4e3a\u6807\u51c6\u7684\u65b9\u6cd5\u53ef\u80fd\u4f4e\u4f30\u8840\u5c0f\u677f\u7684\u6d41\u91cf\u51e0\u767e\u4e07\u6570\u91cf\u7ea7\uff0c\u7279\u522b\u662f\u964d\u4f4e\u4e86\u5f53\u524d\u8840\u5c0f\u677f\u529f\u80fd\u6d4b\u8bd5\u7684\u6709\u6548\u6027\u3002<\/span><\/p>\n


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    \u57fa\u4e8e\u4e24\u56fd\u94fe\u63a5\u8ffd\u8e2a\u62bd\u6837<\/strong><\/span><\/p>\n

    \u7684\u8de8\u56fd\u793e\u4f1a\u9886\u57df\u6d4b\u91cf<\/strong><\/span><\/strong><\/p>\n

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


    <\/p>\n

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

    Measuring transnational social fields through binational link-tracing sampling<\/span><\/h1>\n

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

    https:\/\/arxiv.org\/abs\/2006.11380<\/span><\/p>\n

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

    Marian-Gabriel H\u00e2ncean,Miranda J. Lubbers,Jos\u00e9 Luis Molina<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>We advance binational link-tracing sampling design, an innovative data collection methodology for sampling from transnational social fields, i.e., transnational networks embedding migrants and non-migrants. This paper shows the practical challenges of such a design, the representativeness of the samples and the qualities of the resulted networks. We performed 303 face-to-face structured interviews on sociodemographic variables, migration trajectories and personal networks of people living in a Romanian migration sending community (D\u00e2mbovi\u0163a) and in a migration receiving Spanish town (Castell\u00f3n), simultaneously in both sites. Inter-connecting the personal networks, we built a multi-layered complex network structure embedding 4,855 nominated people, 5,477 directed ties (nominations) and 2,540 edges. Results indicate that the participants’ unique identification is a particularly difficult challenge, the representativeness of the data is not optimal (homophily on observed attributes was detected in the nomination patterns), and the relational and attribute data allow to explore the social organization of the Romanian migrant enclave in Castell\u00f3n, as well as its connectivity to other places. Furthermore, we provide methodological suggestions for improving link-tracing sampling from transnational networks of migration. Our research contributes to the emerging efforts of applying social network analysis to the study of international migration.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u63d0\u51fa\u4e86\u4e24\u56fd\u94fe\u63a5\u8ffd\u8e2a\u62bd\u6837\u8bbe\u8ba1\uff0c\u8fd9\u662f\u4e00\u79cd\u521b\u65b0\u7684\u6570\u636e\u6536\u96c6\u65b9\u6cd5\uff0c\u7528\u4e8e\u8de8\u56fd\u793e\u4f1a\u9886\u57df\u7684\u62bd\u6837\uff0c\u5373\u5305\u542b\u79fb\u6c11\u548c\u975e\u79fb\u6c11\u7684\u8de8\u56fd\u7f51\u7edc\u3002\u672c\u6587\u6307\u51fa\u4e86\u8fd9\u79cd\u8bbe\u8ba1\u7684\u5b9e\u9645\u6311\u6218\uff0c\u6837\u672c\u7684\u4ee3\u8868\u6027\u548c\u7531\u6b64\u4ea7\u751f\u7684\u7f51\u7edc\u7684\u8d28\u91cf\u3002\u6211\u4eec\u8fdb\u884c\u4e86303\u6b21\u9762\u5bf9\u9762\u7684\u7ed3\u6784\u5316\u8bbf\u8c08\uff0c\u5185\u5bb9\u6d89\u53ca\u793e\u4f1a\u4eba\u53e3\u53d8\u91cf\u3001\u79fb\u6c11\u8f68\u8ff9\u548c\u4e2a\u4eba\u7f51\u7edc\uff0c\u8fd9\u4e9b\u4eba\u751f\u6d3b\u5728\u7f57\u9a6c\u5c3c\u4e9a\u79fb\u6c11\u8f93\u51fa\u793e\u533a(d ^ ambovi c { t } a)\u548c\u79fb\u6c11\u63a5\u6536\u897f\u73ed\u7259\u57ce\u9547(Castell\u2018 on) \uff0c\u540c\u65f6\u5728\u8fd9\u4e24\u4e2a\u5730\u70b9\u8fdb\u884c\u3002\u901a\u8fc7\u4e2a\u4eba\u7f51\u7edc\u7684\u76f8\u4e92\u8fde\u63a5\uff0c\u6211\u4eec\u5efa\u7acb\u4e86\u4e00\u4e2a\u591a\u5c42\u6b21\u7684\u590d\u6742\u7f51\u7edc\u7ed3\u6784\uff0c\u5305\u62ec4855\u4e2a\u63d0\u540d\u4eba\uff0c5477\u4e2a\u6709\u5411\u8054\u7cfb(\u63d0\u540d)\u548c2540\u4e2a\u8fb9\u3002\u7ed3\u679c\u8868\u660e\uff0c\u53c2\u4e0e\u8005\u7684\u72ec\u7279\u8eab\u4efd\u8bc6\u522b\u662f\u4e00\u4e2a\u7279\u522b\u56f0\u96be\u7684\u6311\u6218\uff0c\u6570\u636e\u7684\u4ee3\u8868\u6027\u4e0d\u662f\u6700\u4f73\u7684(\u5728\u63d0\u540d\u6a21\u5f0f\u4e2d\u53d1\u73b0\u4e86\u89c2\u5bdf\u5230\u7684\u5c5e\u6027\u7684\u540c\u76f8\u6027) \uff0c\u5173\u7cfb\u548c\u5c5e\u6027\u6570\u636e\u5141\u8bb8\u63a2\u7d22 Castell\u2018 on \u7f57\u9a6c\u5c3c\u4e9a\u79fb\u6c11\u98de\u5730\u7684\u793e\u4f1a\u7ec4\u7ec7\u53ca\u5176\u4e0e\u5176\u4ed6\u5730\u65b9\u7684\u8fde\u901a\u6027\u3002\u6b64\u5916\uff0c\u6211\u4eec\u8fd8\u4e3a\u6539\u8fdb\u8de8\u56fd\u79fb\u6c11\u7f51\u7edc\u4e2d\u7684\u94fe\u63a5\u8ffd\u8e2a\u62bd\u6837\u63d0\u4f9b\u4e86\u65b9\u6cd5\u4e0a\u7684\u5efa\u8bae\u3002\u6211\u4eec\u7684\u7814\u7a76\u6709\u52a9\u4e8e\u5c06\u793e\u4f1a\u7f51\u7edc\u5206\u6790\u5e94\u7528\u4e8e\u56fd\u9645\u79fb\u6c11\u7814\u7a76\u3002<\/span><\/p>\n


    <\/p>\n

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    <\/p>\n

    \n
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    \u9690\u6027\u4f17\u5305\u8bc6\u522b<\/strong><\/span><\/p>\n

    \u5728\u7ebf\u793e\u4ea4\u7f51\u7edc\u4e2d\u7684\u6ee5\u7528\u884c\u4e3a<\/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

    Implicit Crowdsourcing for Identifying Abusive Behavior in Online Social Networks<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.11456<\/span><\/p>\n

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

    Abiola Osho,Ethan Tucker,George Amariucai<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>The increased use of online social networks for the dissemination of information comes with the misuse of the internet for cyberbullying, cybercrime, spam, vandalism, amongst other things. To proactively identify abuse in the networks, we propose a model to identify abusive posts by crowdsourcing. The crowdsourcing part of the detection mechanism is implemented implicitly, by simply observing the natural interaction between users encountering the messages. We explore the node-to-node spread of information on Twitter and propose a model that predicts the abuse level (abusive, hate, spam, normal) associated with the tweet by observing the attributes of the message, along with those of the users interacting with it. We demonstrate that the difference in users’ interactions with abusive posts can be leveraged in identifying posts of varying abuse levels.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u968f\u7740\u5728\u7ebf\u793e\u4ea4\u7f51\u7edc\u7528\u4e8e\u4fe1\u606f\u4f20\u64ad\u7684\u589e\u52a0\uff0c\u7f51\u7edc\u88ab\u6ee5\u7528\u4e8e\u7f51\u7edc\u6b3a\u51cc\u3001\u7f51\u7edc\u72af\u7f6a\u3001\u5783\u573e\u90ae\u4ef6\u3001\u7834\u574f\u516c\u7269\u7b49\u7b49\u3002\u4e3a\u4e86\u4e3b\u52a8\u8bc6\u522b\u7f51\u7edc\u4e2d\u7684\u6ee5\u7528\u884c\u4e3a\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u4e2a\u901a\u8fc7\u4f17\u5305\u8bc6\u522b\u6ee5\u7528\u5e16\u5b50\u7684\u6a21\u578b\u3002\u68c0\u6d4b\u673a\u5236\u7684\u4f17\u5305\u90e8\u5206\u662f\u9690\u5f0f\u5b9e\u73b0\u7684\uff0c\u901a\u8fc7\u7b80\u5355\u5730\u89c2\u5bdf\u9047\u5230\u6d88\u606f\u7684\u7528\u6237\u4e4b\u95f4\u7684\u81ea\u7136\u4ea4\u4e92\u3002\u6211\u4eec\u7814\u7a76\u4e86 Twitter \u4e0a\u4fe1\u606f\u7684\u8282\u70b9\u5230\u8282\u70b9\u7684\u4f20\u64ad\uff0c\u5e76\u63d0\u51fa\u4e86\u4e00\u4e2a\u6a21\u578b\uff0c\u901a\u8fc7\u89c2\u5bdf\u6d88\u606f\u7684\u5c5e\u6027\u4ee5\u53ca\u4e0e\u4e4b\u4ea4\u4e92\u7684\u7528\u6237\u7684\u5c5e\u6027\uff0c\u6765\u9884\u6d4b\u4e0e\u8be5\u6d88\u606f\u76f8\u5173\u7684\u6ee5\u7528\u7a0b\u5ea6(\u8fb1\u9a82\u3001\u4ec7\u6068\u3001\u5783\u573e\u90ae\u4ef6\u3001\u6b63\u5e38)\u3002\u6211\u4eec\u8bc1\u660e\uff0c\u7528\u6237\u4e0e\u6ee5\u53d1\u5e16\u5b50\u4e92\u52a8\u7684\u5dee\u5f02\u53ef\u4ee5\u7528\u6765\u8bc6\u522b\u4e0d\u540c\u7a0b\u5ea6\u7684\u6ee5\u7528\u5e16\u5b50\u3002<\/span><\/p>\n


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

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    \u5728\u9ed1\u6697\u7f51\u7edc\u4e2d\u6316\u6398\u7528\u6237<\/strong><\/span><\/p>\n

    \u4ea4\u4e92\u6a21\u5f0f\u6765\u9884\u6d4b\u4f01\u4e1a\u7f51\u7edc\u4e8b\u4ef6<\/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

    Mining user interaction patterns in the darkweb to predict enterprise cyber incidents<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/1909.11592<\/span><\/p>\n

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

    Soumajyoti Sarkar,Mohammad Almukaynizi,Jana Shakarian,Paulo Shakarian<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>With rise in security breaches over the past few years, there has been an increasing need to mine insights from social m\u3002edia platforms to raise alerts of possible attacks in an attempt to defend conflict during competition. In this study, we attempt to build a framework that utilizes unconventional signals from the darkweb forums by leveraging the reply network structure of user interactions with the goal of predicting enterprise related external cyber attacks. We use both unsupervised and supervised learning models that address the challenges that come with the lack of enterprise attack metadata for ground truth validation as well as insufficient data for training the models. We validate our models on a binary classification problem that attempts to predict cyber attacks on a daily basis for an organization. Using several controlled studies on features leveraging the network structure, we measure the extent to which the indicators from the darkweb forums can be successfully used to predict attacks. We use information from 53 forums in the darkweb over a span of 17 months for the task. Our framework to predict real world organization cyber attacks of 3 different security events, suggest that focusing on the reply path structure between groups of users based on random walk transitions and community structures has an advantage in terms of better performance solely relying on forum or user posting statistics prior to attacks.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5728\u8fc7\u53bb\u7684\u51e0\u5e74\u91cc\uff0c\u968f\u7740\u6570\u5b57\u8bc1\u4e66\u8ba4\u8bc1\u673a\u6784\u6570\u91cf\u7684\u4e0a\u5347\uff0c\u4eba\u4eec\u8d8a\u6765\u8d8a\u9700\u8981\u4ece\u793e\u4ea4\u5a92\u4f53\u5e73\u53f0\u4e2d\u6316\u6398\u6d1e\u5bdf\u529b\uff0c\u63d0\u9ad8\u5bf9\u53ef\u80fd\u53d1\u751f\u7684\u653b\u51fb\u7684\u8b66\u62a5\uff0c\u4ee5\u4fbf\u5728\u7ade\u4e89\u4e2d\u4fdd\u62a4\u51b2\u7a81\u3002\u5728\u8fd9\u9879\u7814\u7a76\u4e2d\uff0c\u6211\u4eec\u5c1d\u8bd5\u5efa\u7acb\u4e00\u4e2a\u6846\u67b6\uff0c\u5229\u7528\u9ed1\u6697\u7f51\u7edc\u8bba\u575b\u7684\u975e\u5e38\u89c4\u4fe1\u53f7\uff0c\u5229\u7528\u7528\u6237\u4ea4\u4e92\u7684\u56de\u590d\u7f51\u7edc\u7ed3\u6784\uff0c\u4ee5\u9884\u6d4b\u4f01\u4e1a\u76f8\u5173\u7684\u5916\u90e8\u7f51\u7edc\u653b\u51fb\u3002\u6211\u4eec\u540c\u65f6\u4f7f\u7528\u65e0\u76d1\u7763\u6a21\u578b\u548c\u76d1\u7763\u5f0f\u5b66\u4e60\u6a21\u578b\u6765\u89e3\u51b3\u7f3a\u4e4f\u4f01\u4e1a\u653b\u51fb\u5143\u6570\u636e\u8fdb\u884c\u5b9e\u5730\u9a8c\u8bc1\u4ee5\u53ca\u57f9\u8bad\u6a21\u578b\u7684\u6570\u636e\u4e0d\u8db3\u6240\u5e26\u6765\u7684\u6311\u6218\u3002\u6211\u4eec\u5728\u4e00\u4e2a\u4e8c\u5143\u5206\u7c7b\u95ee\u9898\u4e0a\u9a8c\u8bc1\u4e86\u6211\u4eec\u7684\u6a21\u578b\uff0c\u8be5\u95ee\u9898\u8bd5\u56fe\u6bcf\u5929\u4e3a\u7ec4\u7ec7\u9884\u6d4b\u7f51\u7edc\u653b\u51fb\u3002\u901a\u8fc7\u5bf9\u7f51\u7edc\u7ed3\u6784\u7279\u5f81\u7684\u4e00\u4e9b\u53d7\u63a7\u7814\u7a76\uff0c\u6211\u4eec\u6d4b\u91cf\u4e86\u6765\u81ea\u9ed1\u6697\u7f51\u7edc\u8bba\u575b\u7684\u6307\u6807\u80fd\u591f\u6210\u529f\u7528\u4e8e\u9884\u6d4b\u653b\u51fb\u7684\u7a0b\u5ea6\u3002\u6211\u4eec\u572817\u4e2a\u6708\u7684\u65f6\u95f4\u91cc\u4f7f\u7528\u4e8653\u4e2a\u8bba\u575b\u7684\u6697\u7f51\u4fe1\u606f\u6765\u5b8c\u6210\u8fd9\u9879\u4efb\u52a1\u3002\u6211\u4eec\u7684\u6846\u67b6\u9884\u6d4b\u73b0\u5b9e\u4e16\u754c\u7ec4\u7ec7\u76843\u4e2a\u4e0d\u540c\u7684\u5b89\u5168\u4e8b\u4ef6\u7684\u7f51\u7edc\u653b\u51fb\uff0c\u5efa\u8bae\u5173\u6ce8\u7528\u6237\u7ec4\u4e4b\u95f4\u7684\u56de\u590d\u8def\u5f84\u7ed3\u6784\u57fa\u4e8e\u968f\u673a\u6f2b\u6b65\u8fc7\u6e21\u548c\u793e\u533a\u7ed3\u6784\u7684\u4f18\u52bf\u5728\u4e8e\u66f4\u597d\u7684\u6027\u80fd\u5b8c\u5168\u4f9d\u8d56\u4e8e\u8bba\u575b\u6216\u7528\u6237\u53d1\u5e03\u653b\u51fb\u524d\u7684\u7edf\u8ba1\u6570\u636e\u3002<\/span><\/p>\n


