{"id":20229,"date":"2020-07-03T18:19:49","date_gmt":"2020-07-03T10:19:49","guid":{"rendered":"https:\/\/swarma.org\/?p=20229"},"modified":"2020-07-03T18:19:49","modified_gmt":"2020-07-03T10:19:49","slug":"%e5%85%b3%e9%97%ad%e5%92%8c%e9%87%8d%e6%96%b0%e5%bc%80%e6%94%be-%e5%ad%a6%e6%a0%a1%e5%9c%a8%e6%ac%a7%e6%b4%b2%e6%96%b0%e5%9e%8b%e5%86%a0%e7%8a%b6%e7%97%85%e6%af%92%e8%82%ba%e7%82%8e%e4%bc%a0%e6%92%ad","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=20229","title":{"rendered":"\u5173\u95ed\u548c\u91cd\u65b0\u5f00\u653e: \u5b66\u6821\u5728\u6b27\u6d32\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u4f20\u64ad\u4e2d\u7684\u4f5c\u7528 | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901233\u7bc7"},"content":{"rendered":"
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    \u5229\u7528\u795e\u7ecf\u7f51\u7edc\u53d1\u73b0 SU (N)\u8d39\u7c73\u5b50\u9690\u85cf\u7279\u5f81\u7684\u542f\u53d1\u5f0f\u673a\u5236\uff1b<\/span><\/h2>\n<\/li>\n
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    \u5df4\u897f\u5df4\u4f0a\u4e9a\u5dde\u548c\u5723\u5361\u5854\u7433\u5a1c\u7684 SARS-CoV-2\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6d41\u884c\u7684\u6700\u4f18\u63a7\u5236\u95ee\u9898\uff1b<\/span><\/h2>\n<\/li>\n
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    \u5173\u95ed\u548c\u91cd\u65b0\u5f00\u653e: <\/strong><\/span><\/p>\n

    \u5b66\u6821\u5728\u6b27\u6d32\u65b0\u578b\u51a0\u72b6<\/strong><\/span><\/p>\n

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

    Shut and re-open: the role of schools in the spread of COVID-19 in Europe<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14158<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Helena B. Stage,Joseph Shingleton,Sanmitra Ghosh,Francesca Scarabel,Lorenzo Pellis,Thomas Finnie<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>We investigate the effect of school closure and subsequent reopening on the transmission of COVID-19, by considering Denmark, Norway, Sweden, and German states as case studies. By comparing the growth rates in daily hospitalisations or confirmed cases under different interventions, we provide evidence that the effect of school closure is visible as a reduction in the growth rate approximately 9 days after implementation. Limited school attendance, such as older students sitting exams or the partial return of younger year groups, does not appear to significantly affect community transmission. A large-scale reopening of schools while controlling or suppressing the epidemic appears feasible in countries such as Denmark or Norway, where community transmission is generally low. However, school reopening can contribute to significant increases in the growth rate in countries like Germany, where community transmission is relatively high. Our findings underscore the need for a cautious evaluation of reopening strategies that ensure low classroom occupancy and a solid infrastructure to quickly identify and isolate new infections.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u5c06\u4e39\u9ea6\u3001\u632a\u5a01\u3001\u745e\u5178\u548c\u5fb7\u56fd\u5404\u5dde\u4f5c\u4e3a\u4e2a\u6848\u7814\u7a76\uff0c\u8c03\u67e5\u5b66\u6821\u5173\u95ed\u548c\u968f\u540e\u7684\u91cd\u65b0\u5f00\u653e\u5bf9\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u4f20\u64ad\u7684\u5f71\u54cd\u3002\u901a\u8fc7\u6bd4\u8f83\u4e0d\u540c\u5e72\u9884\u63aa\u65bd\u4e0b\u6bcf\u65e5\u4f4f\u9662\u6216\u786e\u8bca\u75c5\u4f8b\u7684\u589e\u957f\u7387\uff0c\u6211\u4eec\u63d0\u4f9b\u7684\u8bc1\u636e\u8868\u660e\uff0c\u5173\u95ed\u5b66\u6821\u7684\u5f71\u54cd\u662f\u663e\u800c\u6613\u89c1\u7684\uff0c\u56e0\u4e3a\u5b9e\u65bd\u63aa\u65bd\u5927\u7ea69\u5929\u540e\u589e\u957f\u7387\u4e0b\u964d\u3002\u6709\u9650\u7684\u5b66\u6821\u51fa\u52e4\uff0c\u4f8b\u5982\u9ad8\u5e74\u7ea7\u5b66\u751f\u53c2\u52a0\u8003\u8bd5\u6216\u90e8\u5206\u8fd4\u56de\u5e74\u8f7b\u7fa4\u4f53\uff0c\u4f3c\u4e4e\u6ca1\u6709\u663e\u7740\u5f71\u54cd\u793e\u533a\u4f20\u64ad\u3002\u5728\u793e\u533a\u4f20\u64ad\u7387\u666e\u904d\u8f83\u4f4e\u7684\u4e39\u9ea6\u6216\u632a\u5a01\u7b49\u56fd\u5bb6\uff0c\u5728\u63a7\u5236\u6216\u6291\u5236\u8fd9\u4e00\u6d41\u884c\u75c5\u7684\u540c\u65f6\u5927\u89c4\u6a21\u91cd\u65b0\u5f00\u653e\u5b66\u6821\u4f3c\u4e4e\u662f\u53ef\u884c\u7684\u3002\u7136\u800c\uff0c\u5728\u50cf\u5fb7\u56fd\u8fd9\u6837\u7684\u793e\u533a\u4f20\u64ad\u7387\u76f8\u5bf9\u8f83\u9ad8\u7684\u56fd\u5bb6\uff0c\u5b66\u6821\u91cd\u65b0\u5f00\u5b66\u53ef\u4ee5\u4fc3\u8fdb\u589e\u957f\u7387\u7684\u663e\u8457\u63d0\u9ad8\u3002\u6211\u4eec\u7684\u7814\u7a76\u7ed3\u679c\u5f3a\u8c03\uff0c\u9700\u8981\u8c28\u614e\u8bc4\u4f30\u91cd\u65b0\u5f00\u653e\u7684\u7b56\u7565\uff0c\u4ee5\u786e\u4fdd\u4f4e\u8bfe\u5802\u5360\u7528\u7387\u548c\u575a\u5b9e\u7684\u57fa\u7840\u8bbe\u65bd\uff0c\u4ee5\u5feb\u901f\u8bc6\u522b\u548c\u9694\u79bb\u65b0\u7684\u611f\u67d3\u3002<\/span><\/section>\n

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    \u57fa\u4e8e\u54c1\u724c\u4f20\u64ad\u7684<\/strong><\/span><\/p>\n

    \u5728\u7ebf\u793e\u4f1a\u7f51\u7edc\u5f71\u54cd\u8282\u70b9\u8bc6\u522b<\/strong><\/span><\/p>\n

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

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

    Identify Influential Nodes in Online Social Network for Brand Communication<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14104<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Yuxin Mao,Lujie Zhou,Naixue Xiong<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Online social networks have become incredibly popular in recent years, which prompts an increasing number of companies to promote their brands and products through social media. This paper presents an approach for identifying influential nodes in online social network for brand communication. We first construct a weighted network model for the users and their relationships extracted from the brand-related contents. We quantitatively measure the individual value of the nodes in the community from both the network structure and brand engagement aspects. Then an algorithm for identifying the influential nodes from the virtual brand community is proposed. The algorithm evaluates the importance of the nodes by their individual values as well as the individual values of their surrounding nodes. We extract and construct a virtual brand community for a specific brand from a real-life online social network as the dataset and empirically evaluate the proposed approach. The experimental results have shown that the proposed approach was able to identify influential nodes in online social network. We can get an identification result with higher ratio of verified users and user coverage by using the approach.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u8fd1\u5e74\u6765\uff0c\u5728\u7ebf\u793e\u4ea4\u7f51\u7edc\u53d8\u5f97\u975e\u5e38\u6d41\u884c\uff0c\u4fc3\u4f7f\u8d8a\u6765\u8d8a\u591a\u7684\u516c\u53f8\u901a\u8fc7\u793e\u4ea4\u5a92\u4f53\u63a8\u5e7f\u81ea\u5df1\u7684\u54c1\u724c\u548c\u4ea7\u54c1\u3002\u672c\u6587\u63d0\u51fa\u4e86\u4e00\u79cd\u57fa\u4e8e\u54c1\u724c\u4f20\u64ad\u7684\u5728\u7ebf\u793e\u4f1a\u7f51\u7edc\u4e2d\u6709\u5f71\u54cd\u529b\u8282\u70b9\u7684\u8bc6\u522b\u65b9\u6cd5\u3002\u6211\u4eec\u9996\u5148\u4ece\u54c1\u724c\u76f8\u5173\u5185\u5bb9\u4e2d\u62bd\u53d6\u7528\u6237\u53ca\u5176\u5173\u7cfb\uff0c\u5efa\u7acb\u4e00\u4e2a\u52a0\u6743\u7f51\u7edc\u6a21\u578b\u3002\u6211\u4eec\u4ece\u7f51\u7edc\u7ed3\u6784\u548c\u54c1\u724c\u53c2\u4e0e\u4e24\u4e2a\u65b9\u9762\u5b9a\u91cf\u8861\u91cf\u793e\u533a\u4e2d\u8282\u70b9\u7684\u4e2a\u4eba\u4ef7\u503c\u3002\u7136\u540e\u63d0\u51fa\u4e86\u4e00\u79cd\u4ece\u865a\u62df\u54c1\u724c\u793e\u533a\u4e2d\u8bc6\u522b\u6709\u5f71\u54cd\u529b\u8282\u70b9\u7684\u7b97\u6cd5\u3002\u8be5\u7b97\u6cd5\u901a\u8fc7\u8282\u70b9\u7684\u5355\u4e2a\u503c\u4ee5\u53ca\u5468\u56f4\u8282\u70b9\u7684\u5355\u4e2a\u503c\u6765\u8bc4\u4f30\u8282\u70b9\u7684\u91cd\u8981\u6027\u3002\u6211\u4eec\u4ece\u73b0\u5b9e\u751f\u6d3b\u4e2d\u7684\u5728\u7ebf\u793e\u4f1a\u7f51\u7edc\u4e2d\u62bd\u53d6\u548c\u6784\u5efa\u4e00\u4e2a\u7279\u5b9a\u54c1\u724c\u7684\u865a\u62df\u54c1\u724c\u793e\u533a\u4f5c\u4e3a\u6570\u636e\u96c6\uff0c\u5e76\u5bf9\u6240\u63d0\u51fa\u7684\u65b9\u6cd5\u8fdb\u884c\u5b9e\u8bc1\u8bc4\u4ef7\u3002\u5b9e\u9a8c\u7ed3\u679c\u8868\u660e\uff0c\u8be5\u65b9\u6cd5\u80fd\u591f\u8bc6\u522b\u5728\u7ebf\u793e\u4f1a\u7f51\u7edc\u4e2d\u5177\u6709\u5f71\u54cd\u529b\u7684\u8282\u70b9\u3002\u8be5\u65b9\u6cd5\u53ef\u4ee5\u5f97\u5230\u66f4\u9ad8\u7684\u7528\u6237\u8bc6\u522b\u7387\u548c\u7528\u6237\u8986\u76d6\u7387\u3002<\/span><\/section>\n

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    \u56fe\u7ed3\u6784\u4e3b\u9898\u795e\u7ecf\u7f51\u7edc<\/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

    Graph Structural-topic Neural Network<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14278<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Qingqing Long,Yilun Jin,Guojie Song,Yi Li,Wei Lin<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Graph Convolutional Networks (GCNs) achieved tremendous success by effectively gathering local features for nodes. However, commonly do GCNs focus more on node features but less on graph structures within the neighborhood, especially higher-order structural patterns. However, such local structural patterns are shown to be indicative of node properties in numerous fields. In addition, it is not just single patterns, but the distribution over all these patterns matter, because networks are complex and the neighborhood of each node consists of a mixture of various nodes and structural patterns. Correspondingly, in this paper, we propose Graph Structural-topic Neural Network, abbreviated GraphSTONE, a GCN model that utilizes topic models of graphs, such that the structural topics capture indicative graph structures broadly from a probabilistic aspect rather than merely a few structures. Specifically, we build topic models upon graphs using anonymous walks and Graph Anchor LDA, an LDA variant that selects significant structural patterns first, so as to alleviate the complexity and generate structural topics efficiently. In addition, we design multi-view GCNs to unify node features and structural topic features and utilize structural topics to guide the aggregation. We evaluate our model through both quantitative and qualitative experiments, where our model exhibits promising performance, high efficiency, and clear interpretability.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u56fe\u5377\u79ef\u7f51\u7edc\u901a\u8fc7\u6709\u6548\u5730\u6536\u96c6\u8282\u70b9\u7684\u5c40\u90e8\u7279\u5f81\u53d6\u5f97\u4e86\u5de8\u5927\u7684\u6210\u529f\u3002\u7136\u800c\uff0c\u901a\u5e38 GCNs \u66f4\u591a\u5730\u5173\u6ce8\u8282\u70b9\u7279\u5f81\uff0c\u800c\u8f83\u5c11\u5173\u6ce8\u90bb\u57df\u5185\u7684\u56fe\u7ed3\u6784\uff0c\u7279\u522b\u662f\u9ad8\u9636\u7ed3\u6784\u6a21\u5f0f\u3002\u7136\u800c\uff0c\u8fd9\u79cd\u5c40\u90e8\u7ed3\u6784\u6a21\u5f0f\u5728\u8bb8\u591a\u9886\u57df\u4e2d\u8868\u660e\u8282\u70b9\u5c5e\u6027\u3002\u6b64\u5916\uff0c\u5b83\u4e0d\u4ec5\u4ec5\u662f\u5355\u4e00\u7684\u6a21\u5f0f\uff0c\u800c\u662f\u6240\u6709\u8fd9\u4e9b\u6a21\u5f0f\u7684\u5206\u5e03\u90fd\u5f88\u91cd\u8981\uff0c\u56e0\u4e3a\u7f51\u7edc\u662f\u590d\u6742\u7684\uff0c\u6bcf\u4e2a\u8282\u70b9\u7684\u90bb\u5c45\u662f\u7531\u5404\u79cd\u8282\u70b9\u548c\u7ed3\u6784\u6a21\u5f0f\u7684\u6df7\u5408\u7269\u7ec4\u6210\u7684\u3002\u76f8\u5e94\u5730\uff0c\u672c\u6587\u63d0\u51fa\u4e86\u56fe\u7ed3\u6784\u4e3b\u9898\u795e\u7ecf\u7f51\u7edc\uff0c\u7b80\u79f0 GraphSTONE\uff0c\u4e00\u79cd\u5229\u7528\u56fe\u7684\u4e3b\u9898\u6a21\u578b\u7684 GCN \u6a21\u578b\uff0c\u4f7f\u7ed3\u6784\u4e3b\u9898\u4ece\u6982\u7387\u7684\u89d2\u5ea6\u5e7f\u6cdb\u5730\u6355\u6349\u6307\u793a\u6027\u56fe\u5f62\u7ed3\u6784\uff0c\u800c\u4e0d\u4ec5\u4ec5\u662f\u5c11\u6570\u51e0\u79cd\u7ed3\u6784\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u6211\u4eec\u4f7f\u7528\u533f\u540d\u6f2b\u6b65\u548c\u56fe\u951a LDA (Graph Anchor LDA) \uff0c\u4e00\u79cd\u4f18\u5148\u9009\u62e9\u91cd\u8981\u7ed3\u6784\u6a21\u5f0f\u7684 LDA \u53d8\u91cf\uff0c\u5728\u56fe\u4e0a\u5efa\u7acb\u4e3b\u9898\u6a21\u578b\uff0c\u4ee5\u51cf\u5c11\u590d\u6742\u6027\uff0c\u6709\u6548\u5730\u751f\u6210\u7ed3\u6784\u4e3b\u9898\u3002\u6b64\u5916\uff0c\u6211\u4eec\u8bbe\u8ba1\u4e86\u591a\u89c6\u56fe GCNs \u6765\u7edf\u4e00\u8282\u70b9\u7279\u5f81\u548c\u7ed3\u6784\u4e3b\u9898\u7279\u5f81\uff0c\u5e76\u5229\u7528\u7ed3\u6784\u4e3b\u9898\u6765\u6307\u5bfc\u805a\u5408\u3002\u6211\u4eec\u901a\u8fc7\u5b9a\u91cf\u548c\u5b9a\u6027\u5b9e\u9a8c\u6765\u8bc4\u4f30\u6211\u4eec\u7684\u6a21\u578b\uff0c\u5728\u8fd9\u91cc\u6211\u4eec\u7684\u6a21\u578b\u8868\u73b0\u51fa\u826f\u597d\u7684\u6027\u80fd\uff0c\u9ad8\u6548\u7387\u548c\u6e05\u6670\u7684\u53ef\u89e3\u91ca\u6027\u3002<\/span><\/section>\n

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    \u533a\u57df\u9650\u5236\u641c\u7d22\u4e0a\u7684\u968f\u673a\u6f2b\u6b65<\/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

    A random walk on Area Restricted Search<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14318<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Simone Santini<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>These notes from a graduate class at the Unuversidad Autonoma de Madrid analyze a search behavior known as Area Resticted Search (ARS), widespread in the animal kingdom, and optimal when the resources that one is after are “patchy”. In the first section we study the importance of the behavior in animal and its dependence on the dopamine as a indicator of reward. In the second section we put together a genetic algorithm to determine the optimality of ARS and its characteristics. Finally, we relate ARS to a type of random walks known as “Levy Walks”, in which the probability of jumping at a distance d from the current location follows a power law distribution.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u8fd9\u4e9b\u7b14\u8bb0\u6765\u81ea Unuversidad Autonoma de Madrid \u7684\u4e00\u4e2a\u7814\u7a76\u751f\u73ed\uff0c\u5b83\u4eec\u5206\u6790\u4e86\u4e00\u79cd\u88ab\u79f0\u4e3a\u201c\u533a\u57df\u9650\u5236\u641c\u7d22\u201d(ARS)\u7684\u641c\u7d22\u884c\u4e3a\uff0c\u8fd9\u79cd\u641c\u7d22\u5728\u52a8\u7269\u738b\u56fd\u4e2d\u5f88\u666e\u904d\uff0c\u5f53\u4eba\u4eec\u6240\u8ffd\u6c42\u7684\u8d44\u6e90\u201c\u4e0d\u5b8c\u6574\u201d\u65f6\uff0c\u8fd9\u79cd\u641c\u7d22\u884c\u4e3a\u662f\u6700\u7406\u60f3\u7684\u3002\u5728\u7b2c\u4e00\u90e8\u5206\uff0c\u6211\u4eec\u7814\u7a76\u4e86\u52a8\u7269\u884c\u4e3a\u7684\u91cd\u8981\u6027\uff0c\u4ee5\u53ca\u52a8\u7269\u5bf9\u591a\u5df4\u80fa\u4f5c\u4e3a\u5956\u8d4f\u6307\u6807\u7684\u4f9d\u8d56\u6027\u3002\u5728\u7b2c\u4e8c\u90e8\u5206\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u4e2a\u9057\u4f20\u7b97\u6cd5\uff0c\u4ee5\u786e\u5b9a\u7684\u6700\u4f18\u6027\u519c\u4e1a\u7814\u7a76\u7cfb\u7edf\u53ca\u5176\u7279\u70b9\u3002\u6700\u540e\uff0c\u6211\u4eec\u5c06 ARS \u4e0e\u4e00\u79cd\u79f0\u4e3a\u201c Levy Walks\u201d\u7684\u968f\u673a\u6e38\u52a8\u8054\u7cfb\u8d77\u6765\uff0c\u5728\u8fd9\u79cd\u968f\u673a\u6e38\u52a8\u4e2d\uff0c\u4ece\u5f53\u524d\u4f4d\u7f6e\u7684\u8ddd\u79bb d \u5904\u8df3\u8dc3\u7684\u6982\u7387\u670d\u4ece\u5e42\u5f8b\u5206\u5e03\u3002<\/span><\/section>\n

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    \u544a\u5bc6\u8005\u4f24\u75d5\u7d2f\u7d2f: \u5173\u4e8e\u544a\u5bc6\u7684\u96be\u5ea6<\/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