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    \n
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    \u7528\u4e8e\u68c0\u7d22\u6d41\u91cf Tweets \u7684\u81ea\u52a8\u67e5\u8be2\u4f18\u5316<\/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

    Automatic Query Optimization for Retrieving Traffic Tweets<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.11887<\/span><\/p>\n

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

    Emory Hufbauer,Hana Khamfroush<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Twitter, like many social media and data brokering companies, makes their data available through a search API (application programming interface). In addition to filtering results by date and location, researchers can search for tweets with specific content with a boolean text query, using {it AND}, {it OR}, and {it NOT} operators to select the combinations of phrases which must, or must not, appear in matching tweets. This boolean text search system is not at all unique to Twitter and is found in many different contexts, including academic, legal, and medical databases, however it is stretched to its limits in Twitter’s use case because of the relative volume and brevity of tweets. In addition, the semi-automated use of such systems was well studied under the topic of Information Retrieval during the 1980s and 1990s, however the study of such systems has greatly declined since that time. As such, we propose updated methods for automatically selecting and refining complex boolean search queries that can isolate relevant results with greater specificity and completeness. Furthermore, we present preliminary results of using an optimized query to collect a sample of traffic-incident-related tweets, along with the results of manually classifying and analyzing them.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u50cf\u8bb8\u591a\u793e\u4ea4\u5a92\u4f53\u548c\u6570\u636e\u4ee3\u7406\u516c\u53f8\u4e00\u6837\uff0cTwitter \u901a\u8fc7\u4e00\u4e2a\u641c\u7d22 API (\u5e94\u7528\u7a0b\u5e8f\u7f16\u7a0b\u63a5\u53e3)\u63d0\u4f9b\u6570\u636e\u3002\u9664\u4e86\u6309\u65e5\u671f\u548c\u4f4d\u7f6e\u8fc7\u6ee4\u7ed3\u679c\uff0c\u7814\u7a76\u4eba\u5458\u8fd8\u53ef\u4ee5\u901a\u8fc7\u5e03\u5c14\u6587\u672c\u67e5\u8be2\u641c\u7d22\u5177\u6709\u7279\u5b9a\u5185\u5bb9\u7684 tweets\uff0c\u4f7f\u7528{ it AND }\u3001{ it OR }\u548c{ it NOT }\u64cd\u4f5c\u7b26\u6765\u9009\u62e9\u5fc5\u987b\u6216\u4e0d\u80fd\u51fa\u73b0\u5728\u5339\u914d tweets \u4e2d\u7684\u77ed\u8bed\u7ec4\u5408\u3002\u8fd9\u4e2a\u5e03\u5c14\u578b\u6587\u672c\u641c\u7d22\u7cfb\u7edf\u5e76\u4e0d\u662f Twitter \u72ec\u6709\u7684\uff0c\u53ef\u4ee5\u5728\u5f88\u591a\u4e0d\u540c\u7684\u73af\u5883\u4e2d\u627e\u5230\uff0c\u5305\u62ec\u5b66\u672f\u3001\u6cd5\u5f8b\u548c\u533b\u7597\u6570\u636e\u5e93\uff0c\u7136\u800c\u5728 Twitter \u7684\u4f7f\u7528\u6848\u4f8b\u4e2d\uff0c\u7531\u4e8e\u63a8\u6587\u7684\u76f8\u5bf9\u6570\u91cf\u548c\u7b80\u6d01\u6027\uff0c\u5b83\u5df2\u7ecf\u8fbe\u5230\u4e86\u6781\u9650\u3002\u6b64\u5916\uff0c\u572820\u4e16\u7eaa80\u5e74\u4ee3\u548c90\u5e74\u4ee3\uff0c\u8fd9\u79cd\u7cfb\u7edf\u7684\u534a\u81ea\u52a8\u5316\u4f7f\u7528\u5728\u4fe1\u606f\u68c0\u7d22\u7684\u4e3b\u9898\u4e0b\u5f97\u5230\u4e86\u5f88\u597d\u7684\u7814\u7a76\uff0c\u7136\u800c\u81ea\u90a3\u65f6\u4ee5\u6765\uff0c\u8fd9\u79cd\u7cfb\u7edf\u7684\u7814\u7a76\u5df2\u7ecf\u5927\u5927\u51cf\u5c11\u4e86\u3002\u56e0\u6b64\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u66f4\u65b0\u7684\u65b9\u6cd5\uff0c\u81ea\u52a8\u9009\u62e9\u548c\u7cbe\u70bc\u590d\u6742\u7684\u5e03\u5c14\u641c\u7d22\u67e5\u8be2\uff0c\u53ef\u4ee5\u9694\u79bb\u76f8\u5173\u7684\u7ed3\u679c\u66f4\u5177\u6709\u7279\u5f02\u6027\u548c\u5b8c\u6574\u6027\u3002\u6b64\u5916\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u521d\u6b65\u7684\u7ed3\u679c\uff0c\u4f7f\u7528\u4f18\u5316\u7684\u67e5\u8be2\u6536\u96c6\u4ea4\u901a\u4e8b\u4ef6\u76f8\u5173\u7684 tweets \u6837\u672c\uff0c\u4ee5\u53ca\u624b\u52a8\u5206\u7c7b\u548c\u5206\u6790\u7684\u7ed3\u679c\u3002<\/span><\/p>\n


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    \u5229\u7528\u56fe\u5f62\u795e\u7ecf\u7f51\u7edc\u5b66\u4e60\u4f20\u67d3\u6e90\u7684\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

    Finding Patient Zero: Learning Contagion Source with Graph Neural Networks<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.11913<\/span><\/p>\n

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

    Chintan Shah,Nima Dehmamy,Nicola Perra,Matteo Chinazzi,Albert-L\u00e1szl\u00f3 Barab\u00e1si,Alessandro Vespignani,Rose Yu<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Locating the source of an epidemic, or patient zero (P0), can provide critical insights into the infection’s transmission course and allow efficient resource allocation. Existing methods use graph-theoretic centrality measures and expensive message-passing algorithms, requiring knowledge of the underlying dynamics and its parameters. In this paper, we revisit this problem using graph neural networks (GNNs) to learn P0. We establish a theoretical limit for the identification of P0 in a class of epidemic models. We evaluate our method against different epidemic models on both synthetic and a real-world contact network considering a disease with history and characteristics of COVID-19. % We observe that GNNs can identify P0 close to the theoretical bound on accuracy, without explicit input of dynamics or its parameters. In addition, GNN is over 100 times faster than classic methods for inference on arbitrary graph topologies. Our theoretical bound also shows that the epidemic is like a ticking clock, emphasizing the importance of early contact-tracing. We find a maximum time after which accurate recovery of the source becomes impossible, regardless of the algorithm used.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u627e\u5230\u6d41\u884c\u75c5\u7684\u6e90\u5934\uff0c\u6216\u8005\u8bf4\u96f6\u53f7\u75c5\u4eba(P0) \uff0c\u53ef\u4ee5\u5bf9\u611f\u67d3\u7684\u4f20\u64ad\u8fc7\u7a0b\u63d0\u4f9b\u5173\u952e\u7684\u6d1e\u5bdf\u529b\uff0c\u4ece\u800c\u5b9e\u73b0\u6709\u6548\u7684\u8d44\u6e90\u914d\u7f6e\u3002\u73b0\u6709\u7684\u65b9\u6cd5\u4f7f\u7528\u56fe\u8bba\u4e2d\u5fc3\u6027\u5ea6\u91cf\u548c\u4ee3\u4ef7\u9ad8\u6602\u7684\u6d88\u606f\u4f20\u9012\u7b97\u6cd5\uff0c\u9700\u8981\u4e86\u89e3\u5e95\u5c42\u52a8\u6001\u53ca\u5176\u53c2\u6570\u3002\u672c\u6587\u5229\u7528\u56fe\u795e\u7ecf\u7f51\u7edc(GNNs)\u5b66\u4e60 P0\uff0c\u91cd\u65b0\u7814\u7a76\u4e86\u8fd9\u4e2a\u95ee\u9898\u3002\u5efa\u7acb\u4e86\u4e00\u7c7b\u4f20\u67d3\u75c5\u6a21\u578b\u4e2d P0\u5224\u522b\u7684\u7406\u8bba\u6781\u9650\u3002\u6211\u4eec\u8bc4\u4f30\u6211\u4eec\u7684\u65b9\u6cd5\u9488\u5bf9\u4e0d\u540c\u7684\u4f20\u67d3\u75c5\u6a21\u578b\u5728\u5408\u6210\u548c\u73b0\u5b9e\u4e16\u754c\u63a5\u89e6\u7f51\u7edc\u8003\u8651\u4e00\u4e2a\u75be\u75c5\u7684\u5386\u53f2\u548c\u7279\u70b9\u7684\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u3002\u6211\u4eec\u89c2\u5bdf\u5230 GNNs \u80fd\u591f\u8bc6\u522b\u63a5\u8fd1\u7406\u8bba\u754c\u9650\u7684\u7cbe\u5ea6\uff0c\u6ca1\u6709\u660e\u786e\u7684\u8f93\u5165\u52a8\u529b\u5b66\u6216\u5176\u53c2\u6570\u3002\u6b64\u5916\uff0cGNN \u6bd4\u4f20\u7edf\u7684\u56fe\u62d3\u6251\u63a8\u7406\u65b9\u6cd5\u5feb100\u500d\u4ee5\u4e0a\u3002\u6211\u4eec\u7684\u7406\u8bba\u754c\u9650\u8fd8\u8868\u660e\uff0c\u8fd9\u79cd\u6d41\u884c\u75c5\u5c31\u50cf\u4e00\u4e2a\u6ef4\u7b54\u4f5c\u54cd\u7684\u65f6\u949f\uff0c\u5f3a\u8c03\u4e86\u65e9\u671f\u63a5\u89e6\u8ffd\u8e2a\u7684\u91cd\u8981\u6027\u3002\u6211\u4eec\u53d1\u73b0\u4e00\u4e2a\u6700\u5927\u7684\u65f6\u95f4\u4e4b\u540e\uff0c\u51c6\u786e\u6062\u590d\u6e90\u53d8\u5f97\u4e0d\u53ef\u80fd\uff0c\u65e0\u8bba\u4f7f\u7528\u7684\u7b97\u6cd5\u3002<\/span><\/p>\n


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    (\u975e)\u7a33\u5b9a\u7f51\u7edc\u7684 Nash \u5747\u8861<\/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

    Nash Equilibria on (Un)Stable Networks<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/1901.00373<\/span><\/p>\n

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

    Anton Badev<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>In response to a change, individuals may choose to follow the responses of their friends or, alternatively, to change their friends. To model these decisions, consider a game where players choose their behaviors and friendships. In equilibrium, players internalize the need for consensus in forming friendships and choose their optimal strategies on subsets of k players – a form of bounded rationality. The k-player consensual dynamic delivers a probabilistic ranking of a game’s equilibria, and, via a varying k, facilitates estimation of such games. Applying the model to adolescents’ smoking suggests that: (a.) the response of the friendship network to changes in tobacco price amplifies the intended effect of price changes on smoking, (b.) racial desegregation of high-schools decreases the overall smoking prevalence, (c.) peer effect complementarities are substantially stronger between smokers compared to between non-smokers. (d.) the magnitude of the spillover effects from small scale policies targeting individuals’ smoking choices are roughly double compared to the scale of these policies.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u4f5c\u4e3a\u5bf9\u53d8\u5316\u7684\u56de\u5e94\uff0c\u4e2a\u4f53\u53ef\u80fd\u4f1a\u9009\u62e9\u8ddf\u968f\u4ed6\u4eec\u670b\u53cb\u7684\u56de\u5e94\uff0c\u6216\u8005\uff0c\u6539\u53d8\u4ed6\u4eec\u7684\u670b\u53cb\u3002\u4e3a\u4e86\u5efa\u7acb\u8fd9\u4e9b\u51b3\u5b9a\u7684\u6a21\u578b\uff0c\u8003\u8651\u4e00\u4e2a\u6e38\u620f\uff0c\u5176\u4e2d\u73a9\u5bb6\u9009\u62e9\u4ed6\u4eec\u7684\u884c\u4e3a\u548c\u53cb\u8c0a\u3002\u5728\u5747\u8861\u72b6\u6001\u4e0b\uff0c\u73a9\u5bb6\u5728\u5efa\u7acb\u53cb\u8c0a\u65f6\u5185\u5728\u5730\u9700\u8981\u8fbe\u6210\u5171\u8bc6\uff0c\u5e76\u5728 k \u4e2a\u73a9\u5bb6\u5b50\u96c6\u4e0a\u9009\u62e9\u4ed6\u4eec\u7684\u6700\u4f73\u7b56\u7565\uff0c\u8fd9\u662f\u4e00\u79cd\u6709\u9650\u7406\u6027\u7684\u5f62\u5f0f\u3002K- \u73a9\u5bb6\u4e00\u81f4\u540c\u610f\u52a8\u6001\u63d0\u4f9b\u4e86\u4e00\u4e2a\u6e38\u620f\u5747\u8861\u7684\u6982\u7387\u6392\u540d\uff0c\u5e76\u901a\u8fc7\u53d8\u5316\u7684 k\uff0c\u4fc3\u8fdb\u4e86\u8fd9\u7c7b\u6e38\u620f\u7684\u4f30\u8ba1\u3002\u5c06\u8be5\u6a21\u578b\u5e94\u7528\u4e8e\u9752\u5c11\u5e74\u5438\u70df\u7684\u7814\u7a76\u8868\u660e: (a)\u53cb\u8c0a\u7f51\u7edc\u5bf9\u70df\u8349\u4ef7\u683c\u53d8\u5316\u7684\u53cd\u5e94\u653e\u5927\u4e86\u4ef7\u683c\u53d8\u5316\u5bf9\u5438\u70df\u7684\u9884\u671f\u5f71\u54cd; (b)\u9ad8\u4e2d\u53d6\u6d88\u79cd\u65cf\u9694\u79bb\u964d\u4f4e\u4e86\u603b\u4f53\u5438\u70df\u7387; (c)\u5438\u70df\u8005\u4e4b\u95f4\u7684\u540c\u4f34\u6548\u5e94\u4e92\u8865\u6027\u660e\u663e\u9ad8\u4e8e\u975e\u5438\u70df\u8005\u3002(d)\u9488\u5bf9\u4e2a\u4eba\u5438\u70df\u9009\u62e9\u7684\u5c0f\u89c4\u6a21\u653f\u7b56\u7684\u6ea2\u51fa\u6548\u5e94\u5927\u81f4\u662f\u8fd9\u4e9b\u653f\u7b56\u89c4\u6a21\u7684\u4e24\u500d\u3002<\/span><\/p>\n


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    \u6fb3\u5927\u5229\u4e9a\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e<\/strong><\/span><\/p>\n

    \u6d41\u884c\u5f15\u8d77\u7684\u793e\u533a\u60c5\u7eea\u52a8\u6001\u7814\u7a76<\/strong><\/span><\/p>\n

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


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    \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span>
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    Examination of community sentiment dynamics due to covid-19 pandemic: a case study from Australia<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.12185<\/span><\/p>\n

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

    Jianlong Zhou,Shuiqiao Yang,Chun Xiao,Fang Chen<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>The outbreak of the novel Coronavirus Disease 2019 (COVID-19) has caused unprecedented impacts to people’s daily life around the world. Various measures and policies such as lock-down and social-distancing are implemented by governments to combat the disease during the pandemic period. These measures and policies as well as virus itself may cause different mental health issues to people such as depression, anxiety, sadness, etc. In this paper, we exploit the massive text data posted by Twitter users to analyse the sentiment dynamics of people living in the state of New South Wales (NSW) in Australia during the pandemic period. Different from the existing work that mostly focuses the country-level and static sentiment analysis, we analyse the sentiment dynamics at the fine-grained local government areas (LGAs). Based on the analysis of around 94 million tweets that posted by around 183 thousand users located at different LGAs in NSW in five months, we found that people in NSW showed an overall positive sentimental polarity and the COVID-19 pandemic decreased the overall positive sentimental polarity during the pandemic period. The fine-grained analysis of sentiment in LGAs found that despite the dominant positive sentiment most of days during the study period, some LGAs experienced significant sentiment changes from positive to negative. This study also analysed the sentimental dynamics delivered by the hot topics in Twitter such as government policies (e.g. the Australia’s JobKeeper program, lock-down, social-distancing) as well as the focused social events (e.g. the Ruby Princess Cruise). The results showed that the policies and events did affect people’s overall sentiment, and they affected people’s overall sentiment differently at different stages.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>2019\u5e74\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u75c5\u7684\u7206\u53d1\u5bf9\u4e16\u754c\u5404\u5730\u4eba\u4eec\u7684\u65e5\u5e38\u751f\u6d3b\u9020\u6210\u4e86\u524d\u6240\u672a\u6709\u7684\u5f71\u54cd\u3002\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u75c5\u6bd2\u611f\u67d3\u662f\u4e00\u79cd\u4f20\u67d3\u75c5\u3002\u5404\u56fd\u653f\u5e9c\u5b9e\u65bd\u4e86\u5404\u79cd\u63aa\u65bd\u548c\u653f\u7b56\uff0c\u5982\u5c01\u9501\u548c\u793e\u4f1a\u758f\u8fdc\uff0c\u4ee5\u5728\u5927\u6d41\u884c\u671f\u95f4\u9632\u6cbb\u8be5\u75be\u75c5\u3002\u8fd9\u4e9b\u63aa\u65bd\u548c\u653f\u7b56\u4ee5\u53ca\u75c5\u6bd2\u672c\u8eab\u90fd\u53ef\u80fd\u5bf9\u4eba\u4eec\u9020\u6210\u4e0d\u540c\u7684\u5fc3\u7406\u5065\u5eb7\u95ee\u9898\uff0c\u5982\u6291\u90c1\u3001\u7126\u8651\u3001\u60b2\u4f24\u7b49\u3002\u5728\u8fd9\u7bc7\u8bba\u6587\u4e2d\uff0c\u6211\u4eec\u5229\u7528 Twitter \u7528\u6237\u53d1\u5e03\u7684\u5927\u91cf\u6587\u672c\u6570\u636e\u6765\u5206\u6790\u6fb3\u5927\u5229\u4e9a\u65b0\u5357\u5a01\u5c14\u58eb\u5dde(NSW)\u6d41\u611f\u5927\u6d41\u884c\u671f\u95f4\u4eba\u4eec\u7684\u60c5\u7eea\u52a8\u6001\u3002\u4e0d\u540c\u4e8e\u73b0\u6709\u7684\u4e3b\u8981\u96c6\u4e2d\u5728\u56fd\u5bb6\u5c42\u9762\u548c\u9759\u6001\u60c5\u7eea\u5206\u6790\u7684\u5de5\u4f5c\uff0c\u6211\u4eec\u5206\u6790\u60c5\u7eea\u52a8\u6001\u5728\u7ec6\u7c92\u5ea6\u7684\u5730\u65b9\u653f\u5e9c\u9886\u57df(lga)\u3002\u6839\u636e\u5bf9\u65b0\u5357\u5a01\u5c14\u58eb\u5dde\u4e0d\u540c\u5730\u533a\u768418.3\u4e07\u7528\u6237\u57285\u4e2a\u6708\u5185\u53d1\u5e03\u7684\u5927\u7ea69400\u4e07\u6761\u63a8\u6587\u7684\u5206\u6790\uff0c\u6211\u4eec\u53d1\u73b0\u65b0\u5357\u5a01\u5c14\u58eb\u5dde\u7684\u4eba\u4eec\u8868\u73b0\u51fa\u4e86\u4e00\u79cd\u603b\u4f53\u4e0a\u79ef\u6781\u7684\u60c5\u611f\u6781\u6027\uff0c\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6d41\u884c\u75c5\u5728\u6d41\u884c\u671f\u95f4\u51cf\u5c11\u4e86\u603b\u4f53\u4e0a\u79ef\u6781\u7684\u60c5\u611f\u6781\u6027\u3002\u5bf9 LGAs \u4e2d\u60c5\u7eea\u7684\u7ec6\u81f4\u5206\u6790\u53d1\u73b0\uff0c\u5c3d\u7ba1\u5728\u7814\u7a76\u671f\u95f4\u7684\u5927\u591a\u6570\u65e5\u5b50\u91cc\uff0c\u79ef\u6781\u60c5\u7eea\u5360\u4e3b\u5bfc\u5730\u4f4d\uff0c\u4f46\u4e00\u4e9b LGAs \u7ecf\u5386\u4e86\u4ece\u79ef\u6781\u5230\u6d88\u6781\u7684\u91cd\u5927\u60c5\u7eea\u53d8\u5316\u3002\u8fd9\u9879\u7814\u7a76\u8fd8\u5206\u6790\u4e86 Twitter \u4e0a\u70ed\u95e8\u8bdd\u9898\u6240\u4f20\u9012\u7684\u60c5\u611f\u52a8\u6001\uff0c\u6bd4\u5982\u653f\u5e9c\u653f\u7b56(\u6bd4\u5982\u6fb3\u5927\u5229\u4e9a\u7684\u201c\u5c31\u4e1a\u5b88\u62a4\u8005\u201d\u8ba1\u5212\u3001\u5c01\u9501\u3001\u793e\u4f1a\u758f\u8fdc)\u4ee5\u53ca\u91cd\u70b9\u793e\u4f1a\u4e8b\u4ef6(\u6bd4\u5982\u201c\u7ea2\u5b9d\u77f3\u516c\u4e3b\u90ae\u8f6e\u201d)\u3002\u7ed3\u679c\u8868\u660e\uff0c\u653f\u7b56\u548c\u4e8b\u4ef6\u786e\u5b9e\u5f71\u54cd\u4e86\u4eba\u4eec\u7684\u6574\u4f53\u60c5\u7eea\uff0c\u5e76\u4e14\u5728\u4e0d\u540c\u7684\u9636\u6bb5\u5bf9\u4eba\u4eec\u7684\u6574\u4f53\u60c5\u7eea\u6709\u4e0d\u540c\u7684\u5f71\u54cd\u3002<\/span><\/p>\n


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    \u8003\u8651\u79c1\u6709\u8282\u70b9\u7684<\/strong><\/span><\/p>\n

    \u968f\u673a\u6f2b\u6b65\u793e\u4f1a\u7f51\u7edc\u6027\u8d28\u4f30\u8ba1<\/strong><\/span><\/p>\n

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

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

    Estimating Properties of Social Networks via Random Walk considering Private Nodes<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.12196<\/span><\/p>\n

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

    Kazuki Nakajima,Kazuyuki Shudo<\/span><\/p>\n


    <\/p>\n

    Abstract<\/span><\/strong>\uff1a<\/span><\/strong>Accurately analyzing graph properties of social networks is a challenging task because of access limitations to the graph data. To address this challenge, several algorithms to obtain unbiased estimates of properties from few samples via a random walk have been studied. However, existing algorithms do not consider private nodes who hide their neighbors in real social networks, leading to some practical problems. Here we design random walk-based algorithms to accurately estimate properties without any problems caused by private nodes. First, we design a random walk-based sampling algorithm that comprises the neighbor selection to obtain samples having the Markov property and the calculation of weights for each sample to correct the sampling bias. Further, for two graph property estimators, we propose the weighting methods to reduce not only the sampling bias but also estimation errors due to private nodes. The proposed algorithms improve the estimation accuracy of the existing algorithms by up to 92.6% on real-world datasets<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u51c6\u786e\u5730\u5206\u6790\u793e\u4f1a\u7f51\u7edc\u7684\u56fe\u5f62\u5c5e\u6027\u662f\u4e00\u4e2a\u5177\u6709\u6311\u6218\u6027\u7684\u4efb\u52a1\uff0c\u56e0\u4e3a\u5bf9\u56fe\u5f62\u6570\u636e\u7684\u8bbf\u95ee\u53d7\u5230\u9650\u5236\u3002\u4e3a\u4e86\u5e94\u5bf9\u8fd9\u4e00\u6311\u6218\uff0c\u4eba\u4eec\u7814\u7a76\u4e86\u51e0\u79cd\u901a\u8fc7\u968f\u673a\u6e38\u52a8\u4ece\u5c11\u6570\u6837\u672c\u4e2d\u83b7\u5f97\u65e0\u504f\u4f30\u8ba1\u7684\u7b97\u6cd5\u3002\u7136\u800c\uff0c\u73b0\u6709\u7684\u7b97\u6cd5\u6ca1\u6709\u8003\u8651\u5230\u9690\u85cf\u5728\u771f\u5b9e\u793e\u4f1a\u7f51\u7edc\u4e2d\u90bb\u5c45\u7684\u79c1\u6709\u8282\u70b9\uff0c\u8fd9\u5c31\u5bfc\u81f4\u4e86\u4e00\u4e9b\u5b9e\u9645\u95ee\u9898\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u8bbe\u8ba1\u4e86\u57fa\u4e8e\u968f\u673a\u6f2b\u6b65\u7684\u7b97\u6cd5\uff0c\u4ee5\u51c6\u786e\u5730\u4f30\u8ba1\u5c5e\u6027\u6ca1\u6709\u4efb\u4f55\u95ee\u9898\u6240\u9020\u6210\u7684\u79c1\u6709\u8282\u70b9\u3002\u9996\u5148\uff0c\u6211\u4eec\u8bbe\u8ba1\u4e86\u4e00\u4e2a\u57fa\u4e8e\u968f\u673a\u6e38\u8d70\u7684\u62bd\u6837\u7b97\u6cd5\uff0c\u5305\u62ec\u90bb\u5c45\u9009\u62e9\uff0c\u4ee5\u83b7\u5f97\u5177\u6709\u9a6c\u5c14\u53ef\u592b\u6027\u7684\u6837\u672c\uff0c\u5e76\u8ba1\u7b97\u6bcf\u4e2a\u6837\u672c\u7684\u6743\u91cd\u6765\u6821\u6b63\u62bd\u6837\u504f\u5dee\u3002\u8fdb\u4e00\u6b65\uff0c\u5bf9\u4e8e\u4e24\u4e2a\u56fe\u7684\u6027\u8d28\u4f30\u8ba1\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u52a0\u6743\u65b9\u6cd5\uff0c\u4ee5\u51cf\u5c11\u62bd\u6837\u504f\u5dee\u548c\u4f30\u8ba1\u8bef\u5dee\u7531\u4e8e\u79c1\u6709\u8282\u70b9\u3002\u5728\u771f\u5b9e\u6570\u636e\u96c6\u4e0a\uff0c\u8be5\u7b97\u6cd5\u7684\u4f30\u8ba1\u7cbe\u5ea6\u63d0\u9ad8\u4e8692.6%\u3002<\/span><\/p>\n


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    \u4e9a\u6d32\u56fd\u5bb6\u7684\u98ce\u9669\u6c9f\u901a: <\/strong><\/span><\/p>\n

    \u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u5728 Twitter \u4e0a\u7684\u6f14\u8bb2<\/strong><\/span><\/p>\n

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

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

    Risk Communication in Asian Countries: COVID-19 Discourse on Twitter<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.12218<\/span><\/p>\n

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

    Sungkyu Park,Sungwon Han,Jeongwook Kim,Mir Majid Molaie,Hoang Dieu Vu,Karandeep Singh,Jiyoung Han,Wonjae Lee,Meeyoung Cha<\/span><\/p>\n