    Snitches Get Stitches: On The Difficulty of Whistleblowing<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14407<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Mansoor Ahmed-Rengers,Ross Anderson,Darija Halatova,Ilia Shumailov<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>One of the most critical security protocol problems for humans is when you are betraying a trust, perhaps for some higher purpose, and the world can turn against you if you’re caught. In this short paper, we report on efforts to enable whistleblowers to leak sensitive documents to journalists more safely. Following a survey of cases where whistleblowers were discovered due to operational or technological issues, we propose a game-theoretic model capturing the power dynamics involved in whistleblowing. We find that the whistleblower is often at the mercy of motivations and abilities of others. We identify specific areas where technology may be used to mitigate the whistleblower’s risk. However we warn against technical solutionism: the main constraints are often institutional.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u5bf9\u4e8e\u4eba\u7c7b\u6765\u8bf4\uff0c\u6700\u5173\u952e\u7684\u5b89\u5168\u534f\u8bae\u95ee\u9898\u4e4b\u4e00\u5c31\u662f\u5f53\u4f60\u80cc\u53db\u4e86\u4e00\u4e2a\u4fe1\u4efb\uff0c\u6216\u8bb8\u662f\u4e3a\u4e86\u67d0\u79cd\u66f4\u9ad8\u7684\u76ee\u7684\uff0c\u5982\u679c\u4f60\u88ab\u6293\u4f4f\u4e86\uff0c\u6574\u4e2a\u4e16\u754c\u90fd\u4f1a\u53cd\u8fc7\u6765\u5bf9\u4ed8\u4f60\u3002\u5728\u8fd9\u7bc7\u7b80\u77ed\u7684\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u62a5\u9053\u4e86\u5982\u4f55\u8ba9\u4e3e\u62a5\u8005\u66f4\u5b89\u5168\u5730\u5c06\u654f\u611f\u6587\u4ef6\u6cc4\u9732\u7ed9\u8bb0\u8005\u3002\u5728\u8c03\u67e5\u4e86\u7531\u4e8e\u64cd\u4f5c\u6216\u6280\u672f\u95ee\u9898\u800c\u53d1\u73b0\u4e3e\u62a5\u8005\u7684\u6848\u4f8b\u4e4b\u540e\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u4e2a\u6355\u6349\u4e3e\u62a5\u6240\u6d89\u53ca\u7684\u6743\u529b\u52a8\u6001\u7684\u535a\u5f08\u8bba\u6a21\u578b\u3002\u6211\u4eec\u53d1\u73b0\u544a\u5bc6\u8005\u7ecf\u5e38\u53d7\u5230\u5176\u4ed6\u4eba\u7684\u52a8\u673a\u548c\u80fd\u529b\u7684\u652f\u914d\u3002\u6211\u4eec\u786e\u5b9a\u4e86\u53ef\u4ee5\u5229\u7528\u6280\u672f\u6765\u51cf\u8f7b\u544a\u5bc6\u8005\u98ce\u9669\u7684\u5177\u4f53\u9886\u57df\u3002\u7136\u800c\uff0c\u6211\u4eec\u5bf9\u6280\u672f\u89e3\u51b3\u65b9\u6848\u4e3b\u4e49\u63d0\u51fa\u4e86\u8b66\u544a: \u4e3b\u8981\u7684\u5236\u7ea6\u56e0\u7d20\u5f80\u5f80\u662f\u5236\u5ea6\u6027\u7684\u3002<\/span><\/section>\n

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    \u8054\u5408\u56fd\u5929\u57fa\u4fe1\u606f\u5e73\u53f0: <\/strong><\/span><\/p>\n

    \u9009\u62e9\u6027\u5730\u5212\u5206<\/strong><\/span><\/p>\n

    \u76f8\u4e92\u5173\u8054\u7684\u6570\u636e\u548c\u5b9e\u4f53\u5173\u7cfb<\/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

    SPIDER: Selective Plotting of Interconnected Data and Entity Relations<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14416<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Pranav Addepalli,Eric Wu,Douglas Bossart,Christina Lin,Allistar Smith<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Intelligence analysts have long struggled with an abundance of data that must be investigated on a daily basis. In the U.S. Army, this activity involves reconciling information from various sources, a process that has been automated to a certain extent, but which remains highly manual. To promote automation, a semantic analysis prototype was designed to aid in the intelligence analysis process. This tool, called Selective Plotting of Interconnected Data and Entity Relations (SPIDER), extracts entities and their relationships from text in order to streamline investigations. SPIDER is a web application that can be remotely-accessed via a web browser, and has three major components: (1) a Java API that reads documents, extracts entities and relationships using Stanford CoreNLP, (2) a Neo4j graph database that stores entities, relationships, and properties; (3) a JavaScript-based SigmaJS visualization tool for displaying the graph on the browser. SPIDER can scale document analysis to thousands of files for quick visualization, making the intelligence analysis process more efficient, and allowing military leadership quicker insights into a vast array of potentially-hidden knowledge.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u957f\u671f\u4ee5\u6765\uff0c\u60c5\u62a5\u5206\u6790\u4eba\u5458\u4e00\u76f4\u5728\u4e0e\u5927\u91cf\u5fc5\u987b\u6bcf\u5929\u8c03\u67e5\u7684\u6570\u636e\u4f5c\u6597\u4e89\u3002\u5728\u7f8e\u56fd\u9646\u519b\uff0c\u8fd9\u79cd\u6d3b\u52a8\u5305\u62ec\u8c03\u8282\u6765\u81ea\u4e0d\u540c\u6765\u6e90\u7684\u4fe1\u606f\uff0c\u8fd9\u4e2a\u8fc7\u7a0b\u5728\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u5df2\u7ecf\u81ea\u52a8\u5316\uff0c\u4f46\u4ecd\u7136\u662f\u9ad8\u5ea6\u624b\u52a8\u7684\u3002\u4e3a\u4e86\u4fc3\u8fdb\u81ea\u52a8\u5316\uff0c\u8bbe\u8ba1\u4e86\u4e00\u4e2a\u8bed\u4e49\u5206\u6790\u539f\u578b\u6765\u8f85\u52a9\u667a\u80fd\u5206\u6790\u8fc7\u7a0b\u3002\u8fd9\u4e2a\u5de5\u5177\uff0c\u79f0\u4e3a\u9009\u62e9\u6027\u7ed8\u5236\u76f8\u4e92\u5173\u8054\u7684\u6570\u636e\u548c\u5b9e\u4f53\u5173\u7cfb(SPIDER) \uff0c\u4ece\u6587\u672c\u4e2d\u63d0\u53d6\u5b9e\u4f53\u53ca\u5176\u5173\u7cfb\uff0c\u4ee5\u7b80\u5316\u8c03\u67e5\u3002Spider \u662f\u4e00\u4e2a\u53ef\u4ee5\u901a\u8fc7 web \u6d4f\u89c8\u5668\u8fdc\u7a0b\u8bbf\u95ee\u7684 web \u5e94\u7528\u7a0b\u5e8f\uff0c\u5b83\u6709\u4e09\u4e2a\u4e3b\u8981\u7ec4\u6210\u90e8\u5206: (1)\u4e00\u4e2a Java API\uff0c\u5b83\u53ef\u4ee5\u4f7f\u7528 Stanford CoreNLP \u8bfb\u53d6\u6587\u6863\u3001\u63d0\u53d6\u5b9e\u4f53\u548c\u5173\u7cfb; (2)\u4e00\u4e2a Neo4j \u56fe\u5f62\u6570\u636e\u5e93\uff0c\u5b83\u5b58\u50a8\u5b9e\u4f53\u3001\u5173\u7cfb\u548c\u5c5e\u6027; (3)\u4e00\u4e2a\u57fa\u4e8e javascript \u7684 sigs \u53ef\u89c6\u5316\u5de5\u5177\uff0c\u7528\u4e8e\u5728\u6d4f\u89c8\u5668\u4e0a\u663e\u793a\u56fe\u5f62\u3002\u5929\u57fa\u4fe1\u606f\u5e73\u53f0\u53ef\u4ee5\u5c06\u6587\u4ef6\u5206\u6790\u6269\u5927\u5230\u6570\u5343\u4e2a\u6587\u4ef6\uff0c\u4ee5\u4fbf\u5feb\u901f\u53ef\u89c6\u5316\uff0c\u4f7f\u60c5\u62a5\u5206\u6790\u8fc7\u7a0b\u66f4\u52a0\u6709\u6548\uff0c\u5e76\u4f7f\u519b\u4e8b\u9886\u5bfc\u4eba\u80fd\u591f\u66f4\u5feb\u5730\u6d1e\u5bdf\u5927\u91cf\u6f5c\u5728\u9690\u85cf\u7684\u77e5\u8bc6\u3002<\/span><\/section>\n

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    \u5206\u5e03\u4f4d\u79fb\u4e0b\u65f6\u6001\u56fe\u4e0a<\/strong><\/span><\/p>\n

    \u56fe\u5f62\u795e\u7ecf\u7f51\u7edc\u7684\u589e\u91cf\u5f0f\u8bad\u7ec3<\/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

    Incremental Training of Graph Neural Networks on Temporal Graphs under Distribution Shift<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14422<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Lukas Galke,Iacopo Vagliano,Ansgar Scherp<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Current graph neural networks (GNNs) are promising, especially when the entire graph is known for training. However, it is not yet clear how to efficiently train GNNs on temporal graphs, where new vertices, edges, and even classes appear over time. We face two challenges: First, shifts in the label distribution (including the appearance of new labels), which require adapting the model. Second, the growth of the graph, which makes it, at some point, infeasible to train over all vertices and edges. We address these issues by applying a sliding window technique, i.e., we incrementally train GNNs on limited window sizes and analyze their performance. For our experiments, we have compiled three new temporal graph datasets based on scientific publications and evaluate isotropic and anisotropic GNN architectures. Our results show that both GNN types provide good results even for a window size of just 1 time step. With window sizes of 3 to 4 time steps, GNNs achieve at least 95% accuracy compared to using the entire timeline of the graph. With window sizes of 6 or 8, at least 99% accuracy could be retained. These discoveries have direct consequences for training GNNs over temporal graphs. We provide the code (https:\/\/github.com\/Incremental-GNNs) and the newly compiled datasets (https:\/\/zenodo.org\/record\/3764770) for reproducibility and reuse.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u5f53\u524d\u7684\u56fe\u5f62\u795e\u7ecf\u7f51\u7edc(gnn)\u662f\u5f88\u6709\u524d\u9014\u7684\uff0c\u7279\u522b\u662f\u5f53\u6574\u4e2a\u56fe\u5f62\u5df2\u77e5\u7684\u8bad\u7ec3\u3002\u7136\u800c\uff0c\u76ee\u524d\u8fd8\u4e0d\u6e05\u695a\u5982\u4f55\u6709\u6548\u5730\u5728\u65f6\u6001\u56fe\u4e0a\u8bad\u7ec3 gnn\uff0c\u5728\u65f6\u6001\u56fe\u4e0a\uff0c\u65b0\u7684\u9876\u70b9\u3001\u8fb9\u751a\u81f3\u7c7b\u90fd\u4f1a\u968f\u7740\u65f6\u95f4\u7684\u63a8\u79fb\u51fa\u73b0\u3002\u6211\u4eec\u9762\u4e34\u4e24\u4e2a\u6311\u6218: \u7b2c\u4e00\uff0c\u6807\u7b7e\u5206\u5e03\u7684\u53d8\u5316(\u5305\u62ec\u65b0\u6807\u7b7e\u7684\u51fa\u73b0) \uff0c\u8fd9\u9700\u8981\u8c03\u6574\u6a21\u578b\u3002\u7b2c\u4e8c\uff0c\u56fe\u7684\u589e\u957f\uff0c\u8fd9\u4f7f\u5f97\u5728\u67d0\u4e00\u70b9\u4e0a\uff0c\u5bf9\u6240\u6709\u9876\u70b9\u548c\u8fb9\u8fdb\u884c\u8bad\u7ec3\u662f\u4e0d\u53ef\u884c\u7684\u3002\u6211\u4eec\u901a\u8fc7\u5e94\u7528\u6ed1\u52a8\u7a97\u53e3\u6280\u672f\u6765\u89e3\u51b3\u8fd9\u4e9b\u95ee\u9898\uff0c\u4e5f\u5c31\u662f\u8bf4\uff0c\u6211\u4eec\u5728\u6709\u9650\u7684\u7a97\u53e3\u5927\u5c0f\u4e0a\u9010\u6b65\u8bad\u7ec3 gnn \u5e76\u5206\u6790\u5b83\u4eec\u7684\u6027\u80fd\u3002\u5728\u5b9e\u9a8c\u4e2d\uff0c\u6211\u4eec\u57fa\u4e8e\u79d1\u5b66\u6587\u732e\u7f16\u5236\u4e86\u4e09\u4e2a\u65b0\u7684\u65f6\u95f4\u56fe\u6570\u636e\u96c6\uff0c\u5e76\u5bf9\u5404\u5411\u540c\u6027\u548c\u5404\u5411\u5f02\u6027\u7684 GNN \u7ed3\u6784\u8fdb\u884c\u4e86\u8bc4\u4f30\u3002\u6211\u4eec\u7684\u7ed3\u679c\u663e\u793a\uff0c\u8fd9\u4e24\u79cd GNN \u7c7b\u578b\u63d0\u4f9b\u4e86\u826f\u597d\u7684\u7ed3\u679c\uff0c\u5373\u4f7f\u7a97\u53e3\u5927\u5c0f\u53ea\u67091\u4e2a\u65f6\u95f4\u6b65\u957f\u3002\u7a97\u53e3\u5927\u5c0f\u4e3a3\u81f34\u4e2a\u65f6\u95f4\u6b65\u957f\uff0c\u4e0e\u4f7f\u7528\u56fe\u7684\u6574\u4e2a\u65f6\u95f4\u7ebf\u76f8\u6bd4\uff0cgnn \u7684\u51c6\u786e\u7387\u81f3\u5c11\u8fbe\u523095% \u3002\u7a97\u53e3\u5927\u5c0f\u4e3a6\u62168\u65f6\uff0c\u81f3\u5c11\u53ef\u4fdd\u630199% \u7684\u51c6\u786e\u5ea6\u3002\u8fd9\u4e9b\u53d1\u73b0\u5bf9\u5728\u65f6\u6001\u56fe\u4e0a\u8bad\u7ec3 gnn \u6709\u76f4\u63a5\u7684\u5f71\u54cd\u3002\u6211\u4eec\u63d0\u4f9b\u4ee3\u7801\u3002<\/span><\/section>\n

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    \u57fa\u4e8e\u547d\u4e2d\u6982\u7387\u7684\u6709\u5411\u56fe<\/strong><\/span><\/p>\n

    \u548c\u9a6c\u5c14\u53ef\u592b\u94fe\u4e0a\u7684\u5ea6\u91cf<\/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

    A metric on directed graphs and Markov chains based on hitting probabilities<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14482<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Zachary M. Boyd,Nicolas Fraiman,Jeremy L. Marzuola,Peter J. Mucha,Braxton Osting,Jonathan Weare<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>The shortest-path, commute time, and diffusion distances on undirected graphs have been widely employed in applications such as dimensionality reduction, link prediction, and trip planning. Increasingly, there is interest in using asymmetric structure of data derived from Markov chains and directed graphs, but few metrics are specifically adapted to this task. We introduce a metric on the state space of any ergodic, finite-state, time-homogeneous Markov chain and, in particular, on any Markov chain derived from a directed graph. Our construction is based on hitting probabilities, with nearness in the metric space related to the transfer of random walkers from one node to another at stationarity. Notably, our metric is insensitive to shortest and average path distances, thus giving new information compared to existing metrics. We use possible degeneracies in the metric to develop an interesting structural theory of directed graphs and explore a related quotienting procedure. Our metric can be computed in <\/span>O(n3) time, where <\/span>n is the number of states, and in examples we scale up to <\/span>n=10,000 nodes and <\/span>\u224838M edges on a desktop computer. In several examples, we explore the nature of the metric, compare it to alternative methods, and demonstrate its utility for weak recovery of community structure in dense graphs, visualization, structure recovering, dynamics exploration, and multiscale cluster detection.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u65e0\u5411\u56fe\u4e0a\u7684\u6700\u77ed\u8def\u5f84\u3001\u901a\u52e4\u65f6\u95f4\u548c\u6269\u6563\u8ddd\u79bb\u88ab\u5e7f\u6cdb\u5e94\u7528\u4e8e\u964d\u7ef4\u3001\u94fe\u8def\u9884\u6d4b\u548c\u884c\u7a0b\u89c4\u5212\u7b49\u5e94\u7528\u4e2d\u3002\u4ece\u9a6c\u5c14\u53ef\u592b\u94fe\u548c\u6709\u5411\u56fe\u5bfc\u51fa\u7684\u6570\u636e\u7684\u975e\u5bf9\u79f0\u7ed3\u6784\u8d8a\u6765\u8d8a\u5f15\u8d77\u4eba\u4eec\u7684\u5174\u8da3\uff0c\u4f46\u5f88\u5c11\u6709\u5ea6\u91cf\u6807\u51c6\u4e13\u95e8\u9002\u7528\u4e8e\u8fd9\u9879\u4efb\u52a1\u3002\u6211\u4eec\u5728\u4efb\u610f\u904d\u5386\u3001\u6709\u9650\u72b6\u6001\u3001\u65f6\u9f50\u9a6c\u6c0f\u94fe\u7684\u72b6\u6001\u7a7a\u95f4\u4e0a\uff0c\u7279\u522b\u662f\u5728\u7531\u6709\u5411\u56fe\u5bfc\u51fa\u7684\u4efb\u610f\u9a6c\u6c0f\u94fe\u4e0a\uff0c\u5f15\u5165\u4e86\u4e00\u4e2a\u5ea6\u91cf\u3002\u6211\u4eec\u7684\u7ed3\u6784\u662f\u57fa\u4e8e\u547d\u4e2d\u6982\u7387\uff0c\u5728\u5ea6\u91cf\u7a7a\u95f4\u4e2d\u7684\u8d34\u8fd1\u5ea6\u4e0e\u968f\u673a\u884c\u8d70\u8005\u4ee5\u5e73\u7a33\u7684\u65b9\u5f0f\u4ece\u4e00\u4e2a\u8282\u70b9\u8f6c\u79fb\u5230\u53e6\u4e00\u4e2a\u8282\u70b9\u6709\u5173\u3002\u503c\u5f97\u6ce8\u610f\u7684\u662f\uff0c\u6211\u4eec\u7684\u5ea6\u91cf\u5bf9\u6700\u77ed\u8def\u5f84\u8ddd\u79bb\u548c\u5e73\u5747\u8def\u5f84\u8ddd\u79bb\u4e0d\u654f\u611f\uff0c\u56e0\u6b64\u4e0e\u73b0\u6709\u5ea6\u91cf\u76f8\u6bd4\u63d0\u4f9b\u4e86\u65b0\u7684\u4fe1\u606f\u3002\u6211\u4eec\u5229\u7528\u5ea6\u91cf\u4e2d\u53ef\u80fd\u7684\u9000\u5316\u6765\u53d1\u5c55\u4e00\u4e2a\u6709\u8da3\u7684\u6709\u5411\u56fe\u7684\u7ed3\u6784\u7406\u8bba\uff0c\u5e76\u63a2\u7d22\u76f8\u5173\u7684\u5546\u7a0b\u5e8f\u3002\u6211\u4eec\u7684\u5ea6\u91cf\u53ef\u4ee5\u7528<\/span>O(n3) \u662f\u72b6\u6001\u7684\u6570\u91cf\uff0c\u5728\u4f8b\u5b50\u4e2d\u6211\u4eec\u653e\u5927\u5230<\/span>n=10,000  \u8282\u70b9\u548c<\/span>\u224838M \u684c\u9762\u7535\u8111\u7684\u8fb9\u7f18\u3002\u5728\u51e0\u4e2a\u5b9e\u4f8b\u4e2d\uff0c\u6211\u4eec\u63a2\u8ba8\u4e86\u5ea6\u91cf\u7684\u672c\u8d28\uff0c\u5e76\u5c06\u5176\u4e0e\u5176\u4ed6\u65b9\u6cd5\u8fdb\u884c\u4e86\u6bd4\u8f83\uff0c\u8bc1\u660e\u4e86\u5b83\u5728\u7a20\u5bc6\u56fe\u5f62\u4e2d\u7684\u793e\u533a\u7ed3\u6784\u5f31\u6062\u590d\u3001\u53ef\u89c6\u5316\u3001\u7ed3\u6784\u6062\u590d\u3001\u52a8\u6001\u63a2\u7d22\u548c\u591a\u5c3a\u5ea6\u805a\u7c7b\u68c0\u6d4b\u65b9\u9762\u7684\u5b9e\u7528\u6027\u3002<\/span><\/section>\n

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    \u5173\u4e8eCOVID-19\u5927\u6d41\u884c\u7684<\/strong><\/span><\/p>\n

    \u8bed\u4e49\u6ce8\u91ca\u63a8\u6587\u7684\u77e5\u8bc6\u5e93<\/strong><\/span><\/p>\n