    <\/p>\n

    A<\/span><\/strong>bstract\uff1a<\/strong>COVID-19 has become one of the most widely talked about topics on social media. This research characterizes risk communication patterns by analyzing the public discourse on the novel coronavirus from four Asian countries: South Korea, Iran, Vietnam, and India, which suffered the outbreak to different degrees. The temporal analysis shows that the official epidemic phases issued by governments do not match well with the online attention on COVID-19. This finding calls for a need to analyze the public discourse by new measures, such as topical dynamics. Here, we propose an automatic method to detect topical phase transitions and compare similarities in major topics across these countries over time. We examine the time lag difference between social media attention and confirmed patient counts. For dynamics, we find an inverse relationship between the tweet count and topical diversity.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u5df2\u7ecf\u6210\u4e3a\u793e\u4ea4\u5a92\u4f53\u4e0a\u88ab\u5e7f\u6cdb\u8ba8\u8bba\u7684\u8bdd\u9898\u4e4b\u4e00\u3002\u8fd9\u9879\u7814\u7a76\u901a\u8fc7\u5206\u6790\u6765\u81ea\u56db\u4e2a\u4e9a\u6d32\u56fd\u5bb6: \u97e9\u56fd\u3001\u4f0a\u6717\u3001\u8d8a\u5357\u548c\u5370\u5ea6\u7684\u5173\u4e8e\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u7684\u516c\u5171\u8bdd\u8bed\u6765\u63cf\u8ff0\u98ce\u9669\u4f20\u64ad\u6a21\u5f0f\u3002\u65f6\u95f4\u5206\u6790\u8868\u660e\uff0c\u653f\u5e9c\u53d1\u5e03\u7684\u5b98\u65b9\u6d41\u884c\u75c5\u9636\u6bb5\u5e76\u4e0d\u80fd\u5f88\u597d\u5730\u5339\u914d\u7f51\u4e0a\u5bf9\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u7684\u5173\u6ce8\u3002\u8fd9\u4e2a\u53d1\u73b0\u9700\u8981\u901a\u8fc7\u65b0\u7684\u65b9\u6cd5\u6765\u5206\u6790\u516c\u5171\u8bdd\u8bed\uff0c\u6bd4\u5982\u65f6\u4e8b\u52a8\u6001\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u79cd\u81ea\u52a8\u68c0\u6d4b\u5c40\u90e8\u76f8\u53d8\u7684\u65b9\u6cd5\uff0c\u5e76\u968f\u7740\u65f6\u95f4\u7684\u63a8\u79fb\u6bd4\u8f83\u8fd9\u4e9b\u56fd\u5bb6\u7684\u4e3b\u8981\u8bae\u9898\u7684\u76f8\u4f3c\u6027\u3002\u6211\u4eec\u8c03\u67e5\u4e86\u793e\u4ea4\u5a92\u4f53\u5173\u6ce8\u5ea6\u548c\u786e\u8bca\u60a3\u8005\u6570\u91cf\u4e4b\u95f4\u7684\u65f6\u6ede\u5dee\u5f02\u3002\u5728\u52a8\u6001\u65b9\u9762\uff0c\u6211\u4eec\u53d1\u73b0\u4e86\u63a8\u6587\u6570\u91cf\u548c\u4e3b\u9898\u591a\u6837\u6027\u4e4b\u95f4\u7684\u53cd\u6bd4\u5173\u7cfb\u3002<\/span><\/p>\n


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    \u5f53\u793e\u4f1a\u5f71\u54cd\u529b\u4fc3\u8fdb\u7fa4\u4f53\u667a\u6167\u65f6<\/strong><\/span><\/p>\n

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

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

    When social influence promotes the wisdom of crowds<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.12471<\/span><\/p>\n

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

    Abdullah Almaatouq,M. Amin Rahimian,Abdulla Alhajri<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Questions regarding whether, and if so, under what conditions, groups exhibit ”crowd wisdom” have spurred numerous studies in many disciplines, including management and organizational science, psychology, sociology, complex systems, and computer science. Substantial effort in previous research on these questions has focused on investigating the role of social influence in promoting the wisdom of the crowd or, conversely, leading the crowd astray. Specifically, many previous studies have sought to infer the importance of social influence network attributes (such as influence centralization) to explain the accuracy of collective estimates. In this paper, we argue that this approach is limited and can lead to inconsistent conclusions. Based on our theoretical analysis, numerical simulation, and reanalysis of four previously published experiments (which included a total of 4,002 human participants, organized in 131 independent groups), we demonstrate that the wisdom of crowds in estimation tasks depends on the interaction between the following two factors: (i) centralization of the social influence network, and (ii) the features of the estimation context—i.e., the distribution of the initial (pre-influence) estimates. Specifically, we find that centralized influence is desirable in situations where a crowd is predisposed to overestimation bias and\/or have a high likelihood of committing egregious errors. By adopting a framework that integrates both the structure of social influence and the estimation context, we bring the previously conflicting results under one theoretical framework and clarify the effects of influence centralization on the quality of crowd wisdom.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5173\u4e8e\u662f\u5426\uff0c\u5982\u679c\u662f\uff0c\u5728\u4ec0\u4e48\u6761\u4ef6\u4e0b\uff0c\u7fa4\u4f53\u8868\u73b0\u51fa\u201c\u7fa4\u4f53\u667a\u6167\u201d\u7684\u95ee\u9898\u5df2\u7ecf\u6fc0\u53d1\u4e86\u8bb8\u591a\u5b66\u79d1\u7684\u5927\u91cf\u7814\u7a76\uff0c\u5305\u62ec\u7ba1\u7406\u548c\u7ec4\u7ec7\u79d1\u5b66\u3001\u5fc3\u7406\u5b66\u3001\u793e\u4f1a\u5b66\u3001\u590d\u6742\u7cfb\u7edf\u548c\u8ba1\u7b97\u673a\u79d1\u5b66\u3002\u4ee5\u524d\u5bf9\u8fd9\u4e9b\u95ee\u9898\u7684\u5927\u91cf\u7814\u7a76\u90fd\u96c6\u4e2d\u5728\u8c03\u67e5\u793e\u4f1a\u5f71\u54cd\u529b\u5728\u63d0\u5347\u7fa4\u4f53\u667a\u6167\u4e2d\u7684\u4f5c\u7528\uff0c\u6216\u8005\u76f8\u53cd\uff0c\u5f15\u5bfc\u7fa4\u4f53\u8bef\u5165\u6b67\u9014\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u8bb8\u591a\u4ee5\u524d\u7684\u7814\u7a76\u90fd\u8bd5\u56fe\u63a8\u65ad\u793e\u4f1a\u5f71\u54cd\u7f51\u7edc\u5c5e\u6027(\u5982\u5f71\u54cd\u96c6\u4e2d)\u7684\u91cd\u8981\u6027\u6765\u89e3\u91ca\u96c6\u4f53\u4f30\u8ba1\u7684\u51c6\u786e\u6027\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u8ba4\u4e3a\u8fd9\u79cd\u65b9\u6cd5\u662f\u6709\u9650\u7684\uff0c\u5e76\u53ef\u80fd\u5bfc\u81f4\u4e0d\u4e00\u81f4\u7684\u7ed3\u8bba\u3002\u57fa\u4e8e\u6211\u4eec\u7684\u7406\u8bba\u5206\u6790\u3001\u6570\u503c\u6a21\u62df\u4ee5\u53ca\u5bf9\u4ee5\u524d\u53d1\u8868\u7684\u56db\u4e2a\u5b9e\u9a8c\u7684\u91cd\u65b0\u5206\u6790(\u5305\u62ec\u603b\u51714002\u4e2a\u4eba\u53c2\u4e0e\u8005\uff0c\u7ec4\u7ec7\u5728131\u4e2a\u72ec\u7acb\u7684\u5c0f\u7ec4\u4e2d) \uff0c\u6211\u4eec\u8bc1\u660e\u4e86\u7fa4\u4f53\u5728\u8bc4\u4f30\u4efb\u52a1\u4e2d\u7684\u667a\u6167\u53d6\u51b3\u4e8e\u4ee5\u4e0b\u4e24\u4e2a\u56e0\u7d20\u4e4b\u95f4\u7684\u76f8\u4e92\u4f5c\u7528: (i)\u793e\u4f1a\u5f71\u54cd\u7f51\u7edc\u7684\u96c6\u4e2d\u6027\uff0c\u548c(ii)\u8bc4\u4f30\u73af\u5883\u7684\u7279\u5f81\u2014\u2014\u5373\u521d\u59cb(\u9884\u5f71\u54cd)\u8bc4\u4f30\u7684\u5206\u5e03\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u6211\u4eec\u53d1\u73b0\uff0c\u5728\u4eba\u7fa4\u503e\u5411\u4e8e\u8fc7\u9ad8\u4f30\u8ba1\u504f\u5dee\u548c \/ \u6216\u72af\u4e0b\u4e25\u91cd\u9519\u8bef\u7684\u53ef\u80fd\u6027\u5f88\u9ad8\u7684\u60c5\u51b5\u4e0b\uff0c\u96c6\u4e2d\u5f71\u54cd\u662f\u53ef\u53d6\u7684\u3002\u901a\u8fc7\u91c7\u7528\u4e00\u4e2a\u6574\u5408\u4e86\u793e\u4f1a\u5f71\u54cd\u529b\u7ed3\u6784\u548c\u8bc4\u4ef7\u8bed\u5883\u7684\u6846\u67b6\uff0c\u6211\u4eec\u628a\u4ee5\u524d\u76f8\u4e92\u51b2\u7a81\u7684\u7ed3\u679c\u653e\u5728\u4e00\u4e2a\u7406\u8bba\u6846\u67b6\u4e0b\uff0c\u9610\u660e\u4e86\u5f71\u54cd\u529b\u96c6\u4e2d\u5bf9\u7fa4\u4f53\u667a\u6167\u8d28\u91cf\u7684\u5f71\u54cd\u3002<\/span><\/p>\n


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    \u5b9e\u9a8c\u8026\u5408 logistic \u6620\u5c04\u4e2d<\/strong><\/span><\/p>\n

    \u7684\u5206\u5c94\u548c\u6ede\u540e\u73b0\u8c61<\/strong><\/span><\/p>\n

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

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

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

    Observation of bifurcations and hysteresis in experimentally coupled logistic maps<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.11378<\/span><\/p>\n

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

    Carac\u00e9 Guti\u00e9rrez,Cecilia Cabeza,Nicol\u00e1s Rubido<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Initially, the logistic map became popular as a simplified model for population growth. In spite of its apparent simplicity, as the population growth-rate is increased the map exhibits a broad range of dynamics, which include bifurcation cascades going from periodic to chaotic solutions. Studying coupled maps allows to identify other qualitative changes in the collective dynamics, such as pattern formations or hysteresis. Particularly, hysteresis is the appearance of different attracting sets, a set when the control parameter is increased and another set when it is decreased — a multi-stable region. In this work, we present an experimental study on the bifurcations and hysteresis of nearly identical, coupled, logistic maps. Our logistic maps are an electronic system that has a discrete-time evolution with a high signal-to-noise ratio (<\/span>\u223c106), resulting in simple, precise, and reliable experimental manipulations, which include the design of a modifiable diffusive coupling configuration circuit. We find that the characterisations of the isolated and coupled logistic-maps’ dynamics agrees excellently with the theoretical and numerical predictions (such as the critical bifurcation points and Feigenbaum’s bifurcation velocity). Here, we report multi-stable regions appearing robustly across configurations, even though our configurations had parameter mismatch (which we measure directly from the components of the circuit and also infer from the resultant dynamics for each map) and were unavoidably affected by electronic noise.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u6700\u521d\uff0c\u903b\u8f91\u5730\u56fe\u4f5c\u4e3a\u4eba\u53e3\u589e\u957f\u7684\u7b80\u5316\u6a21\u578b\u800c\u6d41\u884c\u8d77\u6765\u3002\u5c3d\u7ba1\u5b83\u770b\u8d77\u6765\u7b80\u5355\uff0c\u4f46\u662f\u968f\u7740\u4eba\u53e3\u589e\u957f\u7387\u7684\u589e\u52a0\uff0c\u8fd9\u4e2a\u6620\u5c04\u5c55\u793a\u4e86\u4e00\u4e2a\u5e7f\u6cdb\u7684\u52a8\u529b\u5b66\u8303\u56f4\uff0c\u5176\u4e2d\u5305\u62ec\u4ece\u5468\u671f\u89e3\u5230\u6df7\u6c8c\u89e3\u7684\u5206\u53c9\u7ea7\u8054\u3002\u7814\u7a76\u8026\u5408\u6620\u5c04\u5141\u8bb8\u8bc6\u522b\u96c6\u4f53\u52a8\u529b\u5b66\u7684\u5176\u4ed6\u5b9a\u6027\u53d8\u5316\uff0c\u5982\u56fe\u6848\u5f62\u6210\u6216\u6ede\u540e\u3002\u7279\u522b\u5730\uff0c\u6ede\u540e\u662f\u6307\u4e0d\u540c\u5438\u5f15\u96c6\u7684\u51fa\u73b0\uff0c\u63a7\u5236\u53c2\u6570\u589e\u52a0\u65f6\u51fa\u73b0\u7684\u6ede\u540e\u73b0\u8c61\uff0c\u63a7\u5236\u53c2\u6570\u51cf\u5c11\u65f6\u51fa\u73b0\u7684\u6ede\u540e\u73b0\u8c61\u2014\u2014\u591a\u7a33\u5b9a\u533a\u57df\u3002\u5728\u8fd9\u9879\u5de5\u4f5c\u4e2d\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u4e2a\u5b9e\u9a8c\u7814\u7a76\u5206\u5c94\u548c\u6ede\u540e\u7684\u51e0\u4e4e\u76f8\u540c\uff0c\u8026\u5408\uff0c\u903b\u8f91\u65af\u8c1b\u6620\u5c04\u3002\u6211\u4eec\u7684\u903b\u8f91\u5730\u56fe\u662f\u4e00\u4e2a\u7535\u5b50\u7cfb\u7edf\uff0c\u5177\u6709\u79bb\u6563\u65f6\u95f4\u6f14\u53d8\u7684\u9ad8\u4fe1\u566a\u6bd4(<\/span>\u223c106), \u8bbe\u8ba1\u4e86\u4e00\u79cd\u53ef\u53d8\u6269\u6563\u8026\u5408\u7ed3\u6784\u7535\u8def\uff0c\u5b9e\u73b0\u4e86\u7b80\u5355\u3001\u7cbe\u786e\u3001\u53ef\u9760\u7684\u5b9e\u9a8c\u64cd\u4f5c\u3002\u6211\u4eec\u53d1\u73b0\u5b64\u7acb\u548c\u8026\u5408 logistic \u6620\u5c04\u7684\u52a8\u529b\u5b66\u7279\u5f81\u4e0e\u7406\u8bba\u548c\u6570\u503c\u9884\u6d4b(\u5982\u4e34\u754c\u5206\u5c94\u70b9\u548c Feigenbaum \u5206\u5c94\u901f\u5ea6)\u975e\u5e38\u543b\u5408\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u62a5\u544a\u4e86\u591a\u7a33\u5b9a\u533a\u57df\u5728\u914d\u7f6e\u4e0a\u8868\u73b0\u5f3a\u52b2\uff0c\u5c3d\u7ba1\u6211\u4eec\u7684\u914d\u7f6e\u6709\u53c2\u6570\u4e0d\u5339\u914d(\u6211\u4eec\u76f4\u63a5\u4ece\u7535\u8def\u7684\u5143\u4ef6\u6d4b\u91cf\uff0c\u5e76\u4ece\u6bcf\u4e2a\u6620\u5c04\u7684\u7ed3\u679c\u52a8\u529b\u5b66\u63a8\u65ad) \uff0c\u5e76\u4e14\u4e0d\u53ef\u907f\u514d\u5730\u53d7\u5230\u7535\u5b50\u566a\u58f0\u7684\u5f71\u54cd\u3002<\/span><\/p>\n


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    \u5728\u7f51\u7edc\u5316\u52a8\u6001\u7cfb\u7edf\u4e2d\u8bc6\u522b<\/strong><\/span><\/p>\n

    \u6709\u52a9\u4e8e\u4ea7\u751f\u6781\u7aef\u4e8b\u4ef6\u7684\u8fb9\u754c<\/strong><\/span><\/p>\n

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

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

    Identifying edges that facilitate the generation of extreme events in networked dynamical systems<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.11410<\/span><\/p>\n

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

    Timo Br\u00f6hl,Klaus Lehnertz<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>The collective dynamics of complex networks of FitzHugh-Nagumo units exhibits rare and recurrent events of high amplitude (extreme events) that are preceded by so-called proto-events during which a certain fraction of the units become excited. Although it is well known that a sufficiently large fraction of excited units is required to turn a proto-event into an extreme event, it is not yet clear how the other units are being recruited into the final generation of an extreme event. Addressing this question and mimicking typical experimental situations, we investigate the centrality of edges in time-dependent interaction networks. We derived these networks from time series of the units’ dynamics employing a widely used bivariate analysis technique. Using our recently proposed edge centrality concepts together with an edge-based network decomposition technique, we observe that the recruitment is primarily facilitated by sets of certain edges that have no equivalent in the underlying topology. Our finding might aid to improve the understanding of generation of extreme events in natural networked dynamical systems.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>Fitzhugh-nagumo \u5355\u5143\u590d\u6742\u7f51\u7edc\u7684\u96c6\u4f53\u52a8\u529b\u5b66\u8868\u73b0\u51fa\u7f55\u89c1\u7684\u9ad8\u632f\u5e45\u53cd\u590d\u4e8b\u4ef6(\u6781\u7aef\u4e8b\u4ef6) \uff0c\u8fd9\u4e9b\u4e8b\u4ef6\u4e4b\u524d\u53d1\u751f\u6240\u8c13\u7684\u539f\u59cb\u4e8b\u4ef6\uff0c\u5728\u6b64\u671f\u95f4\u67d0\u4e9b\u5355\u5143\u88ab\u6fc0\u53d1\u3002\u867d\u7136\u4f17\u6240\u5468\u77e5\uff0c\u8981\u628a\u4e00\u4e2a\u539f\u59cb\u4e8b\u4ef6\u53d8\u6210\u4e00\u4e2a\u6781\u7aef\u4e8b\u4ef6\uff0c\u9700\u8981\u4e00\u5c0f\u90e8\u5206\u6fc0\u53d1\u5355\u4f4d\u7684\u8db3\u591f\u5927\uff0c\u4f46\u662f\u5176\u4ed6\u5355\u4f4d\u662f\u5982\u4f55\u88ab\u62db\u52df\u5230\u4e00\u4e2a\u6781\u7aef\u4e8b\u4ef6\u7684\u6700\u540e\u4e00\u4ee3\u4e2d\u8fd8\u4e0d\u6e05\u695a\u3002\u9488\u5bf9\u8fd9\u4e2a\u95ee\u9898\uff0c\u6a21\u62df\u5178\u578b\u7684\u5b9e\u9a8c\u60c5\u51b5\uff0c\u6211\u4eec\u7814\u7a76\u4e86\u4f9d\u8d56\u65f6\u95f4\u7684\u4ea4\u4e92\u7f51\u7edc\u4e2d\u8fb9\u7f18\u7684\u4e2d\u5fc3\u6027\u3002\u6211\u4eec\u5229\u7528\u5e7f\u6cdb\u4f7f\u7528\u7684\u53cc\u53d8\u91cf\u5206\u6790\u6280\u672f\uff0c\u4ece\u5355\u4f4d\u52a8\u6001\u7684\u65f6\u95f4\u5e8f\u5217\u4e2d\u63a8\u5bfc\u51fa\u8fd9\u4e9b\u7f51\u7edc\u3002\u4f7f\u7528\u6211\u4eec\u6700\u8fd1\u63d0\u51fa\u7684\u8fb9\u7f18\u4e2d\u5fc3\u6027\u6982\u5ff5\u548c\u57fa\u4e8e\u8fb9\u7f18\u7684\u7f51\u7edc\u5206\u89e3\u6280\u672f\uff0c\u6211\u4eec\u89c2\u5bdf\u5230\u62db\u52df\u4e3b\u8981\u662f\u7531\u67d0\u4e9b\u8fb9\u7684\u96c6\u5408\u4fc3\u6210\u7684\uff0c\u8fd9\u4e9b\u8fb9\u5728\u5e95\u5c42\u62d3\u6251\u4e2d\u6ca1\u6709\u7b49\u4ef7\u7269\u3002\u6211\u4eec\u7684\u53d1\u73b0\u53ef\u80fd\u6709\u52a9\u4e8e\u63d0\u9ad8\u5bf9\u81ea\u7136\u7f51\u7edc\u52a8\u529b\u7cfb\u7edf\u4e2d\u6781\u7aef\u4e8b\u4ef6\u4ea7\u751f\u7684\u7406\u89e3\u3002<\/span><\/p>\n


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    \u8fdb\u5316\u6cb9\u85cf\u8ba1\u7b97\u7f51\u7edc\u4e2d\u7684\u529f\u80fd\u5dee\u5f02<\/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

    Functional differentiations in evolutionary reservoir computing networks<\/span><\/h2>\n

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

    http:\/\/arxiv.org\/abs\/2006.11507<\/span><\/p>\n

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

    Yutaka Yamaguti,Ichiro Tsuda<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>We propose an extended reservoir computer that shows the functional differentiation of neurons. The reservoir computer is developed to enable changing of the internal reservoir using evolutionary dynamics, and we call it an evolutionary reservoir computer. To develop neuronal units to show specificity, depending on the input information, the internal dynamics should be controlled to produce contracting dynamics after expanding dynamics. Expanding dynamics magnifies the difference of input information, while contracting dynamics contributes to forming clusters of input information, thereby producing multiple attractors. The simultaneous appearance of both dynamics indicates the existence of chaos. In contrast, sequential appearance of these dynamics during finite time intervals may induce functional differentiations. In this paper, we show how specific neuronal units are yielded in the evolutionary reservoir computer.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u4e2a\u6269\u5c55\u7684\u50a8\u5907\u8ba1\u7b97\u673a\uff0c\u663e\u793a\u795e\u7ecf\u5143\u7684\u529f\u80fd\u5206\u5316\u3002\u6cb9\u85cf\u8ba1\u7b97\u673a\u662f\u5229\u7528\u6f14\u5316\u52a8\u529b\u5b66\u6765\u5b9e\u73b0\u6cb9\u85cf\u5185\u90e8\u53d8\u5316\u7684\uff0c\u6211\u4eec\u79f0\u4e4b\u4e3a\u6f14\u5316\u6cb9\u85cf\u8ba1\u7b97\u673a\u3002\u4e3a\u4e86\u53d1\u5c55\u795e\u7ecf\u5143\u5355\u4f4d\u4ee5\u663e\u793a\u7279\u5f02\u6027\uff0c\u6839\u636e\u8f93\u5165\u4fe1\u606f\uff0c\u63a7\u5236\u5185\u90e8\u52a8\u529b\u5b66\uff0c\u5728\u6269\u5c55\u52a8\u529b\u5b66\u540e\u4ea7\u751f\u6536\u7f29\u52a8\u529b\u5b66\u3002\u6269\u5c55\u7684\u52a8\u529b\u5b66\u653e\u5927\u4e86\u8f93\u5165\u4fe1\u606f\u7684\u5dee\u5f02\uff0c\u800c\u6536\u7f29\u7684\u52a8\u529b\u5b66\u6709\u52a9\u4e8e\u5f62\u6210\u8f93\u5165\u4fe1\u606f\u7684\u96c6\u7fa4\uff0c\u4ece\u800c\u4ea7\u751f\u591a\u4e2a\u5438\u5f15\u5b50\u3002\u8fd9\u4e24\u79cd\u52a8\u529b\u5b66\u7684\u540c\u65f6\u51fa\u73b0\u8868\u660e\u6df7\u6c8c\u7684\u5b58\u5728\u3002\u76f8\u53cd\uff0c\u5728\u6709\u9650\u7684\u65f6\u95f4\u95f4\u9694\u5185\uff0c\u8fd9\u4e9b\u52a8\u529b\u5b66\u7684\u8fde\u7eed\u51fa\u73b0\u53ef\u80fd\u8bf1\u5bfc\u529f\u80fd\u5206\u5316\u3002\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u5c55\u793a\u4e86\u7279\u5b9a\u7684\u795e\u7ecf\u5143\u5355\u4f4d\u662f\u5982\u4f55\u5728\u8fdb\u5316\u50a8\u5907\u8ba1\u7b97\u673a\u4e2d\u4ea7\u751f\u7684\u3002<\/span><\/p>\n


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    \u793e\u4f1a\u4fe1\u4efb\u7f51\u7edc\u4e2d\u7684\u610f\u89c1\u6700\u5927<\/strong><\/span><\/strong><\/p>\n

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


    <\/p>\n

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

    Opinion Maximization in Social Trust Networks<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.10961<\/span><\/p>\n

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

    Pinghua Xu,Wenbin Hu,Jia Wu,Weiwei Liu<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Social media sites are now becoming very important platforms for product promotion or marketing campaigns. Therefore, there is broad interest in determining ways to guide a site to react more positively to a product with a limited budget. However, the practical significance of the existing studies on this subject is limited for two reasons. First, most studies have investigated the issue in oversimplified networks in which several important network characteristics are ignored. Second, the opinions of individuals are modeled as bipartite states(e.g., support or not) in numerous studies, however, this setting is too strict for many real scenarios. In this study, we focus on social trust networks(STNs), which have the significant characteristics ignored in the previous studies. We generalized a famed continuous-valued opinion dynamics model for STNs, which is more consistent with real scenarios. We subsequently formalized two novel problems for solving the issue in STNs. Moreover, we developed two matrix-based methods for these two problems and experiments on real-world datasets to demonstrate the practical utility of our methods.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u793e\u4ea4\u5a92\u4f53\u7f51\u7ad9\u73b0\u5728\u6b63\u6210\u4e3a\u4ea7\u54c1\u63a8\u5e7f\u6216\u8425\u9500\u6d3b\u52a8\u7684\u91cd\u8981\u5e73\u53f0\u3002\u56e0\u6b64\uff0c\u6709\u5e7f\u6cdb\u7684\u5174\u8da3\uff0c\u786e\u5b9a\u5982\u4f55\u6307\u5bfc\u7f51\u7ad9\u53cd\u5e94\u66f4\u79ef\u6781\u7684\u4ea7\u54c1\u4e0e\u6709\u9650\u7684\u9884\u7b97\u3002\u7136\u800c\uff0c\u7531\u4e8e\u4e24\u4e2a\u65b9\u9762\u7684\u539f\u56e0\uff0c\u73b0\u6709\u7814\u7a76\u7684\u73b0\u5b9e\u610f\u4e49\u6709\u9650\u3002\u9996\u5148\uff0c\u5927\u591a\u6570\u7814\u7a76\u90fd\u662f\u5728\u8fc7\u5206\u7b80\u5316\u7684\u7f51\u7edc\u4e2d\u8fdb\u884c\u7814\u7a76\uff0c\u5ffd\u7565\u4e86\u4e00\u4e9b\u91cd\u8981\u7684\u7f51\u7edc\u7279\u5f81\u3002\u5176\u6b21\uff0c\u5728\u8bb8\u591a\u7814\u7a76\u4e2d\uff0c\u4e2a\u4eba\u7684\u610f\u89c1\u88ab\u5efa\u6a21\u4e3a\u4e24\u90e8\u5206\u7684\u72b6\u6001(\u4f8b\u5982\uff0c\u652f\u6301\u4e0e\u5426) \uff0c\u7136\u800c\uff0c\u8fd9\u79cd\u8bbe\u7f6e\u5bf9\u4e8e\u8bb8\u591a\u771f\u5b9e\u7684\u573a\u666f\u6765\u8bf4\u8fc7\u4e8e\u4e25\u683c\u3002\u672c\u7814\u7a76\u4ee5\u793e\u4f1a\u4fe1\u4efb\u7f51\u7edc\u4e3a\u7814\u7a76\u5bf9\u8c61\uff0c\u4ee5\u5f80\u7684\u7814\u7a76\u5ffd\u7565\u4e86\u5176\u91cd\u8981\u7279\u5f81\u3002\u6211\u4eec\u63a8\u5e7f\u4e86\u4e00\u4e2a\u8457\u540d\u7684\u8fde\u7eed\u503c\u89c2\u70b9\u52a8\u529b\u5b66\u6a21\u578b\uff0c\u8be5\u6a21\u578b\u66f4\u7b26\u5408\u5b9e\u9645\u573a\u666f\u3002\u968f\u540e\uff0c\u6211\u4eec\u5c06\u4e24\u4e2a\u65b0\u95ee\u9898\u6b63\u5f0f\u5316\uff0c\u4ee5\u89e3\u51b3\u5728 stn \u4e2d\u7684\u95ee\u9898\u3002\u6b64\u5916\uff0c\u6211\u4eec\u8fd8\u9488\u5bf9\u8fd9\u4e24\u4e2a\u95ee\u9898\u5f00\u53d1\u4e86\u4e24\u79cd\u57fa\u4e8e\u77e9\u9635\u7684\u65b9\u6cd5\uff0c\u5e76\u5728\u5b9e\u9645\u6570\u636e\u96c6\u4e0a\u8fdb\u884c\u4e86\u5b9e\u9a8c\uff0c\u4ee5\u8bc1\u660e\u6211\u4eec\u7684\u65b9\u6cd5\u7684\u5b9e\u7528\u6027\u3002<\/span><\/p>\n