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


    <\/h2>\n

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

    TweetsCOV19 — A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14492<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Dimitar Dimitrov,Erdal Baran,Pavlos Fafalios,Ran Yu,Xiaofei Zhu,Matth\u00e4us Zloch,Stefan Dietze<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Publicly available social media archives facilitate research in the social sciences and provide corpora for training and testing a wide range of machine learning, NLP and information retrieval methods. With respect to the recent outbreak of COVID-19, online discourse on Twitter reflects public opinion and perception related to the pandemic itself as well as mitigating measures and their societal impact. Understanding such discourse, its evolution and interdependencies with real-world events or (mis)information can foster valuable insights. On the other hand, such corpora are crucial facilitators for computational methods addressing tasks such as sentiment analysis, event detection or entity recognition. However, obtaining, archiving and semantically annotating large amounts of tweets is costly. In this paper, we describe TweetsCOV19, a publicly available knowledge base of currently more than 8 million tweets, spanning the period Oct’19-Apr’20. Metadata about the tweets as well as extracted entities, hashtags, user mentions, sentiments, and URLs are exposed using established RDF\/S vocabularies, providing an unprecedented knowledge base for a range of knowledge discovery tasks. Next to a description of the dataset and its extraction and annotation process, we present an initial analysis, use cases and usage of the corpus.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u516c\u5f00\u7684\u793e\u4f1a\u5a92\u4f53\u6863\u6848\u4fc3\u8fdb\u4e86\u793e\u4f1a\u79d1\u5b66\u7684\u7814\u7a76\uff0c\u5e76\u4e3a\u57f9\u8bad\u548c\u6d4b\u8bd5\u5e7f\u6cdb\u7684\u673a\u5668\u5b66\u4e60\u3001 NLP \u548c\u4fe1\u606f\u68c0\u7d22\u65b9\u6cd5\u63d0\u4f9b\u4e86\u8bed\u6599\u5e93\u3002\u5173\u4e8e\u6700\u8fd1\u7206\u53d1\u7684\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u75ab\u60c5\uff0cTwitter \u4e0a\u7684\u5728\u7ebf\u8ba8\u8bba\u53cd\u6620\u4e86\u516c\u4f17\u5bf9\u5927\u6d41\u884c\u75c5\u672c\u8eab\u7684\u610f\u89c1\u548c\u770b\u6cd5\uff0c\u4ee5\u53ca\u7f13\u89e3\u63aa\u65bd\u548c\u5b83\u4eec\u7684\u793e\u4f1a\u5f71\u54cd\u3002\u7406\u89e3\u8fd9\u6837\u7684\u8bdd\u8bed\uff0c\u5b83\u7684\u6f14\u53d8\u548c\u4e0e\u73b0\u5b9e\u4e16\u754c\u4e8b\u4ef6\u6216(\u9519\u8bef\u7684)\u4fe1\u606f\u7684\u76f8\u4e92\u4f9d\u8d56\u53ef\u4ee5\u57f9\u517b\u6709\u4ef7\u503c\u7684\u6d1e\u5bdf\u529b\u3002\u53e6\u4e00\u65b9\u9762\uff0c\u8fd9\u6837\u7684\u8bed\u6599\u5e93\u5bf9\u4e8e\u5904\u7406\u60c5\u611f\u5206\u6790\u3001\u4e8b\u4ef6\u68c0\u6d4b\u6216\u5b9e\u4f53\u8bc6\u522b\u7b49\u4efb\u52a1\u7684\u8ba1\u7b97\u65b9\u6cd5\u5177\u6709\u91cd\u8981\u7684\u4fc3\u8fdb\u4f5c\u7528\u3002\u7136\u800c\uff0c\u83b7\u53d6\u3001\u5f52\u6863\u548c\u5bf9\u5927\u91cf tweets \u8fdb\u884c\u8bed\u4e49\u6ce8\u91ca\u7684\u4ee3\u4ef7\u662f\u6602\u8d35\u7684\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u63cf\u8ff0\u4e86 TweetsCOV19\uff0c\u8fd9\u662f\u4e00\u4e2a\u516c\u5f00\u7684\u77e5\u8bc6\u5e93\uff0c\u76ee\u524d\u6709\u8d85\u8fc7800\u4e07\u6761\u63a8\u8baf\uff0c\u65f6\u95f4\u8de8\u5ea6\u4ece10\u670819\u65e5\u52304\u670820\u65e5\u3002\u5173\u4e8e tweets \u7684\u5143\u6570\u636e\u4ee5\u53ca\u63d0\u53d6\u7684\u5b9e\u4f53\u3001 hashtags\u3001\u7528\u6237\u63d0\u53ca\u3001 sentiments \u548c url \u90fd\u4f7f\u7528\u5df2\u5efa\u7acb\u7684 rdf \/ s \u8bcd\u6c47\u8868\u516c\u5f00\uff0c\u4e3a\u4e00\u7cfb\u5217\u77e5\u8bc6\u53d1\u73b0\u4efb\u52a1\u63d0\u4f9b\u4e86\u524d\u6240\u672a\u6709\u7684\u77e5\u8bc6\u5e93\u3002\u5728\u63cf\u8ff0\u6570\u636e\u96c6\u53ca\u5176\u63d0\u53d6\u548c\u6ce8\u91ca\u8fc7\u7a0b\u7684\u57fa\u7840\u4e0a\uff0c\u6211\u4eec\u7ed9\u51fa\u4e86\u4e00\u4e2a\u521d\u6b65\u7684\u5206\u6790\u3001\u7528\u4f8b\u548c\u8bed\u6599\u5e93\u7684\u4f7f\u7528\u3002<\/span><\/section>\n

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    \u5173\u4e8e RNNs \u7684 Lyapunov \u6307\u6570: <\/strong><\/span><\/p>\n

    \u7528\u52a8\u6001\u7cfb\u7edf\u5de5\u5177\u7406\u89e3\u4fe1\u606f\u4f20\u64ad<\/strong><\/span><\/p>\n

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


    <\/h2>\n

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

    On Lyapunov Exponents for RNNs: Understanding Information Propagation Using Dynamical Systems Tools<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14123<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Ryan Vogt,Maximilian Puelma Touzel,Eli Shlizerman,Guillaume Lajoie<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Recurrent neural networks (RNNs) have been successfully applied to a variety of problems involving sequential data, but their optimization is sensitive to parameter initialization, architecture, and optimizer hyperparameters. Considering RNNs as dynamical systems, a natural way to capture stability, i.e., the growth and decay over long iterates, are the Lyapunov Exponents (LEs), which form the Lyapunov spectrum. The LEs have a bearing on stability of RNN training dynamics because forward propagation of information is related to the backward propagation of error gradients. LEs measure the asymptotic rates of expansion and contraction of nonlinear system trajectories, and generalize stability analysis to the time-varying attractors structuring the non-autonomous dynamics of data-driven RNNs. As a tool to understand and exploit stability of training dynamics, the Lyapunov spectrum fills an existing gap between prescriptive mathematical approaches of limited scope and computationally-expensive empirical approaches. To leverage this tool, we implement an efficient way to compute LEs for RNNs during training, discuss the aspects specific to standard RNN architectures driven by typical sequential datasets, and show that the Lyapunov spectrum can serve as a robust readout of training stability across hyperparameters. With this exposition-oriented contribution, we hope to draw attention to this understudied, but theoretically grounded tool for understanding training stability in RNNs.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u9012\u5f52\u795e\u7ecf\u7f51\u7edc(rnn)\u5df2\u7ecf\u6210\u529f\u5730\u5e94\u7528\u4e8e\u5404\u79cd\u6d89\u53ca\u5e8f\u5217\u6570\u636e\u7684\u95ee\u9898\uff0c\u4f46\u5176\u4f18\u5316\u5bf9\u53c2\u6570\u521d\u59cb\u5316\u3001\u7ed3\u6784\u548c\u4f18\u5316\u5668\u8d85\u53c2\u6570\u90fd\u5f88\u654f\u611f\u3002\u5c06 RNNs \u770b\u4f5c\u52a8\u6001\u7cfb\u7edf\uff0c\u4e00\u79cd\u6355\u83b7\u7a33\u5b9a\u6027\u7684\u81ea\u7136\u65b9\u6cd5\uff0c\u5373\u957f\u8fed\u4ee3\u8fc7\u7a0b\u4e2d\u7684\u589e\u957f\u548c\u8870\u51cf\uff0c\u662f\u6784\u6210 Lyapunov \u8c31\u7684 Lyapunov \u6307\u6570(LEs)\u3002\u7531\u4e8e\u4fe1\u606f\u7684\u6b63\u5411\u4f20\u64ad\u4e0e\u8bef\u5dee\u68af\u5ea6\u7684\u53cd\u5411\u4f20\u64ad\u6709\u5173\uff0c\u6545\u8bad\u7ec3\u6837\u672c\u7684\u7a33\u5b9a\u6027\u76f4\u63a5\u5173\u7cfb\u5230\u8bad\u7ec3\u6837\u672c\u7684\u7a33\u5b9a\u6027\u3002Les \u6d4b\u91cf\u975e\u7ebf\u6027\u8f68\u8ff9\u6269\u5c55\u548c\u6536\u7f29\u7684\u6e10\u8fd1\u901f\u7387\uff0c\u5e76\u5c06\u7a33\u5b9a\u6027\u5206\u6790\u63a8\u5e7f\u5230\u6784\u9020\u6570\u636e\u9a71\u52a8 RNNs \u7684\u975e\u81ea\u6cbb\u52a8\u6001\u7684\u65f6\u53d8\u5438\u5f15\u5b50\u3002\u4f5c\u4e3a\u7406\u89e3\u548c\u5229\u7528\u8bad\u7ec3\u52a8\u529b\u5b66\u7a33\u5b9a\u6027\u7684\u5de5\u5177\uff0c\u674e\u96c5\u666e\u8bfa\u592b\u8c31\u586b\u8865\u4e86\u6709\u9650\u8303\u56f4\u7684\u89c4\u5b9a\u6027\u6570\u5b66\u65b9\u6cd5\u548c\u8ba1\u7b97\u4ee3\u4ef7\u6602\u8d35\u7684\u7ecf\u9a8c\u65b9\u6cd5\u4e4b\u95f4\u73b0\u6709\u7684\u7a7a\u767d\u3002\u4e3a\u4e86\u5145\u5206\u5229\u7528\u8fd9\u4e00\u5de5\u5177\uff0c\u6211\u4eec\u5b9e\u73b0\u4e86\u4e00\u79cd\u6709\u6548\u7684\u65b9\u6cd5\u6765\u8ba1\u7b97\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d RNNs \u7684 LEs\uff0c\u8ba8\u8bba\u4e86\u6807\u51c6\u7684 RNN \u7ed3\u6784\u5728\u5178\u578b\u7684\u5e8f\u5217\u6570\u636e\u96c6\u9a71\u52a8\u4e0b\u7684\u7279\u5b9a\u65b9\u9762\uff0c\u5e76\u8868\u660e Lyapunov \u8c31\u53ef\u4ee5\u4f5c\u4e3a\u4e00\u4e2a\u8de8\u8d85\u53c2\u6570\u7684\u8bad\u7ec3\u7a33\u5b9a\u6027\u7684\u9c81\u68d2\u8bfb\u51fa\u3002\u6709\u4e86\u8fd9\u4e2a\u4ee5\u8bba\u6587\u4e3a\u5bfc\u5411\u7684\u8d21\u732e\uff0c\u6211\u4eec\u5e0c\u671b\u5f15\u8d77\u4eba\u4eec\u5bf9\u8fd9\u4e2a\u672a\u5145\u5206\u7814\u7a76\uff0c\u4f46\u662f\u7406\u8bba\u4e0a\u624e\u5b9e\u7684\u5de5\u5177\u7684\u6ce8\u610f\uff0c\u4ee5\u7406\u89e3 RNNs \u7684\u8bad\u7ec3\u7a33\u5b9a\u6027\u3002<\/span><\/section>\n

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    \u591a\u5c42\u52a8\u6001\u5f02\u7f51\u7edc\u4e2d\u7684\u7206\u70b8\u540c\u6b65<\/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

    Explosive Synchronization in Multilayer Dynamically Dissimilar Networks<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14161<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Sarika Jalan,Ajay Deep Kachhvah,Hawoong Jeong<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>The phenomenon of explosive synchronization, which originates from hypersensitivity to small perturbation caused by some form of frustration prevailed in various physical and biological systems, has been shown to lead events of cascading failure of the power grid to chronic pain or epileptic seizure in the brain. Furthermore, networks provide a powerful model to understand and predict the properties of a diverse range of real-world complex systems. Recently, a multilayer network has been realized as a better suited framework for the representation of complex systems having multiple types of interactions among the same set of constituents. This article shows that by tuning the properties of one layer (network) of a multilayer network, one can regulate the dynamical behavior of another layer (network). By taking an example of a multiplex network comprising two different types of networked Kuramoto oscillators representing two different layers, this article attempts to provide a glimpse of opportunities and emerging phenomena multiplexing can induce which is otherwise not possible for a network in isolation. Here we consider explosive synchronization to demonstrate the potential of multilayer networks framework. To the end, we discuss several possible extensions of the model considered here by incorporating real-world properties.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u7206\u53d1\u6027\u540c\u6b65\u73b0\u8c61\uff0c\u8fd9\u79cd\u73b0\u8c61\u8d77\u6e90\u4e8e\u8fc7\u654f\uff0c\u7531\u4e8e\u67d0\u79cd\u5f62\u5f0f\u7684\u632b\u6298\u611f\u5728\u5404\u79cd\u7269\u7406\u548c\u751f\u7269\u7cfb\u7edf\u4e2d\u666e\u904d\u5b58\u5728\u800c\u5f15\u8d77\u7684\u5fae\u5c0f\u6270\u52a8\uff0c\u5df2\u7ecf\u88ab\u8bc1\u660e\u5bfc\u81f4\u7535\u7f51\u8fde\u9501\u6545\u969c\u4e8b\u4ef6\uff0c\u5bfc\u81f4\u6162\u6027\u75bc\u75db\u6216\u5927\u8111\u4e2d\u7684\u766b\u75eb\u53d1\u4f5c\u3002\u6b64\u5916\uff0c\u7f51\u7edc\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5f3a\u5927\u7684\u6a21\u578b\u6765\u7406\u89e3\u548c\u9884\u6d4b\u5404\u79cd\u5404\u6837\u7684\u73b0\u5b9e\u4e16\u754c\u590d\u6742\u7cfb\u7edf\u7684\u6027\u8d28\u3002\u8fd1\u5e74\u6765\uff0c\u591a\u5c42\u7f51\u7edc\u4f5c\u4e3a\u4e00\u79cd\u66f4\u9002\u5408\u7684\u6846\u67b6\u88ab\u5b9e\u73b0\uff0c\u7528\u4e8e\u8868\u793a\u540c\u4e00\u7ec4\u6210\u90e8\u5206\u4e4b\u95f4\u5177\u6709\u591a\u79cd\u7c7b\u578b\u76f8\u4e92\u4f5c\u7528\u7684\u590d\u6742\u7cfb\u7edf\u3002\u672c\u6587\u8868\u660e\uff0c\u901a\u8fc7\u8c03\u6574\u591a\u5c42\u7f51\u7edc\u7684\u4e00\u5c42(\u7f51\u7edc)\u7684\u6027\u8d28\uff0c\u53ef\u4ee5\u8c03\u8282\u53e6\u4e00\u5c42(\u7f51\u7edc)\u7684\u52a8\u6001\u884c\u4e3a\u3002\u672c\u6587\u4ee5\u4e24\u79cd\u4e0d\u540c\u7c7b\u578b\u7684 Kuramoto \u632f\u8361\u5668\u7ec4\u6210\u7684\u591a\u8def\u590d\u7528\u7f51\u7edc\u4e3a\u4f8b\uff0c\u8bd5\u56fe\u63d0\u4f9b\u4e00\u4e2a\u673a\u4f1a\u548c\u65b0\u51fa\u73b0\u7684\u73b0\u8c61\u591a\u8def\u590d\u7528\u53ef\u4ee5\u8bf1\u5bfc\uff0c\u5426\u5219\u4e0d\u53ef\u80fd\u5728\u4e00\u4e2a\u7f51\u7edc\u4e2d\u5b64\u7acb\u3002\u8fd9\u91cc\u6211\u4eec\u8003\u8651\u7206\u70b8\u6027\u540c\u6b65\u6765\u8bc1\u660e\u591a\u5c42\u7f51\u7edc\u6846\u67b6\u7684\u6f5c\u529b\u3002\u6700\u540e\uff0c\u6211\u4eec\u8ba8\u8bba\u4e86\u8fd9\u91cc\u8003\u8651\u7684\u6a21\u578b\u7684\u51e0\u4e2a\u53ef\u80fd\u7684\u6269\u5c55\uff0c\u5b83\u4eec\u7ed3\u5408\u4e86\u771f\u5b9e\u4e16\u754c\u7684\u5c5e\u6027\u3002<\/span><\/section>\n

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    \u7528\u4e8e\u6781\u7aef\u795e\u7ecf\u5f62\u6001\u667a\u80fd<\/strong><\/span><\/p>\n

    \u7684\u8d85\u4f4e\u529f\u8017 FDSOI \u795e\u7ecf\u7535\u8def<\/strong><\/span><\/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

    Ultra-Low-Power FDSOI Neural Circuits for Extreme-Edge Neuromorphic Intelligence<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14270<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Arianna Rubino,Can Livanelioglu,Ning Qiao,Melika Payvand,Giacomo Indiveri<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Recent years have seen an increasing interest in the development of artificial intelligence circuits and systems for edge computing applications. In-memory computing mixed-signal neuromorphic architectures provide promising ultra-low-power solutions for edge-computing sensory-processing applications, thanks to their ability to emulate spiking neural networks in real-time. The fine-grain parallelism offered by this approach allows such neural circuits to process the sensory data efficiently by adapting their dynamics to the ones of the sensed signals, without having to resort to the time-multiplexed computing paradigm of von Neumann architectures. To reduce power consumption even further, we present a set of mixed-signal analog\/digital circuits that exploit the features of advanced Fully-Depleted Silicon on Insulator (FDSOI) integration processes. Specifically, we explore the options of advanced FDSOI technologies to address analog design issues and optimize the design of the synapse integrator and of the adaptive neuron circuits accordingly. We present circuit simulation results and demonstrate the circuit’s ability to produce biologically plausible neural dynamics with compact designs, optimized for the realization of large-scale spiking neural networks in neuromorphic processors.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u8fd1\u5e74\u6765\uff0c\u4eba\u4eec\u8d8a\u6765\u8d8a\u5173\u6ce8\u7528\u4e8e\u8fb9\u7f18\u8ba1\u7b97\u5e94\u7528\u7684\u4eba\u5de5\u667a\u80fd\u7535\u8def\u548c\u7cfb\u7edf\u7684\u53d1\u5c55\u3002\u5185\u5b58\u8ba1\u7b97\u6df7\u5408\u4fe1\u53f7\u795e\u7ecf\u5f62\u6001\u7ed3\u6784\u7531\u4e8e\u5177\u6709\u5b9e\u65f6\u6a21\u62df\u8109\u51b2\u795e\u7ecf\u7f51\u7edc\u7684\u80fd\u529b\uff0c\u4e3a\u8fb9\u7f18\u8ba1\u7b97\u611f\u89c9\u5904\u7406\u5e94\u7528\u63d0\u4f9b\u4e86\u6709\u524d\u9014\u7684\u8d85\u4f4e\u529f\u8017\u89e3\u51b3\u65b9\u6848\u3002\u8fd9\u79cd\u65b9\u6cd5\u6240\u63d0\u4f9b\u7684\u7ec6\u7c92\u5ea6\u5e76\u884c\u6027\u4f7f\u5f97\u8fd9\u4e9b\u795e\u7ecf\u56de\u8def\u80fd\u591f\u901a\u8fc7\u5c06\u5176\u52a8\u529b\u5b66\u9002\u5e94\u4e8e\u611f\u77e5\u4fe1\u53f7\u800c\u6709\u6548\u5730\u5904\u7406\u611f\u89c9\u6570\u636e\uff0c\u800c\u4e0d\u5fc5\u8bc9\u8bf8\u4e8e\u51af \u00b7 \u8bfa\u4f9d\u66fc\u7ed3\u6784\u7684\u65f6\u95f4\u590d\u7528\u8ba1\u7b97\u6a21\u5f0f\u3002\u4e3a\u4e86\u8fdb\u4e00\u6b65\u964d\u4f4e\u529f\u8017\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u5957\u6df7\u5408\u4fe1\u53f7\u6a21\u62df \/ \u6570\u5b57\u7535\u8def\uff0c\u5b83\u5229\u7528\u4e86\u5148\u8fdb\u7684\u5168\u8017\u5c3d SOI \u96c6\u6210\u8fc7\u7a0b\u7684\u7279\u70b9\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u6211\u4eec\u63a2\u7d22\u5148\u8fdb\u7684 FDSOI \u6280\u672f\u7684\u9009\u9879\uff0c\u4ee5\u89e3\u51b3\u6a21\u62df\u8bbe\u8ba1\u95ee\u9898\u548c\u4f18\u5316\u8bbe\u8ba1\u7684\u7a81\u89e6\u79ef\u5206\u5668\u548c\u81ea\u9002\u5e94\u795e\u7ecf\u5143\u7535\u8def\u76f8\u5e94\u3002\u6211\u4eec\u7ed9\u51fa\u4e86\u7535\u8def\u4eff\u771f\u7ed3\u679c\uff0c\u5e76\u8bc1\u660e\u4e86\u8be5\u7535\u8def\u80fd\u591f\u4ee5\u7d27\u51d1\u7684\u8bbe\u8ba1\u4ea7\u751f\u751f\u7269\u5b66\u4e0a\u4f3c\u662f\u800c\u975e\u7684\u795e\u7ecf\u52a8\u529b\u5b66\uff0c\u4e3a\u5728\u795e\u7ecf\u5f62\u6001\u5904\u7406\u5668\u4e2d\u5b9e\u73b0\u5927\u89c4\u6a21\u8109\u51b2\u795e\u7ecf\u7f51\u7edc\u8fdb\u884c\u4e86\u4f18\u5316\u3002<\/span><\/section>\n

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

    \u901a\u8fc7\u7cbe\u786e\u5b9a\u65f6\u7684\u8109\u51b2<\/strong><\/span><\/p>\n

    \u63a7\u5236\u632f\u8361\u7cfb\u7efc\u4e2d\u7684\u96c6\u4f53\u540c\u6b65<\/strong><\/span><\/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