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    \u76d1\u6d4b\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e<\/strong><\/span><\/p>\n

    \u6d41\u611f\u5927\u6d41\u884c\u4e0b\u7684\u5168\u7403\u60c5\u7eea<\/strong><\/span><\/p>\n

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

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

    SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.10842<\/span><\/p>\n

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

    Qiang Yang,Hind Alamro,Somayah Albaradei,Adil Salhi,Xiaoting Lv,Changsheng Ma,Manal Alshehri,Inji Jaber,Faroug Tifratene,Wei Wang,Takashi Gojobori,Carlos M. Duarte,Xin Gao,Xiangliang Zhang<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Since the first alert launched by the World Health Organization (5 January, 2020), COVID-19 has been spreading out to over 180 countries and territories. As of June 18, 2020, in total, there are now over 8,400,000 cases and over 450,000 related deaths. This causes massive losses in the economy and jobs globally and confining about 58% of the global population. In this paper, we introduce SenWave, a novel sentimental analysis work using 105+ million collected tweets and Weibo messages to evaluate the global rise and falls of sentiments during the COVID-19 pandemic. To make a fine-grained analysis on the feeling when we face this global health crisis, we annotate 10K tweets in English and 10K tweets in Arabic in 10 categories, including optimistic, thankful, empathetic, pessimistic, anxious, sad, annoyed, denial, official report, and joking. We then utilize an integrated transformer framework, called simpletransformer, to conduct multi-label sentimental classification by fine-tuning the pre-trained language model on the labeled data. Meanwhile, in order for a more complete analysis, we also translate the annotated English tweets into different languages (Spanish, Italian, and French) to generated training data for building sentiment analysis models for these languages. SenWave thus reveals the sentiment of global conversation in six different languages on COVID-19 (covering English, Spanish, French, Italian, Arabic and Chinese), followed the spread of the epidemic. The conversation showed a remarkably similar pattern of rapid rise and slow decline over time across all nations, as well as on special topics like the herd immunity strategies, to which the global conversation reacts strongly negatively. Overall, SenWave shows that optimistic and positive sentiments increased over time, foretelling a desire to seek, together, a reset for an improved COVID-19 world.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u81ea\u4ece\u4e16\u754c\u536b\u751f\u7ec4\u7ec7\u53d1\u5e03\u7b2c\u4e00\u4e2a\u9884\u8b66(2020\u5e741\u67085\u65e5)\u4ee5\u6765\uff0c\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u5df2\u7ecf\u5411180\u591a\u4e2a\u56fd\u5bb6\u548c\u5730\u533a\u4f20\u64ad\u3002\u622a\u81f32020\u5e746\u670818\u65e5\uff0c\u603b\u5171\u6709\u8d85\u8fc78,400,000\u4e2a\u75c5\u4f8b\u548c\u8d85\u8fc7450,000\u4e2a\u76f8\u5173\u6b7b\u4ea1\u3002\u8fd9\u5bfc\u81f4\u5168\u7403\u7ecf\u6d4e\u548c\u5c31\u4e1a\u673a\u4f1a\u5927\u91cf\u51cf\u5c11\uff0c\u9650\u5236\u4e86\u5168\u7403\u7ea658% \u7684\u4eba\u53e3\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u4ecb\u7ecd SenWave\uff0c\u4e00\u4e2a\u65b0\u9896\u7684\u60c5\u611f\u5206\u6790\u5de5\u4f5c\uff0c\u4f7f\u7528\u6536\u96c6\u76841.05\u4ebf\u6761\u63a8\u7279\u548c\u5fae\u535a\u4fe1\u606f\u6765\u8bc4\u4f30\u5168\u7403\u5728\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6d41\u611f\u5927\u6d41\u884c\u671f\u95f4\u60c5\u611f\u7684\u5174\u8870\u3002\u4e3a\u4e86\u5bf9\u6211\u4eec\u9762\u5bf9\u8fd9\u573a\u5168\u7403\u5065\u5eb7\u5371\u673a\u65f6\u7684\u611f\u53d7\u8fdb\u884c\u7ec6\u81f4\u7684\u5206\u6790\uff0c\u6211\u4eec\u5c0610K \u6761\u63a8\u6587\u7528\u82f1\u8bed\u6ce8\u91ca\uff0c10K \u6761\u63a8\u6587\u7528\u963f\u62c9\u4f2f\u8bed\u6ce8\u91ca\uff0c\u5206\u4e3a10\u4e2a\u7c7b\u522b\uff0c\u5305\u62ec\u4e50\u89c2\u3001\u611f\u6069\u3001\u540c\u60c5\u3001\u60b2\u89c2\u3001\u7126\u8651\u3001\u60b2\u4f24\u3001\u70e6\u607c\u3001\u5426\u8ba4\u3001\u5b98\u65b9\u62a5\u544a\u548c\u5f00\u73a9\u7b11\u3002\u7136\u540e\uff0c\u6211\u4eec\u5229\u7528\u4e00\u4e2a\u96c6\u6210\u7684\u53d8\u6362\u5668\u6846\u67b6\uff0c\u79f0\u4e3a\u7b80\u5355\u53d8\u6362\u5668\uff0c\u8fdb\u884c\u591a\u6807\u7b7e\u60c5\u611f\u5206\u7c7b\uff0c\u901a\u8fc7\u5fae\u8c03\u524d\u8bad\u7ec3\u8bed\u8a00\u6a21\u578b\u7684\u6807\u8bb0\u6570\u636e\u3002\u4e0e\u6b64\u540c\u65f6\uff0c\u4e3a\u4e86\u8fdb\u884c\u66f4\u5168\u9762\u7684\u5206\u6790\uff0c\u6211\u4eec\u8fd8\u5c06\u5e26\u6ce8\u91ca\u7684\u82f1\u6587 tweets \u7ffb\u8bd1\u6210\u4e0d\u540c\u7684\u8bed\u8a00(\u897f\u73ed\u7259\u8bed\u3001\u610f\u5927\u5229\u8bed\u548c\u6cd5\u8bed) \uff0c\u4ee5\u751f\u6210\u7528\u4e8e\u6784\u5efa\u8fd9\u4e9b\u8bed\u8a00\u7684\u60c5\u611f\u5206\u6790\u6a21\u578b\u7684\u57f9\u8bad\u6570\u636e\u3002\u56e0\u6b64\uff0cSenWave \u63ed\u793a\u4e86\u6d41\u884c\u75c5\u4f20\u64ad\u4e4b\u540e\uff0c\u5168\u7403\u5bf9\u8bdd\u5728\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u4e0a\u4ee56\u79cd\u4e0d\u540c\u8bed\u8a00(\u5305\u62ec\u82f1\u8bed\u3001\u897f\u73ed\u7259\u8bed\u3001\u6cd5\u8bed\u3001\u610f\u5927\u5229\u8bed\u3001\u963f\u62c9\u4f2f\u8bed\u548c\u4e2d\u6587)\u8fdb\u884c\u7684\u60c5\u7eea\u3002\u968f\u7740\u65f6\u95f4\u7684\u63a8\u79fb\uff0c\u5728\u6240\u6709\u56fd\u5bb6\uff0c\u4ee5\u53ca\u5728\u8bf8\u5982\u7fa4\u4f53\u514d\u75ab\u7b56\u7565\u8fd9\u6837\u7684\u7279\u6b8a\u8bdd\u9898\u4e0a\uff0c\u5168\u7403\u5bf9\u8bdd\u90fd\u4f1a\u4ea7\u751f\u5f3a\u70c8\u7684\u8d1f\u9762\u53cd\u5e94\uff0c\u8fd9\u79cd\u5bf9\u8bdd\u663e\u793a\u51fa\u4e00\u79cd\u975e\u5e38\u76f8\u4f3c\u7684\u5feb\u901f\u4e0a\u5347\u548c\u7f13\u6162\u4e0b\u964d\u7684\u6a21\u5f0f\u3002\u603b\u7684\u6765\u8bf4\uff0cSenWave \u663e\u793a\u4e50\u89c2\u548c\u79ef\u6781\u7684\u60c5\u7eea\u968f\u7740\u65f6\u95f4\u7684\u63a8\u79fb\u800c\u589e\u957f\uff0c\u9884\u793a\u7740\u4e00\u79cd\u5171\u540c\u5bfb\u6c42\u6539\u5584\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u4e16\u754c\u7684\u613f\u671b\u3002<\/span><\/p>\n


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    \u57fa\u4e8e\u8d44\u6e90\u5206\u914d\u7684\u8d85\u8fb9\u754c\u9884\u6d4b<\/strong><\/span><\/p>\n

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

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

    HPRA: Hyperedge Prediction using Resource Allocation<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.10842<\/span><\/p>\n

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

    Tarun Kumar,K Darwin,Srinivasan Parthasarathy,Balaraman Ravindran<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Many real-world systems involve higher-order interactions and thus demand complex models such as hypergraphs. For instance, a research article could have multiple collaborating authors, and therefore the co-authorship network is best represented as a hypergraph. In this work, we focus on the problem of hyperedge prediction. This problem has immense applications in multiple domains, such as predicting new collaborations in social networks, discovering new chemical reactions in metabolic networks, etc. Despite having significant importance, the problem of hyperedge prediction hasn’t received adequate attention, mainly because of its inherent complexity. In a graph with<\/span>n nodes the number of potential edges is <\/span>O(n2), whereas in a hypergraph, the number of potential hyperedges is <\/span>O(2n). To avoid searching through such a huge space, current methods restrain the original problem in the following two ways. One class of algorithms assume the hypergraphs to be <\/span>k-uniform. However, many real-world systems are not confined only to have interactions involving <\/span>k components. Thus, these algorithms are not suitable for many real-world applications. The second class of algorithms requires a candidate set of hyperedges from which the potential hyperedges are chosen. In the absence of domain knowledge, the candidate set can have <\/span>O(2n) possible hyperedges, which makes this problem intractable. We propose HPRA – Hyperedge Prediction using Resource Allocation, the first of its kind algorithm, which overcomes these issues and predicts hyperedges of any cardinality without using any candidate hyperedge set. HPRA is a similarity-based method working on the principles of the resource allocation process. In addition to recovering missing hyperedges, we demonstrate that HPRA can predict future hyperedges in a wide range of hypergraphs.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u8bb8\u591a\u73b0\u5b9e\u4e16\u754c\u7684\u7cfb\u7edf\u6d89\u53ca\u5230\u9ad8\u9636\u7684\u76f8\u4e92\u4f5c\u7528\uff0c\u56e0\u6b64\u9700\u8981\u590d\u6742\u7684\u6a21\u578b\uff0c\u5982\u8d85\u56fe\u3002\u4f8b\u5982\uff0c\u4e00\u7bc7\u7814\u7a76\u8bba\u6587\u53ef\u80fd\u6709\u591a\u4e2a\u5408\u4f5c\u4f5c\u8005\uff0c\u56e0\u6b64\u5408\u4f5c\u4f5c\u8005\u7f51\u7edc\u6700\u597d\u7528\u8d85\u56fe\u6765\u8868\u793a\u3002\u672c\u6587\u4e3b\u8981\u7814\u7a76\u8d85\u8fb9\u754c\u9884\u6d4b\u95ee\u9898\u3002\u8fd9\u4e2a\u95ee\u9898\u5728\u591a\u4e2a\u9886\u57df\u6709\u7740\u5e7f\u6cdb\u7684\u5e94\u7528\uff0c\u4f8b\u5982\u5728\u793e\u4ea4\u7f51\u7edc\u4e2d\u9884\u6d4b\u65b0\u7684\u5408\u4f5c\uff0c\u5728\u65b0\u9648\u4ee3\u8c22\u7f51\u7edc\u4e2d\u53d1\u73b0\u65b0\u7684\u5316\u5b66\u53cd\u5e94\u7b49\u3002\u8d85\u8fb9\u7f18\u9884\u6d4b\u95ee\u9898\u867d\u7136\u5177\u6709\u91cd\u8981\u7684\u610f\u4e49\uff0c\u4f46\u7531\u4e8e\u5176\u56fa\u6709\u7684\u590d\u6742\u6027\uff0c\u4e00\u76f4\u6ca1\u6709\u5f97\u5230\u8db3\u591f\u7684\u91cd\u89c6\u3002\u5728\u4e00\u4e2a\u5e26\u6709<\/span>n \u8282\u70b9\u7684\u4f4d\u52bf\u8fb9\u7684\u6570\u76ee\u662f<\/span>O(n2), \u7136\u800c\u5728\u8d85\u56fe\u4e2d\uff0c\u52bf\u8d85\u8fb9\u7684\u4e2a\u6570\u662f<\/span>O(2n). \u4e3a\u4e86\u907f\u514d\u5728\u8fd9\u6837\u4e00\u4e2a\u5de8\u5927\u7684\u7a7a\u95f4\u4e2d\u8fdb\u884c\u641c\u7d22\uff0c\u73b0\u6709\u7684\u65b9\u6cd5\u4e3b\u8981\u4ece\u4ee5\u4e0b\u4e24\u4e2a\u65b9\u9762\u6765\u6291\u5236\u539f\u95ee\u9898\u3002\u4e00\u7c7b\u7b97\u6cd5\u5047\u8bbe\u8d85\u56fe\u662f<\/span>k- \u7edf\u4e00\u3002\u7136\u800c\uff0c\u8bb8\u591a\u73b0\u5b9e\u4e16\u754c\u7684\u7cfb\u7edf\u5e76\u4e0d\u53ea\u5c40\u9650\u4e8e\u5305\u62ec<\/span>k \u7ec4\u4ef6\u3002\u56e0\u6b64\uff0c\u8fd9\u4e9b\u7b97\u6cd5\u4e0d\u9002\u7528\u4e8e\u8bb8\u591a\u73b0\u5b9e\u4e16\u754c\u7684\u5e94\u7528\u3002\u7b2c\u4e8c\u7c7b\u7b97\u6cd5\u9700\u8981\u4e00\u7ec4\u5019\u9009\u8d85\u8fb9\uff0c\u4ece\u4e2d\u9009\u62e9\u6f5c\u5728\u7684\u8d85\u8fb9\u3002\u5728\u6ca1\u6709\u9886\u57df\u77e5\u8bc6\u7684\u60c5\u51b5\u4e0b\uff0c\u5019\u9009\u96c6\u53ef\u4ee5\u5177\u6709<\/span>O(2n) \u53ef\u80fd\u662f\u8d85\u8fb9\uff0c\u8fd9\u4f7f\u5f97\u8fd9\u4e2a\u95ee\u9898\u5f88\u68d8\u624b\u3002\u672c\u6587\u63d0\u51fa\u4e86\u4e00\u79cd\u57fa\u4e8e\u8d44\u6e90\u5206\u914d\u7684 HPRA-\u8d85\u8fb9\u9884\u6d4b\u7b97\u6cd5\uff0c\u8be5\u7b97\u6cd5\u514b\u670d\u4e86\u8fd9\u4e9b\u95ee\u9898\uff0c\u4e0d\u9700\u8981\u4f7f\u7528\u4efb\u4f55\u5019\u9009\u8d85\u8fb9\u96c6\u5c31\u53ef\u4ee5\u9884\u6d4b\u4efb\u610f\u57fa\u6570\u7684\u8d85\u8fb9\u3002Hpra \u662f\u4e00\u79cd\u57fa\u4e8e\u76f8\u4f3c\u6027\u7684\u65b9\u6cd5\uff0c\u5b83\u9075\u5faa\u8d44\u6e90\u5206\u914d\u8fc7\u7a0b\u7684\u539f\u5219\u3002\u9664\u4e86\u6062\u590d\u4e22\u5931\u7684\u8d85\u8fb9\u5916\uff0c\u6211\u4eec\u8bc1\u660e\u4e86 HPRA \u80fd\u591f\u5728\u5e7f\u6cdb\u7684\u8d85\u56fe\u4e2d\u9884\u6d4b\u672a\u6765\u7684\u8d85\u8fb9\u3002<\/span><\/p>\n


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    \u9752\u5c11\u5e74\u5e78\u798f\u611f\u6d4b\u91cf:<\/strong><\/span><\/p>\n

    \u5e7c\u7a1a\u6570\u5b57\u75d5\u8ff9\u4e0e\u8c03\u67e5\u6570\u636e\u7684\u5bf9\u5e94<\/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

    Measuring Adolescents’ Well-being: Correspondence of Naive Digital Traces to Survey Data<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.11176<\/span><\/p>\n

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

    Elizaveta Sivak,Ivan Smirnov<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Digital traces are often used as a substitute for survey data. However, it is unclear whether and how digital traces actually correspond to the survey-based traits they purport to measure. This paper examines correlations between self-reports and digital trace proxies of depression, anxiety, mood, social integration and sleep among high school students. The study is based on a small but rich multilayer data set (N = 144). The data set contains mood and sleep measures, assessed daily over a 4-month period, along with survey measures at two points in time and information about online activity from VK, the most popular social networking site in Russia. Our analysis indicates that 1) the sentiments expressed in social media posts are correlated with depression; namely, adolescents with more severe symptoms of depression write more negative posts, 2) late-night posting indicates less sleep and poorer sleep quality, and 3) students who were nominated less often as somebody’s friend in the survey have fewer friends on VK and their posts receive fewer “likes.” However, these correlations are generally weak. These results demonstrate that digital traces can serve as useful supplements to, rather than substitutes for, survey data in studies on adolescents’ well-being. These estimates of correlations between survey and digital trace data could provide useful guidelines for future research on the topic.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u6570\u5b57\u75d5\u8ff9\u7ecf\u5e38\u88ab\u7528\u4f5c\u8c03\u67e5\u6570\u636e\u7684\u66ff\u4ee3\u54c1\u3002\u7136\u800c\uff0c\u76ee\u524d\u8fd8\u4e0d\u6e05\u695a\u6570\u5b57\u75d5\u8ff9\u662f\u5426\u4ee5\u53ca\u5982\u4f55\u4e0e\u4ed6\u4eec\u58f0\u79f0\u8981\u6d4b\u91cf\u7684\u57fa\u4e8e\u8c03\u67e5\u7684\u7279\u5f81\u76f8\u5bf9\u5e94\u3002\u672c\u6587\u7814\u7a76\u4e86\u9ad8\u4e2d\u751f\u81ea\u6211\u62a5\u544a\u4e0e\u6291\u90c1\u3001\u7126\u8651\u3001\u60c5\u7eea\u3001\u793e\u4f1a\u6574\u5408\u548c\u7761\u7720\u7684\u6570\u5b57\u75d5\u8ff9\u4ee3\u7406\u4e4b\u95f4\u7684\u76f8\u5173\u6027\u3002\u8fd9\u9879\u7814\u7a76\u662f\u57fa\u4e8e\u4e00\u4e2a\u5c0f\u800c\u4e30\u5bcc\u7684\u591a\u5c42\u6570\u636e\u96c6(n = 144)\u3002\u8fd9\u4e9b\u6570\u636e\u5305\u62ec\u60c5\u7eea\u548c\u7761\u7720\u6d4b\u91cf\u6570\u636e\uff0c\u57284\u4e2a\u6708\u7684\u65f6\u95f4\u91cc\u6bcf\u5929\u8fdb\u884c\u8bc4\u4f30\uff0c\u540c\u65f6\u8fd8\u5305\u62ec\u4e24\u4e2a\u65f6\u95f4\u70b9\u7684\u8c03\u67e5\u6d4b\u91cf\u6570\u636e\uff0c\u4ee5\u53ca\u4fc4\u7f57\u65af\u6700\u53d7\u6b22\u8fce\u7684\u793e\u4ea4\u7f51\u7ad9 VK \u63d0\u4f9b\u7684\u6709\u5173\u5728\u7ebf\u6d3b\u52a8\u7684\u4fe1\u606f\u3002\u6211\u4eec\u7684\u5206\u6790\u8868\u660e: 1)\u793e\u4ea4\u5a92\u4f53\u5e16\u5b50\u4e2d\u8868\u8fbe\u7684\u60c5\u7eea\u4e0e\u6291\u90c1\u75c7\u6709\u5173; \u4e5f\u5c31\u662f\u8bf4\uff0c\u6291\u90c1\u75c7\u75c7\u72b6\u66f4\u4e25\u91cd\u7684\u9752\u5c11\u5e74\u5199\u7684\u8d1f\u9762\u5e16\u5b50\u66f4\u591a; 2)\u6df1\u591c\u53d1\u5e16\u610f\u5473\u7740\u7761\u7720\u66f4\u5c11\u3001\u7761\u7720\u8d28\u91cf\u66f4\u5dee; 3)\u5728\u8c03\u67e5\u4e2d\u88ab\u63d0\u540d\u4e3a\u670b\u53cb\u7684\u5b66\u751f\u5728 VK \u4e0a\u7684\u670b\u53cb\u66f4\u5c11\uff0c\u4ed6\u4eec\u7684\u5e16\u5b50\u5f97\u5230\u7684\u201c\u8d5e\u201d\u4e5f\u66f4\u5c11 \u7136\u800c\uff0c\u8fd9\u4e9b\u76f8\u5173\u6027\u666e\u904d\u8f83\u5f31\u3002\u8fd9\u4e9b\u7ed3\u679c\u8868\u660e\uff0c\u5728\u9752\u5c11\u5e74\u5e78\u798f\u7814\u7a76\u4e2d\uff0c\u6570\u5b57\u75d5\u8ff9\u53ef\u4ee5\u4f5c\u4e3a\u8c03\u67e5\u6570\u636e\u7684\u6709\u7528\u8865\u5145\uff0c\u800c\u4e0d\u662f\u66ff\u4ee3\u3002\u8fd9\u4e9b\u5173\u4e8e\u8c03\u67e5\u548c\u6570\u5b57\u75d5\u8ff9\u6570\u636e\u4e4b\u95f4\u76f8\u5173\u6027\u7684\u4f30\u8ba1\u53ef\u4ee5\u4e3a\u4eca\u540e\u5173\u4e8e\u8fd9\u4e00\u4e3b\u9898\u7684\u7814\u7a76\u63d0\u4f9b\u6709\u7528\u7684\u6307\u5bfc\u3002<\/span><\/p>\n


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    \u81ea\u9002\u5e94\u6269\u6563\u4e0b\u7684\u591a\u89c2\u6d4b\u8c23\u8a00\u6e90\u68c0\u6d4b<\/strong><\/span><\/p>\n

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

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

    Rumor source detection with multiple observations under adaptive diffusions<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.11176<\/span><\/p>\n

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

    Miklos Z. Racz,Jacob Richey<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Recent work, motivated by anonymous messaging platforms, has introduced adaptive diffusion protocols which can obfuscate the source of a rumor: a “snapshot adversary” with access to the subgraph of “infected” nodes can do no better than randomly guessing the entity of the source node. What happens if the adversary has access to multiple independent snapshots? We study this question when the underlying graph is the infinite<\/span>d-regular tree. We show that (1) a weak form of source obfuscation is still possible in the case of two independent snapshots, but (2) already with three observations there is a simple algorithm that finds the rumor source with constant probability, regardless of the adaptive diffusion protocol. We also characterize the tradeoff between local spreading and source obfuscation for adaptive diffusion protocols (under a single snapshot). These results raise questions about the robustness of anonymity guarantees when spreading information in social networks.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u6700\u8fd1\uff0c\u5728\u533f\u540d\u6d88\u606f\u4f20\u9012\u5e73\u53f0\u7684\u63a8\u52a8\u4e0b\uff0c\u5f15\u5165\u4e86\u81ea\u9002\u5e94\u6269\u6563\u534f\u8bae\uff0c\u8fd9\u79cd\u534f\u8bae\u53ef\u4ee5\u6a21\u7cca\u8c23\u8a00\u7684\u6765\u6e90: \u6709\u6743\u8bbf\u95ee\u201c\u88ab\u611f\u67d3\u201d\u8282\u70b9\u5b50\u56fe\u7684\u201c\u5bf9\u624b\u5feb\u7167\u201d\u53ea\u80fd\u968f\u673a\u731c\u6d4b\u6e90\u8282\u70b9\u7684\u5b9e\u4f53\u3002\u5982\u679c\u5bf9\u624b\u53ef\u4ee5\u8bbf\u95ee\u591a\u4e2a\u72ec\u7acb\u7684\u5feb\u7167\u4f1a\u53d1\u751f\u4ec0\u4e48\uff1f\u5f53\u5e95\u56fe\u662f\u65e0\u7a77\u5927\u65f6\uff0c\u6211\u4eec\u7814\u7a76\u8fd9\u4e2a\u95ee\u9898d- \u666e\u901a\u6811\u3002\u6211\u4eec\u8bc1\u660e: (1)\u5728\u4e24\u4e2a\u72ec\u7acb\u7684\u5feb\u7167\u60c5\u51b5\u4e0b\uff0c\u4ecd\u7136\u53ef\u80fd\u5b58\u5728\u4e00\u79cd\u5f31\u5f62\u5f0f\u7684\u6e90\u6df7\u6dc6\uff0c\u4f46\u662f(2)\u5df2\u7ecf\u6709\u4e86\u4e09\u4e2a\u89c2\u5bdf\u503c\uff0c\u6709\u4e00\u4e2a\u7b80\u5355\u7684\u7b97\u6cd5\uff0c\u4e0d\u7ba1\u81ea\u9002\u5e94\u6269\u6563\u534f\u8bae\u5982\u4f55\uff0c\u90fd\u53ef\u4ee5\u627e\u5230\u5177\u6709\u5e38\u6570\u6982\u7387\u7684\u8c23\u8a00\u6e90\u3002\u6211\u4eec\u8fd8\u523b\u753b\u4e86\u81ea\u9002\u5e94\u6269\u6563\u534f\u8bae\u5728\u672c\u5730\u6269\u6563\u548c\u6e90\u6a21\u7cca\u4e4b\u95f4\u7684\u6743\u8861(\u5728\u5355\u4e2a\u5feb\u7167\u4e0b)\u3002\u8fd9\u4e9b\u7ed3\u679c\u63d0\u51fa\u4e86\u5173\u4e8e\u5728\u793e\u4f1a\u7f51\u7edc\u4e2d\u4f20\u64ad\u4fe1\u606f\u65f6\u533f\u540d\u4fdd\u8bc1\u7684\u7a33\u5065\u6027\u7684\u95ee\u9898\u3002<\/span><\/p>\n


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    \u5fc3\u7406\u7269\u7406\u5b66: <\/strong><\/span><\/p>\n

    \u4e24\u4e2a\u8026\u5408\u7684\u6b63\u65b9\u5f62\u8109\u51b2\u795e\u7ecf\u5143<\/strong><\/span><\/p>\n

    \u5728\u4e34\u754c\u72b6\u6001\u4e0b\u5177\u6709\u5de8\u5927\u7684\u52a8\u6001\u8303\u56f4<\/strong><\/span><\/p>\n

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

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

    Physics of Psychophysics: two coupled square lattices of spiking neurons have huge dynamic range at criticality<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.11254<\/span><\/p>\n

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

    Emilio F. Galera,Osame Kinouchi<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Psychophysics try to relate physical input magnitudes to psychological or neural correlates. Microscopic models to account for macroscopic psychophysical laws, in the sense of statistical physics, are an almost unexplored area. Here we examine a sensory epithelium composed of two connected square lattices of stochastic integrate-and-fire cells. With one square lattice we obtain a Stevens’s law<\/span>\u03c1\u221dhm with Stevens’s exponent <\/span>m=0.254 and a sigmoidal saturation, where <\/span>\u03c1 is the neuronal network activity and <\/span>h is the input intensity (external field). We relate Stevens’s power law exponent with the field critical exponent as <\/span>m=1\/\u03b4h=\u03b2\/\u03c3. We also show that this system pertains to the Directed Percolation (DP) universality class (or perhaps the Compact-DP class). With stacked two layers of square lattices, and a fraction of connectivity between the first and second layer, we obtain at the output layer <\/span>\u03c12\u221dhm2, with <\/span>m2=0.08\u2248m2, which corresponds to a huge dynamic range. This enhancement of the dynamic range only occurs when the layers are close to their critical point.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5fc3\u7406\u7269\u7406\u5b66\u8bd5\u56fe\u5c06\u7269\u7406\u8f93\u5165\u91cf\u4e0e\u5fc3\u7406\u6216\u795e\u7ecf\u76f8\u5173\u8054\u3002\u7528\u5fae\u89c2\u6a21\u578b\u6765\u89e3\u91ca\u5b8f\u89c2\u7684\u5fc3\u7406\u7269\u7406\u5b9a\u5f8b\uff0c\u5728\u7edf\u8ba1\u7269\u7406\u5b66\u7684\u610f\u4e49\u4e0a\uff0c\u662f\u4e00\u4e2a\u51e0\u4e4e\u672a\u88ab\u63a2\u7d22\u7684\u9886\u57df\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u68c0\u67e5\u4e00\u4e2a\u611f\u89c9\u4e0a\u76ae\u7ec4\u6210\u7684\u4e24\u4e2a\u8fde\u63a5\u7684\u65b9\u683c\u968f\u673a\u79ef\u5206\u548c\u706b\u7ec6\u80de\u3002\u5bf9\u4e8e\u4e00\u4e2a\u6b63\u65b9\u5f62\u70b9\u9635\uff0c\u6211\u4eec\u5f97\u5230\u4e86\u4e00\u4e2a\u65af\u8482\u6587\u65af\u5b9a\u5f8b<\/span>\u03c1\u221dhm \u53f2\u8482\u6587\u65af\u7684\u6307\u6570<\/span>m=0.254 \u548c\u76f8\u4f3c\u7684\u9971\u548c\u5ea6\uff0c\u5176\u4e2d<\/span>\u03c1 \u662f\u662f\u795e\u7ecf\u5143\u7f51\u7edc\u6d3b\u52a8\uff0c<\/span>h \u662f \u8f93\u5165\u5f3a\u5ea6 \uff0c\u6211\u4eec\u5c06\u65af\u8482\u6587\u65af\u7684\u5e42\u5f8b\u6307\u6570\u4e0e\u573a\u4e34\u754c\u6307\u6570\u5173\u7cfb\u5982\u4e0b:<\/span>m=1\/\u03b4h=\u03b2\/\u03c3.\u6211\u4eec\u8fd8\u8bc1\u660e\u4e86\u8be5\u7cfb\u7edf\u5c5e\u4e8e\u5b9a\u5411 Percolation (DP)\u666e\u9002\u6027\u7c7b(\u6216\u8005\u53ef\u80fd\u5c5e\u4e8e compact-DP \u7c7b)\u3002\u901a\u8fc7\u53e0\u52a0\u4e24\u5c42\u6b63\u65b9\u5f62\u6676\u683c\uff0c\u4ee5\u53ca\u7b2c\u4e00\u5c42\u548c\u7b2c\u4e8c\u5c42\u4e4b\u95f4\u8fde\u901a\u6027\u7684\u4e00\u5c0f\u90e8\u5206\uff0c\u6211\u4eec\u53ef\u4ee5\u5728\u8f93\u51fa\u5c42\u83b7\u5f97<\/span>\u03c12\u221dhm2, <\/span>m2=0.08\u2248m2,\u76f8\u5f53\u4e8e\u4e00\u4e2a\u5de8\u5927\u7684\u52a8\u6001\u8303\u56f4\u3002\u8fd9\u79cd\u52a8\u6001\u8303\u56f4\u7684\u589e\u5f3a\u53ea\u53d1\u751f\u5728\u5c42\u63a5\u8fd1\u5176\u4e34\u754c\u70b9\u7684\u65f6\u5019\u3002<\/span><\/p>\n