    Controlling collective synchrony in oscillatory ensembles by precisely timed pulses<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14355<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Michael Rosenblum<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>We present an efficient technique for control of synchrony in a globally coupled ensemble by pulsatile action. We assume that we can observe the collective oscillation and can stimulate all elements of the ensemble simultaneously. We pay special attention to the minimization of intervention into the system. The key idea is to stimulate only at the most sensitive phase. To find this phase we implement an adaptive feedback control. Estimating the instantaneous phase of the collective mode on the fly, we achieve efficient suppression using a few pulses per oscillatory cycle. We discuss the possible relevance of the results for neuroscience, namely for the development of advanced algorithms for deep brain stimulation, a medical technique used to treat Parkinson’s disease.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u4e2a\u6709\u6548\u7684\u6280\u672f\u63a7\u5236\u540c\u6b65\u5728\u4e00\u4e2a\u5168\u7403\u8026\u5408\u7cfb\u7efc\u8109\u52a8\u884c\u52a8\u3002\u6211\u4eec\u5047\u8bbe\u6211\u4eec\u53ef\u4ee5\u89c2\u5bdf\u5230\u96c6\u4f53\u632f\u8361\uff0c\u5e76\u4e14\u53ef\u4ee5\u540c\u65f6\u6fc0\u53d1\u7cfb\u7efc\u7684\u6240\u6709\u5143\u7d20\u3002\u6211\u4eec\u7279\u522b\u6ce8\u610f\u5c3d\u91cf\u51cf\u5c11\u5bf9\u7cfb\u7edf\u7684\u5e72\u9884\u3002\u5173\u952e\u662f\u53ea\u5728\u6700\u654f\u611f\u7684\u9636\u6bb5\u523a\u6fc0\u3002\u4e3a\u4e86\u627e\u5230\u8fd9\u4e2a\u9636\u6bb5\uff0c\u6211\u4eec\u5b9e\u73b0\u4e86\u4e00\u4e2a\u81ea\u9002\u5e94\u53cd\u9988\u63a7\u5236\u3002\u901a\u8fc7\u52a8\u6001\u4f30\u7b97\u96c6\u4f53\u6a21\u5f0f\u7684\u77ac\u65f6\u9891\u7387\uff0c\u6211\u4eec\u53ef\u4ee5\u5728\u6bcf\u4e2a\u632f\u8361\u5468\u671f\u4e2d\u4f7f\u7528\u51e0\u4e2a\u8109\u51b2\u6765\u5b9e\u73b0\u6709\u6548\u7684\u6291\u5236\u3002\u6211\u4eec\u8ba8\u8bba\u7684\u53ef\u80fd\u76f8\u5173\u7684\u7ed3\u679c\uff0c\u795e\u7ecf\u79d1\u5b66\uff0c\u5373\u5148\u8fdb\u7684\u7b97\u6cd5\u7684\u53d1\u5c55\u8111\u6df1\u90e8\u523a\u6fc0\uff0c\u4e00\u79cd\u533b\u7597\u6280\u672f\u7528\u4e8e\u6cbb\u7597\u5e15\u91d1\u68ee\u6c0f\u75c7\u3002<\/span><\/section>\n

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    \u6700\u5927\u591a\u5c3a\u5ea6\u71b5\u4e0e\u795e\u7ecf\u7f51\u7edc\u6b63\u5219\u5316<\/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

    Maximum Multiscale Entropy and Neural Network Regularization<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14614<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Amir R. Asadi,Emmanuel Abbe<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>A well-known result across information theory, machine learning, and statistical physics shows that the maximum entropy distribution under a mean constraint has an exponential form called the Gibbs-Boltzmann distribution. This is used for instance in density estimation or to achieve excess risk bounds derived from single-scale entropy regularizers (Xu-Raginsky ’17). This paper investigates a generalization of these results to a multiscale setting. We present different ways of generalizing the maximum entropy result by incorporating the notion of scale. For different entropies and arbitrary scale transformations, it is shown that the distribution maximizing a multiscale entropy is characterized by a procedure which has an analogy to the renormalization group procedure in statistical physics. For the case of decimation transformation, it is further shown that this distribution is Gaussian whenever the optimal single-scale distribution is Gaussian. This is then applied to neural networks, and it is shown that in a teacher-student scenario, the multiscale Gibbs posterior can achieve a smaller excess risk than the single-scale Gibbs posterior.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u4fe1\u606f\u8bba\u3001\u673a\u5668\u5b66\u4e60\u548c\u7edf\u8ba1\u7269\u7406\u5b66\u7684\u4e00\u4e2a\u8457\u540d\u7ed3\u679c\u8868\u660e\uff0c\u5728\u5747\u503c\u7ea6\u675f\u4e0b\u7684\u6700\u5927\u71b5\u5206\u5e03\u5448\u6307\u6570\u5f62\u5f0f\uff0c\u79f0\u4e3a\u5409\u5e03\u65af-\u6ce2\u5c14\u5179\u66fc\u5206\u5e03\u3002\u4f8b\u5982\uff0c\u8fd9\u7528\u4e8e\u5bc6\u5ea6\u4f30\u8ba1\u6216\u5b9e\u73b0\u4ece\u5355\u5c3a\u5ea6\u71b5\u6b63\u5219\u5316\u8005(Xu-Raginsky\u201917)\u63a8\u5bfc\u51fa\u7684\u8d85\u989d\u98ce\u9669\u754c\u9650\u3002\u672c\u6587\u7814\u7a76\u4e86\u8fd9\u4e9b\u7ed3\u679c\u5728\u591a\u5c3a\u5ea6\u73af\u5883\u4e0b\u7684\u63a8\u5e7f\u3002\u6211\u4eec\u63d0\u51fa\u4e86\u4e0d\u540c\u7684\u65b9\u6cd5\u901a\u8fc7\u7eb3\u5165\u89c4\u6a21\u7684\u6982\u5ff5\u6765\u63a8\u5e7f\u6700\u5927\u71b5\u7684\u7ed3\u679c\u3002\u5bf9\u4e8e\u4e0d\u540c\u7684\u71b5\u548c\u4efb\u610f\u5c3a\u5ea6\u7684\u53d8\u6362\uff0c\u6211\u4eec\u8bc1\u660e\u4e86\u6700\u5927\u5316\u591a\u5c3a\u5ea6\u71b5\u7684\u5206\u5e03\u62e5\u6709\u5c5e\u6027\u662f\u4e00\u4e2a\u4e0e\u7edf\u8ba1\u7269\u7406\u5b66\u4e2d\u7684\u91cd\u6574\u5316\u7fa4\u8fc7\u7a0b\u7c7b\u4f3c\u7684\u8fc7\u7a0b\u3002\u5728\u62bd\u53d6\u53d8\u6362\u7684\u60c5\u51b5\u4e0b\uff0c\u8fdb\u4e00\u6b65\u8bc1\u660e\u4e86\u5f53\u6700\u4f18\u5355\u5c3a\u5ea6\u5206\u5e03\u662f\u9ad8\u65af\u5206\u5e03\u65f6\uff0c\u62bd\u53d6\u53d8\u6362\u7684\u62bd\u6837\u5206\u5e03\u662f\u9ad8\u65af\u5206\u5e03\u3002\u7136\u540e\u5c06\u5176\u5e94\u7528\u4e8e\u795e\u7ecf\u7f51\u7edc\uff0c\u7ed3\u679c\u8868\u660e\uff0c\u5728\u5e08\u751f\u60c5\u666f\u4e0b\uff0c\u591a\u5c3a\u5ea6\u5409\u5e03\u65af\u540e\u9a8c\u76f8\u5bf9\u4e8e\u5355\u5c3a\u5ea6\u5409\u5e03\u65af\u540e\u9a8c\u53ef\u4ee5\u5b9e\u73b0\u66f4\u5c0f\u7684\u8fc7\u5ea6\u98ce\u9669\u3002<\/span><\/section>\n

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    \u5229\u7528\u795e\u7ecf\u7f51\u7edc\u53d1\u73b0 <\/strong><\/span><\/p>\n

    SU (N)\u8d39\u7c73\u5b50\u9690\u85cf\u7279\u5f81\u7684\u542f\u53d1\u5f0f\u673a\u5236<\/strong><\/span><\/p>\n

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


    <\/h2>\n

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

    Heuristic machinery to uncover hidden features of SU(N) Fermions with neural networks<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14142<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Entong Zhao,Jeongwon Lee,Chengdong He,Zejian Ren,Elnur Hajiyev,Junwei Liu,Gyu-Boong Jo<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>The power of machine learning (ML) provides the possibility of analyzing experimental measurements with an unprecedented sensitivity. However, it still remains challenging to uncover hidden features directly related to physical observables and to understand physics behind from ordinary experimental data using ML. Here, we introduce a heuristic machinery by combining the power of ML and the “trial and error” in a supervised way. We use our machinery to reveal hidden thermodynamic features in the density profile of ultracold fermions interacting within SU(<\/span>N<\/span>) spin symmetry prepared in a quantum simulator, and discover their connection to spin multiplicity. Although such spin symmetry should manifest itself in a many-body wavefuction, it is elusive how the momentum distribution of fermions, the most ordinary measurement, reveals the effect of spin symmetry. Using a fully trained convolutional neural network (NN) with a remarkably high accuracy of <\/span>\u223c<\/span>94<\/span>%<\/span> for detection of the spin multiplicity, we investigate the dependency of accuracy on various hidden features with filtered measurements. Guided by our machinery, we verify how the NN extracts a thermodynamic compressibility from density fluctuations within the single image. Our machine learning framework shows a potential to validate theoretical descriptions of SU(<\/span>N<\/span>) Fermi liquids, and to identify hidden features even for highly complex quantum matters with minimal prior understanding.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u673a\u5668\u5b66\u4e60(ML)\u7684\u529b\u91cf\u63d0\u4f9b\u4e86\u4ee5\u524d\u6240\u672a\u6709\u7684\u7075\u654f\u5ea6\u5206\u6790\u5b9e\u9a8c\u6d4b\u91cf\u7684\u53ef\u80fd\u6027\u3002\u7136\u800c\uff0c\u8981\u53d1\u73b0\u4e0e\u7269\u7406\u89c2\u6d4b\u76f4\u63a5\u76f8\u5173\u7684\u9690\u85cf\u7279\u5f81\uff0c\u5e76\u5229\u7528\u673a\u5668\u5b66\u4e60\u4ece\u666e\u901a\u5b9e\u9a8c\u6570\u636e\u4e2d\u7406\u89e3\u7269\u7406\u5b66\uff0c\u4ecd\u7136\u662f\u4e00\u4e2a\u6311\u6218\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u4ecb\u7ecd\u4e86\u4e00\u79cd\u542f\u53d1\u5f0f\u673a\u5236\u7ed3\u5408\u7684\u6743\u529b\u7684\u673a\u5668\u5b66\u4e60\u548c\u201c\u8bd5\u9519\u201d\u5728\u76d1\u7763\u7684\u65b9\u5f0f\u3002\u6211\u4eec\u5229\u7528\u6211\u4eec\u7684\u673a\u5236\u63ed\u793a\u4e86\u8d85\u51b7\u8d39\u7c73\u5b50\u5728 SU (\u4e2d\u76f8\u4e92\u4f5c\u7528\u7684\u5bc6\u5ea6\u5206\u5e03\u4e2d\u9690\u85cf\u7684\u70ed\u529b\u5b66\u7279\u5f81<\/span>N\u81ea\u65cb\u5bf9\u79f0\u6027\uff0c\u5e76\u53d1\u73b0\u5b83\u4eec\u4e0e\u81ea\u65cb\u591a\u91cd\u6027\u7684\u8054\u7cfb\u3002\u867d\u7136\u8fd9\u79cd\u81ea\u65cb\u5bf9\u79f0\u6027\u672c\u8eab\u5e94\u8be5\u8868\u73b0\u5728\u591a\u4f53\u6ce2\u524d\u4e2d\uff0c\u4f46\u662f\u8d39\u7c73\u5b50\u7684\u52a8\u91cf\u5206\u5e03\uff0c\u8fd9\u79cd\u6700\u666e\u901a\u7684\u6d4b\u91cf\u65b9\u6cd5\uff0c\u5982\u4f55\u63ed\u793a\u81ea\u65cb\u5bf9\u79f0\u6027\u7684\u5f71\u54cd\uff0c\u5374\u662f\u4ee4\u4eba\u8d39\u89e3\u7684\u3002\u4f7f\u7528\u4e00\u4e2a\u5b8c\u5168\u8bad\u7ec3\u6709\u7d20\u7684\u5377\u79ef\u795e\u7ecf\u7f51\u7edc~94%\u4e3a\u4e86\u68c0\u6d4b\u65cb\u91cf\u591a\u91cd\u6027\uff0c\u6211\u4eec\u8c03\u67e5\u4e86\u5404\u79cd\u9690\u85cf\u7279\u5f81\u4e0e\u8fc7\u6ee4\u6d4b\u91cf\u7684\u7cbe\u786e\u5ea6\u4f9d\u8d56\u6027\u3002\u5728\u6211\u4eec\u7684\u673a\u5236\u6307\u5bfc\u4e0b\uff0c\u6211\u4eec\u9a8c\u8bc1\u4e86\u795e\u7ecf\u7f51\u7edc\u5982\u4f55\u4ece\u5355\u4e2a\u56fe\u50cf\u7684\u5bc6\u5ea6\u8d77\u4f0f\u4e2d\u63d0\u53d6\u70ed\u529b\u5b66\u538b\u7f29\u6027\u3002\u6211\u4eec\u7684\u673a\u5668\u5b66\u4e60\u6846\u67b6\u663e\u793a\u4e86\u9a8c\u8bc1 SU (\u7406\u8bba\u63cf\u8ff0\u7684\u6f5c\u529bN\uff09\u8d39\u7c73\u6db2\u4f53\uff0c\u5e76\u786e\u5b9a\u9690\u85cf\u7684\u7279\u70b9\uff0c\u5373\u4f7f\u662f\u9ad8\u5ea6\u590d\u6742\u7684\u91cf\u5b50\u95ee\u9898\u7684\u6700\u4f4e\u4e8b\u5148\u4e86\u89e3\u3002<\/span><\/section>\n

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    \u8109\u51b2\u661f\u8ba1\u65f6\u9635\u5217\u5404\u5411<\/strong><\/span><\/p>\n

    \u5f02\u6027\u5f15\u529b\u6ce2\u80cc\u666f\u641c\u7d22\u7684 Fisher \u516c\u5f0f<\/strong><\/span><\/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

    Fisher formalism for anisotropic gravitational-wave background searches with pulsar timing arrays<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14570<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Yacine Ali-Ha\u00efmoud,Tristan L. Smith,Chiara M. F. Mingarelli<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Pulsar timing arrays (PTAs) are currently the only experiments directly sensitive to gravitational waves with decade-long periods. Within the next five to ten years, PTAs are expected to detect the stochastic gravitational-wave background (SGWB) collectively sourced by inspiralling supermassive black hole binaries. It is expected that this background is mostly isotropic, and current searches focus on the monopole part of the SGWB. Looking ahead, anisotropies in the SGWB may provide a trove of additional information both on known and unknown astrophysical and cosmological sources. In this paper, we build a simple yet realistic Fisher formalism for anisotropic SGWB searches with PTAs. Our formalism is able to accommodate realistic properties of PTAs, and allows simple and accurate forecasts. We illustrate our approach with an idealized PTA consisting of identical, isotropically distributed pulsars. In a companion paper, we apply our formalism to current PTAs and show that it can be a powerful tool to guide and optimize real data analysis.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u8109\u51b2\u661f\u5b9a\u65f6\u9635\u5217(PTAs)\u662f\u76ee\u524d\u552f\u4e00\u5bf9\u5f15\u529b\u6ce2\u76f4\u63a5\u654f\u611f\u7684\u5341\u5e74\u5468\u671f\u5b9e\u9a8c\u3002\u5728\u63a5\u4e0b\u6765\u76845\u523010\u5e74\u5185\uff0c\u9884\u8ba1 PTAs \u5c06\u63a2\u6d4b\u5230\u7531\u5438\u5165\u7684\u8d85\u91cd\u9ed1\u6d1e\u53cc\u661f\u5171\u540c\u83b7\u5f97\u7684\u968f\u673a\u5f15\u529b\u6ce2\u80cc\u666f(SGWB)\u3002\u9884\u8ba1\u8fd9\u79cd\u80cc\u666f\u5927\u591a\u662f\u5404\u5411\u540c\u6027\u7684\uff0c\u76ee\u524d\u7684\u641c\u7d22\u4e3b\u8981\u96c6\u4e2d\u5728 SGWB \u7684\u5355\u6781\u5b50\u90e8\u5206\u3002\u5c55\u671b\u672a\u6765\uff0cSGWB \u7684\u5404\u5411\u5f02\u6027\u53ef\u80fd\u4f1a\u63d0\u4f9b\u5173\u4e8e\u5df2\u77e5\u548c\u672a\u77e5\u5929\u4f53\u7269\u7406\u5b66\u548c\u5b87\u5b99\u5b66\u6765\u6e90\u7684\u989d\u5916\u4fe1\u606f\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u5efa\u7acb\u4e86\u4e00\u4e2a\u7528\u4e8e\u5404\u5411\u5f02\u6027 SGWB \u641c\u7d22\u7684\u7b80\u5355\u800c\u5b9e\u9645\u7684 Fisher \u5f62\u5f0f\u3002\u6211\u4eec\u7684\u5f62\u5f0f\u4e3b\u4e49\u80fd\u591f\u9002\u5e94 pta \u7684\u73b0\u5b9e\u7279\u6027\uff0c\u5e76\u5141\u8bb8\u7b80\u5355\u548c\u51c6\u786e\u7684\u9884\u6d4b\u3002\u6211\u4eec\u7528\u4e00\u4e2a\u7406\u60f3\u5316\u7684 PTA \u6765\u8bf4\u660e\u6211\u4eec\u7684\u65b9\u6cd5\uff0cPTA \u7531\u76f8\u540c\u7684\u3001\u7b49\u70ed\u70b9\u5206\u5e03\u7684\u8109\u51b2\u661f\u7ec4\u6210\u3002\u5728\u4e00\u7bc7\u914d\u5957\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u5c06\u6211\u4eec\u7684\u5f62\u5f0f\u4e3b\u4e49\u5e94\u7528\u4e8e\u5f53\u524d\u7684 pta\uff0c\u5e76\u8868\u660e\u5b83\u53ef\u4ee5\u6210\u4e3a\u6307\u5bfc\u548c\u4f18\u5316\u5b9e\u9645\u6570\u636e\u5206\u6790\u7684\u6709\u529b\u5de5\u5177\u3002<\/span><\/section>\n

    <\/section>\n
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    \u57fa\u4e8e\u968f\u673a SIR \u6a21\u578b\u7684\u9501\u5b9a \/ \u6d4b\u8bd5<\/strong><\/span><\/p>\n

    \u7f13\u89e3\u7b56\u7565\u7814\u7a76\u53ca\u5176<\/strong><\/span><\/p>\n

    \u4e0e\u97e9\u56fd\u3001\u5fb7\u56fd\u548c\u7ebd\u7ea6\u6570\u636e\u7684\u6bd4\u8f83<\/strong><\/span><\/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

    Study of lockdown\/testing mitigation strategies on stochastic SIR model and its comparison with South Korea, Germany and New York data<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14373<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
     Priyanka,Vicky Verma<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>We are currently facing a highly critical case of a world-wide pandemic. The novel coronavirus (SARS-CoV-2, a.k.a. COVID-19) has proved to be extremely contagious and the original outbreak from Asia has now spread to all continents. This situation will fruitfully profit from the study in regards of the spread of the virus, assessing effective countermeasures to weight the impact of the adopted strategies. The standard Susceptible-Infectious-Recovered (SIR) model is a very successful and widely used mathematical model for predicting the spread of an epidemic. We adopt the SIR model on a random network and extend the model to include control strategies {em lockdown} and {em testing} — two often employed mitigation strategies. The ability of these strategies in controlling the pandemic spread is investigated by varying the effectiveness with which they are implemented. The possibility of a second outbreak is evaluated in detail after the mitigation strategies are withdrawn. We notice that, in any case, a sudden interruption of such mitigation strategies will likely induce a resurgence of a second outbreak, whose peak will be correlated to the number of susceptible individuals. In fact, we find that a population will remain vulnerable to the infection until the herd immunity is achieved. We also test our model with real statistics and information on the epidemic spread in South Korea, Germany, and New York and find a remarkable agreement with the simulation data.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u76ee\u524d\u6b63\u9762\u4e34\u5168\u4e16\u754c\u5927\u6d41\u884c\u7684\u4e00\u4e2a\u975e\u5e38\u4e25\u91cd\u7684\u75c5\u4f8b\u3002\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2(SARS-CoV-2\uff0c\u53c8\u540d\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e)\u5df2\u88ab\u8bc1\u660e\u5177\u6709\u6781\u5f3a\u7684\u4f20\u67d3\u6027\uff0c\u6700\u521d\u4ece\u4e9a\u6d32\u7206\u53d1\u7684\u75ab\u60c5\u73b0\u5df2\u8513\u5ef6\u5230\u6240\u6709\u5927\u9646\u3002\u8fd9\u79cd\u60c5\u51b5\u5c06\u6709\u6548\u5730\u53d7\u76ca\u4e8e\u5173\u4e8e\u75c5\u6bd2\u4f20\u64ad\u7684\u7814\u7a76\uff0c\u8bc4\u4f30\u6709\u6548\u7684\u5bf9\u7b56\uff0c\u4ee5\u8861\u91cf\u6240\u91c7\u53d6\u7684\u6218\u7565\u7684\u5f71\u54cd\u3002\u6807\u51c6\u7684\u6613\u611f-\u4f20\u67d3-\u6062\u590d(SIR)\u6a21\u578b\u662f\u4e00\u4e2a\u975e\u5e38\u6210\u529f\u548c\u5e7f\u6cdb\u4f7f\u7528\u7684\u6570\u5b66\u6a21\u578b\u6765\u9884\u6d4b\u4f20\u67d3\u75c5\u7684\u4f20\u64ad\u3002\u6211\u4eec\u5728\u968f\u673a\u7f51\u7edc\u4e0a\u91c7\u7528 SIR \u6a21\u578b\uff0c\u5e76\u5c06\u6a21\u578b\u6269\u5c55\u5230\u5305\u62ec\u63a7\u5236\u7b56\u7565{ em lockdown }\u548c{ em testing }—- \u4e24\u79cd\u5e38\u7528\u7684\u7f13\u89e3\u7b56\u7565\u3002\u901a\u8fc7\u6539\u53d8\u8fd9\u4e9b\u6218\u7565\u7684\u5b9e\u65bd\u6548\u679c\u6765\u8c03\u67e5\u8fd9\u4e9b\u6218\u7565\u63a7\u5236\u5927\u6d41\u884c\u4f20\u64ad\u7684\u80fd\u529b\u3002\u5728\u64a4\u9500\u51cf\u7f13\u6218\u7565\u4e4b\u540e\uff0c\u5c06\u8be6\u7ec6\u8bc4\u4f30\u7b2c\u4e8c\u6b21\u7206\u53d1\u7684\u53ef\u80fd\u6027\u3002\u6211\u4eec\u6ce8\u610f\u5230\uff0c\u5728\u4efb\u4f55\u60c5\u51b5\u4e0b\uff0c\u8fd9\u79cd\u7f13\u89e3\u7b56\u7565\u7684\u7a81\u7136\u4e2d\u65ad\u90fd\u53ef\u80fd\u5bfc\u81f4\u7b2c\u4e8c\u6b21\u75ab\u60c5\u7684\u518d\u6b21\u7206\u53d1\uff0c\u5176\u9ad8\u5cf0\u5c06\u4e0e\u6613\u611f\u4eba\u7fa4\u7684\u6570\u91cf\u76f8\u5173\u3002\u4e8b\u5b9e\u4e0a\uff0c\u6211\u4eec\u53d1\u73b0\u5728\u7fa4\u4f53\u514d\u75ab\u529b\u8fbe\u5230\u4e4b\u524d\uff0c\u7fa4\u4f53\u4ecd\u7136\u5bb9\u6613\u53d7\u5230\u611f\u67d3\u3002\u6211\u4eec\u8fd8\u7528\u97e9\u56fd\u3001\u5fb7\u56fd\u548c\u7ebd\u7ea6\u75ab\u60c5\u4f20\u64ad\u7684\u771f\u5b9e\u7edf\u8ba1\u6570\u636e\u548c\u4fe1\u606f\u5bf9\u6211\u4eec\u7684\u6a21\u578b\u8fdb\u884c\u4e86\u68c0\u9a8c\uff0c\u53d1\u73b0\u4e0e\u6a21\u62df\u6570\u636e\u6709\u663e\u8457\u7684\u4e00\u81f4\u6027\u3002<\/span><\/section>\n

    <\/span><\/section>\n

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    \u4e0d\u5e73\u8861\u72b6\u6001\u4e0b\u4e24\u515a\u515a\u6d3e\u504f\u89c1\u7684\u6d4b\u91cf<\/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

    On measuring two-party partisan bias in unbalanced states<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14067<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    John F. Nagle,Alec Ramsay<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Assuming that partisan fairness and responsiveness are important aspects of redistricting, it is important to measure them. Many measures of partisan bias are satisfactory for states that are balanced with roughly equal proportions of voters for the two major parties. It has been less clear which metrics measure fairness robustly when the proportion of the vote is unbalanced by as little as 60% to 40%. We have addressed this by analyzing past election results for four states with Democratic preferences (CA, IL, MA, and MD), three states with Republican preferences (SC, TN, and TX) and comparing those to results for four nearly balanced states (CO, NC, OH, and PA). We used many past statewide elections in each state to build statistically precise seats for votes and rank for votes graphs to which many measures of partisan bias were applied. In addition to providing values of responsiveness, we find that five of the measures of bias provide mutually consistent values in all states, thereby providing a core of usable measures for unbalanced states. Although all five measures focus on different aspects of partisan bias, normalization of the values across the eleven states provides a suitable way to compare them, and we propose that their average provides a superior measure which we call composite bias. Regarding other measures, we find that the most seemingly plausible symmetry measure fails for unbalanced states. We also consider deviations from the proportionality ideal, but using it is difficult because the political geography of a state can entangle responsiveness with total partisan bias. We do not attempt to separate intentional partisan bias from the implicit bias that results from the interaction of the map drawing rules of a state and its political geography, on the grounds that redistricting should attempt to minimize total partisan bias whatever its provenance.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u5047\u8bbe\u515a\u6d3e\u516c\u5e73\u548c\u53cd\u5e94\u80fd\u529b\u662f\u91cd\u65b0\u5212\u5206\u9009\u533a\u7684\u91cd\u8981\u65b9\u9762\uff0c\u91cd\u8981\u7684\u662f\u8981\u8861\u91cf\u5b83\u4eec\u3002\u8bb8\u591a\u8861\u91cf\u515a\u6d3e\u504f\u89c1\u7684\u6807\u51c6\u5bf9\u4e8e\u4e24\u4e2a\u4e3b\u8981\u653f\u515a\u7684\u9009\u6c11\u6bd4\u4f8b\u5927\u81f4\u76f8\u7b49\u7684\u5dde\u6765\u8bf4\u662f\u4ee4\u4eba\u6ee1\u610f\u7684\u3002\u5982\u679c\u9009\u7968\u7684\u6bd4\u4f8b\u4e0d\u5e73\u8861\uff0c\u53ea\u670960% \u523040% \uff0c\u90a3\u4e48\u54ea\u79cd\u8861\u91cf\u6807\u51c6\u80fd\u591f\u6709\u529b\u5730\u8861\u91cf\u516c\u5e73\u6027\u5c31\u4e0d\u90a3\u4e48\u660e\u786e\u4e86\u3002\u6211\u4eec\u901a\u8fc7\u5206\u6790\u6c11\u4e3b\u515a\u504f\u597d\u7684\u56db\u4e2a\u5dde(CA\uff0cIL\uff0cMA \u548c MD)\u3001\u5171\u548c\u515a\u504f\u597d\u7684\u4e09\u4e2a\u5dde(SC\uff0cTN \u548c TX)\u7684\u8fc7\u53bb\u9009\u4e3e\u7ed3\u679c\u6765\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff0c\u5e76\u5c06\u8fd9\u4e9b\u7ed3\u679c\u4e0e\u56db\u4e2a\u63a5\u8fd1\u5e73\u8861\u7684\u5dde(CO\uff0cNC\uff0cOH \u548c PA)\u7684\u7ed3\u679c\u8fdb\u884c\u6bd4\u8f83\u3002\u6211\u4eec\u5229\u7528\u8fc7\u53bb\u5728\u6bcf\u4e2a\u5dde\u4e3e\u884c\u7684\u8bb8\u591a\u5dde\u7ea7\u9009\u4e3e\uff0c\u4e3a\u9009\u7968\u5efa\u7acb\u4e86\u7edf\u8ba1\u5b66\u4e0a\u7cbe\u786e\u7684\u5e2d\u4f4d\uff0c\u5e76\u5bf9\u9009\u7968\u56fe\u8868\u8fdb\u884c\u6392\u540d\uff0c\u8bb8\u591a\u8861\u91cf\u515a\u6d3e\u504f\u89c1\u7684\u6307\u6807\u90fd\u9002\u7528\u4e8e\u8fd9\u4e9b\u56fe\u8868\u3002\u9664\u4e86\u63d0\u4f9b\u53cd\u5e94\u80fd\u529b\u7684\u4ef7\u503c\uff0c\u6211\u4eec\u53d1\u73b0\u4e94\u79cd\u504f\u5dee\u6d4b\u91cf\u65b9\u6cd5\u5728\u6240\u6709\u72b6\u6001\u4e0b\u63d0\u4f9b\u4e86\u76f8\u4e92\u4e00\u81f4\u7684\u4ef7\u503c\uff0c\u4ece\u800c\u4e3a\u4e0d\u5e73\u8861\u72b6\u6001\u63d0\u4f9b\u4e86\u53ef\u7528\u7684\u6838\u5fc3\u6d4b\u91cf\u65b9\u6cd5\u3002\u5c3d\u7ba1\u6240\u6709\u76845\u9879\u6307\u6807\u90fd\u96c6\u4e2d\u5728\u515a\u6d3e\u504f\u89c1\u7684\u4e0d\u540c\u65b9\u9762\uff0c\u4f46\u662f\u5341\u4e00\u4e2a\u5dde\u4ef7\u503c\u89c2\u7684\u6b63\u5e38\u5316\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5408\u9002\u7684\u65b9\u6cd5\u6765\u6bd4\u8f83\u5b83\u4eec\uff0c\u6211\u4eec\u5efa\u8bae\u4ed6\u4eec\u7684\u5e73\u5747\u503c\u63d0\u4f9b\u4e86\u4e00\u4e2a\u66f4\u597d\u7684\u6307\u6807\uff0c\u6211\u4eec\u79f0\u4e4b\u4e3a\u7efc\u5408\u504f\u89c1\u3002\u81f3\u4e8e\u5176\u4ed6\u7684\u6d4b\u91cf\u65b9\u6cd5\uff0c\u6211\u4eec\u53d1\u73b0\u6700\u770b\u4f3c\u5408\u7406\u7684\u5bf9\u79f0\u6d4b\u91cf\u65b9\u6cd5\u5bf9\u4e0d\u5e73\u8861\u72b6\u6001\u662f\u4e0d\u9002\u7528\u7684\u3002\u6211\u4eec\u4e5f\u8003\u8651\u504f\u79bb\u76f8\u79f0\u6027\u7684\u7406\u60f3\uff0c\u4f46\u4f7f\u7528\u5b83\u662f\u56f0\u96be\u7684\uff0c\u56e0\u4e3a\u4e00\u4e2a\u56fd\u5bb6\u7684\u653f\u6cbb\u5730\u7406\u53ef\u4ee5\u7ea0\u7f20\u4e0e\u5b8c\u5168\u515a\u6d3e\u504f\u89c1\u7684\u53cd\u5e94\u3002\u6211\u4eec\u5e76\u4e0d\u8bd5\u56fe\u5c06\u84c4\u610f\u7684\u515a\u6d3e\u504f\u89c1\u4e0e\u4e00\u4e2a\u56fd\u5bb6\u7684\u5730\u56fe\u7ed8\u5236\u89c4\u5219\u53ca\u5176\u653f\u6cbb\u5730\u7406\u7684\u76f8\u4e92\u4f5c\u7528\u6240\u4ea7\u751f\u7684\u9690\u542b\u504f\u89c1\u533a\u5206\u5f00\u6765\uff0c\u7406\u7531\u662f\u91cd\u65b0\u5212\u5206\u9009\u533a\u5e94\u8bd5\u56fe\u5c3d\u91cf\u51cf\u5c11\u5b8c\u5168\u7684\u515a\u6d3e\u504f\u89c1\uff0c\u65e0\u8bba\u5176\u6765\u6e90\u5982\u4f55\u3002<\/span><\/section>\n

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    \u6811\u7684\u7ebf\u6027\u6392\u5217\u4e2d\u8fb9\u957f\u4e4b\u548c\u7684\u53d8\u5316<\/strong><\/span><\/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 variation of the sum of edge lengths in linear arrangements of trees<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14069<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Ramon Ferrer-i-Cancho,Carlos G\u00f3mez-Rodr\u00edguez,Juan Luis Esteban<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>A fundamental problem in network science is the normalization of the topological or physical distance between vertices, that requires understanding the range of variation of the unnormalized distances. Here we investigate the limits of the variation of the physical distance in linear arrangements of the vertices of trees. In particular, we investigate various problems on the sum of edge lengths in trees of a fixed size: the minimum and the maximum value of the sum for specific trees, the minimum and the maximum in classes of trees (bistar trees and caterpillar trees) and finally the minimum and the maximum for any tree. We establish some foundations for research on optimality scores for spatial networks in one dimension.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u7f51\u7edc\u79d1\u5b66\u4e2d\u7684\u4e00\u4e2a\u57fa\u672c\u95ee\u9898\u662f\u9876\u70b9\u4e4b\u95f4\u7684\u62d3\u6251\u6216\u7269\u7406\u8ddd\u79bb\u7684\u89c4\u8303\u5316\uff0c\u8fd9\u9700\u8981\u7406\u89e3\u975e\u89c4\u8303\u5316\u8ddd\u79bb\u7684\u53d8\u5316\u8303\u56f4\u3002\u8fd9\u91cc\u6211\u4eec\u7814\u7a76\u6811\u7684\u9876\u70b9\u7ebf\u6027\u6392\u5217\u7684\u7269\u7406\u8ddd\u79bb\u53d8\u5316\u7684\u6781\u9650\u3002\u7279\u522b\u5730\uff0c\u6211\u4eec\u7814\u7a76\u4e86\u56fa\u5b9a\u5927\u5c0f\u6811\u7684\u8fb9\u957f\u4e4b\u548c\u7684\u5404\u79cd\u95ee\u9898: \u7279\u5b9a\u6811\u7684\u8fb9\u957f\u4e4b\u548c\u7684\u6700\u5c0f\u503c\u548c\u6700\u5927\u503c\uff0c\u5404\u7c7b\u6811(\u53cc\u661f\u6811\u548c\u6bdb\u866b\u6811)\u7684\u6700\u5c0f\u503c\u548c\u6700\u5927\u503c\uff0c\u6700\u540e\u662f\u4efb\u4f55\u6811\u7684\u6700\u5c0f\u503c\u548c\u6700\u5927\u503c\u3002\u6211\u4eec\u4e3a\u7814\u7a76\u4e00\u7ef4\u7a7a\u95f4\u7f51\u7edc\u7684\u6700\u4f18\u6027\u5f97\u5206\u5960\u5b9a\u4e86\u4e00\u4e9b\u57fa\u7840\u3002<\/span><\/section>\n

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    \u5df4\u897f\u5df4\u4f0a\u4e9a\u5dde\u548c\u5723\u5361\u5854\u7433\u5a1c\u7684 <\/strong><\/span><\/p>\n

    SARS-CoV-2\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e<\/strong><\/span><\/p>\n

    \u6d41\u884c\u7684\u6700\u4f18\u63a7\u5236\u95ee\u9898<\/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

    Optimal Control Concerns Regarding the COVID-19 (SARS-CoV-2) Pandemic in Bahia and Santa Catarina, Brazil<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14108<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Marcelo M. Morato,Igor M. L. Pataro,Marcus V. Americano da Costa,Julio E. Normey-Rico<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>The COVID-19 pandemic is the profoundest health crisis of the 21rst century. The SARS-CoV-2 virus arrived in Brazil around March, 2020 and its social and economical backlashes are catastrophic. In this paper, it is investigated how Model Predictive Control (MPC) could be used to plan appropriate social distancing policies to mitigate the pandemic effects in Bahia and Santa Catarina, two states of different regions, culture, and population demography in Brazil. In addition, the parameters of Susceptible-Infected-Recovered-Deceased (SIRD) models for these two states are identified using an optimization procedure. The control input to the process is a social isolation guideline passed to the population. Two MPC strategies are designed: a) a centralized MPC, which coordinates a single control policy for both states; and b) a decentralized strategy, for which one optimization is solved for each state. Simulation results are shown to illustrate and compare both control strategies. The framework serves as guidelines to deals with such pandemic phenomena.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u662f21\u4e16\u7eaa\u6700\u4e25\u91cd\u7684\u5065\u5eb7\u5371\u673a\u3002Sars-cov-2\u75c5\u6bd2\u4e8e2020\u5e743\u6708\u5de6\u53f3\u62b5\u8fbe\u5df4\u897f\uff0c\u5176\u793e\u4f1a\u548c\u7ecf\u6d4e\u53cd\u5f39\u662f\u707e\u96be\u6027\u7684\u3002\u5728\u8fd9\u7bc7\u8bba\u6587\u4e2d\uff0c\u6211\u4eec\u7814\u7a76\u4e86\u5982\u4f55\u5229\u7528\u6a21\u578b\u9884\u4f30\u8ba1\u63a7\u5236\u536b\u751f\u7ec4\u7ec7\u6765\u5236\u5b9a\u9002\u5f53\u7684\u793e\u4f1a\u758f\u8fdc\u653f\u7b56\uff0c\u4ee5\u51cf\u8f7b\u5df4\u4f0a\u4e9a\u548c\u5723\u5361\u5854\u7433\u5a1c\u8fd9\u4e24\u4e2a\u4e0d\u540c\u5730\u533a\u3001\u4e0d\u540c\u6587\u5316\u548c\u4e0d\u540c\u4eba\u53e3\u7ec4\u6210\u7684\u5dde\u7684\u6d41\u884c\u75c5\u5f71\u54cd\u3002\u6b64\u5916\uff0c\u8fd8\u5229\u7528\u6700\u4f18\u5316\u65b9\u6cd5\u5bf9\u8fd9\u4e24\u79cd\u72b6\u6001\u4e0b\u7684\u6613\u611f-\u611f\u67d3-\u5eb7\u590d-\u6b7b\u4ea1(SIRD)\u6a21\u578b\u7684\u53c2\u6570\u8fdb\u884c\u4e86\u8fa8\u8bc6\u3002\u8be5\u8fc7\u7a0b\u7684\u63a7\u5236\u8f93\u5165\u662f\u4f20\u9012\u7ed9\u603b\u4f53\u7684\u793e\u4f1a\u9694\u79bb\u6307\u5bfc\u65b9\u9488\u3002\u8bbe\u8ba1\u4e86\u4e24\u79cd MPC \u7b56\u7565: \u4e00\u79cd\u662f\u96c6\u4e2d\u5f0f MPC\uff0c\u534f\u8c03\u4e24\u79cd\u72b6\u6001\u7684\u5355\u4e00\u63a7\u5236\u7b56\u7565; \u53e6\u4e00\u79cd\u662f\u5206\u6563\u5f0f MPC\uff0c\u4e3a\u6bcf\u79cd\u72b6\u6001\u89e3\u51b3\u4e00\u4e2a\u4f18\u5316\u95ee\u9898\u3002\u4eff\u771f\u7ed3\u679c\u9a8c\u8bc1\u4e86\u4e24\u79cd\u63a7\u5236\u7b56\u7565\u7684\u6709\u6548\u6027\u3002\u8be5\u6846\u67b6\u662f\u5904\u7406\u8fd9\u79cd\u5927\u6d41\u884c\u73b0\u8c61\u7684\u6307\u5bfc\u65b9\u9488\u3002<\/span><\/section>\n

    <\/section>\n
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    \u98d3\u98ce\u64a4\u79bb\u8fc7\u7a0b\u4e2d\u7684\u9053\u8def\u7f51\u7edc\u53ef\u8fbe\u6027\u8bc4\u4f30<\/strong><\/span><\/p>\n

    \u2014\u2014\u4ee5\u4f5b\u7f57\u91cc\u8fbe\u5dde\u7684\u98d3\u98ce Irma \u4e3a\u4f8b<\/strong><\/span><\/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

    Estimating Road Network Accessibility during a Hurricane Evacuation: A Case Study of Hurricane Irma in Florida<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14137<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Yi-Jie Zhu,Yujie Hu,Jennifer M. Collins<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Understanding the spatiotemporal road network accessibility during a hurricane evacuation, the level of ease of residents in an area in reaching evacuation destination sites through the road network, is a critical component of emergency management. While many studies have attempted to measure road accessibility (either in the scope of evacuation or beyond), few have considered both dynamic evacuation demand and characteristics of a hurricane. This study proposes a methodological framework to achieve this goal. In an interval of every six hours, the method first estimates the evacuation demand in terms of number of vehicles per household in each county subdivision by considering the hurricane’s wind radius and track. The closest facility analysis is then employed to model evacuees’ route choices towards the predefined evacuation destinations. The potential crowdedness index (PCI), a metric capturing the level of crowdedness of each road segment, is then computed by coupling the estimated evacuation demand and route choices. Finally, the road accessibility of each sub-county is measured by calculating the reciprocal of the sum of PCI values of corresponding roads connecting evacuees from the sub-county to the designated destinations. The method is applied to the entire state of Florida during Hurricane Irma in September 2017. Results show that I-75 and I-95 northbound have a high level of congestion, and sub-counties along the northbound I-95 suffer from the worst road accessibility. In addition, this research performs a sensitivity analysis for examining the impacts of different choices of behavioral response curves on accessibility results.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u4e86\u89e3\u98d3\u98ce\u758f\u6563\u671f\u95f4\u7684\u65f6\u7a7a\u9053\u8def\u7f51\u7edc\u53ef\u8fbe\u6027\uff0c\u5373\u901a\u8fc7\u9053\u8def\u7f51\u7edc\u5230\u8fbe\u758f\u6563\u76ee\u7684\u5730\u5730\u533a\u7684\u5c45\u6c11\u7684\u5bb9\u6613\u7a0b\u5ea6\uff0c\u662f\u5e94\u6025\u7ba1\u7406\u7684\u4e00\u4e2a\u91cd\u8981\u7ec4\u6210\u90e8\u5206\u3002\u867d\u7136\u8bb8\u591a\u7814\u7a76\u8bd5\u56fe\u8861\u91cf\u9053\u8def\u7684\u53ef\u8fbe\u6027(\u65e0\u8bba\u662f\u5728\u758f\u6563\u8303\u56f4\u5185\u8fd8\u662f\u4ee5\u5916) \uff0c\u4f46\u5f88\u5c11\u6709\u7814\u7a76\u8003\u8651\u5230\u52a8\u6001\u758f\u6563\u9700\u6c42\u548c\u98d3\u98ce\u7684\u7279\u70b9\u3002\u672c\u7814\u7a76\u63d0\u51fa\u4e86\u5b9e\u73b0\u8fd9\u4e00\u76ee\u6807\u7684\u65b9\u6cd5\u6846\u67b6\u3002\u5728\u6bcf\u516d\u4e2a\u5c0f\u65f6\u7684\u65f6\u95f4\u95f4\u9694\u5185\uff0c\u8be5\u65b9\u6cd5\u9996\u5148\u8003\u8651\u98d3\u98ce\u7684\u98ce\u5411\u534a\u5f84\u548c\u8def\u5f84\uff0c\u4ee5\u6bcf\u4e2a\u53bf\u5206\u533a\u6bcf\u6237\u7684\u8f66\u8f86\u6570\u91cf\u6765\u4f30\u8ba1\u758f\u6563\u9700\u6c42\u3002\u6700\u8fd1\u8bbe\u65bd\u5206\u6790\uff0c\u7136\u540e\u91c7\u7528\u6a21\u578b\u758f\u6563\u4eba\u5458\u7684\u8def\u7ebf\u9009\u62e9\u5230\u9884\u5b9a\u4e49\u7684\u758f\u6563\u76ee\u7684\u5730\u3002\u6f5c\u5728\u62e5\u6324\u5ea6\u6307\u6570(PCI)\u662f\u4e00\u4e2a\u5ea6\u91cf\u6bcf\u4e2a\u8def\u6bb5\u62e5\u6324\u7a0b\u5ea6\u7684\u6307\u6807\uff0c\u7136\u540e\u901a\u8fc7\u8026\u5408\u4f30\u8ba1\u7684\u758f\u6563\u9700\u6c42\u548c\u8def\u7ebf\u9009\u62e9\u8ba1\u7b97\u51fa\u6765\u3002\u6700\u540e\uff0c\u901a\u8fc7\u8ba1\u7b97\u8fde\u63a5\u4ece\u8be5\u6b21\u7ea7\u53bf\u5230\u6307\u5b9a\u76ee\u7684\u5730\u7684\u758f\u6563\u4eba\u5458\u7684\u76f8\u5e94\u9053\u8def PCI \u503c\u4e4b\u548c\u7684\u5012\u6570\u6765\u8861\u91cf\u6bcf\u4e2a\u6b21\u7ea7\u53bf\u7684\u9053\u8def\u53ef\u8fbe\u6027\u3002\u8be5\u65b9\u6cd5\u57282017\u5e749\u6708\u98d3\u98ce\u201c\u5384\u739b\u201d\u671f\u95f4\u9002\u7528\u4e8e\u6574\u4e2a\u4f5b\u7f57\u91cc\u8fbe\u5dde\u3002\u7ed3\u679c\u663e\u793a\uff0cI-75\u548c I-95\u5317\u884c\u8def\u6bb5\u62e5\u5835\u7a0b\u5ea6\u5f88\u9ad8\uff0cI-95\u5317\u884c\u8def\u6bb5\u6cbf\u7ebf\u5404\u53bf\u7684\u9053\u8def\u4ea4\u901a\u72b6\u51b5\u6700\u5dee\u3002\u6b64\u5916\uff0c\u8fd9\u9879\u7814\u7a76\u8fd8\u91c7\u7528\u4e86\u4e00\u4e2a\u654f\u611f\u5ea6\u5206\u6790\u7684\u65b9\u6cd5\u6765\u68c0\u9a8c\u4e0d\u540c\u9009\u62e9\u7684\u884c\u4e3a\u53cd\u5e94\u66f2\u7ebf\u5bf9\u53ef\u8fbe\u6027\u7ed3\u679c\u7684\u5f71\u54cd\u3002<\/span><\/section>\n