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    \u82f1\u56fd\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u7981\u95ed: <\/strong><\/span><\/p>\n

    \u5bf9\u7a7a\u6c14\u6c61\u67d3\u6709\u4ec0\u4e48\u5f71\u54cd<\/strong><\/span><\/p>\n

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

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

    UK COVID-19 Lockdown: What are the impacts on air pollution<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.10785<\/span><\/p>\n

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

    J. E. Higham,M. A. Green,C. Acosta Ramirez<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>A country-wide `lock-down’ imposed on the 23red March 2020 in the UK had a significant impact on the UK’s anthropogenic movements. The closure of work-places and restrictions imposed on visiting friends and family has radically reduced the amount of traffic on the roads. In this short communication, we use data from UK air-quality sensors to quantify air pollution trends pre- and post-lock-down. While we detect large falls in nitrogen dioxide at levels not seen over the last decade, trends in other pollutants were mixed especially when compared to historical data. It suggests that the implication that lock-down was beneficial for the environment was not so obvious.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>2020\u5e743\u670823\u65e5\uff0c\u82f1\u56fd\u5b9e\u65bd\u4e86\u5168\u56fd\u8303\u56f4\u7684\u201c\u5c01\u9501\u201d \uff0c\u5bf9\u82f1\u56fd\u7684\u4eba\u4e3a\u6d3b\u52a8\u4ea7\u751f\u4e86\u91cd\u5927\u5f71\u54cd\u3002\u5de5\u4f5c\u573a\u6240\u7684\u5173\u95ed\u4ee5\u53ca\u5bf9\u63a2\u671b\u670b\u53cb\u548c\u5bb6\u4eba\u7684\u9650\u5236\u5927\u5927\u51cf\u5c11\u4e86\u9053\u8def\u4e0a\u7684\u4ea4\u901a\u91cf\u3002\u5728\u8fd9\u4e2a\u7b80\u77ed\u7684\u4ea4\u6d41\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u6765\u81ea\u82f1\u56fd\u7a7a\u6c14\u8d28\u91cf\u4f20\u611f\u5668\u7684\u6570\u636e\u6765\u91cf\u5316\u5c01\u9501\u524d\u540e\u7684\u7a7a\u6c14\u6c61\u67d3\u8d8b\u52bf\u3002\u867d\u7136\u6211\u4eec\u68c0\u6d4b\u5230\u4e8c\u6c27\u5316\u6c2e\u7684\u6c34\u5e73\u5927\u5e45\u4e0b\u964d\uff0c\u4f46\u662f\u5176\u4ed6\u6c61\u67d3\u7269\u7684\u8d8b\u52bf\u5374\u597d\u574f\u53c2\u534a\uff0c\u5c24\u5176\u662f\u4e0e\u5386\u53f2\u6570\u636e\u76f8\u6bd4\u3002\u8fd9\u8868\u660e\uff0c\u5c01\u9501\u6709\u5229\u4e8e\u73af\u5883\u7684\u542b\u4e49\u5e76\u4e0d\u662f\u90a3\u4e48\u660e\u663e\u3002<\/span><\/p>\n


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    \u51e0\u4f55\uff0c\u63a8\u7406\uff0c\u590d\u6742\u6027\u548c\u6c11\u4e3b<\/strong><\/span><\/p>\n

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

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

    Geometry, Inference, Complexity, and Democracy<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.10879<\/span><\/p>\n

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

    Jordan S. Ellenberg<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Decisions about how the population of the United States should be divided into legislative districts have powerful and not fully understood effects on the outcomes of elections. The problem of understanding what we might mean by “fair districting” intertwines mathematical, political, and legal reasoning; but only in recent years has the academic mathematical community gotten directly involved in the process. I’ll report on recent progress in this area, how newly developed mathematical tools have affected real political decisions, and what remains to be done. This survey represents the content of a lecture presented by the author in the Current Events Bulletin session of the Joint Mathematics Meetings in January 2020.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u5173\u4e8e\u5982\u4f55\u5c06\u7f8e\u56fd\u4eba\u53e3\u5212\u5206\u4e3a\u7acb\u6cd5\u533a\u7684\u51b3\u5b9a\u5bf9\u9009\u4e3e\u7ed3\u679c\u4ea7\u751f\u4e86\u5f3a\u5927\u800c\u53c8\u672a\u88ab\u5145\u5206\u7406\u89e3\u7684\u5f71\u54cd\u3002\u7406\u89e3\u6211\u4eec\u6240\u8bf4\u7684\u201c\u516c\u5e73\u5206\u533a\u201d\u662f\u4ec0\u4e48\u610f\u601d\u7684\u95ee\u9898\u5c06\u6570\u5b66\u3001\u653f\u6cbb\u548c\u6cd5\u5f8b\u63a8\u7406\u8054\u7cfb\u5728\u4e00\u8d77; \u4f46\u662f\u76f4\u5230\u6700\u8fd1\u51e0\u5e74\uff0c\u5b66\u672f\u6570\u5b66\u754c\u624d\u76f4\u63a5\u53c2\u4e0e\u5230\u8fd9\u4e2a\u8fc7\u7a0b\u4e2d\u6765\u3002\u6211\u5c06\u62a5\u544a\u8fd9\u4e00\u9886\u57df\u7684\u6700\u65b0\u8fdb\u5c55\uff0c\u65b0\u5f00\u53d1\u7684\u6570\u5b66\u5de5\u5177\u5982\u4f55\u5f71\u54cd\u771f\u6b63\u7684\u653f\u6cbb\u51b3\u7b56\uff0c\u4ee5\u53ca\u8fd8\u9700\u8981\u505a\u4ec0\u4e48\u3002\u672c\u8c03\u67e5\u662f\u4f5c\u8005\u57282020\u5e741\u6708\u6570\u5b66\u8054\u5e2d\u4f1a\u8bae\u7684\u65f6\u4e8b\u7b80\u62a5\u4f1a\u4e0a\u53d1\u8868\u7684\u4e00\u6b21\u6f14\u8bb2\u7684\u5185\u5bb9\u3002<\/span><\/p>\n


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    \u4f7f\u7528\u6982\u7387\u7ec6\u80de\u81ea\u52a8\u673a\u7814\u7a76<\/strong><\/span><\/p>\n

    \u8ba1\u7b97\u6a21\u578b\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6d41\u884c\u75c5<\/strong><\/span><\/p>\n

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

    Computational model on COVID-19 Pandemic using Probabilistic Cellular Automata<\/span><\/h2>\n

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

    https:\/\/arxiv.org\/abs\/2006.11270<\/span><\/p>\n

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

    Sayantari Ghosh,Saumik Bhattacharya<\/span><\/p>\n


    <\/p>\n

    Abstract\uff1a<\/span><\/strong>Coronavirus disease (COVID-19) which is caused by SARS-COV2 has become a pandemic. This disease is highly infectious and potentially fatal, causing a global public health concern. To contain the spread of COVID-19, governments are adopting nationwide interventions, like lockdown, containment and quarantine, restrictions on travel, cancelling social events and extensive testing. To understand the effects of these measures on the control of the epidemic in a data-driven manner, we propose a probabilistic cellular automata (PCA) based modified SEIQR model. The transitions associated with the model is driven by data available on chronology, symptoms, pathogenesis and transmissivity of the virus. By arguing that the lattice-based model captures the features of the dynamics along with the existing fluctuations, we perform rigorous computational analyses of the model to take into account of the spatial dynamics of social distancing measures imposed on the people. Considering the probabilistic behavioural aspects associated with mitigation strategies, we study the model considering factors like population density and testing efficiency. Using the model, we focus on the variability of epidemic dynamics data for different countries and point out the reasons behind these contrasting observations. To the best of our knowledge, this is the first attempt to model COVID-19 spread using PCA that gives us both spatial and temporal variations of the infection spread with the insight about the contributions of different infection parameters.<\/span><\/p>\n

    \u6458\u8981\uff1a<\/span><\/strong>\u7531 SARS-COV2\u5f15\u8d77\u7684\u51a0\u72b6\u75c5\u6bd2\u75c5(\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e)\u5df2\u7ecf\u6210\u4e3a\u4e00\u79cd\u6d41\u884c\u75c5\u3002\u8fd9\u79cd\u75be\u75c5\u5177\u6709\u9ad8\u5ea6\u4f20\u67d3\u6027\uff0c\u53ef\u80fd\u81f4\u547d\uff0c\u5f15\u8d77\u5168\u7403\u516c\u5171\u536b\u751f\u5173\u6ce8\u3002\u4e3a\u4e86\u904f\u5236\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u7684\u8513\u5ef6\uff0c\u5404\u56fd\u653f\u5e9c\u6b63\u5728\u91c7\u53d6\u5168\u56fd\u6027\u7684\u5e72\u9884\u63aa\u65bd\uff0c\u5982\u5c01\u9501\u3001\u9694\u79bb\u548c\u9694\u79bb\u3001\u9650\u5236\u65c5\u884c\u3001\u53d6\u6d88\u793e\u4f1a\u6d3b\u52a8\u548c\u5e7f\u6cdb\u7684\u68c0\u6d4b\u3002\u4e3a\u4e86\u4ee5\u6570\u636e\u9a71\u52a8\u7684\u65b9\u5f0f\u7406\u89e3\u8fd9\u4e9b\u63aa\u65bd\u5bf9\u6d41\u884c\u75c5\u63a7\u5236\u7684\u5f71\u54cd\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u79cd\u57fa\u4e8e\u6982\u7387\u5143\u80de\u81ea\u52a8\u673a(PCA)\u7684\u6539\u8fdb SEIQR \u6a21\u578b\u3002\u4e0e\u8be5\u6a21\u578b\u76f8\u5173\u7684\u8f6c\u53d8\u662f\u7531\u5173\u4e8e\u75c5\u6bd2\u7684\u5e74\u4ee3\u5b66\u3001\u75c7\u72b6\u3001\u53d1\u75c5\u673a\u5236\u548c\u4f20\u64ad\u7387\u7684\u6570\u636e\u9a71\u52a8\u7684\u3002\u901a\u8fc7\u8bba\u8bc1\u57fa\u4e8e\u683c\u5b50\u7684\u6a21\u578b\u6355\u83b7\u4e86\u52a8\u529b\u5b66\u7684\u7279\u5f81\u4ee5\u53ca\u5b58\u5728\u7684\u6ce2\u52a8\uff0c\u6211\u4eec\u5bf9\u6a21\u578b\u8fdb\u884c\u4e86\u4e25\u683c\u7684\u8ba1\u7b97\u5206\u6790\uff0c\u4ee5\u8003\u8651\u5f3a\u52a0\u5728\u4eba\u4eec\u8eab\u4e0a\u7684\u793e\u4f1a\u8ddd\u79bb\u63aa\u65bd\u7684\u7a7a\u95f4\u52a8\u529b\u5b66\u3002\u8003\u8651\u5230\u4e0e\u51cf\u707e\u7b56\u7565\u76f8\u5173\u7684\u6982\u7387\u884c\u4e3a\u65b9\u9762\uff0c\u6211\u4eec\u7814\u7a76\u4e86\u8003\u8651\u79cd\u7fa4\u5bc6\u5ea6\u548c\u6d4b\u8bd5\u6548\u7387\u7b49\u56e0\u7d20\u7684\u6a21\u578b\u3002\u5229\u7528\u8be5\u6a21\u578b\uff0c\u6211\u4eec\u91cd\u70b9\u5173\u6ce8\u4e0d\u540c\u56fd\u5bb6\u7684\u6d41\u884c\u75c5\u52a8\u6001\u6570\u636e\u7684\u53ef\u53d8\u6027\uff0c\u5e76\u6307\u51fa\u8fd9\u4e9b\u5bf9\u6bd4\u89c2\u5bdf\u80cc\u540e\u7684\u539f\u56e0\u3002\u636e\u6211\u4eec\u6240\u77e5\uff0c\u8fd9\u662f\u7b2c\u4e00\u6b21\u5c1d\u8bd5\u4f7f\u7528 PCA \u6a21\u578b\u6765\u6a21\u62df\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u4f20\u64ad\uff0c\u8be5\u6a21\u578b\u901a\u8fc7\u6d1e\u5bdf\u4e0d\u540c\u611f\u67d3\u53c2\u6570\u7684\u4f5c\u7528\uff0c\u4e3a\u6211\u4eec\u63d0\u4f9b\u4e86\u611f\u67d3\u4f20\u64ad\u7684\u65f6\u7a7a\u53d8\u5316\u3002<\/span><\/p>\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
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    <\/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><\/div>\n","protected":false},"excerpt":{"rendered":"

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