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    \u4f30\u8ba1\u7f8e\u56fd\u90ae\u653f\u7f16\u7801\u4e4b\u95f4<\/strong><\/span><\/p>\n

    \u7684\u5927\u9a71\u52a8\u65f6\u95f4\u77e9\u9635:<\/strong><\/span><\/p>\n

    \u5dee\u5206\u62bd\u6837\u65b9\u6cd5<\/strong><\/span><\/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

    Estimating a Large Drive Time Matrix between Zip Codes in the United States: A Differential Sampling Approach<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14138<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Yujie Hu,Changzhen Wang,Ruiyang Li,Fahui Wang<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Estimating a massive drive time matrix between locations is a practical but challenging task. The challenges include availability of reliable road network (including traffic) data, programming expertise, and access to high-performance computing resources. This research proposes a method for estimating a nationwide drive time matrix between ZIP code areas in the U.S.–a geographic unit at which many national datasets such as health information are compiled and distributed. The method (1) does not rely on intensive efforts in data preparation or access to advanced computing resources, (2) uses algorithms of varying complexity and computational time to estimate drive times of different trip lengths, and (3) accounts for both interzonal and intrazonal drive times. The core design samples ZIP code pairs with various intensities according to trip lengths and derives the drive times via Google Maps API, and the Google times are then used to adjust and improve some primitive estimates of drive times with low computational costs. The result provides a valuable resource for researchers.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u4f30\u7b97\u4e0d\u540c\u4f4d\u7f6e\u4e4b\u95f4\u7684\u5927\u89c4\u6a21\u9a71\u52a8\u65f6\u95f4\u77e9\u9635\u662f\u4e00\u9879\u5b9e\u9645\u4f46\u5177\u6709\u6311\u6218\u6027\u7684\u4efb\u52a1\u3002\u6311\u6218\u5305\u62ec\u53ef\u9760\u7684\u9053\u8def\u7f51\u7edc(\u5305\u62ec\u4ea4\u901a)\u6570\u636e\u7684\u53ef\u7528\u6027\u3001\u7f16\u7a0b\u4e13\u4e1a\u77e5\u8bc6\u4ee5\u53ca\u5bf9\u9ad8\u6027\u80fd\u8ba1\u7b97\u8d44\u6e90\u7684\u8bbf\u95ee\u3002\u8fd9\u9879\u7814\u7a76\u63d0\u51fa\u4e86\u4e00\u79cd\u4f30\u7b97\u7f8e\u56fd\u90ae\u653f\u7f16\u7801\u5730\u533a\u4e4b\u95f4\u7684\u5168\u56fd\u9a71\u52a8\u65f6\u95f4\u77e9\u9635\u7684\u65b9\u6cd5—- \u8fd9\u662f\u4e00\u4e2a\u5730\u7406\u5355\u5143\uff0c\u8bb8\u591a\u56fd\u5bb6\u6570\u636e\u96c6\uff0c\u5982\u5065\u5eb7\u4fe1\u606f\u662f\u5728\u8fd9\u91cc\u6c47\u7f16\u548c\u5206\u53d1\u7684\u3002\u8be5\u65b9\u6cd5(1)\u4e0d\u4f9d\u8d56\u4e8e\u5728\u6570\u636e\u51c6\u5907\u6216\u8bbf\u95ee\u9ad8\u7ea7\u8ba1\u7b97\u8d44\u6e90\u65b9\u9762\u7684\u5bc6\u96c6\u52aa\u529b\uff0c(2)\u4f7f\u7528\u4e0d\u540c\u590d\u6742\u5ea6\u548c\u8ba1\u7b97\u65f6\u95f4\u7684\u7b97\u6cd5\u6765\u4f30\u8ba1\u4e0d\u540c\u884c\u7a0b\u957f\u5ea6\u7684\u9a71\u52a8\u65f6\u95f4\uff0c(3)\u517c\u987e\u4e86\u533a\u95f4\u548c\u533a\u5185\u9a71\u52a8\u65f6\u95f4\u3002\u6838\u5fc3\u8bbe\u8ba1\u6839\u636e\u884c\u7a0b\u957f\u5ea6\u91c7\u6837\u4e0d\u540c\u5f3a\u5ea6\u7684\u90ae\u653f\u7f16\u7801\u5bf9\uff0c\u5e76\u901a\u8fc7\u8c37\u6b4c\u5730\u56fe API \u8ba1\u7b97\u9a71\u52a8\u65f6\u95f4\uff0c\u7136\u540e\u4f7f\u7528\u8c37\u6b4c\u65f6\u95f4\u8c03\u6574\u548c\u6539\u8fdb\u4e00\u4e9b\u4f4e\u8ba1\u7b97\u6210\u672c\u7684\u539f\u59cb\u9a71\u52a8\u65f6\u95f4\u4f30\u8ba1\u3002\u7814\u7a76\u7ed3\u679c\u4e3a\u7814\u7a76\u4eba\u5458\u63d0\u4f9b\u4e86\u5b9d\u8d35\u7684\u8d44\u6e90\u3002<\/span><\/section>\n

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    \u57ce\u5e02\u5c3a\u5ea6\u5f8b\u4e2d\u7684\u7a7a\u95f4\u76f8\u4e92\u4f5c\u7528<\/strong><\/span><\/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

    Spatial interactions in urban scaling laws<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14140<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Eduardo G. Altmann<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Analyses of urban scaling laws assume that observations in different cities are independent of the existence of nearby cities. Here we introduce generative models and data-analysis methods that overcome this limitation by modelling explicitly the effect of interactions between individuals at different locations. Parameters that describe the scaling law and the spatial interactions are inferred from data simultaneously, allowing for rigorous (Bayesian) model comparison and overcoming the problem of defining the boundaries of urban regions. Results in five different datasets show that including spatial interactions typically leads to better models and a change in the exponent of the scaling law. Data and codes are provided in Ref. [1].<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u5bf9\u57ce\u5e02\u5c3a\u5ea6\u5f8b\u7684\u5206\u6790\u5047\u5b9a\u4e0d\u540c\u57ce\u5e02\u7684\u89c2\u6d4b\u503c\u4e0e\u90bb\u8fd1\u57ce\u5e02\u7684\u5b58\u5728\u65e0\u5173\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u4ecb\u7ecd\u751f\u6210\u6a21\u578b\u548c\u6570\u636e\u5206\u6790\u65b9\u6cd5\uff0c\u514b\u670d\u8fd9\u4e00\u5c40\u9650\u6027\uff0c\u660e\u786e\u5efa\u6a21\u4e2a\u4eba\u4e4b\u95f4\u7684\u76f8\u4e92\u4f5c\u7528\u5728\u4e0d\u540c\u5730\u70b9\u7684\u5f71\u54cd\u3002\u63cf\u8ff0\u5c3a\u5ea6\u5f8b\u548c\u7a7a\u95f4\u76f8\u4e92\u4f5c\u7528\u7684\u53c2\u6570\u540c\u65f6\u4ece\u6570\u636e\u4e2d\u63a8\u65ad\u51fa\u6765\uff0c\u5141\u8bb8\u8fdb\u884c\u4e25\u683c\u7684(\u8d1d\u53f6\u65af)\u6a21\u578b\u6bd4\u8f83\uff0c\u514b\u670d\u4e86\u754c\u5b9a\u57ce\u5e02\u533a\u57df\u8fb9\u754c\u7684\u95ee\u9898\u3002\u4e94\u4e2a\u4e0d\u540c\u6570\u636e\u96c6\u7684\u7ed3\u679c\u8868\u660e\uff0c\u5305\u62ec\u7a7a\u95f4\u76f8\u4e92\u4f5c\u7528\u901a\u5e38\u4f1a\u5bfc\u81f4\u66f4\u597d\u7684\u6a21\u578b\u548c\u6807\u5ea6\u5f8b\u6307\u6570\u7684\u53d8\u5316\u3002\u6570\u636e\u548c\u4ee3\u7801\u63d0\u4f9b\u53c2\u8003\u6587\u732e\u3002[1]\u3002<\/span><\/section>\n

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    20\u4e16\u7eaa90\u5e74\u4ee3\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\uff0c<\/strong><\/span><\/p>\n

    \u7f8e\u56fd\u5927\u57ce\u5e02\u70ed\u70b9\u5730\u533a\u7684<\/strong><\/span><\/p>\n

    \u79fb\u52a8\u548c\u8bbf\u95ee\u7684\u4e0d\u540c\u6a21\u5f0f<\/strong><\/span><\/p>\n

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


    <\/h2>\n

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

    Disparate Patterns of Movements and Visits to Points of Interests Located in Urban Hotspots across U.S. Metropolitan Cities during COVID-19<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14157<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Qingchun Li,Liam Bessell,Xin Xiao,Chao Fan,Xinyu Gao,Ali Mostafavi<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>We examined the effect of social distancing on changes in visits to urban hotspot points of interest. Urban hotspots, such as central business districts, are gravity activity centers orchestrating movement and mobility patterns in cities. In a pandemic situation, urban hotspots could be potential superspreader areas as visits to urban hotspots can increase the risk of contact and transmission of a disease among a population. We mapped origin-destination networks from census block groups to points of interest (POIs) in sixteen cities in the United States. We adopted a coarse-grain approach to study movement patterns of visits to POIs among the hotspots and non-hotspots from January to May 2020. Also, we conducted chi-square tests to identify POIs with significant flux-in changes during the analysis period. The results showed disparate patterns across cities in terms of reduction in POI visits to hotspot areas. The sixteen cities are divided into two categories based on visits to POIs in hotspot areas. In one category, which includes the cities of, San Francisco, Seattle, and Chicago, we observe a considerable decrease in visits to POIs in hotspot areas, while in another category, including the cites of, Austin, Houston, and San Diego, the visits to hotspot areas did not greatly decrease during the social distancing period. In addition, while all the cities exhibited overall decreasing visits to POIs, one category maintained the proportion of visits to POIs in the hotspots. The proportion of visits to some POIs (e.g., Restaurant and Other Eating Places) remained stable during the social distancing period, while some POIs had an increased proportion of visits (e.g., Grocery Stores). The findings highlight that social distancing orders do yield disparate patterns of reduction in movements to hotspots POIs.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u7814\u7a76\u4e86\u793e\u4f1a\u8ddd\u79bb\u5bf9\u57ce\u5e02\u70ed\u70b9\u5730\u533a\u6e38\u5ba2\u6570\u91cf\u53d8\u5316\u7684\u5f71\u54cd\u3002\u57ce\u5e02\u70ed\u70b9\u5730\u533a\uff0c\u5982\u4e2d\u592e\u5546\u52a1\u533a\uff0c\u662f\u91cd\u529b\u6d3b\u52a8\u4e2d\u5fc3\uff0c\u534f\u8c03\u57ce\u5e02\u7684\u8fd0\u52a8\u548c\u6d41\u52a8\u6a21\u5f0f\u3002\u5728\u5927\u6d41\u884c\u7684\u60c5\u51b5\u4e0b\uff0c\u57ce\u5e02\u70ed\u70b9\u53ef\u80fd\u662f\u6f5c\u5728\u7684\u8d85\u7ea7\u4f20\u64ad\u5730\u533a\uff0c\u56e0\u4e3a\u8bbf\u95ee\u57ce\u5e02\u70ed\u70b9\u53ef\u4ee5\u589e\u52a0\u4eba\u53e3\u4e4b\u95f4\u63a5\u89e6\u548c\u4f20\u64ad\u75be\u75c5\u7684\u98ce\u9669\u3002\u6211\u4eec\u7ed8\u5236\u4e86\u7f8e\u56fd\u5341\u516d\u4e2a\u57ce\u5e02\u4ece\u4eba\u53e3\u666e\u67e5\u533a\u7ec4\u5230\u611f\u5174\u8da3\u70b9(POIs)\u7684\u8d77\u70b9-\u76ee\u7684\u5730\u7f51\u7edc\u3002\u6211\u4eec\u91c7\u7528\u7c97\u7c92\u5ea6\u65b9\u6cd5\u7814\u7a762020\u5e741\u6708\u81f35\u6708\u5728\u70ed\u70b9\u548c\u975e\u70ed\u70b9\u5730\u533a\u8bbf\u95ee POIs \u7684\u6d41\u52a8\u6a21\u5f0f\u3002\u6b64\u5916\uff0c\u6211\u4eec\u8fd8\u8fdb\u884c\u4e86\u5361\u65b9\u68c0\u9a8c\uff0c\u4ee5\u786e\u5b9a\u5728\u5206\u6790\u671f\u95f4\u6709\u663e\u8457\u901a\u91cf\u53d8\u5316\u7684 poi\u3002\u8c03\u67e5\u7ed3\u679c\u663e\u793a\uff0c\u5404\u4e2a\u57ce\u5e02\u7684 POI \u70ed\u70b9\u5730\u533a\u8bbf\u95ee\u91cf\u4e0b\u964d\u7684\u60c5\u51b5\u5404\u4e0d\u76f8\u540c\u3002\u8fd9\u5341\u516d\u4e2a\u57ce\u5e02\u6839\u636e\u70ed\u70b9\u5730\u533a\u7684 POIs \u8bbf\u95ee\u91cf\u88ab\u5206\u4e3a\u4e24\u7c7b\u3002\u5728\u4e00\u4e2a\u7c7b\u522b\u4e2d\uff0c\u5305\u62ec\u65e7\u91d1\u5c71\u3001\u897f\u96c5\u56fe\u548c\u829d\u52a0\u54e5\uff0c\u6211\u4eec\u89c2\u5bdf\u5230\u5728\u70ed\u70b9\u5730\u533a\u8bbf\u95ee POIs \u7684\u5927\u91cf\u51cf\u5c11\uff0c\u800c\u5728\u53e6\u4e00\u4e2a\u7c7b\u522b\u4e2d\uff0c\u5305\u62ec\u5965\u65af\u6c40\u3001\u4f11\u65af\u987f\u548c\u5723\u5730\u4e9a\u54e5\uff0c\u8bbf\u95ee\u70ed\u70b9\u5730\u533a\u5728\u793e\u4f1a\u758f\u8fdc\u671f\u95f4\u5e76\u6ca1\u6709\u5927\u91cf\u51cf\u5c11\u3002\u6b64\u5916\uff0c\u867d\u7136\u6240\u6709\u57ce\u5e02\u5bf9 POIs \u7684\u8bbf\u95ee\u603b\u4f53\u5448\u4e0b\u964d\u8d8b\u52bf\uff0c\u4f46\u6709\u4e00\u7c7b\u8bbf\u95ee\u70ed\u70b9\u5730\u533a\u7684 POIs \u7684\u6bd4\u4f8b\u4fdd\u6301\u4e0d\u53d8\u3002\u5728\u793e\u4ea4\u8ddd\u79bb\u62c9\u5927\u671f\u95f4\uff0c\u90e8\u5206\u53c2\u89c2\u70b9(\u4f8b\u5982\u98df\u8086\u53ca\u5176\u4ed6\u98df\u8086)\u7684\u8bbf\u95ee\u6bd4\u4f8b\u4fdd\u6301\u7a33\u5b9a\uff0c\u800c\u90e8\u5206\u53c2\u89c2\u70b9(\u4f8b\u5982\u98df\u54c1\u6742\u8d27\u5e97)\u7684\u8bbf\u95ee\u6bd4\u4f8b\u5219\u6709\u6240\u589e\u52a0\u3002\u7814\u7a76\u7ed3\u679c\u7a81\u51fa\u8868\u660e\uff0c\u793e\u4f1a\u758f\u8fdc\u79e9\u5e8f\u786e\u5b9e\u4f1a\u5bfc\u81f4\u4e0d\u540c\u6a21\u5f0f\u7684 POIs \u70ed\u70b9\u79fb\u52a8\u51cf\u5c11\u3002<\/span><\/section>\n

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    \u6709\u4e89\u8bae\u7684\u4fe1\u606f\u5728 Reddit \u4e0a<\/strong><\/span><\/p>\n

    \u4f20\u64ad\u5f97\u8d8a\u6765\u8d8a\u5feb\uff0c\u8d8a\u6765\u8d8a\u8fdc<\/strong><\/span><\/p>\n

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

    Controversial information spreads faster and further in Reddit<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.13991<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Jasser Jasser,Ivan Garibay,Steve Scheinert,Alexander V. Mantzaris<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Online users discuss and converse about all sorts of topics on social networks. Facebook, Twitter, Reddit are among many other networks where users can have this freedom of information sharing. The abundance of information shared over these networks makes them an attractive area for investigating all aspects of human behavior on information dissemination. Among the many interesting behaviors, controversiality within social cascades is of high interest to us. It is known that controversiality is bound to happen within online discussions. The online social network platform Reddit has the feature to tag comments as controversial if the users have mixed opinions about that comment. The difference between this study and previous attempts at understanding controversiality on social networks is that we do not investigate topics that are known to be controversial. On the contrary, we examine typical cascades with comments that the readers deemed to be controversial concerning the matter discussed. This work asks whether controversially initiated information cascades have distinctive characteristics than those not controversial in Reddit. We used data collected from Reddit consisting of around 17 million posts and their corresponding comments related to cybersecurity issues to answer these emerging questions. From the comparative analyses conducted, controversial content travels faster and further from its origin. Understanding this phenomenon would shed light on how users or organization might use it to their help in controlling and spreading a specific beneficiary message.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u5728\u7ebf\u7528\u6237\u5728\u793e\u4ea4\u7f51\u7edc\u4e0a\u8ba8\u8bba\u548c\u4ea4\u8c08\u5404\u79cd\u5404\u6837\u7684\u8bdd\u9898\u3002\u5728 Facebook\uff0cTwitter\uff0cReddit \u7b49\u5176\u4ed6\u7f51\u7ad9\u4e0a\uff0c\u7528\u6237\u53ef\u4ee5\u81ea\u7531\u5730\u5206\u4eab\u4fe1\u606f\u3002\u901a\u8fc7\u8fd9\u4e9b\u7f51\u7edc\u5171\u4eab\u7684\u4e30\u5bcc\u4fe1\u606f\u4f7f\u5b83\u4eec\u6210\u4e3a\u7814\u7a76\u4eba\u7c7b\u4fe1\u606f\u4f20\u64ad\u884c\u4e3a\u5404\u4e2a\u65b9\u9762\u7684\u6709\u5438\u5f15\u529b\u7684\u9886\u57df\u3002\u5728\u8bb8\u591a\u6709\u8da3\u7684\u884c\u4e3a\u4e2d\uff0c\u793e\u4f1a\u7ea7\u8054\u4e2d\u7684\u4e89\u8bae\u662f\u6211\u4eec\u975e\u5e38\u611f\u5174\u8da3\u7684\u3002\u4f17\u6240\u5468\u77e5\uff0c\u5728\u7f51\u7edc\u8ba8\u8bba\u4e2d\u80af\u5b9a\u4f1a\u6709\u4e89\u8bae\u3002\u5728\u7ebf\u793e\u4ea4\u7f51\u7edc\u5e73\u53f0 Reddit \u6709\u4e00\u4e2a\u529f\u80fd\uff0c\u5982\u679c\u7528\u6237\u5bf9\u8bc4\u8bba\u6709\u4e0d\u540c\u7684\u610f\u89c1\uff0c\u53ef\u4ee5\u5c06\u8bc4\u8bba\u6807\u8bb0\u4e3a\u6709\u4e89\u8bae\u7684\u3002\u8fd9\u9879\u7814\u7a76\u4e0e\u4e4b\u524d\u5c1d\u8bd5\u7406\u89e3\u793e\u4ea4\u7f51\u7edc\u4e0a\u7684\u4e89\u8bae\u7684\u4e0d\u540c\u4e4b\u5904\u5728\u4e8e\uff0c\u6211\u4eec\u4e0d\u8c03\u67e5\u90a3\u4e9b\u5df2\u77e5\u5177\u6709\u4e89\u8bae\u6027\u7684\u8bdd\u9898\u3002\u76f8\u53cd\uff0c\u6211\u4eec\u68c0\u67e5\u5178\u578b\u7684\u7011\u5e03\u4e0e\u8bc4\u8bba\uff0c\u8bfb\u8005\u8ba4\u4e3a\u662f\u6709\u4e89\u8bae\u7684\u4e8b\u9879\u8ba8\u8bba\u3002\u8fd9\u9879\u7814\u7a76\u63d0\u51fa\u4e86\u8fd9\u6837\u4e00\u4e2a\u95ee\u9898: \u4e0e Reddit \u4e0a\u90a3\u4e9b\u6ca1\u6709\u4e89\u8bae\u7684\u4fe1\u606f\u7ea7\u8054\u76f8\u6bd4\uff0c\u8fd9\u4e9b\u6709\u4e89\u8bae\u7684\u4fe1\u606f\u7ea7\u8054\u662f\u5426\u5177\u6709\u72ec\u7279\u7684\u7279\u5f81\u3002\u6211\u4eec\u4f7f\u7528\u4ece Reddit \u4e0a\u6536\u96c6\u7684\u5927\u7ea61700\u4e07\u7bc7\u5e16\u5b50\u7684\u6570\u636e\uff0c\u4ee5\u53ca\u4ed6\u4eec\u76f8\u5e94\u7684\u4e0e\u7f51\u7edc\u5b89\u5168\u95ee\u9898\u76f8\u5173\u7684\u8bc4\u8bba\u6765\u56de\u7b54\u8fd9\u4e9b\u65b0\u51fa\u73b0\u7684\u95ee\u9898\u3002\u4ece\u6240\u8fdb\u884c\u7684\u6bd4\u8f83\u5206\u6790\u6765\u770b\uff0c\u6709\u4e89\u8bae\u7684\u5185\u5bb9\u4f20\u64ad\u5f97\u8d8a\u6765\u8d8a\u5feb\uff0c\u800c\u4e14\u79bb\u5b83\u7684\u8d77\u6e90\u8d8a\u6765\u8d8a\u8fdc\u3002\u4e86\u89e3\u8fd9\u4e00\u73b0\u8c61\u5c06\u6709\u52a9\u4e8e\u4e86\u89e3\u7528\u6237\u6216\u7ec4\u7ec7\u5982\u4f55\u5229\u7528\u5b83\u6765\u5e2e\u52a9\u63a7\u5236\u548c\u4f20\u64ad\u7279\u5b9a\u7684\u53d7\u76ca\u4eba\u4fe1\u606f\u3002<\/span><\/section>\n

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    \u52a0\u5f3a\u4f01\u4e1a\u7f51\u7edc\u4e2d\u77e5\u8bc6\u8f6c\u79fb\u7684\u5e72\u9884\u60c5\u666f<\/strong><\/span><\/p>\n

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

    Intervention scenarios to enhance knowledge transfer in a network of firm<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    https:\/\/arxiv.org\/abs\/2006.14249<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Frank Schweitzer,Yan Zhang,Giona Casiraghi<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>We investigate a multi-agent model of firms in an R&D network. Each firm is characterized by its knowledge stock <\/span>xi(t), which follows a non-linear dynamics. It can grow with the input from other firms, i.e., by knowledge transfer, and decays otherwise. Maintaining interactions is costly. Firms can leave the network if their expected knowledge growth is not realized, which may cause other firms to also leave the network. The paper discusses two bottom-up intervention scenarios to prevent, reduce, or delay cascades of firms leaving. The first one is based on the formalism of network controllability, in which driver nodes are identified and subsequently incentivized, by reducing their costs. The second one combines node interventions and network interventions. It proposes the controlled removal of a single firm and the random replacement of firms leaving. This allows to generate small cascades, which prevents the occurrence of large cascades. We find that both approaches successfully mitigate cascades and thus improve the resilience of the R&D network.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u7814\u7a76\u4e86 r & d \u7f51\u7edc\u4e2d\u4f01\u4e1a\u7684\u591a\u667a\u80fd\u4f53\u6a21\u578b\uff0c\u6bcf\u4e2a\u4f01\u4e1a\u90fd\u6709\u5176\u77e5\u8bc6\u5b58\u91cf<\/span>xi(t),\u672c\u6587\u63d0\u51fa\u4e86\u4e00\u79cd\u57fa\u4e8e\u975e\u7ebf\u6027\u52a8\u529b\u5b66\u7684\u975e\u7ebf\u6027\u52a8\u529b\u5b66\u65b9\u6cd5\u3002\u5b83\u53ef\u4ee5\u968f\u7740\u5176\u4ed6\u516c\u53f8\u7684\u6295\u5165\u800c\u589e\u957f\uff0c\u4f8b\u5982\uff0c\u901a\u8fc7\u77e5\u8bc6\u8f6c\u79fb\uff0c\u5426\u5219\u5c31\u4f1a\u8870\u9000\u3002\u7ef4\u62a4\u4ea4\u4e92\u662f\u6602\u8d35\u7684\u3002\u5982\u679c\u4f01\u4e1a\u9884\u671f\u7684\u77e5\u8bc6\u589e\u957f\u4e0d\u80fd\u5b9e\u73b0\uff0c\u4f01\u4e1a\u53ef\u4ee5\u79bb\u5f00\u7f51\u7edc\uff0c\u8fd9\u53ef\u80fd\u5bfc\u81f4\u5176\u4ed6\u4f01\u4e1a\u4e5f\u79bb\u5f00\u7f51\u7edc\u3002\u672c\u6587\u8ba8\u8bba\u4e86\u4e24\u79cd\u81ea\u4e0b\u800c\u4e0a\u7684\u5e72\u9884\u60c5\u666f\uff0c\u4ee5\u9632\u6b62\u3001\u51cf\u5c11\u6216\u5ef6\u8fdf\u4f01\u4e1a\u79bb\u5f00\u7684\u7ea7\u8054\u6548\u5e94\u3002\u7b2c\u4e00\u79cd\u662f\u57fa\u4e8e\u7f51\u7edc\u53ef\u63a7\u6027\u7684\u5f62\u5f0f\u4e3b\u4e49\uff0c\u5373\u901a\u8fc7\u964d\u4f4e\u6210\u672c\u6765\u8bc6\u522b\u9a71\u52a8\u8282\u70b9\u5e76\u968f\u540e\u6fc0\u52b1\u5b83\u4eec\u3002\u7b2c\u4e8c\u79cd\u662f\u7ed3\u5408\u8282\u70b9\u5e72\u9884\u548c\u7f51\u7edc\u5e72\u9884\u3002\u5b83\u5efa\u8bae\u6709\u63a7\u5236\u5730\u53d6\u6d88\u4e00\u5bb6\u516c\u53f8\uff0c\u968f\u673a\u53d6\u4ee3\u79bb\u5f00\u7684\u516c\u53f8\u3002\u8fd9\u5141\u8bb8\u751f\u6210\u5c0f\u7ea7\u8054\uff0c\u4ece\u800c\u9632\u6b62\u51fa\u73b0\u5927\u7ea7\u8054\u3002\u6211\u4eec\u53d1\u73b0\u8fd9\u4e24\u79cd\u65b9\u6cd5\u90fd\u6210\u529f\u5730\u51cf\u5c11\u4e86\u7ea7\u8054\uff0c\u4ece\u800c\u63d0\u9ad8\u4e86\u7814\u53d1\u7f51\u7edc\u7684\u5f39\u6027\u3002<\/span><\/section>\n

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    \u57fa\u4e8e\u5fd7\u613f\u8005\u56f0\u5883\u535a\u5f08\u7684<\/strong><\/span><\/p>\n

    \u7d27\u6025\u758f\u6563\u6551\u52a9\u884c\u4e3a\u6a21\u578b\u7814\u7a76<\/strong><\/span><\/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

    Modeling Helping Behavior in Emergency Evacuations Using Volunteer’s Dilemma Game<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14207<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Jaeyoung Kwak,Michael H Lees,Wentong Cai,Marcus EH Ong<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>People often help others who are in trouble, especially in emergency evacuation situations. For instance, during the 2005 London bombings, it was reported that evacuees helped injured persons to escape the place of danger. In terms of game theory, it can be understood that such helping behavior provides a collective good while it is a costly behavior because the volunteers spend extra time to assist the injured persons in case of emergency evacuations. In order to study the collective effects of helping behavior in emergency evacuations, we have performed numerical simulations of helping behavior among evacuees in a room evacuation scenario. Our simulation model is based on the volunteer’s dilemma game reflecting volunteering cost. The game theoretic model is coupled with a social force model to understand the relationship between the spatial and social dynamics of evacuation scenarios. By systematically changing the cost parameter of helping behavior, we observed different patterns of collective helping behaviors and these collective patterns are summarized with a phase diagram.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u4eba\u4eec\u7ecf\u5e38\u5e2e\u52a9\u6709\u56f0\u96be\u7684\u4eba\uff0c\u7279\u522b\u662f\u5728\u7d27\u6025\u64a4\u79bb\u7684\u60c5\u51b5\u4e0b\u3002\u4f8b\u5982\uff0c\u57282005\u5e74\u4f26\u6566\u7206\u70b8\u6848\u671f\u95f4\uff0c\u636e\u62a5\u9053\uff0c\u64a4\u79bb\u4eba\u5458\u5e2e\u52a9\u53d7\u4f24\u8005\u9003\u79bb\u5371\u9669\u5730\u70b9\u3002\u4ece\u535a\u5f08\u8bba\u7684\u89d2\u5ea6\u6765\u770b\uff0c\u53ef\u4ee5\u7406\u89e3\u7684\u662f\uff0c\u8fd9\u79cd\u5e2e\u52a9\u884c\u4e3a\u63d0\u4f9b\u4e86\u4e00\u79cd\u96c6\u4f53\u5229\u76ca\uff0c\u800c\u8fd9\u662f\u4e00\u79cd\u4ee3\u4ef7\u9ad8\u6602\u7684\u884c\u4e3a\uff0c\u56e0\u4e3a\u5728\u7d27\u6025\u64a4\u79bb\u65f6\uff0c\u5fd7\u613f\u8005\u4f1a\u82b1\u8d39\u989d\u5916\u7684\u65f6\u95f4\u6765\u5e2e\u52a9\u53d7\u4f24\u7684\u4eba\u3002\u4e3a\u4e86\u7814\u7a76\u7d27\u6025\u758f\u6563\u4e2d\u5e2e\u52a9\u884c\u4e3a\u7684\u96c6\u4f53\u6548\u5e94\uff0c\u6211\u4eec\u5bf9\u4e00\u4e2a\u623f\u95f4\u758f\u6563\u573a\u666f\u4e2d\u88ab\u758f\u6563\u8005\u7684\u5e2e\u52a9\u884c\u4e3a\u8fdb\u884c\u4e86\u6570\u503c\u6a21\u62df\u3002\u6211\u4eec\u7684\u6a21\u62df\u6a21\u578b\u662f\u57fa\u4e8e\u53cd\u6620\u5fd7\u613f\u8005\u6210\u672c\u7684\u5fd7\u613f\u8005\u56f0\u5883\u535a\u5f08\u3002\u8be5\u535a\u5f08\u8bba\u6a21\u578b\u4e0e\u793e\u4f1a\u529b\u91cf\u6a21\u578b\u76f8\u7ed3\u5408\uff0c\u4ee5\u7406\u89e3\u758f\u6563\u573a\u666f\u7684\u7a7a\u95f4\u548c\u793e\u4f1a\u52a8\u6001\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u901a\u8fc7\u7cfb\u7edf\u5730\u6539\u53d8\u5e2e\u52a9\u884c\u4e3a\u7684\u6210\u672c\u53c2\u6570\uff0c\u6211\u4eec\u89c2\u5bdf\u5230\u4e86\u4e0d\u540c\u7684\u96c6\u4f53\u5e2e\u52a9\u884c\u4e3a\u6a21\u5f0f\uff0c\u5e76\u7528\u76f8\u56fe\u5bf9\u8fd9\u4e9b\u96c6\u4f53\u6a21\u5f0f\u8fdb\u884c\u4e86\u603b\u7ed3\u3002<\/span><\/section>\n

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

    \u533a\u5ba4\u6a21\u578b\u7684\u7ed3\u6784\u53ef\u8bc6\u522b\u6027\u548c\u53ef\u89c2\u6d4b\u6027<\/strong><\/span><\/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

    Structural Identifiability and Observability of Compartmental Models of the COVID-19 Pandemic<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14295<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Gemma Massonis,Julio R. Banga,Alejandro F. Villaverde<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>The recent coronavirus disease (COVID-19) outbreak has dramatically increased the public awareness and appreciation of the utility of dynamic models. At the same time, the dissemination of contradictory model predictions has highlighted their limitations. If some parameters and\/or state variables of a model cannot be determined from output measurements, its ability to yield correct insights — as well as the possibility of controlling the system — may be compromised. Epidemic dynamics are commonly analysed using compartmental models, and many variations of such models have been used for analysing and predicting the evolution of the COVID-19 pandemic. In this paper we survey the different models proposed in the literature, assembling a list of 36 model structures and assessing their ability to provide reliable information. We address the problem using the control theoretic concepts of structural identifiability and observability. Since some parameters can vary during the course of an epidemic, we consider both the constant and time-varying parameter assumptions. We analyse the structural identifiability and observability of all of the models, considering all plausible choices of outputs and time-varying parameters, which leads us to analyse 255 different model versions. We classify the models according to their structural identifiability and observability under the different assumptions and discuss the implications of the results. We also illustrate with an example several alternative ways of remedying the lack of observability of a model. Our analyses provide guidelines for choosing the most informative model for each purpose, taking into account the available knowledge and measurements.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u6700\u8fd1\u7684\u51a0\u72b6\u75c5\u6bd2\u75c5(\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e)\u7684\u7206\u53d1\u6781\u5927\u5730\u63d0\u9ad8\u4e86\u516c\u4f17\u5bf9\u52a8\u6001\u6a21\u578b\u7684\u6548\u7528\u7684\u8ba4\u8bc6\u548c\u6b23\u8d4f\u3002\u4e0e\u6b64\u540c\u65f6\uff0c\u77db\u76fe\u6a21\u578b\u9884\u6d4b\u7684\u4f20\u64ad\u7a81\u51fa\u4e86\u5b83\u4eec\u7684\u5c40\u9650\u6027\u3002\u5982\u679c\u4e00\u4e2a\u6a21\u578b\u7684\u67d0\u4e9b\u53c2\u6570\u548c \/ \u6216\u72b6\u6001\u53d8\u91cf\u4e0d\u80fd\u4ece\u8f93\u51fa\u6d4b\u91cf\u4e2d\u786e\u5b9a\uff0c\u90a3\u4e48\u5b83\u4ea7\u751f\u6b63\u786e\u6d1e\u5bdf\u529b\u7684\u80fd\u529b\u2014\u2014\u4ee5\u53ca\u63a7\u5236\u7cfb\u7edf\u7684\u53ef\u80fd\u6027\u2014\u2014\u53ef\u80fd\u4f1a\u53d7\u5230\u635f\u5bb3\u3002\u6d41\u884c\u75c5\u52a8\u529b\u5b66\u901a\u5e38\u4f7f\u7528\u5206\u9694\u6a21\u578b\u8fdb\u884c\u5206\u6790\uff0c\u8fd9\u4e9b\u6a21\u578b\u7684\u8bb8\u591a\u53d8\u4f53\u5df2\u88ab\u7528\u4e8e\u5206\u6790\u548c\u9884\u6d4b\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6d41\u884c\u75c5\u7684\u6f14\u53d8\u3002\u672c\u6587\u7efc\u8ff0\u4e86\u6587\u732e\u4e2d\u63d0\u51fa\u7684\u5404\u79cd\u6a21\u578b\uff0c\u6536\u96c6\u4e8636\u79cd\u6a21\u578b\u7ed3\u6784\uff0c\u5e76\u8bc4\u4f30\u4e86\u5b83\u4eec\u63d0\u4f9b\u53ef\u9760\u4fe1\u606f\u7684\u80fd\u529b\u3002\u6211\u4eec\u7528\u7ed3\u6784\u53ef\u8bc6\u522b\u6027\u548c\u53ef\u89c2\u6d4b\u6027\u7684\u63a7\u5236\u7406\u8bba\u6982\u5ff5\u6765\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\u3002\u7531\u4e8e\u67d0\u4e9b\u53c2\u6570\u5728\u4f20\u67d3\u75c5\u8fc7\u7a0b\u4e2d\u4f1a\u53d1\u751f\u53d8\u5316\uff0c\u6211\u4eec\u8003\u8651\u4e86\u5e38\u6570\u548c\u65f6\u53d8\u53c2\u6570\u7684\u5047\u8bbe\u3002\u6211\u4eec\u5206\u6790\u4e86\u6240\u6709\u6a21\u578b\u7684\u7ed3\u6784\u53ef\u8bc6\u522b\u6027\u548c\u53ef\u89c2\u6d4b\u6027\uff0c\u8003\u8651\u4e86\u6240\u6709\u53ef\u80fd\u7684\u8f93\u51fa\u9009\u62e9\u548c\u65f6\u53d8\u53c2\u6570\uff0c\u8fd9\u4f7f\u6211\u4eec\u5206\u6790\u4e86255\u4e2a\u4e0d\u540c\u7684\u6a21\u578b\u7248\u672c\u3002\u6839\u636e\u6a21\u578b\u5728\u4e0d\u540c\u5047\u8bbe\u6761\u4ef6\u4e0b\u7684\u7ed3\u6784\u53ef\u8bc6\u522b\u6027\u548c\u53ef\u89c2\u6d4b\u6027\u5bf9\u6a21\u578b\u8fdb\u884c\u4e86\u5206\u7c7b\uff0c\u5e76\u8ba8\u8bba\u4e86\u5206\u7c7b\u7ed3\u679c\u7684\u610f\u4e49\u3002\u6211\u4eec\u8fd8\u7528\u4e00\u4e2a\u4f8b\u5b50\u6765\u8bf4\u660e\u51e0\u79cd\u53ef\u4f9b\u9009\u62e9\u7684\u65b9\u6cd5\u6765\u5f25\u8865\u6a21\u578b\u53ef\u89c2\u6d4b\u6027\u7684\u4e0d\u8db3\u3002\u6211\u4eec\u7684\u5206\u6790\u4e3a\u6bcf\u4e2a\u76ee\u7684\u9009\u62e9\u6700\u6709\u4ef7\u503c\u7684\u6a21\u578b\u63d0\u4f9b\u4e86\u6307\u5bfc\u65b9\u9488\uff0c\u540c\u65f6\u8003\u8651\u5230\u73b0\u6709\u7684\u77e5\u8bc6\u548c\u6d4b\u91cf\u3002<\/span><\/section>\n

    <\/section>\n
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    \u57fa\u4e8e\u8d1f\u91c7\u6837\u9ad8\u9636\u8df3\u56fe\u7684\u65f6\u53d8\u56fe\u8868\u793a\u5b66\u4e60<\/strong><\/span><\/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

    Time-varying Graph Representation Learning via Higher-Order Skip-Gram with Negative Sampling<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14330<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Simone Piaggesi,Andr\u00e9 Panisson<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Representation learning models for graphs are a successful family of techniques that project nodes into feature spaces that can be exploited by other machine learning algorithms. Since many real-world networks are inherently dynamic, with interactions among nodes changing over time, these techniques can be defined both for static and for time-varying graphs. Here, we build upon the fact that the skip-gram embedding approach implicitly performs a matrix factorization, and we extend it to perform implicit tensor factorization on different tensor representations of time-varying graphs. We show that higher-order skip-gram with negative sampling (HOSGNS) is able to disentangle the role of nodes and time, with a small fraction of the number of parameters needed by other approaches. We empirically evaluate our approach using time-resolved face-to-face proximity data, showing that the learned time-varying graph representations outperform state-of-the-art methods when used to solve downstream tasks such as network reconstruction, and to predict the outcome of dynamical processes such as disease spreading. The source code and data are publicly available at https:\/\/github.com\/simonepiaggesi\/hosgns.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u56fe\u8868\u793a\u5b66\u4e60\u6a21\u578b\u662f\u4e00\u7cfb\u5217\u6210\u529f\u7684\u6280\u672f\uff0c\u5b83\u4eec\u5c06\u8282\u70b9\u6295\u5c04\u5230\u7279\u5f81\u7a7a\u95f4\uff0c\u8fd9\u4e9b\u7279\u5f81\u7a7a\u95f4\u53ef\u4ee5\u88ab\u5176\u4ed6\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u5229\u7528\u3002\u7531\u4e8e\u8bb8\u591a\u73b0\u5b9e\u4e16\u754c\u7684\u7f51\u7edc\u672c\u8d28\u4e0a\u662f\u52a8\u6001\u7684\uff0c\u8282\u70b9\u4e4b\u95f4\u7684\u4ea4\u4e92\u968f\u65f6\u95f4\u53d8\u5316\uff0c\u8fd9\u4e9b\u6280\u672f\u53ef\u4ee5\u5b9a\u4e49\u4e3a\u9759\u6001\u56fe\u548c\u65f6\u53d8\u56fe\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u5efa\u7acb\u5728\u8df3\u8fc7\u683c\u62c9\u59c6\u5d4c\u5165\u65b9\u6cd5\u9690\u5f0f\u5730\u6267\u884c\u77e9\u9635\u5206\u89e3\u5206\u89e3\u7684\u4e8b\u5b9e\u4e4b\u4e0a\uff0c\u6211\u4eec\u5c06\u5176\u6269\u5c55\u5230\u5bf9\u65f6\u53d8\u56fe\u7684\u4e0d\u540c\u5f20\u91cf\u8868\u793a\u6267\u884c\u9690\u5f0f\u5f20\u91cf\u56e0\u5f0f\u5206\u89e3\u3002\u6211\u4eec\u8bc1\u660e\u4e86\u9ad8\u9636\u8d1f\u91c7\u6837\u8df3\u56fe(HOSGNS)\u80fd\u591f\u533a\u5206\u8282\u70b9\u548c\u65f6\u95f4\u7684\u4f5c\u7528\uff0c\u53ea\u9700\u8981\u5176\u4ed6\u65b9\u6cd5\u6240\u9700\u53c2\u6570\u7684\u4e00\u5c0f\u90e8\u5206\u3002\u6211\u4eec\u4f7f\u7528\u65f6\u95f4\u5206\u8fa8\u7684\u9762\u5bf9\u9762\u63a5\u8fd1\u6570\u636e\u5bf9\u6211\u4eec\u7684\u65b9\u6cd5\u8fdb\u884c\u4e86\u5b9e\u8bc1\u8bc4\u4f30\uff0c\u8868\u660e\u5b66\u4e60\u7684\u65f6\u53d8\u56fe\u8868\u793a\u6cd5\u5728\u89e3\u51b3\u7f51\u7edc\u91cd\u5efa\u7b49\u4e0b\u6e38\u4efb\u52a1\u548c\u9884\u6d4b\u75be\u75c5\u4f20\u64ad\u7b49\u52a8\u6001\u8fc7\u7a0b\u7684\u7ed3\u679c\u65f6\u4f18\u4e8e\u6700\u5148\u8fdb\u7684\u65b9\u6cd5\u3002\u6e90\u4ee3\u7801\u548c\u6570\u636e\u53ef\u5728https:\/\/github.com\/simonepiaggesi\/hosgns<\/span><\/section>\n

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    \n
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    \u76f8\u79f0\u793e\u533a\u7ed3\u6784\u7684\u63a8\u8bba\u7edf\u8ba1\u5b66<\/strong><\/span><\/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

    Statistical inference of assortative community structures<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14493<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Lizhi Zhang,Tiago P. Peixoto<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>We develop a principled methodology to infer assortative communities in networks based on a nonparametric Bayesian formulation of the planted partition model. We show that this approach succeeds in finding statistically significant assortative modules in networks, unlike alternatives such as modularity maximization, which systematically overfits both in artificial as well as in empirical examples. In addition, we show that our method is not subject to a resolution limit, and can uncover an arbitrarily large number of communities, as long as there is statistical evidence for them. Our formulation is amenable to model selection procedures, which allow us to compare it to more general approaches based on the stochastic block model, and in this way reveal whether assortativity is in fact the dominating large-scale mixing pattern. We perform this comparison with several empirical networks, and identify numerous cases where the network’s assortativity is exaggerated by traditional community detection methods, and we show how a more faithful degree of assortativity can be identified.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u57fa\u4e8e\u79cd\u690d\u5212\u5206\u6a21\u578b\u7684\u975e\u53c2\u6570\u8d1d\u53f6\u65af\u516c\u5f0f\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u79cd\u539f\u5219\u6027\u7684\u65b9\u6cd5\u6765\u63a8\u65ad\u7f51\u7edc\u4e2d\u7684\u5206\u7c7b\u7fa4\u843d\u3002\u6211\u4eec\u8bc1\u660e\uff0c\u8fd9\u79cd\u65b9\u6cd5\u6210\u529f\u5730\u5728\u7f51\u7edc\u4e2d\u53d1\u73b0\u4e86\u7edf\u8ba1\u610f\u4e49\u91cd\u5927\u7684\u76f8\u79f0\u6a21\u5757\uff0c\u800c\u4e0d\u50cf\u6a21\u5757\u5316\u6700\u5927\u5316\u8fd9\u6837\u7684\u66ff\u4ee3\u65b9\u6848\uff0c\u8fd9\u79cd\u66ff\u4ee3\u65b9\u6848\u7cfb\u7edf\u5730\u8d85\u8d8a\u4e86\u4eba\u5de5\u548c\u7ecf\u9a8c\u5b9e\u4f8b\u3002\u6b64\u5916\uff0c\u6211\u4eec\u8bc1\u660e\u4e86\u6211\u4eec\u7684\u65b9\u6cd5\u4e0d\u53d7\u5206\u8fa8\u7387\u9650\u5236\uff0c\u5e76\u4e14\u53ef\u4ee5\u63ed\u793a\u4efb\u610f\u5927\u91cf\u7684\u793e\u533a\uff0c\u53ea\u8981\u6709\u5b83\u4eec\u7684\u7edf\u8ba1\u8bc1\u636e\u3002\u6211\u4eec\u7684\u516c\u5f0f\u662f\u987a\u4ece\u6a21\u578b\u9009\u62e9\u7a0b\u5e8f\uff0c\u8fd9\u4f7f\u6211\u4eec\u80fd\u591f\u6bd4\u8f83\u5b83\u4e0e\u66f4\u4e00\u822c\u7684\u65b9\u6cd5\u57fa\u4e8e\u968f\u673a\u5757\u6a21\u578b\uff0c\u5728\u8fd9\u79cd\u65b9\u5f0f\u4e0b\u63ed\u793a\u662f\u5426\u534f\u8c03\u6027\u5b9e\u9645\u4e0a\u662f\u4e3b\u8981\u7684\u5927\u89c4\u6a21\u6df7\u5408\u6a21\u5f0f\u3002\u6211\u4eec\u5bf9\u51e0\u4e2a\u7ecf\u9a8c\u7f51\u7edc\u8fdb\u884c\u4e86\u6bd4\u8f83\uff0c\u53d1\u73b0\u4e86\u5927\u91cf\u7684\u7f51\u7edc\u7684\u534f\u8c03\u6027\u88ab\u4f20\u7edf\u7684\u793e\u533a\u68c0\u6d4b\u65b9\u6cd5\u5938\u5927\u7684\u6848\u4f8b\uff0c\u5e76\u8bf4\u660e\u4e86\u5982\u4f55\u80fd\u591f\u8bc6\u522b\u51fa\u66f4\u52a0\u5fe0\u5b9e\u7684\u534f\u8c03\u6027\u7a0b\u5ea6\u3002<\/span><\/section>\n

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    \u7a33\u5065\u7f51\u7edc\u8fde\u901a\u6027\u7684\u903e\u6e17\u9608\u503c<\/strong><\/span><\/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

    Percolation Thresholds for Robust Network Connectivity<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14496<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Arman Mohseni-Kabir,Mihir Pant,Don Towsley,Saikat Guha,Ananthram Swami<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>Communication networks, power grids, and transportation networks are all examples of networks whose performance depends on reliable connectivity of their underlying network components even in the presence of usual network dynamics due to mobility, node or edge failures, and varying traffic loads. Percolation theory quantifies the threshold value of a local control parameter such as a node occupation (resp., deletion) probability or an edge activation (resp., removal) probability above (resp., below) which there exists a giant connected component (GCC), a connected component comprising of a number of occupied nodes and active edges whose size is proportional to the size of the network itself. Any pair of occupied nodes in the GCC is connected via at least one path comprised of active edges and occupied nodes. The mere existence of the GCC itself does not guarantee that the long-range connectivity would be robust, e.g., to random link or node failures due to network dynamics. In this paper, we explore new percolation thresholds that guarantee not only spanning network connectivity, but also robustness. We define and analyze four measures of robust network connectivity, explore their interrelationships, and numerically evaluate the respective robust percolation thresholds for the 2D square lattice.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u901a\u4fe1\u7f51\u7edc\u3001\u7535\u529b\u7f51\u7edc\u548c\u4ea4\u901a\u7f51\u7edc\u90fd\u662f\u7f51\u7edc\u7684\u4f8b\u5b50\uff0c\u5b83\u4eec\u7684\u6027\u80fd\u53d6\u51b3\u4e8e\u5176\u5e95\u5c42\u7f51\u7edc\u7ec4\u4ef6\u7684\u53ef\u9760\u8fde\u63a5\uff0c\u5373\u4f7f\u5b58\u5728\u901a\u5e38\u7684\u7f51\u7edc\u52a8\u6001\uff0c\u7531\u4e8e\u79fb\u52a8\u6027\u3001\u8282\u70b9\u6216\u8fb9\u7f18\u6545\u969c\u548c\u4e0d\u540c\u7684\u4ea4\u901a\u8d1f\u8377\u3002\u903e\u6e17\u7406\u8bba\u91cf\u5316\u4e86\u4e00\u4e2a\u5c40\u90e8\u63a7\u5236\u53c2\u6570\u7684\u9608\u503c\uff0c\u5982\u8282\u70b9\u5360\u7528(\u547c\u5438\uff0c\u5220\u9664)\u6982\u7387\u6216\u8fb9\u7f18\u6fc0\u6d3b(\u547c\u5438\uff0c\u5220\u9664)\u6982\u7387\u5927\u4e8e(\u547c\u5438\uff0c\u5220\u9664)\u5b58\u5728\u4e00\u4e2a\u5de8\u5927\u7684\u8fde\u63a5\u5143\u4ef6(\u56fe\u8bba)(GCC) \uff0c\u4e00\u4e2a\u7531\u82e5\u5e72\u88ab\u5360\u7528\u7684\u8282\u70b9\u548c\u6d3b\u8dc3\u7684\u8fb9\u7f18\u7ec4\u6210\u7684\u8fde\u63a5\u5143\u4ef6(\u56fe\u8bba) \uff0c\u5176\u5927\u5c0f\u4e0e\u7f51\u7edc\u672c\u8eab\u7684\u5927\u5c0f\u6210\u6b63\u6bd4\u3002\u6240\u8ff0 GCC \u4e2d\u7684\u4efb\u610f\u4e00\u5bf9\u88ab\u5360\u7528\u7684\u8282\u70b9\u901a\u8fc7\u81f3\u5c11\u4e00\u6761\u7531\u6d3b\u52a8\u8fb9\u548c\u88ab\u5360\u7528\u8282\u70b9\u7ec4\u6210\u7684\u8def\u5f84\u8fde\u63a5\u3002\u4ec5\u4ec5\u6d77\u6e7e\u5408\u4f5c\u59d4\u5458\u4f1a\u672c\u8eab\u7684\u5b58\u5728\u5e76\u4e0d\u80fd\u4fdd\u8bc1\u8fdc\u7a0b\u8fde\u63a5\u662f\u5065\u58ee\u7684\uff0c\u4f8b\u5982\uff0c\u5bf9\u4e8e\u7531\u7f51\u7edc\u52a8\u6001\u5f15\u8d77\u7684\u968f\u673a\u94fe\u63a5\u6216\u8282\u70b9\u6545\u969c\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u63a2\u7d22\u65b0\u7684\u903e\u6e17\u9608\u503c\uff0c\u4e0d\u4ec5\u4fdd\u8bc1\u8de8\u8d8a\u7f51\u7edc\u8fde\u901a\u6027\uff0c\u800c\u4e14\u5065\u58ee\u6027\u3002\u6211\u4eec\u5b9a\u4e49\u5e76\u5206\u6790\u4e86\u56db\u79cd\u7a33\u5065\u7f51\u7edc\u8fde\u901a\u6027\u5ea6\u91cf\uff0c\u63a2\u8ba8\u4e86\u5b83\u4eec\u4e4b\u95f4\u7684\u76f8\u4e92\u5173\u7cfb\uff0c\u5e76\u5bf9\u4e8c\u7ef4\u6b63\u65b9\u5f62\u7f51\u683c\u7684\u7a33\u5065\u6e17\u6d41\u9608\u503c\u8fdb\u884c\u4e86\u6570\u503c\u8bc4\u4f30\u3002<\/span><\/section>\n

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    \u9884\u6d4b\u5370\u5ea6\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e<\/strong><\/span><\/p>\n

    \u5927\u6d41\u884c\u7684\u6bcf\u65e5\u548c\u7d2f\u79ef\u75c5\u4f8b\u6570<\/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

    Forecasting the daily and cumulative number of cases for the COVID-19 pandemic in India<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14575<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Subhas Khajanchi,Kankan Sarkar<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/span><\/strong>The ongoing novel coronavirus epidemic has been announced a pandemic by the World Health Organization on March 11, 2020, and the Govt. of India has declared a nationwide lockdown from March 25, 2020, to prevent community transmission of COVID-19. Due to absence of specific antivirals or vaccine, mathematical modeling play an important role to better understand the disease dynamics and designing strategies to control rapidly spreading infectious diseases. In our study, we developed a new compartmental model that explains the transmission dynamics of COVID-19. We calibrated our proposed model with daily COVID-19 data for the four Indian provinces, namely Jharkhand, Gujarat, Andhra Pradesh, and Chandigarh. We study the qualitative properties of the model including feasible equilibria and their stability with respect to the basic reproduction number <\/span>R0. The disease-free equilibrium becomes stable and the endemic equilibrium becomes unstable when the recovery rate of infected individuals increased but if the disease transmission rate remains higher then the endemic equilibrium always remain stable. For the estimated model parameters, <\/span>R0>1 for all the four provinces, which suggests the significant outbreak of COVID-19. Short-time prediction shows the increasing trend of daily and cumulative cases of COVID-19 for the four provinces of India.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>2020\u5e743\u670811\u65e5\uff0c\u4e16\u754c\u536b\u751f\u7ec4\u7ec7\u548c\u653f\u5e9c\u5ba3\u5e03\u6b63\u5728\u8fdb\u884c\u7684\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u75ab\u60c5\u5927\u6d41\u884c\u3002\u5370\u5ea6\u653f\u5e9c\u5ba3\u5e03\u4ece2020\u5e743\u670825\u65e5\u8d77\u5b9e\u884c\u5168\u56fd\u5c01\u9501\uff0c\u4ee5\u9632\u6b62\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u5728\u793e\u533a\u4f20\u64ad\u3002\u7531\u4e8e\u7f3a\u4e4f\u7279\u5b9a\u7684\u6297\u75c5\u6bd2\u836f\u7269\u6216\u75ab\u82d7\uff0c\u6570\u5b66\u6a21\u578b\u5728\u66f4\u597d\u5730\u7406\u89e3\u75be\u75c5\u52a8\u6001\u548c\u8bbe\u8ba1\u63a7\u5236\u4f20\u67d3\u75c5\u5feb\u901f\u4f20\u64ad\u7684\u7b56\u7565\u65b9\u9762\u53d1\u6325\u7740\u91cd\u8981\u4f5c\u7528\u3002\u5728\u6211\u4eec\u7684\u7814\u7a76\u4e2d\uff0c\u6211\u4eec\u5f00\u53d1\u4e86\u4e00\u4e2a\u65b0\u7684\u5206\u5ba4\u6a21\u578b\u6765\u89e3\u91ca\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u7684\u4f20\u64ad\u52a8\u529b\u5b66\u3002\u6211\u4eec\u7528\u5370\u5ea64\u4e2a\u7701\u4efd—- \u8d3e\u574e\u5fb7\u3001\u53e4\u5409\u62c9\u7279\u3001 Andhra Pradesh \u548c Chandigarh—- \u7684\u6bcf\u65e5\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6570\u636e\u6821\u51c6\u4e86\u6211\u4eec\u7684\u6a21\u578b\u3002\u6211\u4eec\u7814\u7a76\u4e86\u6a21\u578b\u7684\u5b9a\u6027\u6027\u8d28\uff0c\u5305\u62ec\u53ef\u884c\u5e73\u8861\u70b9\u53ca\u5176\u76f8\u5bf9\u4e8e\u57fa\u672c\u4f20\u67d3\u6570\u7684\u7a33\u5b9a\u6027<\/span>R0.\u5f53\u611f\u67d3\u8005\u7684\u6062\u590d\u7387\u589e\u52a0\u65f6\uff0c\u65e0\u75c5\u5e73\u8861\u53d8\u5f97\u7a33\u5b9a\uff0c\u5730\u65b9\u75c5\u5e73\u8861\u53d8\u5f97\u4e0d\u7a33\u5b9a\uff0c\u4f46\u5982\u679c\u4f20\u67d3\u7387\u4fdd\u6301\u8f83\u9ad8\uff0c\u5219\u5730\u65b9\u75c5\u5e73\u8861\u59cb\u7ec8\u4fdd\u6301\u7a33\u5b9a\u3002\u5bf9\u4e8e\u4f30\u8ba1\u7684\u6a21\u578b\u53c2\u6570,<\/span>R0>1 \u6240\u6709\u56db\u4e2a\u7701\u4efd\uff0c\u8fd9\u610f\u5473\u7740\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u7684\u5927\u89c4\u6a21\u7206\u53d1\u3002\u77ed\u671f\u9884\u6d4b\u663e\u793a\uff0c\u5370\u5ea64\u4e2a\u7701\u6bcf\u65e5\u548c\u7d2f\u79ef\u7684\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u75c5\u4f8b\u5448\u4e0a\u5347\u8d8b\u52bf\u3002<\/span><\/section>\n

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    \u53ef\u4ee5\u5e2e\u52a9\u4eba\u4eec\u6446\u8131\u56da\u5f92\u56f0\u5883<\/strong><\/span><\/p>\n

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

    Topology dependent payoffs can lead to escape from prisoner’s dilemma<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/span><\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.14593<\/span><\/section>\n
    \u4f5c\u8005:<\/span><\/strong><\/span><\/section>\n
    Saptarshi Sinha,Deep Nath,Soumen Roy<\/span><\/section>\n

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    Abstract\uff1a<\/span><\/strong>Evolutionary game theory attempts to understand the stability of cooperation in spatially restricted populations. Maintenance of cooperation is difficult, especially in the absence of spatial restrictions. There have been numerous studies of games played on graphs. It is well recognised that the underlying graph topology significantly influences the outcome of such games. A natural yet unexplored question is whether the topology of the underlying structures on which the games are played possess no role whatsoever in the determination of payoffs. Herein, we introduce a form of game payoff, which is weakly dependent on the underlying topology. Our approach is inspired by the well-known microbial phenomenon of quorum sensing. We demonstrate that even with such a weak dependence, the basic game dynamics and indeed the very nature of the game may be altered.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/span><\/strong>\u8fdb\u5316\u535a\u5f08\u8bba\u8bd5\u56fe\u7406\u89e3\u7a7a\u95f4\u53d7\u9650\u79cd\u7fa4\u4e2d\u5408\u4f5c\u7684\u7a33\u5b9a\u6027\u3002\u7ef4\u6301\u5408\u4f5c\u662f\u56f0\u96be\u7684\uff0c\u7279\u522b\u662f\u5728\u6ca1\u6709\u7a7a\u95f4\u9650\u5236\u7684\u60c5\u51b5\u4e0b\u3002\u5df2\u7ecf\u6709\u5f88\u591a\u5173\u4e8e\u56fe\u5f62\u6e38\u620f\u7684\u7814\u7a76\u3002\u4f17\u6240\u5468\u77e5\uff0c\u5e95\u5c42\u56fe\u5f62\u7684\u62d3\u6251\u7ed3\u6784\u663e\u8457\u5730\u5f71\u54cd\u7740\u8fd9\u7c7b\u6e38\u620f\u7684\u7ed3\u679c\u3002\u4e00\u4e2a\u81ea\u7136\u800c\u672a\u88ab\u63a2\u7d22\u7684\u95ee\u9898\u662f\uff0c\u5728\u6e38\u620f\u8fdb\u884c\u7684\u57fa\u7840\u7ed3\u6784\u7684\u62d3\u6251\u7ed3\u6784\u662f\u5426\u5728\u786e\u5b9a\u6536\u76ca\u65b9\u9762\u6ca1\u6709\u4efb\u4f55\u4f5c\u7528\u3002\u5728\u6b64\uff0c\u6211\u4eec\u5f15\u5165\u4e86\u5bf9\u7b56\u652f\u4ed8\u7684\u4e00\u79cd\u5f62\u5f0f\uff0c\u5b83\u5f31\u4f9d\u8d56\u4e8e\u5e95\u5c42\u62d3\u6251\u3002\u6211\u4eec\u7684\u65b9\u6cd5\u662f\u542f\u53d1\u4f17\u6240\u5468\u77e5\u7684\u5fae\u751f\u7269\u7fa4\u4f53\u611f\u5e94\u73b0\u8c61\u3002\u6211\u4eec\u8bc1\u660e\uff0c\u5373\u4f7f\u6709\u8fd9\u6837\u4e00\u4e2a\u5f31\u7684\u4f9d\u8d56\u6027\uff0c\u57fa\u672c\u7684\u6e38\u620f\u52a8\u6001\u548c\u5b9e\u9645\u4e0a\u7684\u6027\u8d28\u7684\u6e38\u620f\u53ef\u80fd\u88ab\u6539\u53d8\u3002<\/span><\/section>\n

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

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

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